FerretDBis in the Database Technology Industry

FerretDB

ferretdb

Data-ferret is a tiny, yet powerful to scan or transform deeply nested and complex object-like data with ease. It is available as an open-source project under an MIT license. util library It can and complex, data whose interface or shape cannot be guaranteed ( ), in other words, . search transform schemaless messy data data-ferret is designed to be extensible. It or iterables, beyond the native JavaScript data types. supports custom class instances Additionally, it provides handling objects with . first-class support for circular references I would like to give away to my readers that could benefit from using data-ferret to build a business. my top 5 picks for viable SaaS products Startup Ideas 1. A Search Engine One of the most potential applications for is building the quintessential search engine. You can use data-ferret’s function or create your custom function for specific capabilities. data-ferret locateText() traverse() This can be applied to various industries as a SaaS product. For example; A one-stop shop to find jobs and real estate opportunities by using data from various sources and third-parties listing providers. or your custom will not need data to be normalized to work with it. Users can be presented with all properties available in the dataset by using locateText() traverse() getUniqueKeys() . The same concept may apply to a platform that allows users to for posts, photos, and other content based on keywords, hashtags, and other criteria, without needing to know the specific schema of the data. social media search Same for goes where specific text can be matched or any tags or flags that may exist in the dataset or a that allows users to search for academic papers based on keywords, authors, and other criteria, without needing to know the specific schema of the data. news search engine research paper search engine A that allows users to search for answers to common questions and problems, without needing to know the specific schema of the data. customer service chatbot A that allows users to search for case law and other legal documents based on keywords, judges, and other criteria, without needing to know the specific schema of the data. legal search engine A that allows users to search for medical information and research based on keywords, conditions, and other criteria, without needing to know the specific schema of the data. medical search engine 2. Content Redaction/Moderation Service Perhaps your next product idea has GDPR considerations or will have to deal with sensitive data like credit cards, or details that must be anonymized. Or in general, content moderation must be implemented. You can leverage to perform transformations on your dataset and generalize your approach without having to worry about interface/schemas changing. replaceText() 3. Data Migration Service A data migration tool for companies to seamlessly transfer data from one system to another, regardless of the data’s structure or format. You could use in conjunction with to map out the schema of the original dataset and through a dashboard UI, a user can present a new schema as output. Key names may require renaming or deletion, for that and got you covered. getUniqueKeys() locateKey() renameKey() removeKey() 4. Data Visualization Data-ferret ships a browser-ready version of the code, which means it is not just suitable for Node.Js backend services but also frontend. A for businesses to easily process and visualize large amounts of data from various sources, without needing to know the specific schema of the data sounds like a plausible use case. data analytics tool Data-ferret could be used on the backend to consolidate or prepare the initial data. On the front end, a UI can use the same APIs to perform quick search operations on the client side. Web workers could to perform computationally heavy operations in a separate thread to ensure the main thread remains unencumbered to render the page and remain responsive. also be used 5. Data Auditing Sometimes the focus is on what data is there, not necessarily on how it is structured. For example, a data for financial institutions to automatically match and reconcile transactions across multiple systems and data sources may use a custom function or , which could let sniff out missing records, incorrect values, etc, with more ease, and then later apply whatever business rules make sense, whether it be generating a report or applying data correction. reconciliation tool traverse() locateKey() That’s it. I hope this article has got your creative gears going and might prompt you to check out . my project Good luck! Also published . here

Create a Search Engine and Other Startup Ideas Using Data-Ferret

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

“Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat, the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life, so even well-preserved samples from over 1,000 years ago aren’t complete. AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually, and finding similarities in DNA sequences is a similar task. Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal, so they’re ideal mothers to grow a baby mammoth. After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers. “Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat , the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. call AI an extinction-level threat call AI an extinction-level threat How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. shortens the time spent in genome sequencing shortens the time spent in genome sequencing Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life , so even well-preserved samples from over 1,000 years ago aren’t complete. has a 521-year half-life has a 521-year half-life AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually , and finding similarities in DNA sequences is a similar task. saves Netflix $1 billion annually saves Netflix $1 billion annually Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal , so they’re ideal mothers to grow a baby mammoth. the longest pregnancy of any mammal the longest pregnancy of any mammal After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. resurrect the woolly mammoth by 2027 resurrect the woolly mammoth by 2027 Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. successfully cloned a black-footed ferret successfully cloned a black-footed ferret Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. could improve global health could improve global health Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers.

