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In this blog, I will show you how to quickly create an entirely free app using a JavaScript program that runs within the free Oracle database and calls AI, the results of which are stored in the database and can then be accessed using , , Rest, or any other language. All of the source code is, of course, available . Hugging Face SQL JSON here A typical sequence of actions in applications, both in general and particularly in AI applications, involves an application utilizing an AI service. The acquired information is then stored in a database, ready for retrieval, subsequent processing (such as using OML), analysis, and future queries. When the command originates directly from the database, the data is promptly stored and gains higher reliability. This approach is in-process and eliminates the need for an additional network call for persistence. Additionally, by housing the logic and even code within the database, the remarkable features of the robust Oracle database, including high availability, management, and observability, are naturally harnessed. This distinct capability is a hallmark of the Oracle database, which encompasses both Java and, as detailed in this blog, JavaScript runtime engines within the database itself. Hugging Face specializes in crafting tools for constructing applications using machine learning. Particularly notable is its transformers library designed for natural language processing applications, along with its platform facilitating the sharing of machine learning models and datasets. It has garnered significant popularity in recent years. In short, we’ll… Create a cohere.com or huggingface.co account, create and copy an access token, and select a model. Create the Oracle database. Create the database user and table(s). Run a short JavaScript program in the database to make a call to the Cohere or Hugging Face AI model and store the results in the table. Query these results using SQL, JSON, Rest, etc. Setup Cohere account and AI model Go to https://cohere.com and , go to your , and click the button. Sign Up Profile Settings Click on , then API Keys, create a key (or use trial) and copy its value for use later. Dashboard Click on and select the hamburger menu/icon on the left of any page to view the API Reference list and select one. In this case, we will select section and API Reference generate Co.Generate. Select and then select the option in order to see a sample code snippet to call the model. Ianguage JavaScript Setup Hugging Face account and AI model Go to https://huggingface.co and , go to your , and click the button. Sign Up Profile Settings Click on , create a token, and copy its value for use later. Access Tokens Click on and select a model. In this case, we will select a model under the section for Models Natural Language Processing Question Answering. Select a model (perhaps one of the more popular ones), notice the information in the on the left, and then select the drop-down menu on the right. Model Card Deploy Select and then select the option in order to see a sample code snippet to call the model. Inference API JavaScript Setup Oracle Database We can use any flavor of the Oracle database 23c. On the cloud, we can use Oracle always free autonomous database later this year when 23c should be available. We can use the Oracle 23c Free version as it’s available now and we can simply install or use a container image locally. Or of course, we could use the local for dev and cloud for production, etc. and either of these options is very quick to set up… Oracle Database Free… You can go here to set up the Free Oracle Database. Using the container image is very simple. You can simply pull the image: docker pull container-registry.oracle.com/database/free:latest Then, execute the one line below, replacing with a password of your choice (you can also add a parameter with a directory location in order to persist changes across restarts). Notice the param that allows calls out of the container. This is the setting for Macs and will be different if running on a different OS. -e ORACLE_PASSWORD=Welcome12345 -v oracle-volume:/somedirectory --add-host docker.for.mac.host.internal:host-gateway docker run -d -p 1521:1521 — add-host docker.for.mac.host.internal:host-gateway -e ORACLE_PASSWORD=Welcome12345 gvenzl/oracle-free Setup SQLcl (or Database Actions) and connect You can install SQLcl to manage the database by: Downloading and install from this location https://www.oracle.com/database/sqldeveloper/technologies/sqlcl/download/ This will provide an executable that we will use to administer the database. For convenience, you may add to your PATH. [SQLcl_INSTALL_DIR]/bin/sql [SQLcl_INSTALL_DIR]/bin Login replacing with the location of your SQLcl and replacing with the one you provided as when creating the database Here is an example when using a local install (eg Oracle Database Free container image): [SQLcl_INSTALL_DIR] Welcome12345 ORACLE_PASSWORD [SQLcl_INSTALL_DIR]/bin/sql /nolog SQL> connect sys@//localhost:1521/freepdb1 as sysdba Password? (**********?) ************* Connected. Create the users, with appropriate privileges, that will call Cohere and/or Hugging Face via JavaScript. Run the following SQL files to create the user and its necessary grants and ACLs (note that these can be further tightened for better security). aijs @sql/create_aijs_acl.sql SQL> @sql/create_aijs_user.sql``SQL> Now connect the user and create a table to store the results of the call to Hugging Face SQL> connect aijs/Welcome12345; SQL> create table huggingfacejson (id json); SQL> create table coherejson (id json); Now everything is ready to run! Run either the Cohere or HuggingFace queries from the JavaScript code in the database Cohere Example SQL> @sql/coherequery.sql PL/SQL procedure successfully completed. Then, check the JSON results stored in the table by doing a SQL query. SQL> select * from coherejson; [{"id":"1b42f8f9-ea6d-4a65-8f02-bdcf89d9bd79","generations":[{"id":"d6467c0b-4a78-4dd4-b822-c1e2bfa4ecb0","text":"\n LLMs or Large Language Models are artificial intelligence tools that can read, summarize and translate texts and"}],"prompt":"Please explain to me how LLMs work","meta":{"api_version":{"version":"1"}}} Looking at the code we just executed we can see the JavaScript module created and the function being called: create or replace mle module cohere_module language javascript as import "mle-js-fetch"; import "mle-js-oracledb"; export async function cohereDemo(apiToken) { if (apiToken === undefined) { throw Error("must provide an API token"); } const modelId = "generate"; const restAPI = `https://api.cohere.ai/v1/${modelId}`; const headers = { accept: 'application/json', 'content-type': 'application/json', "Authorization": `Bearer ${apiToken}` }; const payload = { max_tokens: 20, return_likelihoods: 'NONE', truncate: 'END', prompt: 'Please explain to me how LLMs work' }; const resp = await fetch( restAPI, { method: "POST", headers: headers, body: JSON.stringify(payload), credentials: "include" } ); const resp_json = await resp.json(); session.execute( `INSERT INTO COHEREJSON (id) VALUES (:resp_json)`, [ JSON.stringify(resp_json) ] ); } / create or replace procedure coherequery( p_API_token varchar2 ) as mle module cohere_module signature 'cohereDemo(string)'; / -- this is how you can test the API call begin utl_http.set_wallet('system:'); coherequery('[yourcoheretokenhere]'); end; / Hugging Face Example SQL> @sql/huggingfacequery.sql PL/SQL procedure successfully completed. Then, check the JSON results stored in the table by doing a SQL query. SQL> select * from huggingfacejson; [{"score":0.16964051127433777,"token":2053,"token_str":"no","sequence":"the answer to the universe is no."},{"score":0.07344779372215271,"token":2498,"token_str":"nothing","sequence":"the answer to the universe is nothing."},{"score":0.05803246051073074,"token":2748,"token_str":"yes","sequence":"the answer to the universe is yes."},{"score":0.043957971036434174,"token":4242,"token_str":"unknown","sequence":"the answer to the universe is unknown."},{"score":0.04015727713704109,"token":3722,"token_str":"simple","sequence":"the answer to the universe is simple."}] Looking at the code we just executed we can see the JavaScript module created and the function being called: create or replace mle module huggingface_module language javascript as import "mle-js-fetch"; import "mle-js-oracledb"; export async function huggingfaceDemo(apiToken) { if (apiToken === undefined) { throw Error("must provide an API token"); } const payload = { inputs: "The answer to the universe is [MASK]." }; const modelId = "bert-base-uncased"; const headers = { "Authorization": `Bearer ${apiToken}` }; const restAPI = `https://api-inference.huggingface.co/models/${modelId}`; const resp = await fetch( restAPI, { method: "POST", headers: headers, body: JSON.stringify(payload), credentials: "include" } ); const resp_json = await resp.json(); session.execute( `INSERT INTO HUGGINGFACEJSON (id) VALUES (:resp_json)`, [ JSON.stringify(resp_json) ] ); } / create or replace procedure huggingfacequery( p_API_token varchar2 ) as mle module huggingface_module signature 'huggingfaceDemo(string)'; / -- this is how you can test the API call begin utl_http.set_wallet('system:'); huggingfacequery('[hf_yourhuggingfacetokenhere]'); end; / Useful parameters and debug information for Hugging Face can be found . here The results can now be queried, analyzed, etc. using SQL or JSON (simultaneously thanks to the new JSON duality feature), REST, or even the MongoDB API.Oracle Database is the perfect database for AI for a number of reasons, particularly as it is a vector database with capabilities such as the following already available today: Native data types for vector representation and storage: RAW, BLOB, JSON In-Memory Column Store to store & search vector embeddings w/ SIMD kernels for amazing performance Extensible indexing framework to create data-model specific indices (e.g., Text, Spatial) Native Oracle Machine Learning APIs — Modeling, Classification, Scoring, Clustering, etc. Plus, many other capabilities which, used together, make Oracle a better Vector Database DMLs, Parallel Loading, Partitioning, Advanced Compression, Parallel Query, RAC, Sharding, etc. It is also possible to call Oracle OCI AI and other services from within the Oracle database. Conclusion The blog went into how to call Hugging Face APIs from within the Oracle database using JavaScript thus demonstrating a powerful combination of features perfectly suited to a broad array of AI solutions and friendly to JavaScript developers. Some other blogs related to JavaScript in the database, in general, the MultiLingual Engine (MLE) that makes it possible, etc. can be found and . Thanks to Martin and Lucas for all their help. in Martin Bach’s blogs this post about JavaScript modules I look forward to any comments or questions you may have and really appreciate your time reading. Also published . here