The Role of AI in Resurrecting Extinct Species

বিলুপ্ত প্রজাতির পুনরুত্থানে এআই-এর ভূমিকা

Die Rolle der KI bei der Wiederbelebung ausgestorbener Arten

El papel de la IA en la resurrección de especies extintas

Le rôle de l’IA dans la résurrection des espèces disparues

विलुप्त प्रजातियों को पुनर्जीवित करने में एआई की भूमिका

絶滅した種の復活におけるAIの役割

멸종된 종을 부활시키는 데 있어 AI의 역할

O papel da IA na ressurreição de espécies extintas

Роль ИИ в воскрешении вымерших видов

Soyu Tükenmiş Türlerin Diriltilmesinde Yapay Zekanın Rolü

Vai trò của AI trong việc hồi sinh các loài đã tuyệt chủng

人工智能在复活灭绝物种中的作用

This paper is under CC 4.0 license. available on arxiv : Authors (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References CONCLUSION The analysis of the interactions and dialogues that took place during the therapeutic laboratory for autonomies endorsed with the inclusion of the Pepper robot provided an interesting perspective on the communication dynamics of children in the lab. The results obtained showed the effectiveness of the robot in promoting autonomy and functional acquisition, demonstrating that the therapeutic method based on assistive robotics is a valuable resource to support the rehabilitation needs of communication and social skills of autistic children. The analysis highlights new possibilities for their engagement and active participation as a co-designer, as we already experienced in the past with neuro-typical children [19], representing an innovative opportunity to promote the development and wellness of autistic people and opening to new perspectives for therapeutic intervention more aware of the needs of autistic minds. In conclusion, this paper highlights the need to provide the robot with the ability of adapt to the children’s peculiarity and features and sharing her/his vocabulary, especially by recognizing and using memes during interactions, to inspire greater trust in children and allowing the use of common slang, already in use with classmates, allowing them to consider Pepper a peer and effectively insert it in the role of mediator. In addition, it would allow children to use a simplified type of communication during meetings to compensate for the typical communication deficits associated with autism spectrum disorders. In the future we will work in this directions (co-design, user’s adaptations, using and recognizing memes during children-robot communication) having the robot able to communicate and express adapting to user tastes and preferences and and try again to field a real-world evaluation that takes into account the effectiveness of different levels of user adaptation [23]. REFERENCE [1] M. Biondi e M. Maj, DSM-5: diagnostic and statistical manual of mental disorders: text revision, 5. ed. Milano: Raffaello Cortina. [2] E. Hollander, R. Hagerman, e C. Ferretti, Textbook of Autism Spectrum Disorders, Second Edition. American Psychiatric Association Publishing, 2022. [3] ISS - Istituto superiore di sanità, «Osservatorio Nazionale Autismo», OssNA. https://osservatorionazionaleautismo.iss.it [4] P. Pennisi et al., «Autism and social robotics: A systematic review», Autism Research, vol. 9, fasc. 2, pp. 165–183, 2016, doi: 10.1002/aur.1527. [5] S. Shamsuddin, H. Yussof, S. Mohamed, e F. A. 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The Power of Memes: The Pepper Robot as a Communicative Aid for Autistic Children - Conclusion

2022 was a thanks to events that included the high-profile collapse of FTX and subsequent criminal proceedings brought against its founder. Yet if we fast-forward to today, it looks like faith in crypto is making a comeback as . low point in crypto’s history Bitcoin’s price surges to $42,000 But while cryptocurrencies and blockchain technologies in general are decentralized by nature, this doesn’t mean that they should go hand in hand with being deregulated. The technology and the assets they produce are increasingly used for everyday tasks. Governments haven’t been quick to welcome the change but a new generation of policymakers and innovators are keen to usher in a new era for the blockchain that will help to bring the benefits of the technology to users in a more secure and reliable way. For this reason, Hester Peirce, Commissioner of the United States Securities and Exchange Commission (SEC), delivered a fireside chat at Georgetown University on January 26th to mark the launch of USA-UAE Alliance, a blockchain business forum founded by Georgetown University classmates Edward Cullen and Michael Amatulli. Token Safe Harbor 3.0 Hester M. Peirce was appointed by President Trump to the U.S. Securities and Exchange Commission and was in 2018 sworn in. Before joining the SEC, Peirce conducted research on the regulation of financial markets at the Mercatus Center at George Mason University. She was a Senior Counsel on the U.S. Senate Committee on Banking, Housing, and Urban Affairs, where she advised Ranking Member Richard Shelby and other members of the Committee on securities issues. Peirce has also spoken previously about the need for so-called Token Safe Harbors. The idea here is to give crypto and blockchain startups the breathing space to develop and build networks to a more robust stage before having to adhere to federal finance regulations and security protocols. According to notes Peirce released on GitHub in relation to her , the initiative aims to enhance token purchaser protection, more frequent updates on development disclosures and more clarity about the transition process that would take place at the end of the three-year grace period. Token Safe Harbor 2.0 proposal During the event at Georgetown University Edward Cullen, CEO of Crescite and one of the interviewers at the event, asked Peirce whether it may be time for a Token Safe Harbor 3.0 proposal in 2024. She shared that, “A problem that a lot of people had pointed out was that there are a lot of people issuing tokens and there was not a lot of disclosure around them. Obviously, if people are buying these things you want to make sure they know what they’re buying so we wanted a framework designed around the information that buyers would uniquely want.” She continued, “I was trying to get at the safe harbor that I put out would require people to make these disclosures for the initial period when they when they were selling these tokens. And then the idea was that if you really decentralize the blockchain such that no one had more information than anyone else, the disclosures wouldn’t be necessary anymore because all that information would be out there and it would be available for anyone to see there.” Striking balance with regulations Cullen also asked Peirce to share her views on whether freedom in capital markets is a positive or negative. She expanded, “A lot of the way I think about securities regulation is driven because we want to build an economy that allows people to get to get the most out of themselves. You have to have capital markets that allow capital to flow to people who have good ideas and that those good ideas are ferreted out not by a regulator up at the top, but by people who are on the ground, who know the people, who know the technology that they’re building, who can ask those questions.” She concludes “It’s a grassroots kind of thing. And so a properly functioning capital market makes sure that capital flows in the right direction. When I have a choice to maximize liberty, I will but of course, there are statuary mandates and parameters to consider.” The USA-UAE blockchain alliance Said Edward Cullen, CEO of Crescite Innovation Corporation, “The USA-UAE Alliance™ is the latest example of international collaborators coming together and using blockchain technologies for economic growth and social good. We are appreciative of our education as continue to build.” Cullen, earlier a candidate for Mayor of New York City, is an entrepreneur who is known for innovation and regulatory leadership. The USA-UAE Alliance™ team includes Karl P. Kilb III, Co-Founder and Chairman of Crescite Innovation Corporation and former General Counsel of Bloomberg LP. Kilb is the Founder and Chairman of the Entrepreneurial Law Advisory Council at the Fordham University School of Law. Said Michael Amatulli, who helped start the alliance and is CEO of MVNM, “Blockchain is going to become increasingly prominent across financial communities and it’s essential that the development of the technology happens in a fair and sustainable manner.” Featured photo of Edward Cullen, Hester Peirce and Jacquelyn Melinek This article was originally published on The Sociable.