Calling Hugging Face AI From an Oracle Database Using JavaScript

Authors:
(1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research;
(2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind;
(3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Participant Interviews Participant Surveys Discussion and Future Work Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS 7 DISCUSSION AND FUTURE WORK 7.1 Future Work Towards Coherent Story Generation While common sense and logical consistency is an elusive goal for LLMs [7], their utility as a writing tool increases as they generate more coherent outputs. Hierarchical generation can be leveraged in various ways to improve the long-range coherence of long texts. One enhancement to improve coherence, especially suited for screenplays and theatre scripts, is a method to generate complex and complete character arcs. Likewise, generating satisfying scene conclusions in dialogue is rare. One technique to address this is to use hierarchical dialogue generation by constructing each scene’s dialogue from a generated beginning, middle, and end dialogue beat. Finally, to improve stylistic coherence, future work could explore methods to generate thematic outputs satisfying notions of genre. This could be done by writing new prompts or transposing existing ones into a variety of diverse author styles and voices. 7.2 On the Difference between Film and Theatre, and a Critique of the Formulaic Hierarchy in Dramatron As Dramatron relies on top-down hierarchical generation, each subsequent step depends on those that came prior. Several participants noted that this style of writing is more aligned with screenwriting than playwriting. In the words of some industry professionals, they were “drawing a line between screenwriting and script writing for stage. Playwrights do not use the log line in the same way as in screen writing” (p9). Participants 4 and 5, who self-identified as playwrights for the stage, “would never approach a piece of work with a story in [their] head. [They] might come with a more investigative approach with a theme”. In fact, they argued against Dramatron’s constrained top-down hierarchy: “Why generate characters first? At earlier parts of the creation process you might not know the characters. In the way [we] work, [we] often come with characters at the end, and the idea of characters comes last”. The log line itself could be seen as a post-hoc summary, as “often times playwrights find out what the play is about after [they] finish it. I will know the play once it is done” (p9). This said, Dramatron does allow for going back-and-forth between levels in the hierarchy, and through creative prompting, future work could allow for generation steps can happen out of order. Differences in the writing of screenplays and theatre scripts can also be related to cultural and economic factors: “The difference between theatre and screen is that nobody’s making theatre on demand, no outside pressure. Or at least not in the same way that there is for television. Theatre feels less like generating content” (p4, p5) whereas “film scripts, in the industry, want the traditional fourth wall” (p9). This reflection invites us to reconsider the applicability of Dramatron. Since our tool is formulaic by construction, is it suitable to TV or film production? As one respondent noted, “[Dramatron] will be very useful for an average Hollywood movie and for television. It does not need to have a deep understanding of the human soul, unlike Shakespeare. [...] The thing with action movies is that is that actors are not expected to connect with the writer. A screenwriter on a TV set is just like [Dramatron] [...]. It is a sublime skill to be a Hollywood writer because your creative input is small.” (p9). 7.3 Ethical Questions and Risks Posed by AI Writing Tools We describe a co-creative tool built around large language models. It can augment and uplift human artists’ work by providing them with inspiration, as well as challenge them and thereby support their artistic practice. Before conducting our study, we identified three directly relevant risks and ethical implications discussed in previous work [116]: 1) bias and offensive language in the generated output, 2) automation of creative work resulting in “cannibalizing” the work of creative artists engaged in script writing, and 3) copyright infringement by reusing copyrighted data from the training dataset, either knowingly (e.g. through prompting: “write the script in the style of Ursula Le Guin”) or unknowingly (e.g. by virtue of similar training data). Our mitigation strategy is two-fold: we invite the creative human artist into the loop throughout the co-authorship process, and we maintain clarity and transparency on the origin of the generated text. To mitigate copyright issues, the writer could query short parts of the script using a search engine and plagiarism detection tools [59]; this functionality could be built directly into co-creative tools. Writers using these tools should be aware of the origin of the data in the LLM, and their audiences should be aware that those outputs were generated through an interaction between humans and co-creative tools. Interestingly, study participants independently raised these concerns during interviews. From the feedback gathered in the study, some participants reported that outputs from the LLM can sometimes be problematic, stereotypical, or biased: for example, “I am less sexist than the computer” (p3), or “the protagonists are both male characters, and all of the supporting characters are female” (p4, p5). Furthermore, participants raised concerns about the source of the dataset: “If you are putting existing scripts into the dataset, where are they being pulled from?” (p4, p5). Thoughts on this subject ranged from “Plagiarising the corpus of scripts is a problem” (p2) to “In the context of collective and devised creation, [reusing existing published work] is not necessarily a problem, because it can be perceived as an homage to existing work” (p11). The rules and norms for the use of systems trained on copyright-protected material are the subject of ongoing work [9]. For example, Lee et al. (2022) distinguish between verbatim, paraphrase, and idea plagiarism [59]. Finally, participants raised concern about the potential impact of generative tools on creative economies: “It would free the artist from writing formulaic scripts, [but] it also replaces the work opportunities” (p4, p5). In general, participants found our mitigation strategies satisfactory and none reported distress or concern regarding outputs from the model. While not the prime focus of the interview sessions, biases and stereotypes could be systematically explored: future work could explore what sorts of narratives can be written using using AI tools, and how the system performs for different cultural groups. 7.4 Using a Tool or Participating in a Co-Creative System? Previous work has argued for engagement with subject matter experts, literary scholars, and industry professionals [109]. In this work, screenwriters and playwrights co-wrote with Dramatron. In the post-interview surveys, most of the participants felt they did not own the final output. This raises several questions: Should Dramatron be considered merely a writing tool, or should it rather be seen as a co-creative system? Are writers comfortable using and ready to adopt co-creative tools? Participants reflected on the authorship of machine co-created text (“Interesting what this means for the future of writing. Who is the author?”, p6). As a corollary to the issues of authorship and of biases, p3 wondered whether an LLM should generate text from the perspectives of different identities, and if that could amount to cultural appropriation (although they later added: “but I write about many people too, and I am less objective than this AI, because I have seen less data”). Chung et al. [20] discuss how AI-based Creativity Support Tools can be seen as part of the Artist Support Network. These tools may need to conform to the artists’ expectations of collaboration, similarly to the types of interactions they have with human collaborators: for example, sub-contracting, co-creation, or inspiration. Our expert interviews and surveys surfaced similar views towards the interaction with Dramatron and co-creative writing tools in general. This paper is available on arxiv under CC 4.0 license. Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Authors: Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Abstract and Intro Storytelling, The Shape of Stories, and Log Lines Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Evaluating Text Generated by Large Language Models Participant Interviews Participant Interviews Participant Surveys Participant Surveys Discussion and Future Work Discussion and Future Work Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS G. CO-WRITTEN SCRIPTS 7 DISCUSSION AND FUTURE WORK 7.1 Future Work Towards Coherent Story Generation While common sense and logical consistency is an elusive goal for LLMs [7], their utility as a writing tool increases as they generate more coherent outputs. Hierarchical generation can be leveraged in various ways to improve the long-range coherence of long texts. One enhancement to improve coherence, especially suited for screenplays and theatre scripts, is a method to generate complex and complete character arcs. Likewise, generating satisfying scene conclusions in dialogue is rare. One technique to address this is to use hierarchical dialogue generation by constructing each scene’s dialogue from a generated beginning, middle, and end dialogue beat. Finally, to improve stylistic coherence, future work could explore methods to generate thematic outputs satisfying notions of genre. This could be done by writing new prompts or transposing existing ones into a variety of diverse author styles and voices. 7.2 On the Difference between Film and Theatre, and a Critique of the Formulaic Hierarchy in Dramatron As Dramatron relies on top-down hierarchical generation, each subsequent step depends on those that came prior. Several participants noted that this style of writing is more aligned with screenwriting than playwriting. In the words of some industry professionals, they were “drawing a line between screenwriting and script writing for stage. Playwrights do not use the log line in the same way as in screen writing” (p9). Participants 4 and 5, who self-identified as playwrights for the stage, “would never approach a piece of work with a story in [their] head. [They] might come with a more investigative approach with a theme”. In fact, they argued against Dramatron’s constrained top-down hierarchy: “Why generate characters first? At earlier parts of the creation process you might not know the characters. In the way [we] work, [we] often come with characters at the end, and the idea of characters comes last”. The log line itself could be seen as a post-hoc summary, as “often times playwrights find out what the play is about after [they] finish it. I will know the play once it is done” (p9). This said, Dramatron does allow for going back-and-forth between levels in the hierarchy, and through creative prompting, future work could allow for generation steps can happen out of order. Differences in the writing of screenplays and theatre scripts can also be related to cultural and economic factors: “The difference between theatre and screen is that nobody’s making theatre on demand, no outside pressure. Or at least not in the same way that there is for television. Theatre feels less like generating content” (p4, p5) whereas “film scripts, in the industry, want the traditional fourth wall” (p9). This reflection invites us to reconsider the applicability of Dramatron. Since our tool is formulaic by construction, is it suitable to TV or film production? As one respondent noted, “[Dramatron] will be very useful for an average Hollywood movie and for television. It does not need to have a deep understanding of the human soul, unlike Shakespeare. [...] The thing with action movies is that is that actors are not expected to connect with the writer. A screenwriter on a TV set is just like [Dramatron] [...]. It is a sublime skill to be a Hollywood writer because your creative input is small.” (p9). 7.3 Ethical Questions and Risks Posed by AI Writing Tools We describe a co-creative tool built around large language models. It can augment and uplift human artists’ work by providing them with inspiration, as well as challenge them and thereby support their artistic practice. Before conducting our study, we identified three directly relevant risks and ethical implications discussed in previous work [116]: 1) bias and offensive language in the generated output, 2) automation of creative work resulting in “cannibalizing” the work of creative artists engaged in script writing, and 3) copyright infringement by reusing copyrighted data from the training dataset, either knowingly (e.g. through prompting: “write the script in the style of Ursula Le Guin”) or unknowingly (e.g. by virtue of similar training data). Our mitigation strategy is two-fold: we invite the creative human artist into the loop throughout the co-authorship process, and we maintain clarity and transparency on the origin of the generated text. To mitigate copyright issues, the writer could query short parts of the script using a search engine and plagiarism detection tools [59]; this functionality could be built directly into co-creative tools. Writers using these tools should be aware of the origin of the data in the LLM, and their audiences should be aware that those outputs were generated through an interaction between humans and co-creative tools. Interestingly, study participants independently raised these concerns during interviews. From the feedback gathered in the study, some participants reported that outputs from the LLM can sometimes be problematic, stereotypical, or biased: for example, “I am less sexist than the computer” (p3), or “the protagonists are both male characters, and all of the supporting characters are female” (p4, p5). Furthermore, participants raised concerns about the source of the dataset: “If you are putting existing scripts into the dataset, where are they being pulled from?” (p4, p5). Thoughts on this subject ranged from “Plagiarising the corpus of scripts is a problem” (p2) to “In the context of collective and devised creation, [reusing existing published work] is not necessarily a problem, because it can be perceived as an homage to existing work” (p11). The rules and norms for the use of systems trained on copyright-protected material are the subject of ongoing work [9]. For example, Lee et al. (2022) distinguish between verbatim, paraphrase, and idea plagiarism [59]. Finally, participants raised concern about the potential impact of generative tools on creative economies: “It would free the artist from writing formulaic scripts, [but] it also replaces the work opportunities” (p4, p5). In general, participants found our mitigation strategies satisfactory and none reported distress or concern regarding outputs from the model. While not the prime focus of the interview sessions, biases and stereotypes could be systematically explored: future work could explore what sorts of narratives can be written using using AI tools, and how the system performs for different cultural groups. 7.4 Using a Tool or Participating in a Co-Creative System? Previous work has argued for engagement with subject matter experts, literary scholars, and industry professionals [109]. In this work, screenwriters and playwrights co-wrote with Dramatron. In the post-interview surveys, most of the participants felt they did not own the final output. This raises several questions: Should Dramatron be considered merely a writing tool, or should it rather be seen as a co-creative system? Are writers comfortable using and ready to adopt co-creative tools? Participants reflected on the authorship of machine co-created text (“Interesting what this means for the future of writing. Who is the author?”, p6). As a corollary to the issues of authorship and of biases, p3 wondered whether an LLM should generate text from the perspectives of different identities, and if that could amount to cultural appropriation (although they later added: “but I write about many people too, and I am less objective than this AI, because I have seen less data”). Chung et al. [20] discuss how AI-based Creativity Support Tools can be seen as part of the Artist Support Network. These tools may need to conform to the artists’ expectations of collaboration, similarly to the types of interactions they have with human collaborators: for example, sub-contracting, co-creation, or inspiration. Our expert interviews and surveys surfaced similar views towards the interaction with Dramatron and co-creative writing tools in general. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Advancing Coherent Story Generation: Future Directions for AI Writing Tools

Lasers by Hal Hellman, is part of the HackerNoon Books Series. You can jump to any chapter in this book . WHAT’S SO SPECIAL ABOUT COHERENT LIGHT? here WHAT’S SO SPECIAL ABOUT COHERENT LIGHT? So desirable are the qualities of coherent light that the complicated filtering process described above has actually been used. For example, one British experimenter, Dennis Gabor, used it in the 1940s in an attempt to make a better microscope. But so great was the effort, and so meager the resulting light, that this project was abandoned. In the course of Dr. Gabor’s experiments, however, he did manage to make a special kind of picture, using coherent light, which he called a . He derived the name from two Greek words meaning a . We shall see why in a moment. hologram whole picture Ordinary black and white photographs merely record darks and lights, or the intensity of the illumination, thereby providing a scale of grays, nothing more. But because waves of coherent light consistently maintain their relative spacing, they can be used to record additional information, namely the distance from objects. For example, if we shine a beam of coherent (laser) light between two objects we can, knowing the light wavelength, determine the distance between them to a high degree of accuracy. The basic idea is diagramed in . It can be seen that the number of waves times the wavelength gives the precise distance (to within 1 wavelength of light) from the laser source to each object. But this would be a difficult process to implement. Figure 12 A better way, and one that is already in operation, is to use conventional methods to measure the approximate distance and use the laser beam for precise or fine measurement. In the device shown in , the beam is split into two parts. One part is kept in the instrument itself to act as a reference. The other is aimed at a reflector, which sends it back to a detector in the main device, where it is automatically compared with the reference beam. If the two beams are in phase (that is, if their crests are superimposed), the waves combine and produce a high intensity beam at the detector. As the reflector moves closer to or farther away from the laser source the beam intensity decreases and then increases 20again as the wave crests move in and out of phase. The instrument counts the changes and displays the distance the reflector moves, as a function of the wavelengths, on the control cabinet meters. Figure 2 Figure 12 Principle of distance measurement using coherent light. Wavelength times number of waves gives precise distance between laser and object. Distance to be measured Laser Object No. 21 Wavelength Object No. 1 Since the word for the interaction of the waves in a system like this is “interference”, the measurement process is called (pronounced in ter fer OM e try). Although not new, it can now be applied for the first time in machine tool applications, providing the accuracy needed in this age of space technology and microminiaturization. Measurements with a laser interferometer can be made with an accuracy of 0.5 part per million at distances up to 200 inches. Such precision was previously unheard of in a machine shop environment, having been limited to laboratory measurements, and only at a range of a few inches. Under similar laboratory conditions, measurements by laser interferometry now detect movements of 10⁻¹¹ centimeter, a distance approaching the dimensions of an atomic nucleus. interferometry Now let us suppose we expand the laser beam as shown on , and, with the aid of a mirror, direct part of it (the reference beam) at a photographic plate. The remaining portion of the diverging beam is used to illuminate the object to be photographed. Some of this light (the object beam) is reflected toward the plate and carries with it information about the object, as indicated by the wavy line. In the region where these two beams intersect, interference occurs, and a sample of this interference is recorded within the photographic emulsion. Where two crests meet a dark spot is recorded; where the waves are out of phase the processed plate is clear. The result is a hologram, a complex pattern of “fringes”, characteristic of the contour and light and dark areas of the object, as well as its distance from the plate. These fringes have the ability to diffract light rays. When light from a laser, or a point source of white light, is directed at the hologram from the same direction as the reference beam, part of the light is “bent” so that it appears to come from the place once occupied by the object. The result is a remarkably realistic 3-dimensional image. page 22 There, in a nutshell, is the incredible new technique of holography. The extreme order of laser light is illustrated by the regularity of the dots on the cover of this booklet. This strange kind of light provides us with yet other advantages. Indeed, one of the most important is the fact that the energy of the laser is not being sprayed out in all directions. All of it is concentrated in the narrow beam that emerges from the device. And it narrow. Laser light has already been shone on the moon, the beam spreading out to only a few miles in traveling there from earth. The best optical searchlight beam would spread wider than the moon itself, thus dissipating its energy. stays It is for this reason, as well as its temporal coherence, that laser light is being considered for communications. A narrow beam is particularly important for space communications because of the long distances involved. But it is also possible to focus laser light as no light has ever been focused before. At close range a laser beam can be focused down to a circle just a few wavelengths across, concentrating its energy and making it possible to drill holes only 0.0002 inch in diameter. The photo on shows the exquisite control that can be exercised. page 52 Let us see what this focusability means in terms of power. Consider, by way of analogy, a dainty 100-pound lady in a pair of spike-heeled shoes. As she takes a step, her weight will be concentrated on one of those heels. If the area of the heel is, say, one quarter of a square inch 22(½ × ½ inch), the pressure exerted on the poor tile or carpet rises to 400 pounds per square inch (4 × 100) and if the heel is only ¼ inch on a side, the pressure will be 1600 pounds per square inch! Making and Viewing a Hologram MAKING A HOLOGRAM Object Object beam Holographic plate Mirror Reference beam Laser VIEWING A HOLOGRAM Hologram Image Eye Coherent light source What we are getting at, of course, is the fact that the coherence of the laser beam permits it to be concentrated into a tiny area. Thus whatever total energy is being sent out by the laser can be concentrated to the point where its effective energy is tremendous. The sun emits some 6500 watts per square centimeter. Laser beams have already reached 500 watts per square centimeter. million But the power of the laser does not derive solely from its ability to be focused. Even an unfocused beam is several times more powerful than the sun’s output (per square centimeter). Figure 13 The typical hologram, looks like a geometric design, but it contains more information than would an ordinary photograph. The , made from a hologram, show the detail, apparent solidity, and parallax effect of the reconstructed light waves. The parallax effect is the ability to see around the objects just as one could if they were really there. (See .) images below frontispiece Tank, from another angle The crucial difference between the sun’s light or any ordinary kind of light and laser light lies in the extent to which the emission of energy can be controlled. In the production of ordinary light the atoms, as we know, emit spontaneously, or in an uncontrolled fashion. But if the atoms could be forced to take in the proper amount of energy, store it, and release it when we wanted them to, we would have , rather than spontaneous, emission. stimulated This, however, is practically the same as the amplification principle we discussed earlier. In that case, a small radio signal is jacked up into a large one by stimulating an available power source to release its energy at the same wavelength and in step with the smaller signal. The question is, how can we do this with light? About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Hal Hellman (2021). Laser. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/65512/pg65512-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