Embracing Regulation: A Positive Outlook for Blockchain Enthusiasts

This paper is under CC 4.0 license. available on arxiv Authors: (1) Marcin W ˛atorek, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland and marcin.watorek@pk.edu.pl (M.W.); (2) Jarosław Kwapie ´n, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and jaroslaw.kwapien@ifj.edu.pl; (3)  Stanisław Drozd˙ z, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland, Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Radzikowskiego 152, 31-342 Kraków, Poland and stanislaw.drozdz@ifj.edu.pl (S.D.). Table of Links Abstract and Introduction Data and Methodology Results and Discussion Conclusion and References 4. Conclusions Based on the multiscale cross-correlation analysis performed for the data covering almost the last three years, it can be concluded that the cryptocurrency market dynamics is substantially tied to the traditional financial markets. Consistently, the most liquid cryptocurrencies, BTC and ETH, cannot serve as a hedge or safe haven for the stock market investments, not only during the turbulent periods like the Covid-19 panic, where this effect is particularly strong, but also during the recent bear market period on tech stocks, which has been accompanied by the parallel bear market on cryptocurrencies. Many observations show that the Covid-19 pandemic may have changed the paradigm that the cryptocurrency market is a largely autonomous market. The recent market developments and the strong US dollar have additionally increased the strength of the cross-correlations between BTC and ETH on the one side and the US tech stocks on the other side. These observations support some earlier findings on the same subject (e.g., [22,70]). In contrast, as the cryptocurrency market was weakly correlated with other markets during 2021, our results cannot support directly a recent hypothesis that the quantitative easing could actually be responsible for these correlations [22]. The existence of links between the global economy and the cryptocurrency market are further strengthened by the reaction of the price changes of BTC and ETH to economic data, such as CPI inflation, in a similar way to traditional financial instruments. These results are able to remove or, at least, to suppress the uncertainty that recent literature on this topic has brought to the cryptocurrency investors. Now it is more clear that the cryptocurrencies can no longer serve as a convenient hedging target for the investors whose purpose is to diversify the risk. Our study brings a strong indication that the cryptocurrency market has finally become a connected part of the global financial markets after 12 years of the maturation process. Whether such a direction of this market evolution remains in agreement with the early vision of the cryptocurrency creators can be disputed, however. We also face a related question: does the fact that we have got “just another part of the global financial market” deserve devoting so huge amounts of energy to it? Sooner or later this question must be addressed by the policy makers. Nevertheless, what becomes evident now is that it allows the market participants to broaden the spectrum of their investment possibilities. Among the limitations that might have influenced our study and, subsequently, our conclusions, we have to mention that only two principal cryptocurrencies were studied. Although they are the most influential, the most frequently traded, and widely discussed cryptocurrencies, they by no means define the entire market. It is possible that an analysis that included some less important cryptocurrencies would bring different outcomes. This is especially likely for the marginal cryptocurrencies without any thinkable “fundamental” value, whose dynamics is driven predominantly by extreme speculation. However, as the cryptocurrency market is looked at by the most investors through the lens of BTC and ETH (as their capitalization indicates), this particular limitation does not seem discouraging to us. Currently, these two assets shape the whole cryptocurrency market and we expect them to continue doing it in the nearest future. Another limitation of our analysis is the particular selection of the traditional financial instruments. Indeed, they constitute only a small fraction of the available ones. We are convinced, though, that they are among the most observed and the most influential ones in the context of the global economy, which fully justifies our choice. A more general observation that the cryptocurrency market has spontaneously coupled to the technological sector of the stock markets by reacting to some trigger provided by the external data inflow resembles analogous effects of the spontaneous emergence of order among the so-far independent degrees of freedom in the various complex systems. However, as complexity allows for flexible reacting of a system to both the external perturbations and internal processes, such effects of ordering in the financial markets have to be eventually counterbalanced by the opposite processes of disordering. Therefore, the market participants must be aware that the inter-market couplings may not last forever and they can significantly be weakened or even removed completely at some point in future. 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Integration of Cryptocurrency into the Global Financial Market: Conclusion and References

As global talent contends with increasingly rigid immigration policies, the landscape for EB1A visa applicants, especially those of Indian origin, has witnessed significant fluctuations in recent months. Spynn, a PR firm that helps applicants for the demonstrate extraordinary ability, faces new challenges in navigating these changes. EB-1A Visa In July 2023, the priority date for Indian applicants was February 2022 for final action and June 2022 for filing, presenting a backlog of just over a year. However, August 2023 brought an unexpected retrogression, moving the priority date back to January 2012, a drastic ten-year leap. This change, attributed to oversubscription in the Indian category, left the immigration community in disarray​​​​. September 2023 saw no improvement, as the retrogression to January 2012 persisted, underscoring oversubscription. With the start of fiscal year 2024 in October, there were high expectations for significant advancements. Contrary to expectations, the dates only advanced five years to January 2017, showing a significant six-year backlog​​​​. Furthermore, the November Visa Bulletin revealed no movement in any category for India, with EB-1 halting on January 1, 2017. This stagnation further emphasizes the challenges faced by Indian EB-1A applicants​​. In response to these complexities, U.S. Citizenship and Immigration Services (USCIS) issued new policy guidance on September 12, 2023, to clarify the types of evidence evaluated for EB1 eligibility, particularly those in science, technology, engineering, or mathematics (STEM) fields. This update is designed to provide more clarity and transparency, assisting petitioners in submitting appropriate evidence to establish the beneficiary’s eligibility​​. Establishing Extraordinary Ability Spynn, amidst these changes, offers a unique approach to meeting the stringent requirements set by the USCIS, navigating the evolving immigration system for their clients with extraordinary abilities. asserts Matteo Ferretti, the driving force behind Spynn. With a rich background in public relations, Ferretti has steered the firm towards a niche focus: aligning PR efforts with the EB1A Visa's exacting publicity criteria. he emphasizes. "Strategic visibility is paramount," "Our service transcends traditional PR. We craft narratives that resonate with USCIS's definition of extraordinary ability," This strategy stems from a deep understanding of U.S. immigration nuances. Ferretti notes. "The EB1A is more than meeting criteria; it's about weaving a compelling story that showcases unparalleled expertise," Data and Future Trends The demand for EB1A Visas in India has escalated by 20% in 2023 compared to the previous year. Despite the application surge, the approval rate remains steady at around 40%, reflecting the visa's stringent criteria. " ," remarks an industry insider, raising questions about the efficacy of PR tactics in securing the visa. The figures are telling. The EB1A is designed for individuals of exceptional ability, and demonstrating this is quite a challenge Spynn, however, challenges this viewpoint with its impressive track record. ," declares Ferretti from the firm. Their approach encompasses a comprehensive strategy that extends beyond mere publicity. " ," he elaborates. Ferretti anticipates a 25% rise in applications from India over the next five years, signaling an increasing reliance on their specialized services in navigating the complexities of the EB1A visa process. "95% of our Indian clients have successfully obtained their EB1A Visas We customize our strategies to highlight the distinct strengths and accomplishments of each applicant Global Operations and Customized Strategies Spynn's services extend beyond U.S. borders. Ferretti says. The company serves clients from diverse regions and professional backgrounds, offering effective strategies tailored to local needs. "Talent is universal, but opportunities are not always equally accessible. We aim to bridge this gap," However, some critics argue that a one-size-fits-all approach may not be practical for a U.S.-specific program like the EB1A Visa. Ferretti acknowledges this but emphasizes the company's flexibility. he explains. "Each client receives a customized strategy that considers both USCIS criteria and their unique contributions to their field," Spynn's Adaptability and Vision As countries become more selective in their immigration policies, only the most qualified individuals will succeed in crossing borders. Spynn aims to guide these talents. Ferretti concludes. "We're more than a company; we're part of a larger movement to make the world accessible for extraordinary talents," His vision is grounded in a practical understanding of immigration trends. he adds. In a world that's becoming increasingly insular, Spynn offers a glimmer of hope. Their unique approach to the EB1A Visa application process is not just a business strategy; it's a practical necessity in today's competitive global environment. "The rules are changing, and we're prepared to adapt our strategies to meet these new challenges,"