WHAT’S SO SPECIAL ABOUT COHERENT LIGHT?

We live in a unique time, a time when everything is changing rapidly, thanks to . AI is revolutionizing numerous industries in the market. In this article, we will discuss how multilingual model overcomes language barriers and assists companies in accessing the global market! AI Cohere's But before we will dive in, inviting to build with the latest AI technologies at our ! lablab.ai you AI Hackathons The Challenge Problems and limitations arise when humans and machines attempt to communicate using natural language. These barriers can occur due to differences in linguistic understanding, context, ambiguity, and the limitations of current machine-learning models. Consider an example of a person who wants to order pizza: Human: "I'm craving some pizza. Where's a good place to get one around here?" Machine: "You should try Joe's Pizzeria. It's amazing!" In this scenario, the human expresses a desire for and seeks a recommendation for a good place to get one. However, the machine lacks common ground with the human, meaning it does not possess shared knowledge about the local area, including the availability and quality of pizza establishments. Consequently, the machine responds with a generic suggestion, assuming the human is looking for a general recommendation. pizza The lack of common ground prevents the machine from providing personalized or contextually relevant information. Ideally, a machine equipped with relevant location-based data, user preferences, or access to local reviews could offer more tailored recommendations. Establishing common ground between humans and machines necessitates the machine's ability to leverage shared knowledge, user preferences, or external resources to provide more accurate and contextually appropriate responses. This is where comes in. Cohere’s Multilingual Model How Cohere tackles these challenges For teams working with machine learning, Cohere's Multilingual Model Embed provides a powerful tool for creating text analysis applications. It offers high-performance and accurate embeddings in English as well as over 100 other languages. Its key features include: Build semantic search capability using conversational language Clustering similar topics and discovering thematic trends across a body of text sources Building a recommendation engine to engage users with more relevant content Running topic modeling, semantic search, and recommendations across 100+ languages with just one model Relevance of Multilingual Models Enabling individuals with diverse linguistic backgrounds to transcend language barriers fosters the exchange of knowledge, ideas, and innovations. This, in turn, paves the way for swift advancements in various domains like science and technology, ensuring equitable access to information and opportunities for people across the globe. They have use in many areas: Multilingual Model can help translate text or speech between different languages, making it easier for people to communicate. They can also assist in real-time language conversion during conversations. Translation and Interpretation: This model allows users to search for information in one language and get results in multiple languages, so language barriers don't limit access to information. Finding Information in Different Languages: They can generate articles, reports, or social media posts in different languages. They can also make long texts shorter and easier to understand. Creating and Summarizing Content: Multilingual Model make it possible for chatbots and virtual assistants to help people in different languages, so more people can benefit from their assistance. Chatbots and Virtual Assistants in Multiple Languages: This model can support language learners by providing translations, language exercises, vocabulary help, and grammar correction, making learning more effective for people studying different languages. Language Learning: They can analyze feelings and opinions expressed in different languages, helping organizations track their reputation, customer feedback, and public sentiment on a global scale. Understanding Sentiments and Social Trends: It can improve customer support by enabling multilingual communication, making it easier for customers and support agents to understand each other. Better Customer Support in Different Languages: LLMs can compare cultural aspects, expressions, and sentiments across languages, which is useful for studying different cultures, analyzing markets, and understanding global trends. Cross-cultural Analysis: The model can accurately translate legal and medical documents, making it easier for professionals in these fields to access important information. Translations for Legal and Medical Fields: , a global leader in trustworthy and equitable AI solutions for businesses, has the trust of numerous world-class brands such as , , and . These brands leverage platform to securely and responsibly engage with millions of consumers. With LivePerson, these brands facilitate over a billion conversational interactions each month, which in turn generates a vast and invaluable dataset. platform equips businesses with safety tools and harnesses the potential of generative AI and large language models to drive improved business outcomes. LivePerson HSBC Chipotle Virgin Media LivePerson's Conversational Cloud LivePerson's The impact of conversational solutions, powered by large language models, is immense for customer brands. The utilization of LLMs not only leads to increased customer and employee satisfaction, but it also enables brands to automate a greater number of workflows, reduce operational costs, and optimize resource allocation. By leveraging LLM-powered conversational solutions, brands can redirect their human staff towards higher-value tasks, further enhancing efficiency and maximizing the benefits of AI-driven technologies. LivePerson's Cohere's Summary Cohere's Multilingual Model addresses the challenges of communication barriers between humans and machines by providing a powerful tool for text analysis applications. With high-performance and accurate embeddings available in English and over 100 other languages, Cohere enables teams to build semantic search capabilities, cluster similar topics, and create recommendation engines. This allows businesses to connect and succeed worldwide by engaging users with relevant content and providing contextually appropriate responses in multiple languages. With Cohere's Multilingual Model, businesses can overcome language barriers and tap into global markets more effectively. Big thanks to for writing this article for 💚 Shreya lablab.ai

How Cohere's Multilingual Model is Helping Businesses Connect and Succeed Worldwide

Cómo el modelo multilingüe de Cohere está ayudando a las empresas a conectarse y tener éxito en todo el mundo

Comment le modèle multilingue de Cohere aide les entreprises à se connecter et à réussir dans le monde entier

कैसे कोहेयर का बहुभाषी मॉडल दुनिया भर में व्यवसायों को जुड़ने और सफल होने में मदद कर रहा है

Cohere の多言語モデルが世界中の企業のつながりと成功にどのように役立っているか

Como o modelo multilíngue da Cohere está ajudando as empresas a se conectarem e terem sucesso em todo o mundo

Mô hình đa ngôn ngữ của Cohere đang giúp các doanh nghiệp kết nối và thành công trên toàn thế giới như thế nào

Cohere 的多语言模式如何帮助企业在全球范围内建立联系并取得成功

It struck me today that people are like mosaics. I’m sure I cannot be the first person to make this connection, but it’s a new idea for me. A mosaic is defined (by my Oxford Dictionary) as “a picture or pattern produced by arranging small colored pieces of hard material…” and “a combination of diverse elements forming a more or less coherent whole.” At distance, a mosaic appears as a whole or complete picture. The whole may be a geometric or abstract pattern, a landscape, or a portrait. Anything. As you get closer, you start to notice the mosaic’s pieces. It turns out that the “picture” is composed of many smaller elements or pieces, which may differ in size, shape, color or shade, and texture. The pieces may be smooth and uniform or jagged and irregular, and the “whole” may contain uniform pieces in one area and jagged pieces in another. Some parts may look like broken pieces or fragments and these broken fragments may appear to be leftovers from some other earlier whole object or rejects from another thing. The closer you get, the more you learn how the picture is more diverse, detailed and complicated than it appears from a distance. Mosaics are mounted on some kind of skeleton structure, base, or . And the mosaic’s pieces are held together by a medium in which they rest and bond, forming a “more or less whole.” foundation coherent A “more or less coherent whole”? I’ll take that: the more and the less. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Notes Toward a Unified Theory of the Coherent Person (Mosaic)

Introduction: Ever since the buzz about GenAI and ChatGPT, I have been trying to understand the space and leveraging it into my day-to-day activities as a Data Engineer. No doubt it has been useful for debugging with respective programming and documentation. Personally, it played a significant role in helping me plan my Dubai itinerary. It even contributed to naming my friend’s daughter. However, it works pretty well if you give appropriate and precise requirements. There is a lot of anxiety among people that AI is going to take our jobs as they feel almost everything will be automated through AI. Is LLM as good as everyone claims? In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes, where it requires enhancement, and where human expertise remains irreplaceable. Areas of Strength: Basic Data Querying: 100% the human necessity is going to get disrupted, but the severity based on the complexity is high. Even the SQL practitioners won’t feel much difference here, and self-serve analytics will catch fire in this area which is something phenomenal! Again, to quote positively, data engineers won’t need to guide the stakeholders nearly as much! Troubleshooting Pipeline Failures: LLMs coupled with agents will slash on-call load massively. LLMs processing stack traces/quality failures can recommend the built-in remedies and nudge the ball down the field to address the problem, presumably even over Slack. And that is a fantastic percentage of data engineering hours saved! To be clear, some hard failures require manual troubleshooting. Anomaly Detection: Regardless of the structure, format, source, complexity, timeliness, or even natural play of data, large language models can identify anomalies. LLMs are well-suited to anomaly identification because they can work across a variety of data sources, identify subtle patterns, and distinguish when data isn’t behaving as expected. This will help data engineers to identify and fix data quality problems in advance. Areas for Enhancement: Data Cleansing: Our results suggest that Language Models (LLMs) are highly effective in data cleansing due to their high performance in unstructured data management and semantic error identification and correction, as well as high scalability. Furthermore, our review of the current state-of-the-art suggests that even more substantial improvements are possible, including fine-tuning of LLMs to specific domains or even organization data. In addition, various interactive mechanisms as well as coupling with data quality frameworks can significantly increase the efficiency of LLMs. Overall, LLMs allow for organizing and improving the whole process of data cleansing, helping to obtain high-quality datasets, and, consequently, valuable insights. Visualization and Report Generation: Language Models (LLMs) excel in visualization and documentation tasks by automating the creation of reports, summarizing data insights, and generating narrative-driven reports. Enhancements needed include improving document organization and categorization, data-driven storytelling capabilities integrating with version control systems and visualization tools for seamless output generation, and enhancing interpretability features to ensure clarity and coherence in reports. Writing Pipeline Code in SQL, Spark, or Flink LLM can be a helpful starting point, and for our work, it is a good boilerplate. Still, the generated material is hard to debug — or often not, worth the trouble. Prompt-driven content is still far from being able to turn out a nice, optimized, and absolutely accurate data pipeline. Optimization By providing suggestions on efficient query structures and detecting problems that slow performance, LLMs are especially powerful when it comes to SQL optimization. They can even make recommendations for optimizations on the basis of historical query data. Things for improvement include integrating database-specific optimization techniques into the training of LLM, bridging with query execution plans in order to give context-oriented advice and feedback mechanisms so that one can modify strategies for optimization according to what users prefer as well as performance metrics. Physical Data Model As soon as the logical model is finalized, LLM will be efficient in moving and upgrading this logical (or conceptual) data model into the targeted architecture. The problem that LLM encounters with all the examples is that it’s hard for most people to understand the pipeline, table, and its relationship. Areas Where Human Expertise Remains Irreplaceable: Logical Data Model Data modeling is a very soft skills-focused task. You have to communicate with different stakeholders to understand the business context in detail. Understanding what data is needed, the relationship between entities, and considering different scenarios are human-oriented tasks that LLMs must continue to struggle with. Building Pipeline and Framework Once they reach a certain level of complexity, LLMs begin to give up. Imagine if someone asked ChatGPT, “Can you provide me the source code of Android’s next version?” While LLMs can help with routine data engineering tasks, they do not have the insight or inventiveness unique to humans that allows for new solutions to complex challenges, unlocking creativity in technical terms. Domain Expertise and Intuition: Human data engineers bring a unique blend of domain expertise and intuition to their work that often surpasses what conventional software isn’t able to achieve through automation. This intuition allows them to find subtle patterns in data that no fully rigorous automated system can. Based on their knowledge of this field and their experiences in it, they can predict future trends. They can also make decisions in light of a broader context, including corporate or government needs, ethical considerations, and potential future scenarios. This holistic approach to data engineering surpasses what LLMs have achieved. Although LLMs have great power, they are typically restricted to the patterns and relationships they have been trained to recognize. Conclusion: On the whole, Gen AI can certainly automate trivial procedures such as looking for information, launching programs that don’t work properly, repeatedly correcting faults, and anomaly detection. My gut tells me that Gen AI will win out and excel at tasks such as Data Cleansing, Optimization, and Writing Pipelines — once the team understands gaps and missing patterns. But it can not learn purely human soft skills that are important for building logical data models or a framework. I believe that these skills, which cannot be automated, are also essential for stakeholders to ask for innovation in the first place. After all, no one ever asked Steve Jobs if they needed anything like an iPhone; he just thought up what would become its invention. References: https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b ​​__https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e__ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead Introduction: Ever since the buzz about GenAI and ChatGPT, I have been trying to understand the space and leveraging it into my day-to-day activities as a Data Engineer. No doubt it has been useful for debugging with respective programming and documentation. Personally, it played a significant role in helping me plan my Dubai itinerary. It even contributed to naming my friend’s daughter. However, it works pretty well if you give appropriate and precise requirements. There is a lot of anxiety among people that AI is going to take our jobs as they feel almost everything will be automated through AI. Is LLM as good as everyone claims? In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes, where it requires enhancement, and where human expertise remains irreplaceable. Areas of Strength: Basic Data Querying: 100% the human necessity is going to get disrupted, but the severity based on the complexity is high. Even the SQL practitioners won’t feel much difference here, and self-serve analytics will catch fire in this area which is something phenomenal! Again, to quote positively, data engineers won’t need to guide the stakeholders nearly as much! Troubleshooting Pipeline Failures: LLMs coupled with agents will slash on-call load massively. LLMs processing stack traces/quality failures can recommend the built-in remedies and nudge the ball down the field to address the problem, presumably even over Slack. And that is a fantastic percentage of data engineering hours saved! To be clear, some hard failures require manual troubleshooting. Anomaly Detection: Regardless of the structure, format, source, complexity, timeliness, or even natural play of data, large language models can identify anomalies. LLMs are well-suited to anomaly identification because they can work across a variety of data sources, identify subtle patterns, and distinguish when data isn’t behaving as expected. This will help data engineers to identify and fix data quality problems in advance. Areas for Enhancement: Data Cleansing: Our results suggest that Language Models (LLMs) are highly effective in data cleansing due to their high performance in unstructured data management and semantic error identification and correction, as well as high scalability. Furthermore, our review of the current state-of-the-art suggests that even more substantial improvements are possible, including fine-tuning of LLMs to specific domains or even organization data. In addition, various interactive mechanisms as well as coupling with data quality frameworks can significantly increase the efficiency of LLMs. Overall, LLMs allow for organizing and improving the whole process of data cleansing, helping to obtain high-quality datasets, and, consequently, valuable insights. Visualization and Report Generation: Language Models (LLMs) excel in visualization and documentation tasks by automating the creation of reports, summarizing data insights, and generating narrative-driven reports. Enhancements needed include improving document organization and categorization, data-driven storytelling capabilities integrating with version control systems and visualization tools for seamless output generation, and enhancing interpretability features to ensure clarity and coherence in reports. Writing Pipeline Code in SQL, Spark, or Flink LLM can be a helpful starting point, and for our work, it is a good boilerplate. Still, the generated material is hard to debug — or often not, worth the trouble. Prompt-driven content is still far from being able to turn out a nice, optimized, and absolutely accurate data pipeline. Optimization By providing suggestions on efficient query structures and detecting problems that slow performance, LLMs are especially powerful when it comes to SQL optimization. They can even make recommendations for optimizations on the basis of historical query data. Things for improvement include integrating database-specific optimization techniques into the training of LLM, bridging with query execution plans in order to give context-oriented advice and feedback mechanisms so that one can modify strategies for optimization according to what users prefer as well as performance metrics. Physical Data Model As soon as the logical model is finalized, LLM will be efficient in moving and upgrading this logical (or conceptual) data model into the targeted architecture. The problem that LLM encounters with all the examples is that it’s hard for most people to understand the pipeline, table, and its relationship. Areas Where Human Expertise Remains Irreplaceable: Logical Data Model Data modeling is a very soft skills-focused task. You have to communicate with different stakeholders to understand the business context in detail. Understanding what data is needed, the relationship between entities, and considering different scenarios are human-oriented tasks that LLMs must continue to struggle with. Building Pipeline and Framework Once they reach a certain level of complexity, LLMs begin to give up. Imagine if someone asked ChatGPT, “Can you provide me the source code of Android’s next version?” While LLMs can help with routine data engineering tasks, they do not have the insight or inventiveness unique to humans that allows for new solutions to complex challenges, unlocking creativity in technical terms. Domain Expertise and Intuition: Human data engineers bring a unique blend of domain expertise and intuition to their work that often surpasses what conventional software isn’t able to achieve through automation. This intuition allows them to find subtle patterns in data that no fully rigorous automated system can. Based on their knowledge of this field and their experiences in it, they can predict future trends. They can also make decisions in light of a broader context, including corporate or government needs, ethical considerations, and potential future scenarios. This holistic approach to data engineering surpasses what LLMs have achieved. Although LLMs have great power, they are typically restricted to the patterns and relationships they have been trained to recognize. Conclusion: On the whole, Gen AI can certainly automate trivial procedures such as looking for information, launching programs that don’t work properly, repeatedly correcting faults, and anomaly detection. My gut tells me that Gen AI will win out and excel at tasks such as Data Cleansing, Optimization, and Writing Pipelines — once the team understands gaps and missing patterns. But it can not learn purely human soft skills that are important for building logical data models or a framework. I believe that these skills, which cannot be automated, are also essential for stakeholders to ask for innovation in the first place. After all, no one ever asked Steve Jobs if they needed anything like an iPhone; he just thought up what would become its invention. References: https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b ​​__ https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e __ https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead

LLMs: An Assessment From a Data Engineer

Court Reveals Wright's 2024 MYOB Email Was Manipulated to Support False Claims

Holodeck Heroes: Building AI Companions for the Final Frontier

The Declining Critical Thinking Skills: From Artificial Intelligence to Average Intelligence