Proving Extraordinary Ability for the EB-1A Visa, According to Spynn

229 Stories To Learn About Data Analytics

This paper is available on arxiv under CC 4.0 license. . Authors: Gemini Team, Google Table of Links Abstract and Introduction Model Architecture Training Infrastructure Training Dataset Evaluation Responsible Deployment Discussion and Conclusion, References Contributions and Acknowledgments Appendix 8. Contributions and Acknowledgments Leads Rohan Anil, Co-Lead, Text Sebastian Borgeaud, Co-Lead, Text Yonghui Wu, Co-Lead, Text Jean-Baptiste Alayrac, Co-Lead, MM Vision Jiahui Yu, Co-Lead, MM Vision Radu Soricut*, Co-Lead, MM Vision* Johan Schalkwyk, Lead, MM Audio Andrew M. Dai, Co-Lead, Data Anja Hauth*, Co-Lead, Data* Katie Millican, Co-Lead, Data David Silver, Co-Lead, Fine-Tuning Slav Petrov, Co-Lead, Fine-Tuning Melvin Johnson, Lead, Instruction Tuning Ioannis Antonoglou*, Co-Lead, RL Techniques* Julian Schrittwieser, Co-Lead, RL Techniques Amelia Glaese, Lead, Human Data Jilin Chen, Lead, Safety Emily Pitler, Co-Lead, Tool Use Timothy Lillicrap, Co-Lead, Tool Use Angeliki Lazaridou, Co-Lead, Eval Orhan Firat, Co-Lead, Eval James Molloy, Co-Lead, Infra Michael Isard, Co-Lead, Infra Paul R. Barham, Co-Lead, Infra Tom Hennigan, Co-Lead, Infra Benjamin Lee, Co-Lead, Codebase & Parallelism Fabio Viola, Co-Lead, Codebase & Parallelism Malcolm Reynolds, Co-Lead, Codebase & Parallelism Yuanzhong Xu, Co-Lead, Codebase & Parallelism Ryan Doherty, Lead, Ecosystem Eli Collins, Lead, Product Clemens Meyer, Co-Lead, Operations Eliza Rutherford, Co-Lead, Operations Erica Moreira, Co-Lead, Operations Kareem Ayoub, Co-Lead, Operations Megha Goel, Co-Lead, Operations Core Contributors George Tucker Enrique Piqueras Maxim Krikun Iain Barr Nikolay Savinov Ivo Danihelka Becca Roelofs Core Contributors Anaïs White Anders Andreassen Tamara von Glehn Lakshman Yagati Mehran Kazemi Lucas Gonzalez Misha Khalman Jakub Sygnowski Alexandre Frechette Charlotte Smith Laura Culp Lev Proleev Yi Luan Xi Chen James Lottes Nathan Schucher Federico Lebron Alban Rrustemi Natalie Clay Phil Crone Tomas Kocisky Jeffrey Zhao Bartek Perz Dian Yu Heidi Howard Adam Bloniarz Jack W. Rae Han Lu Laurent Sifre Marcello Maggioni Fred Alcober Dan Garrette Megan Barnes Shantanu Thakoor Jacob Austin Gabriel Barth-Maron William Wong Rishabh Joshi Rahma Chaabouni Deeni Fatiha Arun Ahuja Ruibo Liu Yunxuan Li Sarah Cogan Jeremy Chen Core Contributors Chao Jia Chenjie Gu Qiao Zhang Jordan Grimstad Ale Jakse Hartman Martin Chadwick Gaurav Singh Tomar Xavier Garcia Evan Senter Emanuel Taropa Thanumalayan Sankaranarayana Pillai Jacob Devlin Michael Laskin Diego de Las Casas Dasha Valter Connie Tao Lorenzo Blanco Adrià Puigdomènech Badia David Reitter Mianna Chen Jenny Brennan Clara Rivera Sergey Brin Shariq Iqbal Gabriela Surita Jane Labanowski Abhi Rao Stephanie Winkler Emilio Parisotto Yiming Gu Kate Olszewska Yujing Zhang Ravi Addanki Antoine Miech Annie Louis Laurent El Shafey Denis Teplyashin Geoff Brown Elliot Catt Nithya Attaluri Jan Balaguer Jackie Xiang Pidong Wang Zoe Ashwood Anton Briukhov Albert Webson Sanjay Ganapathy Smit Sanghavi Core Contributors Ajay Kannan Ming-Wei Chang Axel Stjerngren Josip Djolonga Yuting Sun Ankur Bapna Matthew Aitchison Pedram Pejman Henryk Michalewski Tianhe Yu Cindy Wang Juliette Love Junwhan Ahn Dawn Bloxwich Kehang Han Peter Humphreys Thibault Sellam James Bradbury Varun Godbole Sina Samangooei Bogdan Damoc Alex Kaskasoli Sébastien M. R. Arnold Vijay Vasudevan Shubham Agrawal Jason Riesa Dmitry Lepikhin Richard Tanburn Srivatsan Srinivasan Hyeontaek Lim Sarah Hodkinson Pranav Shyam Johan Ferret Steven Hand Ankush Garg Tom Le Paine Jian Li Yujia Li Minh Giang Alexander Neitz Zaheer Abbas Sarah York Machel Reid Elizabeth Cole Aakanksha Chowdhery Dipanjan Das Dominika Rogozińska Vitaly Nikolaev Core Contributors Pablo Sprechmann Zachary Nado Lukas Zilka Flavien Prost Luheng He Marianne Monteiro Gaurav Mishra Chris Welty Josh Newlan Dawei Jia Miltiadis Allamanis Clara Huiyi Hu Raoul de Liedekerke Justin Gilmer Carl Saroufim Shruti Rijhwani Shaobo Hou Disha Shrivastava Anirudh Baddepudi Alex Goldin Adnan Ozturel Albin Cassirer Yunhan Xu Daniel Sohn Devendra Sachan Reinald Kim Amplayo Craig Swanson Dessie Petrova Shashi Narayan Arthur Guez Siddhartha Brahma Jessica Landon Miteyan Patel Ruizhe Zhao Kevin Villela Luyu Wang Wenhao Jia Matthew Rahtz Mai Giménez Legg Yeung Hanzhao Lin James Keeling Petko Georgiev Diana Mincu Boxi Wu Salem Haykal Rachel Saputro Kiran Vodrahalli Core Contributors James Qin Zeynep Cankara Abhanshu Sharma Nick Fernando Will Hawkins Behnam Neyshabur Solomon Kim Adrian Hutter Priyanka Agrawal Alex Castro-Ros George van den Driessche Tao Wang Fan Yang Shuo-yiin Chang Paul Komarek Ross