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 34 of 42. 32. “BitCoin: SEIR-C Propagation models of block and transaction dissemination” (Reliance Document) {ID_000550} / {L3/237/1} 622. The document purports to be precursor work to the Bitcoin White Paper, dated 12 December 2008. (a) COPA’s Reasons for Alleging Forgery 623. The document has been backdated. It is an altered version of a document actually published by Dr Wright in 2017 (the “2017 SSRN Paper”). [PM1 [25-38] and [55]]. 624. The document contains hidden, embedded Grammarly timestamps indicating its true date to be 15 April 2017 or later. [PM1 [48-53]]. 625. The Grammarly software did not exist in 2008 [Madden1 [62c]] but is contemporaneous for 2017. [Madden1 [70-72]]. 626. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. [PM1 [25-33]]. 627. The content referred to in the hidden, embedded traces of the 2017 SSRN paper includes news articles and government publications which did not yet exist in 2008 but which are contemporaneous for 2017. [PM1 [19-21] and [55]]. 628. The document contains equations and formulae which have been corrupted in a manner consistent with conversion from a more modern format to an older format [PM1 [34-45]. The document includes tampered content that apparently represents an attempt to explain away this problem as the result of using older equation-editing MathType software. However, analysis indicates that equations in the document were in fact authored with much later version of that software, which did not yet exist in 2008 but which are contemporaneous to 2017. [PM40 [22-42]]. 629. The document contains references to Microsoft schemas which did not yet exist in 2008 but are contemporaneous to 2010 onwards (including 2017). [PM1 [46-47]]. 630. The document includes anachronistic references to events that had not occurred by 2008 but which had occurred by 2017 [PM1 [21-22]]. 631. The internal metadata records an anomalous edit time in excess of 70 days and the editing period of this document overlaps with several other documents in Dr Wright’s disclosure, consistent with being created using clock manipulation techniques. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 632. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting academic work done by Dr Wright in his supposed development of Bitcoin), contrary to fact. 633. Dr Wright has positively asserted that {ID_000550} is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 634. Dr Wright is recorded as an author of the metadata. The doctored content is phrased by Dr Wright in the first person: “Note- I have released online as an anonymous programmer using the pseudonym "Satoshi Nakamoto". 635. Dr Wright is a user of Grammarly software. 636. {ID_000550} contains hidden text embedded within the document which contradicts the information presented on the face of the document, a characteristic of manipulation of a series of MS Word .DOC files found throughout Dr Wright’s disclosure in these proceedings. 637. The 2017 SSRN Paper is a version of the same document that was published on the SSRN website by Dr Wright himself, bearing a “date written” of 28 March 2017. The “date written” is contemporaneous with the hidden Grammarly timestamp in {ID_000550} and not to the purported date of the document. 638. The metadata of the 2017 SSRN Paper lists “craig” as the creator of the document. 639. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. 640. Dr Wright has not disclosed the 2017 SSRN paper or the common source document. 641. In his first witness statement in these proceedings, Dr Wright lists this document as a document to which he has been referred when preparing his evidence. (c) Dr Wright’s Explanations and COPA’s Rebuttal 642. In Appendix B to Wright11, Dr Wright attempted to explain the presence of embedded text showing a remnant version which referred to Bitcoin as an existing system and included various references to events after 2008 (including for instance to a report of May 2013). He claimed that these references were added “as a result of the corruption of the document, probably while it was kept at nChain or one of the other corporations”: {CSW/2/54}. In cross-examination, he claimed that two documents became merged into one another as a result of both being open at the same time on a Citrix environment and an edit not happening correctly on one of them: {Day2/135:1} to {Day2/138:19}. 643. In Appendix B to Wright11, Dr Wright sought to explain away the presence of the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later by saying that they resulted from “someone in nChain opening the document in 2017 or later” and these elements becoming embedded: {CSW/2/53}; {Day2/143:1} to {Day2/143:23}. It is inherent in that explanation that these elements could become embedded without the person saving or otherwise interacting with the document (since such interaction would cause the metadata timestamps to update). 644. When pressed in cross-examination with the point that the remnant text in the document matches text in a 2017 version of the document uploaded by Dr Wright to the SSRN website, Dr Wright could give no coherent answer. When further pressed that this reliance document contains clear signs of corrupted equations (which appear properly readable in the SSRN version), thus suggesting errors resulting from conversion, he likewise failed to give any intelligible answer. See {Day2/140:7} to {Day2/142:25}. 645. Dr Wright also claimed that the references to CheckBlockHeader, UTXO and Bitcoin Core in this document were there because he used them. {Day2/134:14} to {Day2/140:5} 646. COPA submitted that Dr Wright’s explanation should be rejected as dishonest for the following reasons: 646.1. It presupposes that the features of a shared computing environment would cause two documents to become merged, with remnant text added in the way seen here, but in a manner which resulted in the document not being corrupted and appearing normal on its face. This possibility is rejected by the independent expert evidence. See Madden4, [159]; Madden / Placks joint statement 2 at [8]. 646.2. Dr Wright’s explanation for the presence of anachronistic artefacts in the metadata (including the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later) is contradicted by the clear expert evidence of Mr Madden, which is that such references could not be inserted without the document being interacted with by a user, which would in turn result in updating of the metadata timestamps of the document (notably the “last saved” timestamp). The fact that the metadata timestamps for this document were dated to 2008 but the document contains the elements set out above shows that the document has been forged by backdating. See Madden4, [158]; Madden / Placks joint statement2 at [8]; {Day16/35:19} to {Day16/38:11}; {Day16/125:7} to {Day16/125:18}. 646.3. The correspondence between parts of the hidden remnant text of this document and the 2017 SSRN version of the document can only rationally be explained by this document being a copy forged and backdated in recent years. 646.4. This conclusion is further reinforced by the corrupted equations, which are clearly indicative of a document having been converted from a later version of MS Word into an older format which does not support the correct font or automatically applies an incorrect text editing format: see PM1 [44] {H/1/19}. 646.5. The anachronistic terms Dr Wright says he used are not found in other genuine documents before their first use. These first uses were as follows: 646.5.1. CheckBlockHeader was first introduced into the code in March 2014, Wuille1 [25] {C1/1/6}; 646.5.2. Bitcoin Core was first introduced in version 0.9 of the software in 2014 Wuille1 [50] {C1/1/12}; and 646.5.3. UTXO first introduced in version 0.8 of the software by Mr Wuille, with him first seeing the term on 21 June 2012 Wuille1 [29-32] {C1/1/7}. (d) Conclusion 647. In addition to COPA’s submissions in this section, I also refer to [737]-[759] in the main Judgment, all of which demonstrate that the points gathered in Appendix 1 to Dr Wright’s Closing in relation to this document do not begin to explain all the indications of forgery set out above. Dr Wright plainly forged this document from the 2017 SSRN paper. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 34 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 34 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 32. “BitCoin: SEIR-C Propagation models of block and transaction dissemination” (Reliance Document) {ID_000550} / {L3/237/1} 622. The document purports to be precursor work to the Bitcoin White Paper, dated 12 December 2008. (a) COPA’s Reasons for Alleging Forgery (a) COPA’s Reasons for Alleging Forgery 623. The document has been backdated. It is an altered version of a document actually published by Dr Wright in 2017 (the “2017 SSRN Paper”). [PM1 [25-38] and [55]]. 624. The document contains hidden, embedded Grammarly timestamps indicating its true date to be 15 April 2017 or later. [PM1 [48-53]]. 625. The Grammarly software did not exist in 2008 [Madden1 [62c]] but is contemporaneous for 2017. [Madden1 [70-72]]. 626. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. [PM1 [25-33]]. 627. The content referred to in the hidden, embedded traces of the 2017 SSRN paper includes news articles and government publications which did not yet exist in 2008 but which are contemporaneous for 2017. [PM1 [19-21] and [55]]. 628. The document contains equations and formulae which have been corrupted in a manner consistent with conversion from a more modern format to an older format [PM1 [34-45]. The document includes tampered content that apparently represents an attempt to explain away this problem as the result of using older equation-editing MathType software. However, analysis indicates that equations in the document were in fact authored with much later version of that software, which did not yet exist in 2008 but which are contemporaneous to 2017. [PM40 [22-42]]. 629. The document contains references to Microsoft schemas which did not yet exist in 2008 but are contemporaneous to 2010 onwards (including 2017). [PM1 [46-47]]. 630. The document includes anachronistic references to events that had not occurred by 2008 but which had occurred by 2017 [PM1 [21-22]]. 631. The internal metadata records an anomalous edit time in excess of 70 days and the editing period of this document overlaps with several other documents in Dr Wright’s disclosure, consistent with being created using clock manipulation techniques. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 632. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting academic work done by Dr Wright in his supposed development of Bitcoin), contrary to fact. 633. Dr Wright has positively asserted that {ID_000550} is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 634. Dr Wright is recorded as an author of the metadata. The doctored content is phrased by Dr Wright in the first person: “Note- I have released online as an anonymous programmer using the pseudonym "Satoshi Nakamoto". 635. Dr Wright is a user of Grammarly software. 636. {ID_000550} contains hidden text embedded within the document which contradicts the information presented on the face of the document, a characteristic of manipulation of a series of MS Word .DOC files found throughout Dr Wright’s disclosure in these proceedings. 637. The 2017 SSRN Paper is a version of the same document that was published on the SSRN website by Dr Wright himself, bearing a “date written” of 28 March 2017. The “date written” is contemporaneous with the hidden Grammarly timestamp in {ID_000550} and not to the purported date of the document. 638. The metadata of the 2017 SSRN Paper lists “craig” as the creator of the document. 639. {ID_000550} contains hidden, embedded traces of the 2017 SSRN paper, indicating that both are derived from a common source document. 640. Dr Wright has not disclosed the 2017 SSRN paper or the common source document. 641. In his first witness statement in these proceedings, Dr Wright lists this document as a document to which he has been referred when preparing his evidence. (c) Dr Wright’s Explanations and COPA’s Rebuttal (c) Dr Wright’s Explanations and COPA’s Rebuttal 642. In Appendix B to Wright11, Dr Wright attempted to explain the presence of embedded text showing a remnant version which referred to Bitcoin as an existing system and included various references to events after 2008 (including for instance to a report of May 2013). He claimed that these references were added “as a result of the corruption of the document, probably while it was kept at nChain or one of the other corporations”: {CSW/2/54}. In cross-examination, he claimed that two documents became merged into one another as a result of both being open at the same time on a Citrix environment and an edit not happening correctly on one of them: {Day2/135:1} to {Day2/138:19}. 643. In Appendix B to Wright11, Dr Wright sought to explain away the presence of the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later by saying that they resulted from “someone in nChain opening the document in 2017 or later” and these elements becoming embedded: {CSW/2/53}; {Day2/143:1} to {Day2/143:23}. It is inherent in that explanation that these elements could become embedded without the person saving or otherwise interacting with the document (since such interaction would cause the metadata timestamps to update). 644. When pressed in cross-examination with the point that the remnant text in the document matches text in a 2017 version of the document uploaded by Dr Wright to the SSRN website, Dr Wright could give no coherent answer. When further pressed that this reliance document contains clear signs of corrupted equations (which appear properly readable in the SSRN version), thus suggesting errors resulting from conversion, he likewise failed to give any intelligible answer. See {Day2/140:7} to {Day2/142:25}. 645. Dr Wright also claimed that the references to CheckBlockHeader, UTXO and Bitcoin Core in this document were there because he used them. {Day2/134:14} to {Day2/140:5} 646. COPA submitted that Dr Wright’s explanation should be rejected as dishonest for the following reasons: 646.1. It presupposes that the features of a shared computing environment would cause two documents to become merged, with remnant text added in the way seen here, but in a manner which resulted in the document not being corrupted and appearing normal on its face. This possibility is rejected by the independent expert evidence. See Madden4, [159]; Madden / Placks joint statement 2 at [8]. 646.2. Dr Wright’s explanation for the presence of anachronistic artefacts in the metadata (including the anachronistic Grammarly timestamps, the references to Mathtype software dating from 2017 and references to Microsoft schemas dating from 2010 and later) is contradicted by the clear expert evidence of Mr Madden, which is that such references could not be inserted without the document being interacted with by a user, which would in turn result in updating of the metadata timestamps of the document (notably the “last saved” timestamp). The fact that the metadata timestamps for this document were dated to 2008 but the document contains the elements set out above shows that the document has been forged by backdating. See Madden4, [158]; Madden / Placks joint statement2 at [8]; {Day16/35:19} to {Day16/38:11}; {Day16/125:7} to {Day16/125:18}. 646.3. The correspondence between parts of the hidden remnant text of this document and the 2017 SSRN version of the document can only rationally be explained by this document being a copy forged and backdated in recent years. 646.4. This conclusion is further reinforced by the corrupted equations, which are clearly indicative of a document having been converted from a later version of MS Word into an older format which does not support the correct font or automatically applies an incorrect text editing format: see PM1 [44] {H/1/19}. 646.5. The anachronistic terms Dr Wright says he used are not found in other genuine documents before their first use. These first uses were as follows: 646.5.1. CheckBlockHeader was first introduced into the code in March 2014, Wuille1 [25] {C1/1/6}; CheckBlockHeader 646.5.2. Bitcoin Core was first introduced in version 0.9 of the software in 2014 Wuille1 [50] {C1/1/12}; and Bitcoin Core 646.5.3. UTXO first introduced in version 0.8 of the software by Mr Wuille, with him first seeing the term on 21 June 2012 Wuille1 [29-32] {C1/1/7}. UTXO (d) Conclusion (d) Conclusion 647. In addition to COPA’s submissions in this section, I also refer to [737]-[759] in the main Judgment, all of which demonstrate that the points gathered in Appendix 1 to Dr Wright’s Closing in relation to this document do not begin to explain all the indications of forgery set out above. Dr Wright plainly forged this document from the 2017 SSRN paper. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

Crypto Open Patent Alliance Finds Evidence of Tampering in Wright's Claim to be Bitcoin Founder

Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Conclusion, and References

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 7 of 42. 5. “Project BlackNet” paper (Reliance Document) {ID_001379} / {L1/79/1} 100. This document purports to be a copy of a “Project BlackNet” research paper dated on its face to 3 October 2002. The document contains references to a “Stage 4 – Release Phase” and other textual amendments, referring to “Digital Cash”, as well as other features of purported relevance to Bitcoin. 101. This document includes the text of the BlackNet Abstract (see the section above) pleaded as a forgery in COPA’s Particulars of Claim [26-27], and dealt with in the preceding section. (a) COPA’s Reasons for Alleging Forgery 102. The document has been backdated [PM8 [60-61]. The document is dated on its face to 3 October 2002. However, the internal metadata for the document indicate that it was actually created on 17 February 2014. [PM8 [15]]. 103. The document is an edited version of an earlier document, into which passages of text have been inserted using text from the Bitcoin White Paper. [PM8 [23-33, 60.b.]]. 104. The document is apparently part of a series of documents, all of which carry similar content on their face, but which have been edited or are converted versions of the same file [PM8 [3]]. Some of those documents are consistent with a pattern of editing beginning with an earlier precursor document, and ending with a document which would be deployed as if it was original and authentic, in connection with Dr Wright’s claim to be Satoshi Nakamoto. See in particular Mr Madden’s discussion of {ID_001016} as another backdated document in the sequence [PM8 [34-50]]. 105. The metadata indicates that the file was created using Microsoft Word 2013 as the PDF Producer. Microsoft Word 2013 was not yet published in 2002 (the date on the face of the document) but is contemporaneous for 2014. [PM8 [15]]. 106. The document was created in PDF form by printing to PDF from an underlying precursor DOC or .DOCX document on 17 February 2014 [PM8 [16]]. No such underlying precursor document has been disclosed by Dr Wright. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 107. Dr Wright is listed in the metadata as the author of the document. 108. Dr Wright is listed on the face of the document as its first author. 109. Dr Wright is listed as the sole author in the ‘version control’ section of the document on page 2. 110. The document contains Dr Wright’s address and telephone number. 111. Dr Wright has positively asserted in these proceedings that this is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 112. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting work done by him in 2002, elements of which then appeared in the Bitcoin White Paper), contrary to fact. 113. Dr Wright has posted screenshots of this document or a very similar document on Twitter, apparently in support of his claim to be Satoshi Nakamoto. 114. In his evidence in these proceedings, Dr Wright has claimed this to be a document related to the later Bitcoin White Paper and that it is original to the purported date on its face, contrary to fact. [Exhibit CSW 14]. 115. Dr Wright has relied on this document, or documents with similar content, in previous proceedings and in public in support of his claim to be Satoshi Nakamoto. 116. The metadata records “DeMorgan” as the company from which the document was created. In his evidence in these proceedings, Dr Wright relies on his work on various projects from his time at DeMorgan, as part of his claim to have been working on the concepts behind the Bitcoin White Paper. [Wright1 [32-47]]. 117. The document was created in the time zone UTC+11, consistent with Dr Wright’s location in Australia in February 2014, being the date of creation of the document. [PM8 [18]]. 118. In his Defence in these proceedings, in public social media posts, and elsewhere, Dr Wright has repeatedly stated that the series of research papers (to which this document belongs) are related to the Bitcoin White Paper, and that later applications (submitted in 2009/2010) contained the abstract of the Bitcoin White Paper. (c) Dr Wright’s Explanations and COPA’s Rebuttal 119. In Appendix B to Wright11, Dr Wright accepted that this document was created by conversion of a document to PDF format in 2014. He claimed that this document was obtained from the computer of a former employee who had achieved unauthorised access to Dr Wright’s current wife’s email account, although he did not explain why that claim was relevant. He gave no coherent explanation for why a project paper supposedly prepared for an ITOL application in 2002 would be converted to PDF by an employee of one of his companies in 2014. See: {CSW/2/59} to {CSW/2/60}. 120. In his oral evidence, Dr Wright tried to explain away the fact that (a) authentic versions of this document describe an IT security project (involving an encrypted network), with no reference to any digital transaction system and (b) this version (among others) includes the content of the authentic versions but adds incongruous sections referring to a peer-to-peer digital transaction system involving transactions being structured into “an ongoing chain of hash-based proof-of-work” to preclude double-spending or replay attacks. See {Day2/56:22} to {Day2/77:18}. Key aspects of his explanations and their deficiencies are addressed below. 121. This document is a forgery. Its content is not authentic to 2002, as Dr Wright claims it is. His attempts to explain away the indications of forgery should be rejected for the following reasons: 121.1. A version of this document in disclosure which Mr Madden has established as apparently authentic to 2002 ({ID000013}) is an internally coherent plan for an IT security system with three stages, in which the Stage 3 is described as the Final Phase and the budget covers three stages. It includes nothing about a digital transaction system, still less one involving a chain of hash-based proof-of-work. 121.2. Similarly, a project plan for this IT project which Dr Wright actually submitted to AusIndustry in 2003 has the same content as that apparently authentic document: {L1/113/1} [p8ff. Both the application form {L/113/3} and the project plan {L/113/8} were consistent in their description of the project. Neither says anything about a digital transaction system, still less one involving a chain of hash-based proof-of-work. 121.3. Even an apparently authentic version of the project plan in disclosure which dates to March 2009 {ID_001016} shows the project in the same terms, concluding with Stage 3 and including nothing about a digital currency system. 121.4. In this reliance document {ID_001379}, as in some other (similarly inauthentic) versions of the project plan in disclosure, Dr Wright has added an Abstract (at p4) which describes a digital transaction system similar to Bitcoin. The Abstract bears no obvious relation to the various other parts of the document, which are survivals of the authentic version, such as the Overall Objective section at p6 and the Assessment Against the Selection Criteria section at p8. Dr Wright has also added a few references to a Stage 4 involving the development of the supposed digital transaction system. However, he has forgotten to modify the budget on p11-12, which makes no financial provision for the supposed Stage 4 and still describes Stage 3 as the Final Phase. 121.5. In the Kleiman trial, Dr Wright said that parts of the Bitcoin White Paper went back to his “2002 AusIndustry filings for research and development”: see {L17/327/93}. When that evidence was put to him in this trial, he insisted that the document including the supposed Stage 4 elements of the project (involving a digital transaction system) “would have been filed [with AusIndustry] in 2002 and then in 2003”: {Day2/72:24}. That evidence was flatly contradicted by the actual AusIndustry filing from July 2003 (summarised above). 121.6. Dr Wright has attempted to account for these documents by a confused narrative of different versions of the document being in existence in 2002 and 2009 and used for different purposes, while the version control was in a state of chaos (see transcript reference above). The documents tell their own clear story: that Dr Wright produced an IT security project plan in 2002, which he used for tax concession applications, but he later (from 2014) doctored that plan to bolt on sections reflecting features of the Bitcoin system in order to promote his false claim to have been involved in creating that system. (d) Conclusion 122. I am entirely satisfied that the Project BlackNet paper was forged by Dr Wright. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 7 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 7 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 5. “Project BlackNet” paper (Reliance Document) {ID_001379} / {L1/79/1} 100. This document purports to be a copy of a “Project BlackNet” research paper dated on its face to 3 October 2002. The document contains references to a “Stage 4 – Release Phase” and other textual amendments, referring to “Digital Cash”, as well as other features of purported relevance to Bitcoin. 101. This document includes the text of the BlackNet Abstract (see the section above) pleaded as a forgery in COPA’s Particulars of Claim [26-27], and dealt with in the preceding section. (a) COPA’s Reasons for Alleging Forgery (a) COPA’s Reasons for Alleging Forgery 102. The document has been backdated [PM8 [60-61]. The document is dated on its face to 3 October 2002. However, the internal metadata for the document indicate that it was actually created on 17 February 2014. [PM8 [15]]. 103. The document is an edited version of an earlier document, into which passages of text have been inserted using text from the Bitcoin White Paper. [PM8 [23-33, 60.b.]]. 104. The document is apparently part of a series of documents, all of which carry similar content on their face, but which have been edited or are converted versions of the same file [PM8 [3]]. Some of those documents are consistent with a pattern of editing beginning with an earlier precursor document, and ending with a document which would be deployed as if it was original and authentic, in connection with Dr Wright’s claim to be Satoshi Nakamoto. See in particular Mr Madden’s discussion of {ID_001016} as another backdated document in the sequence [PM8 [34-50]]. 105. The metadata indicates that the file was created using Microsoft Word 2013 as the PDF Producer. Microsoft Word 2013 was not yet published in 2002 (the date on the face of the document) but is contemporaneous for 2014. [PM8 [15]]. 106. The document was created in PDF form by printing to PDF from an underlying precursor DOC or .DOCX document on 17 February 2014 [PM8 [16]]. No such underlying precursor document has been disclosed by Dr Wright. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 107. Dr Wright is listed in the metadata as the author of the document. 108. Dr Wright is listed on the face of the document as its first author. 109. Dr Wright is listed as the sole author in the ‘version control’ section of the document on page 2. 110. The document contains Dr Wright’s address and telephone number. 111. Dr Wright has positively asserted in these proceedings that this is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 112. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting work done by him in 2002, elements of which then appeared in the Bitcoin White Paper), contrary to fact. 113. Dr Wright has posted screenshots of this document or a very similar document on Twitter, apparently in support of his claim to be Satoshi Nakamoto. 114. In his evidence in these proceedings, Dr Wright has claimed this to be a document related to the later Bitcoin White Paper and that it is original to the purported date on its face, contrary to fact. [Exhibit CSW 14]. 115. Dr Wright has relied on this document, or documents with similar content, in previous proceedings and in public in support of his claim to be Satoshi Nakamoto. 116. The metadata records “DeMorgan” as the company from which the document was created. In his evidence in these proceedings, Dr Wright relies on his work on various projects from his time at DeMorgan, as part of his claim to have been working on the concepts behind the Bitcoin White Paper. [Wright1 [32-47]]. 117. The document was created in the time zone UTC+11, consistent with Dr Wright’s location in Australia in February 2014, being the date of creation of the document. [PM8 [18]]. 118. In his Defence in these proceedings, in public social media posts, and elsewhere, Dr Wright has repeatedly stated that the series of research papers (to which this document belongs) are related to the Bitcoin White Paper, and that later applications (submitted in 2009/2010) contained the abstract of the Bitcoin White Paper. (c) Dr Wright’s Explanations and COPA’s Rebuttal (c) Dr Wright’s Explanations and COPA’s Rebuttal 119. In Appendix B to Wright11, Dr Wright accepted that this document was created by conversion of a document to PDF format in 2014. He claimed that this document was obtained from the computer of a former employee who had achieved unauthorised access to Dr Wright’s current wife’s email account, although he did not explain why that claim was relevant. He gave no coherent explanation for why a project paper supposedly prepared for an ITOL application in 2002 would be converted to PDF by an employee of one of his companies in 2014. See: {CSW/2/59} to {CSW/2/60}. 120. In his oral evidence, Dr Wright tried to explain away the fact that (a) authentic versions of this document describe an IT security project (involving an encrypted network), with no reference to any digital transaction system and (b) this version (among others) includes the content of the authentic versions but adds incongruous sections referring to a peer-to-peer digital transaction system involving transactions being structured into “an ongoing chain of hash-based proof-of-work” to preclude double-spending or replay attacks. See {Day2/56:22} to {Day2/77:18}. Key aspects of his explanations and their deficiencies are addressed below. 121. This document is a forgery. Its content is not authentic to 2002, as Dr Wright claims it is. His attempts to explain away the indications of forgery should be rejected for the following reasons: 121.1. A version of this document in disclosure which Mr Madden has established as apparently authentic to 2002 ({ID000013}) is an internally coherent plan for an IT security system with three stages, in which the Stage 3 is described as the Final Phase and the budget covers three stages. It includes nothing about a digital transaction system, still less one involving a chain of hash-based proof-of-work. 121.2. Similarly, a project plan for this IT project which Dr Wright actually submitted to AusIndustry in 2003 has the same content as that apparently authentic document: {L1/113/1} [p8ff. Both the application form {L/113/3} and the project plan {L/113/8} were consistent in their description of the project. Neither says anything about a digital transaction system, still less one involving a chain of hash-based proof-of-work. 121.3. Even an apparently authentic version of the project plan in disclosure which dates to March 2009 {ID_001016} shows the project in the same terms, concluding with Stage 3 and including nothing about a digital currency system. 121.4. In this reliance document {ID_001379}, as in some other (similarly inauthentic) versions of the project plan in disclosure, Dr Wright has added an Abstract (at p4) which describes a digital transaction system similar to Bitcoin. The Abstract bears no obvious relation to the various other parts of the document, which are survivals of the authentic version, such as the Overall Objective section at p6 and the Assessment Against the Selection Criteria section at p8. Dr Wright has also added a few references to a Stage 4 involving the development of the supposed digital transaction system. However, he has forgotten to modify the budget on p11-12, which makes no financial provision for the supposed Stage 4 and still describes Stage 3 as the Final Phase. 121.5. In the Kleiman trial, Dr Wright said that parts of the Bitcoin White Paper went back to his “2002 AusIndustry filings for research and development”: see {L17/327/93}. When that evidence was put to him in this trial, he insisted that the document including the supposed Stage 4 elements of the project (involving a digital transaction system) “would have been filed [with AusIndustry] in 2002 and then in 2003”: {Day2/72:24}. That evidence was flatly contradicted by the actual AusIndustry filing from July 2003 (summarised above). 121.6. Dr Wright has attempted to account for these documents by a confused narrative of different versions of the document being in existence in 2002 and 2009 and used for different purposes, while the version control was in a state of chaos (see transcript reference above). The documents tell their own clear story: that Dr Wright produced an IT security project plan in 2002, which he used for tax concession applications, but he later (from 2014) doctored that plan to bolt on sections reflecting features of the Bitcoin system in order to promote his false claim to have been involved in creating that system. (d) Conclusion (d) Conclusion 122. I am entirely satisfied that the Project BlackNet paper was forged by Dr Wright. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