McIlroy Mario Lučić Guodong Zhang Wael Farhan Michael Sharman Paul Natsev Paul Michel Yong Cheng Yamini Bansal Siyuan Qiao Kris Cao Siamak Shakeri Christina Butterfield Justin Chung Paul Kishan Rubenstein Shivani Agrawal Arthur Mensch Kedar Soparkar Karel Lenc Timothy Chung Aedan Pope Loren Maggiore Jackie Kay Priya Jhakra Shibo Wang Joshua Maynez Mary Phuong Taylor Tobin Andrea Tacchetti Maja Trebacz Kevin Robinson Yash Katariya Sebastian Riedel Core Contributors Paige Bailey Kefan Xiao Nimesh Ghelani Lora Aroyo Ambrose Slone Neil Houlsby Xuehan Xiong Zhen Yang Elena Gribovskaya Jonas Adler Mateo Wirth Lisa Lee Music Li Thais Kagohara Jay Pavagadhi Sophie Bridgers Anna Bortsova Sanjay Ghemawat Zafarali Ahmed Tianqi Liu Richard Powell Vijay Bolina Mariko Iinuma Polina Zablotskaia James Besley Da-Woon Chung Timothy Dozat Ramona Comanescu Xiance Si Jeremy Greer Guolong Su Martin Polacek Raphaël Lopez Kaufman Simon Tokumine Hexiang Hu Elena Buchatskaya Yingjie Miao Mohamed Elhawaty Aditya Siddhant Nenad Tomasev Jinwei Xing Christina Greer Helen Miller Shereen Ashraf Aurko Roy Zizhao Zhang Ada Ma Angelos Filos Core Contributors Milos Besta Rory Blevins Ted Klimenko Chih-Kuan Yeh Soravit Changpinyo Jiaqi Mu Oscar Chang Mantas Pajarskas Carrie Muir Vered Cohen Charline Le Lan Krishna Haridasan Amit Marathe Steven Hansen Sholto Douglas Rajkumar Samuel Mingqiu Wang Sophia Austin Chang Lan Jiepu Jiang Justin Chiu Jaime Alonso Lorenzo Lars Lowe Sjösund Sébastien Cevey Zach Gleicher Thi Avrahami Anudhyan Boral Hansa Srinivasan Vittorio Selo Rhys May Konstantinos Aisopos Léonard Hussenot Livio Baldini Soares Kate Baumli Michael B. Chang Adrià Recasens Ben Caine Alexander Pritzel Filip Pavetic Fabio Pardo Anita Gergely Justin Frye Vinay Ramasesh Dan Horgan Kartikeya Badola Nora Kassner Subhrajit Roy Ethan Dyer Core Contributors Víctor Campos Alex Tomala Yunhao Tang Dalia El Badawy Elspeth White Basil Mustafa Oran Lang Abhishek Jindal Sharad Vikram Zhitao Gong Sergi Caelles Ross Hemsley Gregory Thornton Fangxiaoyu Feng Wojciech Stokowiec Ce Zheng Phoebe Thacker Çağlar Ünlü Zhishuai Zhang Mohammad Saleh James Svensson Max Bileschi Piyush Patil Ankesh Anand Roman Ring Katerina Tsihlas Arpi Vezer Marco Selvi Toby Shevlane Mikel Rodriguez Tom Kwiatkowski Samira Daruki Keran Rong Allan Dafoe Nicholas FitzGerald Keren Gu-Lemberg Mina Khan Lisa Anne Hendricks Marie Pellat Vladimir Feinberg James Cobon-Kerr Tara Sainath Maribeth Rauh Sayed Hadi Hashemi Richard Ives Yana Hasson YaGuang Li Eric Noland Core Contributors Yuan Cao Nathan Byrd Le Hou Qingze Wang Thibault Sottiaux Michela Paganini Jean-Baptiste Lespiau Alexandre Moufarek Samer Hassan Kaushik Shivakumar Joost van Amersfoort Amol Mandhane Pratik Joshi Anirudh Goyal Matthew Tung Andrew Brock Hannah Sheahan Vedant Misra Cheng Li Nemanja Rakićević Mostafa Dehghani Fangyu Liu Sid Mittal Junhyuk Oh Seb Noury Eren Sezener Fantine Huot Matthew Lamm Nicola De Cao Charlie Chen Contributors Gamaleldin Elsayed Ed Chi Mahdis Mahdieh Ian Tenney Nan Hua Ivan Petrychenko Patrick Kane Dylan Scandinaro Rishub Jain Jonathan Uesato Romina Datta Adam Sadovsky Oskar Bunyan Dominik Rabiej Shimu Wu John Zhang Contributors Gautam Vasudevan Edouard Leurent Mahmoud Alnahlawi Ionut Georgescu Nan Wei Ivy Zheng Betty Chan Pam G Rabinovitch Piotr Stanczyk Ye Zhang David Steiner Subhajit Naskar Michael Azzam Matthew Johnson Adam Paszke Chung-Cheng Chiu Jaume Sanchez Elias Afroz Mohiuddin Faizan Muhammad Jin Miao Andrew Lee Nino Vieillard Sahitya Potluri Jane Park Elnaz Davoodi Jiageng Zhang Jeff Stanway Drew Garmon Abhijit Karmarkar Zhe Dong Jong Lee Aviral Kumar Luowei Zhou Jonathan Evens William Isaac Zhe Chen Johnson Jia Anselm Levskaya Zhenkai Zhu Chris Gorgolewski Peter Grabowski Yu Mao Alberto Magni Kaisheng Yao Javier Snaider Norman Casagrande Paul Suganthan Evan Palmer Contributors Geoffrey Irving Edward Loper Manaal Faruqui Isha Arkatkar Nanxin Chen Izhak Shafran Michael Fink Alfonso Castaño Irene Giannoumis Wooyeol Kim Mikołaj Rybiński Ashwin Sreevatsa Jennifer Prendki David Soergel Adrian Goedeckemeyer Willi Gierke Mohsen Jafari Meenu Gaba Jeremy Wiesner Diana Gage Wright Yawen Wei Harsha Vashisht Yana Kulizhskaya Jay Hoover Maigo Le Lu Li Chimezie Iwuanyanwu Lu Liu Kevin Ramirez Andrey Khorlin Albert Cui Tian LIN Marin Georgiev Marcus Wu Ricardo Aguilar Keith Pallo Abhishek Chakladar Alena Repina Xihui Wu Tom van der Weide Priya Ponnapalli Caroline Kaplan Jiri Simsa Shuangfeng Li Olivier Dousse Fan Yang Jeff Piper Nathan Ie Contributors Minnie Lui Rama Pasumarthi Nathan Lintz Anitha Vijayakumar Lam Nguyen Thiet Daniel Andor Pedro Valenzuela Cosmin Paduraru Daiyi Peng Katherine Lee Shuyuan Zhang Somer Greene Duc Dung Nguyen Paula Kurylowicz Sarmishta Velury Sebastian Krause Cassidy Hardin Lucas Dixon Lili Janzer Kiam Choo Ziqiang Feng Biao Zhang Achintya Singhal Tejasi Latkar Mingyang Zhang Quoc Le Elena Allica Abellan Dayou Du Dan McKinnon Natasha Antropova Tolga Bolukbasi Orgad Keller David Reid Daniel Finchelstein Maria Abi Raad Remi Crocker Peter Hawkins Robert Dadashi Colin Gaffney Sid Lall Ken Franko Egor Filonov Anna Bulanova Rémi Leblond Vikas Yadav Shirley Chung Harry Askham Luis C. Cobo Contributors Kelvin Xu Felix Fischer Jun Xu Christina Sorokin Chris Alberti Chu-Cheng Lin Colin Evans Hao Zhou Alek Dimitriev Hannah Forbes Dylan Banarse Zora Tung Jeremiah Liu Mark Omernick Colton Bishop Chintu Kumar Rachel Sterneck Ryan Foley Rohan Jain Swaroop Mishra Jiawei Xia Taylor Bos Geoffrey Cideron Ehsan Amid Francesco Piccinno Xingyu Wang Praseem Banzal Petru Gurita Hila Noga Premal Shah Daniel J. Mankowitz Alex Polozov Nate Kushman Victoria Krakovna Sasha Brown MohammadHossein Bateni Dennis Duan Vlad Firoiu Meghana Thotakuri Tom Natan Anhad Mohananey Matthieu Geist Sidharth Mudgal Sertan Girgin Hui Li Jiayu Ye Ofir Roval Reiko Tojo Contributors Michael Kwong James Lee-Thorp Christopher Yew Quan Yuan Sumit Bagri Danila Sinopalnikov Sabela Ramos John Mellor Abhishek Sharma Aliaksei Severyn Jonathan Lai Kathy Wu Heng-Tze Cheng David Miller Nicolas Sonnerat Denis Vnukov Rory Greig Jennifer Beattie Emily Caveness Libin Bai Julian Eisenschlos Alex Korchemniy Tomy Tsai Mimi Jasarevic Weize Kong Phuong Dao Zeyu Zheng Frederick Liu Fan Yang Rui Zhu Mark Geller Tian Huey Teh Jason Sanmiya Evgeny Gladchenko Nejc Trdin Andrei Sozanschi Daniel Toyama Evan Rosen Sasan Tavakkol Linting Xue Chen Elkind Oliver Woodman John Carpenter George Papamakarios Rupert Kemp Sushant Kafle Tanya Grunina Rishika Sinha Contributors Alice Talbert Abhimanyu Goyal Diane Wu Denese Owusu-Afriyie Cosmo Du Chloe Thornton Jordi Pont-Tuset Pradyumna Narayana Jing Li Saaber Fatehi John Wieting Omar Ajmeri Benigno Uria Tao Zhu Yeongil Ko Laura Knight Amélie Héliou Ning Niu Shane Gu Chenxi Pang Dustin Tran Yeqing Li Nir Levine Ariel Stolovich Norbert Kalb Rebeca Santamaria-Fernandez Sonam Goenka Wenny Yustalim Robin Strudel Ali Elqursh Balaji Lakshminarayanan Charlie Deck Shyam Upadhyay Hyo Lee Mike Dusenberry Zonglin Li Xuezhi Wang Kyle Levin Raphael Hoffmann Dan Holtmann-Rice Olivier Bachem Summer Yue Sho Arora Eric Malmi Daniil Mirylenka Qijun Tan Christy Koh Soheil Hassas Yeganeh Contributors Siim Põder Steven Zheng Francesco Pongetti Mukarram Tariq Yanhua Sun Lucian Ionita Mojtaba Seyedhosseini Pouya Tafti Ragha Kotikalapudi Zhiyu Liu Anmol Gulati Jasmine Liu Xinyu Ye Bart Chrzaszcz Lily Wang Nikhil Sethi Tianrun Li Ben Brown Shreya Singh Wei Fan Aaron Parisi Joe Stanton Chenkai Kuang Vinod Koverkathu Christopher A. Choquette-Choo Yunjie Li TJ Lu Abe Ittycheriah Prakash Shroff Pei Sun Mani Varadarajan Sanaz Bahargam Rob Willoughby David Gaddy Ishita Dasgupta Guillaume Desjardins Marco Cornero Brona Robenek Bhavishya Mittal Ben Albrecht Ashish Shenoy Fedor Moiseev Henrik Jacobsson Alireza Ghaffarkhah Morgane Rivière Alanna Walton Clément Crepy Alicia Parrish Contributors Yuan Liu Zongwei Zhou Clement Farabet Carey Radebaugh Praveen Srinivasan Claudia van der Salm Andreas Fidjeland Salvatore Scellato Eri Latorre-Chimoto Hanna Klimczak-Plucińska David Bridson Dario de Cesare Tom Hudson Piermaria Mendolicchio Lexi Walker Alex Morris Ivo Penchev Matthew Mauger Alexey Guseynov Alison Reid Seth Odoom Lucia Loher Victor Cotruta Madhavi Yenugula Dominik Grewe Anastasia Petrushkina Tom Duerig Antonio Sanchez Steve Yadlowsky Amy Shen Amir Globerson Adam Kurzrok Lynette Webb Sahil Dua Dong Li Preethi Lahoti Surya Bhupatiraju Dan Hurt Haroon Qureshi Ananth Agarwal Tomer Shani Matan Eyal Anuj Khare Shreyas Rammohan Belle Lei Wang Chetan Tekur Mihir Sanjay Kale Jinliang Wei Contributors Ruoxin Sang Brennan Saeta Tyler Liechty Yi Sun Yao Zhao Stephan Lee Pandu Nayak Doug Fritz Manish Reddy Vuyyuru John Aslanides Nidhi Vyas Martin Wicke Xiao Ma Taylan Bilal Evgenii Eltyshev Daniel Balle Nina Martin Hardie Cate James Manyika Keyvan Amiri Yelin Kim Xi Xiong Kai Kang Florian Luisier Nilesh Tripuraneni David Madras Mandy Guo Austin Waters Oliver Wang Joshua Ainslie Jason Baldridge Han Zhang Garima Pruthi Jakob Bauer Feng Yang Riham Mansour Jason Gelman Yang Xu George Polovets Ji Liu Honglong Cai Warren Chen XiangHai Sheng Emily Xue Sherjil Ozair Adams Yu Christof Angermueller Contributors Xiaowei Li Weiren Wang Julia Wiesinger Emmanouil Koukoumidis Yuan Tian Anand Iyer Madhu Gurumurthy Mark Goldenson Parashar Shah MK Blake Hongkun Yu Anthony Urbanowicz Jennimaria Palomaki Chrisantha Fernando Kevin Brooks Ken Durden Harsh Mehta Nikola Momchev Elahe Rahimtoroghi Maria Georgaki Amit Raul Sebastian Ruder Morgan Redshaw Jinhyuk