So, About That BlackNet Paper... Looks Like It’s Not from 2002 After All

Shingling in the Generative AI Era

Facebook. Where do I even begin with this colossal junkyard of a platform? It's like someone took the concept of social networking and doused it in a vat of incompetence. Let's talk about how Facebook has managed to mess up beyond all common sense. Firstly, their idea of content moderation seems to be more about randomly throwing darts at a board than actually using any sort of coherent logic. Legitimate posts in perfectly innocuous groups are being whisked away into the digital ether while spam proliferates like a disease. Seriously, how does a post about genuine advice or a thoughtful discussion get flagged and removed, but the umpteenth spammy post for shady sunglasses or miracle weight loss pills in totally irrelevant groups get a free pass? It's like Facebook has an algorithm that's not just broken but actively malicious. Facebook cares more about profit margins than the user experience. Instead of fostering a community where people can share meaningful ideas and connect on a genuine level, they've turned it into a virtual junkyard where the loudest, most obnoxious, spammy advertisers and paid-to-post parasites reign supreme. What happened to valuing quality over quantity? Now, it's all about how many eyeballs they can shove in front of spammy URL posts or advertisements, regardless of whether those eyeballs are seeing something worthwhile or utter garbage. It's a race to the bottom where meaningful content is drowned out by an endless barrage of clickbait. And let's not forget the privacy nightmares. Facebook has more tentacles in your personal life than an overbearing mother-in-law. They track your every move, every click, every like, and probably your wet dreams if they could. Remember when Facebook was caught red-handed with plans to sell user data and received support from Facebook’s most senior executives, including CEO Mark Zuckerberg and chief operating officer Sheryl Sandberg? Yeah, that happened, and somehow, we're all still here, feeding the beast with our personal information. According to approximately 4,000 pages of leaked internal documents obtained by NBC News, Zuckerberg allegedly supervised strategies aimed at centralizing the social network's influence and stifling competitors by using user data as leverage. This occurred despite public assurances from the company about protecting user data. The documents primarily cover the years 2011 to 2015. Facebook’s (Meta) latest spam joke Case in point: I joined a Facebook group dedicated to blogging and guest posting. Perfect, right? Wrong. I posted a meticulously crafted message with a URL, offering insightful tips on how to craft a perfect guest post. What did Facebook do? They promptly removed my post, citing some obscure community guideline violation. Meanwhile, the same group was inundated with spam posts about irrelevant topics, worthless websites, and other scammers, hawked by individuals paid to spread digital garbage like confetti at a clown convention. Facebook has lost its way. Digital Nonsense It's crystal clear that Facebook has lost its way. They seem more interested in padding their profits than ensuring a quality user experience. Maybe they're scrimping on competent programmers because their algorithms can't tell the difference between genuine content and spammy trash. There are better options out there, folks. Platforms that actually give a damn about quality and fairness. They know how to handle spam: swiftly and decisively, with bans and suspensions flying like justice from a vigilant sheriff. Meanwhile, Facebook twiddles its thumbs, letting the spammers run amok like unruly toddlers in a toy store. It's time for a change It's time for a change. Governments should consider banning this digital travesty of a platform. We deserve better. We deserve platforms that respect our time, our data, and our intelligence. Facebook had its chance. It's time to let it fade into irrelevance, like the distant memory of a regrettable haircut or Myspace. Facebook could have been a force for good - Instead, it’s a spam-loving machine The saddest part is that Facebook could have been a force for good. It could have been a platform where people from all walks of life could come together, share ideas, and foster genuine connections. Instead, it's a bloated, soulless husk of what it could have been—a place where spam rules and meaningful interaction is a rare gem buried under layers of digital detritus. Facebook is a joke Facebook is a joke—a sad, outdated relic in the age of superior social media alternatives. Let's move on, shall we? There's a whole world of digital connectivity out there, waiting for us to embrace platforms that actually value what matters. Facebook's love affair with scammers Facebook's love affair with scammers is a textbook example of corporate greed overshadowing ethical responsibility. They don't just turn a blind eye to scammers; they roll out the red carpet for them. It's like they have a special VIP section reserved for shady operators peddling everything from fake diplomas, get-rich-quick schemes, dodgy products and digital services. It's like they're saying, "Scam away, folks! Just make sure the ad revenue keeps flowing." They rake in cash hand over fist while unsuspecting users fall victim to schemes that would make even the most seasoned con artist blush. Facebook doesn't care. And the best part? Facebook doesn't care. They're too busy counting their ill-gotten gains to bother with pesky details like vetting advertisers or ensuring the legitimacy of promoted services. Who cares if someone's promoting a Ponzi scheme or selling snake oil disguised as miracle cures? Certainly not Facebook, as long as those sweet advertising dollars keep pouring in. Facebook's revenue - Willful ignorance Let's face it: a substantial chunk of Facebook's revenue likely comes from scammers. I mean, do they even bother to check? The obvious answer is no. It's the digital equivalent of renting out billboard space to a carnival barker without asking what's under the tent. They're complicit, whether through negligence or willful ignorance. So, while the rest of us navigate the internet with caution, Facebook gleefully lines its pockets with ad click money earned from the misfortune of others. It's a disgrace, a betrayal of trust, and a stark reminder that when it comes to ethics, Facebook is MIA. Facebook's laissez-faire attitude towards scammy and spammy advertisers is not just irresponsible—it's reprehensible. They owe their users better. They owe the digital community better. It’s time our governments finally put a stop to this mayhem. Hence why I have decided to start my own movement, a reporting agency that finds, responds to and reports every scam we come across. Then, provide a database every month to my national government that reflects the reported scams Meta refuses to remove after being reported. A visionary will emerge - Sooner than you think! Someday, a visionary will emerge—someone smarter, bolder, and more ethical than Zuckerberg and his cohort of profit-driven enablers. This visionary will revolutionize social media, leaving Facebook (or Meta, as they now call it) in the dust. (Watch this space) Picture this: a platform where authenticity reigns supreme. Only real people, genuine advertisers, and vetted content are allowed. No more scams, no more clickbait, no more shady dealings. It's a place where integrity is not just a buzzword but a guiding principle. This new social media titan will prioritize user safety and privacy above all else. Children will be protected, personal data will be guarded like a treasure, and transparency will be the cornerstone of every interaction. Imagine a digital space where users can connect, share, and thrive without fear of exploitation or deception. And yes, this platform will make money. But unlike Facebook, its success won't come at the expense of its users' trust. It will be a fair exchange where value is delivered without compromising principles. Advertising will be relevant and respectful, enhancing the user experience rather than detracting from it.  It’s coming, believe me. Maybe sooner than you think. Meta, beware. The winds of change are blowing, and it's only a matter of time until you become the next Myspace—a relic of a bygone era. The future belongs to those who innovate with integrity and prioritize the well-being of their community. So, brace yourself because the revolution is coming, and when it arrives, the world will finally have a social media platform worthy of its users. So, Facebook, if you're listening (which you probably are because, let's face it, you're always listening), get your act together. Clean up your act. Start valuing quality content and user experience over your bottom line driven by spam and scams. Stop treating us like commodities to be sold to the highest bidder. Until then, I'll be over here, trying to salvage what's left of my faith in social media. And maybe, just maybe, hoping for a platform that actually gives a damn about its legitimate users instead of spammers and scammers. It's time they cleaned up their act and started prioritizing integrity over profit, and governments start handing out major penalties Until then, caveat emptor: let the buyer beware, especially when it comes to anything Facebook promotes on its junkyard of depict, spam and filth. Facebook. Where do I even begin with this colossal junkyard of a platform? It's like someone took the concept of social networking and doused it in a vat of incompetence. Let's talk about how Facebook has managed to mess up beyond all common sense. Firstly, their idea of content moderation seems to be more about randomly throwing darts at a board than actually using any sort of coherent logic. Legitimate posts in perfectly innocuous groups are being whisked away into the digital ether while spam proliferates like a disease. Seriously, how does a post about genuine advice or a thoughtful discussion get flagged and removed, but the umpteenth spammy post for shady sunglasses or miracle weight loss pills in totally irrelevant groups get a free pass? It's like Facebook has an algorithm that's not just broken but actively malicious. Facebook cares more about profit margins than the user experience. Instead of fostering a community where people can share meaningful ideas and connect on a genuine level, they've turned it into a virtual junkyard where the loudest, most obnoxious, spammy advertisers and paid-to-post parasites reign supreme. What happened to valuing quality over quantity? Now, it's all about how many eyeballs they can shove in front of spammy URL posts or advertisements, regardless of whether those eyeballs are seeing something worthwhile or utter garbage. It's a race to the bottom where meaningful content is drowned out by an endless barrage of clickbait. And let's not forget the privacy nightmares. Facebook has more tentacles in your personal life than an overbearing mother-in-law. They track your every move, every click, every like, and probably your wet dreams if they could. Remember when Facebook was caught red-handed with plans to sell user data and received support from Facebook’s most senior executives, including CEO Mark Zuckerberg and chief operating officer Sheryl Sandberg? Yeah, that happened, and somehow, we're all still here, feeding the beast with our personal information. plans to sell user data According to approximately 4,000 pages of leaked internal documents obtained by NBC News, Zuckerberg allegedly supervised strategies aimed at centralizing the social network's influence and stifling competitors by using user data as leverage. This occurred despite public assurances from the company about protecting user data. The documents primarily cover the years 2011 to 2015. Facebook’s (Meta) latest spam joke Case in point: I joined a Facebook group dedicated to blogging and guest posting. Perfect, right? Wrong. I posted a meticulously crafted message with a URL, offering insightful tips on how to craft a perfect guest post . guest post What did Facebook do? What did Facebook do? They promptly removed my post, citing some obscure community guideline violation. Meanwhile, the same group was inundated with spam posts about irrelevant topics, worthless websites, and other scammers , hawked by individuals paid to spread digital garbage like confetti at a clown convention. spam posts other scammers Facebook has lost its way. Digital Nonsense It's crystal clear that Facebook has lost its way. They seem more interested in padding their profits than ensuring a quality user experience. Maybe they're scrimping on competent programmers because their algorithms can't tell the difference between genuine content and spammy trash. There are better options out there, folks. Platforms that actually give a damn about quality and fairness. They know how to handle spam: swiftly and decisively, with bans and suspensions flying like justice from a vigilant sheriff. Meanwhile, Facebook twiddles its thumbs, letting the spammers run amok like unruly toddlers in a toy store. It's time for a change It's time for a change. Governments should consider banning this digital travesty of a platform. We deserve better. We deserve platforms that respect our time, our data, and our intelligence. Facebook had its chance. It's time to let it fade into irrelevance, like the distant memory of a regrettable haircut or Myspace. Facebook could have been a force for good - Instead, it’s a spam-loving machine Facebook could have been a force for good - Instead, it’s a spam-loving machine The saddest part is that Facebook could have been a force for good. It could have been a platform where people from all walks of life could come together, share ideas, and foster genuine connections. Instead, it's a bloated, soulless husk of what it could have been—a place where spam rules and meaningful interaction is a rare gem buried under layers of digital detritus. Facebook Facebook is a joke Facebook is a joke—a sad, outdated relic in the age of superior social media alternatives. Let's move on, shall we? There's a whole world of digital connectivity out there, waiting for us to embrace platforms that actually value what matters. Facebook's love affair with scammers Facebook's love affair with scammers is a textbook example of corporate greed overshadowing ethical responsibility. They don't just turn a blind eye to scammers; they roll out the red carpet for them. It's like they have a special VIP section reserved for shady operators peddling everything from fake diplomas, get-rich-quick schemes, dodgy products and digital services. love affair with scammers shady operators fake It's like they're saying, "Scam away, folks! Just make sure the ad revenue keeps flowing." They rake in cash hand over fist while unsuspecting users fall victim to schemes that would make even the most seasoned con artist blush. Facebook doesn't care. And the best part? Facebook doesn't care. They're too busy counting their ill-gotten gains to bother with pesky details like vetting advertisers or ensuring the legitimacy of promoted services. Who cares if someone's promoting a Ponzi scheme or selling snake oil disguised as miracle cures? Certainly not Facebook, as long as those sweet advertising dollars keep pouring in. Facebook's revenue - Willful ignorance Let's face it: a substantial chunk of Facebook's revenue likely comes from scammers. I mean, do they even bother to check? The obvious answer is no. It's the digital equivalent of renting out billboard space to a carnival barker without asking what's under the tent. They're complicit, whether through negligence or willful ignorance. So, while the rest of us navigate the internet with caution, Facebook gleefully lines its pockets with ad click money earned from the misfortune of others. It's a disgrace, a betrayal of trust, and a stark reminder that when it comes to ethics, Facebook is MIA. Facebook's laissez-faire attitude towards scammy and spammy advertisers is not just irresponsible—it's reprehensible. They owe their users better. They owe the digital community better. It’s time our governments finally put a stop to this mayhem. Hence why I have decided to start my own movement, a reporting agency that finds, responds to and reports every scam we come across. Then, provide a database every month to my national government that reflects the reported scams Meta refuses to remove after being reported. the reported scams A visionary will emerge - Sooner than you think! Someday, a visionary will emerge—someone smarter, bolder, and more ethical than Zuckerberg and his cohort of profit-driven enablers. This visionary will revolutionize social media, leaving Facebook (or Meta, as they now call it) in the dust. (Watch this space) Picture this: a platform where authenticity reigns supreme. Only real people, genuine advertisers, and vetted content are allowed. No more scams, no more clickbait, no more shady dealings. It's a place where integrity is not just a buzzword but a guiding principle. authenticity content This new social media titan will prioritize user safety and privacy above all else. Children will be protected, personal data will be guarded like a treasure, and transparency will be the cornerstone of every interaction. Imagine a digital space where users can connect, share, and thrive without fear of exploitation or deception. And yes, this platform will make money. But unlike Facebook, its success won't come at the expense of its users' trust. It will be a fair exchange where value is delivered without compromising principles. Advertising will be relevant and respectful, enhancing the user experience rather than detracting from it.  It’s coming, believe me. Maybe sooner than you think. Meta, beware. The winds of change are blowing, and it's only a matter of time until you become the next Myspace—a relic of a bygone era. The future belongs to those who innovate with integrity and prioritize the well-being of their community. So, brace yourself because the revolution is coming, and when it arrives, the world will finally have a social media platform worthy of its users. So, Facebook, if you're listening (which you probably are because, let's face it, you're always listening), get your act together. Clean up your act. Start valuing quality content and user experience over your bottom line driven by spam and scams. Stop treating us like commodities to be sold to the highest bidder. Until then, I'll be over here, trying to salvage what's left of my faith in social media. And maybe, just maybe, hoping for a platform that actually gives a damn about its legitimate users instead of spammers and scammers. It's time they cleaned up their act and started prioritizing integrity over profit, and governments start handing out major penalties Until then, caveat emptor: let the buyer beware, especially when it comes to anything Facebook promotes on its junkyard of depict, spam and filth.

Facebook Has Managed To Mess Up Beyond All Common Sense. A Social Media Nightmare 