Lee Komal Jalan Dinghua Li Ginger Perng Blake Hechtman Parker Schuh Milad Nasr Mia Chen Kieran Milan Vladimir Mikulik Trevor Strohman Juliana Franco Tim Green Program Leads Demis Hassabis Koray Kavukcuoglu Overall Technical Leads (equal contribution) Jeffrey Dean Oriol Vinyals The roles are defined as below: • : Individual(s) responsible for the sub-team throughout the project. Lead Individual that had significant impact throughout the project. • Core Contributor: : Individual that had contributions to the project and was partially involved with the effort. • Contributor Responsible for the organizational aspects of the Gemini effort • Program Lead: Responsible for the technical direction of the overall Gemini effort • Overall Technical Lead: Within each role, contributions are equal, and are listed in a randomized order. Ordering within each role does not indicate ordering of the contributions. Gemini is a cross-Google effort, with members from Google DeepMind (GDM), Google Research (GR), Knowledge and Information (K&I), Core ML, Cloud, Labs, and more. We thank our reviewers and colleagues for their valuable discussions and feedback on the report— Alexandra Belias, Arielle Bier, Eleanor Tomlinson, Emily Hossellman, Gaby Pearl, Helen King, Hollie Dobson, Jaclyn Konzelmann, Jennifer Beroshi, Joel Moss, Jon Small, Jonathan Fildes, Oli Gaymond, Rebecca Bland, Reena Jana, and Tom Lue. Our work is made possible by the dedication and efforts of numerous teams at Google. We would like to acknowledge the support from Abhi Mohan, Adekunle Bello, Aishwarya Nagarajan, Alaa Saade, Alejandro Lince, Alexander Chen, Alexander Kolbasov, Alexander Schiffhauer, Amar Subramanya, Ameya Shringi, Amin Vahdat, Anda Rabatić, Anthonie Gross, Antoine Yang, Anthony Green, Anton Ruddock, Art Khurshudov, Artemis Chen, Arthur Argenson, Avinatan Hassidim, Beiye Liu, Benjamin Schroeder, Bin Ni, Brett Daw, Bryan Chiang, Burak Gokturk, Carl Crous, Carrie Grimes Bostock, Charbel Kaed, Charlotte Banks, Che Diaz, Chris Larkin, Christy Lian, Claire Cui, Clare Bycroft, Corentin Tallec, Daniel Herndon, Dave Burke, David Battle, David Engel, Dipannita Shaw, Donghyun Koo, Doug Ritchie, Dragos Stefanescu, Elissa Wolf, Emre Sargin, Eran Ofek, Eric Herren, Estella King, Fatema Alkhanaizi, Felix Gimeno, Fernando Pereira, Florent Altché, Gabriel Carvajal, Gaurav Gandhi, George Powell, Goran Pavičić, Harry Richardson, Hassan Wassel, Hongji Li, Idan Szpektor, Igor Ivanisevic, Ivan Jambrešić, Ivan Jurin, Jade Fowler, James Assiene, Jay Yagnik, Jeanbastien Grill, Jeff Seibert, Jenna LaPlante, Jessica Austin, Jianxing Lu, Jim O’Keeffe, Jin Huang, Joe Heyward, Johannes Welbl, John Jumper, Jonathan Caton, Josh Woodward, Joshua Foster, Kathryn Tunyasuvunakool, Katrina Wong, Kavya Kopparapu, Kelvin Nguyen, Kira Yin, Konstantin Sharlaimov, Kun Li, Lee Hong, Lilly Taylor, Longfei Shen, Luc Mercier, Maciej Mikuła, Mania Abdi, Manuel Sanchez, Maria Ines Aranguren, Mario Carlos Cortes III, Matthew Tait, Matthias Lochbrunner, Mehdi Ghissassi, Micah Mosley, Michael Bendersky, Michael Figurnov, Michael Harris, Michael Mathieu, Michael O’Neill, Michael Vorburger, Mihir Paradkar, Nandita Dukkipati, Nathan Carter, Nathan Watson, Neil Rabinowitz, Nikhil Dandekar, Nishant Ranka, Obaid Sarvana, Olcan Sercinoglu, Olivier Lacombe, Ottavia Bertolli, Paul Caron, Pranesh Srinivasan, Praveen Kumar, Rahul Sukthankar, Raia Hadsell, Rajagopal Ananthanarayanan, Roberto Lupi, Rosie Zou, Sachin Menezes, Sadegh Jazayeri, Sam Cheung, Sameer Bidichandani, Sania Alex, Sanjiv Kumar, Sara Wiltberger, Sarah Fitzgerald, Saz Basu, Sebastian Nowozin, Shannon Hepburn, Shayne Cardwell, Sissie Hsiao, Srinivasan Venkatachary, Sugato Basu, Sundar Pichai, Sundeep Tirumalareddy, Susannah Young, Swetha Vijayaraghavan, Tania Bedrax-Weiss, Taylor Applebaum, Teiva Harsanyi, Terry Chen, Tim Blyth, Ting Liu, Tom Cobley, Tomas Izo, Trystan Upstill, Varun Singhai, Vedrana Klarić Trupčević, Victor Cai, Vladimir Pudovkin, Vu Dang, Wenbo Zhao, Wesley Crow, Wesley Szeng, Xiaodan Song, Yazhou Zu, Ye Tian, Yicong Wang, Yixing Wang, Yossi Matias, Yunlong Jiao, Zachary Jessup, Zhenchuan Pang, Žiga Avsec, Zimeng Yang, and Zoubin Ghahramani. We’d also like to recognize the AlphaCode team, the Borg Scheduling team, the Facilities team, the Gemini Demo Team, the Global Server Ops (GSO) team, the JAX team, the the Legal team, ML SRE team, the ML Supercomputer (MLSC) team, the PartIR team, the Platforms Infrastructure Engineering (PIE) team, and the XLA Compiler team. We thank everyone at Google not explicitly mentioned above, who have shared excitement, given feedback on early Gemini models or created interesting demo uses of Gemini, and worked with or supported the core Gemini team on many aspects of this project.