By now you're probably aware that a certain segment of the population last week experienced what some of us in the know refer to as the Blue Screen of Death™ — causing widespread panic and chaos, albeit briefly. Millions of Windows-based devices that power banks, airports, hospitals, and even broadcast stations went offline and started displaying that dreaded pop-up that usually signifies the unceremonious death of your PC. Imagine not being able to travel, pay, get treatment, or even tune in to your TV to learn what's going on because.. computer problems. 🙄 So, what happened? The way it began was that cybersecurity firm Crowdstrike deployed what should have been a regular, run-of-the mill update for its Falcon software to protect customers' computers against new cyber threats. That update ended up bringing the world to a standstill instead. The exact reason for why the roll out caused problems on Windows was not entirely clear, but experts speculate that a rogue file in the update apparently did not agree with Microsoft's operating system, causing systems to crash. Some speculate that faulty code or lack of quality checks prior to deployment could have been the problem. Whatever the case, all hell broke loose. "This is not a security incident or cyberattack," Crowdstrike Chief Executive George Kurtz tweeted as soon as customers' complaints began piling up. Naturally, questions have arisen around both Crowdstrike and Microsoft, both of whom hold a sizable market of the cybersecurity and OS space, respectively. One twitter user even posted a comparison of how Crowdstrike compared to its competitors (it's the second most valuable cybersecurity firm in the world). Judgment against Crowdstrike has been swift though, with the company's stock price plummeting Friday following the fiasco — wiping billions off of its valuation. Microsoft also saw its stock price fall following the event, dragging the broader stock indices down with it on Friday as the event caused a sell off in tech stocks. Things don't seem to have gotten better, with Dan Ives, a technical analyst on Wall Street, tweeting that IT outage issues still persisted. "This is not good news for George Kurtz & Co. in an already bad situation as it appears a number of businesses are still finding outage issues," Ives wrote. CNN reports that the cost from the global outage could top $1 billion, but there's no telling yet who will exactly foot the bill. There's a very high likelihood that customers will demand compensation from Crowdstrike, dragging it into legal distractions. Worse yet, Crowdstrike's brand reputation is also going to take a massive hit and its competitors may already be trying to eat away at the company's market share. In fact, Ives went on to say that Crowdstrike, which was not a widely known brand prior to this event, had become "a household name, but not in a good way." CrowdStrike CEO Kurtz also has an interesting history in that this isn't the first time he's been at the center of a global tech failure. In 2010, a buggy antivirus update from McAfee caused hundreds of thousands of computers to go kaput! Kurtz was the chief technology officer of McAfee at the time, and it doesn't look like there was a lot of blow back against him at the time. McAfee was reportedly bought by Intel a few months after the event and Kurtz ultimately ended up leaving the company to create CrowdStrike. For now, he's in hot water as US lawmakers have called on Kurtz to testify on the global IT outage caused by his company's update. Meanwhile, Microsoft said the event was a "reminder of how important it is for all of us across the tech ecosystem to prioritize operating with safe deployment and disaster recovery using the mechanisms that exist." The Windows maker estimated that CrowdStrike's update affected 8.5 million devices running its operating system, which was less than 1% of all Windows machines. "While the percentage was small, the broad economic and societal impacts reflect the use of CrowdStrike by enterprises that run many critical services," the company said in a blog post. I suppose that's Microsoft's way of reminding everyone of the 80-20 rule :-) Microsoft ranked #2 on HackerNoon's tech company rankings this week. In Other News.. 📰 Ethereum ETFs Approved by SEC, Bringing Popular Funds to Second-Largest Cryptocurrency — via CoinDesk
Cohere raises $500M to beat back generative AI rivals — via TechCrunch
Hackers are already taking advantage of the CrowdStrike outage chaos — via CNN
Google scraps plan to remove cookies from Chrome — via Reuters
The AI-generated disinformation dystopia that wasn't — via Axios
Autonomous AI workers that talk to each other will arrive in 2025, Capgemini predicts — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page.*Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here.* By now you're probably aware that a certain segment of the population last week experienced what some of us in the know refer to as the Blue Screen of Death™ — causing widespread panic and chaos, albeit briefly. Millions of Windows-based devices that power banks, airports, hospitals, and even broadcast stations went offline and started displaying that dreaded pop-up that usually signifies the unceremonious death of your PC. Millions of Windows-based devices Millions of Windows-based devices Imagine not being able to travel , pay, get treatment, or even tune in to your TV to learn what's going on because.. computer problems. 🙄 travel travel So, what happened? The way it began was that cybersecurity firm Crowdstrike deployed what should have been a regular, run-of-the mill update for its Falcon software to protect customers' computers against new cyber threats. Crowdstrike Crowdstrike That update ended up bringing the world to a standstill instead. The exact reason for why the roll out caused problems on Windows was not entirely clear, but experts speculate that a rogue file in the update apparently did not agree with Microsoft's operating system, causing systems to crash. why rogue file rogue file Some speculate that faulty code or lack of quality checks prior to deployment could have been the problem. Whatever the case, all hell broke loose. speculate speculate "This is not a security incident or cyberattack," Crowdstrike Chief Executive George Kurtz tweeted as soon as customers' complaints began piling up. Naturally, questions have arisen around both Crowdstrike and Microsoft, both of whom hold a sizable market of the cybersecurity and OS space, respectively. One twitter user even posted a comparison of how Crowdstrike compared to its competitors (it's the second most valuable cybersecurity firm in the world). posted posted Judgment against Crowdstrike has been swift though, with the company's stock price plummeting Friday following the fiasco — wiping billions off of its valuation. Microsoft also saw its stock price fall following the event, dragging the broader stock indices down with it on Friday as the event caused a sell off in tech stocks. Things don't seem to have gotten better, with Dan Ives, a technical analyst on Wall Street, tweeting that IT outage issues still persisted. "This is not good news for George Kurtz & Co. in an already bad situation as it appears a number of businesses are still finding outage issues," Ives wrote. tweeting tweeting CNN reports that the cost from the global outage could top $1 billion, but there's no telling yet who will exactly foot the bill. There's a very high likelihood that customers will demand compensation from Crowdstrike, dragging it into legal distractions. Worse yet, Crowdstrike's brand reputation is also going to take a massive hit and its competitors may already be trying to eat away at the company's market share. demand compensation demand compensation In fact, Ives went on to say that Crowdstrike, which was not a widely known brand prior to this event, had become "a household name, but not in a good way." CrowdStrike CEO Kurtz also has an interesting history in that this isn't the first time he's been at the center of a global tech failure. In 2010, a buggy antivirus update from McAfee caused hundreds of thousands of computers to go kaput! isn't the first time isn't the first time Kurtz was the chief technology officer of McAfee at the time, and it doesn't look like there was a lot of blow back against him at the time. McAfee was reportedly bought by Intel a few months after the event and Kurtz ultimately ended up leaving the company to create CrowdStrike. For now, he's in hot water as US lawmakers have called on Kurtz to testify on the global IT outage caused by his company's update. called called Meanwhile, Microsoft said the event was a "reminder of how important it is for all of us across the tech ecosystem to prioritize operating with safe deployment and disaster recovery using the mechanisms that exist." said said The Windows maker estimated that CrowdStrike's update affected 8.5 million devices running its operating system, which was less than 1% of all Windows machines. "While the percentage was small, the broad economic and societal impacts reflect the use of CrowdStrike by enterprises that run many critical services," the company said in a blog post. I suppose that's Microsoft's way of reminding everyone of the 80-20 rule :-) 80-20 rule 80-20 rule Microsoft ranked #2 on HackerNoon's tech company rankings this week. tech company rankings tech company rankings In Other News.. 📰 In Other News.. 📰 Ethereum ETFs Approved by SEC, Bringing Popular Funds to Second-Largest Cryptocurrency — via CoinDesk Cohere raises $500M to beat back generative AI rivals — via TechCrunch Hackers are already taking advantage of the CrowdStrike outage chaos — via CNN Google scraps plan to remove cookies from Chrome — via Reuters The AI-generated disinformation dystopia that wasn't — via Axios Autonomous AI workers that talk to each other will arrive in 2025, Capgemini predicts — via CNBC Ethereum ETFs Approved by SEC, Bringing Popular Funds to Second-Largest Cryptocurrency — via CoinDesk CoinDesk CoinDesk Cohere raises $500M to beat back generative AI rivals — via TechCrunch TechCrunch TechCrunch Hackers are already taking advantage of the CrowdStrike outage chaos — via CNN CNN CNN Google scraps plan to remove cookies from Chrome — via Reuters Reuters Reuters The AI-generated disinformation dystopia that wasn't — via Axios Axios Axios Autonomous AI workers that talk to each other will arrive in 2025, Capgemini predicts — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page.*Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. * *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings here here

How Could an Update Go So Wrong?

Visualizing Colors: The Gradient Oscillation Hyperspace

ভিজ্যুয়ালাইজিং কালার: গ্রেডিয়েন্ট অসিলেশন হাইপারস্পেস

Farben visualisieren: Der Gradientenoszillations-Hyperraum

Visualización de colores: el hiperespacio de oscilación de gradiente

Visualisation des couleurs : l'hyperespace d'oscillation de dégradé

रंगों का दृश्यांकन: ग्रेडिएंट ऑसिलेशन हाइपरस्पेस

色の視覚化: 勾配振動ハイパースペース

색상 시각화: 그라데이션 진동 초공간

Visualizando Cores: O Hiperespaço de Oscilação de Gradiente

Визуализация цветов: гиперпространство градиентных колебаний

Renkleri Görselleştirmek: Degrade Salınım Hiperuzayı

Trực quan hóa màu sắc: Siêu không gian dao động chuyển màu

可视化颜色：梯度振荡超空间

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References Abstract This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles. Keywords Design tools; Data-driven Design; Generative Design; Graphic design; Typography. 1. Introduction Typography is the art of giving our language a visual form. Thus, it is through typography that we materialise and store our knowledge and information [1]. Since the publication of the first typographic book, in the mid-fifteenth century, society has been looking for the most appropriate way to convey a message typographically. In the field of editorial design, this effort has focused on the search for the best principles for designing typographic compositions, such as books [2]. Advances in digital technologies have been changing the work process of graphic designers. The emergence of computational approaches in the design domain enabled designers to explore new perspectives, new conceptual and visual possibilities, and achieve new types of solutions. Furthermore, the emergence of computational approaches comes with a paradigm shift in the role of the designer, who begins to create processes that enable the creation of designs, instead of designing the final solution. In other words, the design concept is translated into a computer program that systematically explores various design possibilities from the original concept. That said, in the particular case of layout design, we consider that the potential of computational approaches is not yet being fully explored. In this work, we explore a computational generative approach for the automatic design of book layouts. The result is a computer system, which operates inside the Adobe InDesign environment, that automatically generates book designs from input content. Figure 1 shows different books created with the presented system. It starts by receiving the input content, namely text and images. Before generating compositions for the input content, the designer can specify restrictions on some of the visual characteristics of the output compositions, e.g. format, size, grid and font. Then, the system automatically typesets the content based on a set of typography rules and principles found in fundamental literature in the field. In the end, the system presents the generated composition to the user as an editable Adobe InDesign document. Overall, the system is capable of creating layout compositions that comply with specific fundamental typographic principles while matching the graphic preferences of the user. In addition, experiments conducted with the system demonstrate its ability to autonomously generate varied compositions with the same input content and also generate visually coherent compositions for different input contents. In addition, it creates functional layout designs in an almost unpredictable manner. This reveals the great potential of this approach to layout design, both in generating outputs that the designer uses as final solutions and in using the outputs as starting points for further explorations. This work is aligned with the experiments previously developed by Ferreira et al. [3] The remaining of the article is organised as follows: Section 2 overviews related works; Section 3 describes the system, namely the interaction process, the engine that runs the system, as well as its inputs and outputs; Section 4 presents an experiment conducted to validate the designs created with the system and discusses the results obtained; Lastly, Section 5 summarises the main contributions of this work and identifies future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References Abstract This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles. Keywords Keywords Design tools; Data-driven Design; Generative Design; Graphic design; Typography. 1. Introduction Typography is the art of giving our language a visual form. Thus, it is through typography that we materialise and store our knowledge and information [1]. Since the publication of the first typographic book, in the mid-fifteenth century, society has been looking for the most appropriate way to convey a message typographically. In the field of editorial design, this effort has focused on the search for the best principles for designing typographic compositions, such as books [2]. Advances in digital technologies have been changing the work process of graphic designers. The emergence of computational approaches in the design domain enabled designers to explore new perspectives, new conceptual and visual possibilities, and achieve new types of solutions. Furthermore, the emergence of computational approaches comes with a paradigm shift in the role of the designer, who begins to create processes that enable the creation of designs, instead of designing the final solution. In other words, the design concept is translated into a computer program that systematically explores various design possibilities from the original concept. That said, in the particular case of layout design, we consider that the potential of computational approaches is not yet being fully explored. In this work, we explore a computational generative approach for the automatic design of book layouts. The result is a computer system, which operates inside the Adobe InDesign environment, that automatically generates book designs from input content. Figure 1 shows different books created with the presented system. It starts by receiving the input content, namely text and images. Before generating compositions for the input content, the designer can specify restrictions on some of the visual characteristics of the output compositions, e.g. format, size, grid and font. Then, the system automatically typesets the content based on a set of typography rules and principles found in fundamental literature in the field. In the end, the system presents the generated composition to the user as an editable Adobe InDesign document. Overall, the system is capable of creating layout compositions that comply with specific fundamental typographic principles while matching the graphic preferences of the user. In addition, experiments conducted with the system demonstrate its ability to autonomously generate varied compositions with the same input content and also generate visually coherent compositions for different input contents. In addition, it creates functional layout designs in an almost unpredictable manner. This reveals the great potential of this approach to layout design, both in generating outputs that the designer uses as final solutions and in using the outputs as starting points for further explorations. This work is aligned with the experiments previously developed by Ferreira et al. [3] The remaining of the article is organised as follows: Section 2 overviews related works; Section 3 describes the system, namely the interaction process, the engine that runs the system, as well as its inputs and outputs; Section 4 presents an experiment conducted to validate the designs created with the system and discusses the results obtained; Lastly, Section 5 summarises the main contributions of this work and identifies future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Towards the Automation of Book Typesetting: Abstract and Introduction

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References 5. Conclusion We have presented a novel approach to computationally design books. The presented system implements a generative design process which takes advantage of the scripting capabilities of Adobe InDesign to procedurally typeset books from content provided by the user. We have shown the ability of the system to (i) create book designs that consistently comply with a series of typographic rules, styles and principles identified in the literature; (ii) produce visually diversified books from the same input content; and (iii) produce visually coherent books with different contents. The work presented in the paper may challenge the typical roles of both the tool and the designer. First, by automatically creating and suggesting design alternatives, the tool ends up playing a more active role in the design process. Then, by modifying and developing custom tools, the designer is no longer a mere tool user and becomes the author of tools tailored to specific needs. We believe this shift can be fruitful since it enables the exploration and discovery of new technical and creative possibilities. This work can hopefully provide directions to further research on generative processes for supporting design exploration and finding unique designs. In the particular case of typography, generative approaches such as the one presented in the paper can be useful and reveal great potential, especially in the current print-on-demand market and digital publishing, where each publication may be unique. Our future work will move in the direction of employing Artificial Intelligence techniques, such as Evolutionary Computation and Machine Learning, to enable a deeper exploration of the vast space of book designs that can be achieved with the system and also to automatically suggest settings to designers according to their needs or goals. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References 5. Conclusion We have presented a novel approach to computationally design books. The presented system implements a generative design process which takes advantage of the scripting capabilities of Adobe InDesign to procedurally typeset books from content provided by the user. We have shown the ability of the system to (i) create book designs that consistently comply with a series of typographic rules, styles and principles identified in the literature; (ii) produce visually diversified books from the same input content; and (iii) produce visually coherent books with different contents. The work presented in the paper may challenge the typical roles of both the tool and the designer. First, by automatically creating and suggesting design alternatives, the tool ends up playing a more active role in the design process. Then, by modifying and developing custom tools, the designer is no longer a mere tool user and becomes the author of tools tailored to specific needs. We believe this shift can be fruitful since it enables the exploration and discovery of new technical and creative possibilities. This work can hopefully provide directions to further research on generative processes for supporting design exploration and finding unique designs. In the particular case of typography, generative approaches such as the one presented in the paper can be useful and reveal great potential, especially in the current print-on-demand market and digital publishing, where each publication may be unique. Our future work will move in the direction of employing Artificial Intelligence techniques, such as Evolutionary Computation and Machine Learning, to enable a deeper exploration of the vast space of book designs that can be achieved with the system and also to automatically suggest settings to designers according to their needs or goals. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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