Gemini -  A Family of Highly Capable Multimodal Models: Contributions and Acknowledgments

Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Unpleasant Discovery in the Patent Office—Levying Black Mail. here Unpleasant Discovery in the Patent Office—Levying Black Mail. "The Patent Office has been, during the past week, in a high state of excitement, occasioned by the discovery of the operations of E. W. W. Griffin, clerk in charge of the draftsmen's division, who, it appears, has been levying black mail on the lady employés of the office, for nearly two years. During the administration of Colonel Fisher, late Commissioner of Patents, a large number of ladies were employed, for the purpose of recopying drawings, when ordered by the inventors, of patents already on file. "These ladies were placed under charge of Griffin, with power to retain them in office so long as their services were satisfactory. It has been proved that Griffin hired the ladies at regular salaries of $1,000 per annum, the most of whom he blackmailed to the amount of $400 per year each. It is estimated that he has made $1,000 per month for the past two years. "The matter was brought to the notice of Commissioner Duncan, and an investigation ordered, which resulted in the dismissal of Griffin. "It is thought that there are other cases of this kind, and the Commissioner expresses his determination to ferret them all out, and make a clean sweep of all parties in his department engaged in swindling operations, against the government or against individuals. "The Patent Office has for a long time been considered a rich field for operations of this kind, and investigations have often been suggested, but passed unheeded by the proper authorities. "It is openly stated that an investigation into the relations existing between certain examiners of patents and certain patent agents, would disclose a more fearful state of blackmailing than exists in all the other government departments combined." [We find the above sensational paragraph among the recent Washington items of the . We are in a position to say that "the high state of excitement" alluded to has existed only in the brain of the newspaper correspondent. The facts, in brief, are these: In July, 1869, a lady, and wife of one of the clerks in the draftsmen's room, made application to Commissioner Fisher for a position in the copying division of the same department; and, upon the urgent solicitation and recommendation of Mr. E. W. W. Griffin, chief of the division, she was appointed, and has held the position from that time until now, receiving as salary $1,000 per annum, which, with the full knowledge of her husband, she has divided with Griffin, in consideration of his services in procuring for her the appointment. About a month ago, one of the lady's friends got hold of the matter, and reported it to the Court, which resulted in an investigation and the subsequent dismissal of Griffin. This is the only case of the kind that we have heard of, and we have no reason to believe that there is any other, or that corruption exists in the Examining Corps, as alleged. Evening Mail —Eds. of testing the purity of samples of water, by watching the rapidity of its action on soap and similar compounds, has been introduced by the French , MM. Boutron and Boudet. The experiment tests, at the same time, the purity of the soap. Dissolved in water in which lime is held in solution, the soap is precipitated in hard white flakes. If the quantity of soap put in the lime water be noted, it will be found that the smaller the quantity producing precipitation, the purer the soap. The (of Paris) reports some experiments, on this subject, by M. F. Schulze. A method savants Journal de Pharmacie et de Chemie .—The fifth State fair of the Mechanics, and Agricultural Fair Association of Louisiana will commence in the city of New Orleans, on Saturday, April 8, 1871, and continue nine days. Over $20,000 in premiums are offered. Rules, regulations, and schedule of premiums may be obtained of the Secretary and Treasurer, Luther Homes, Esq., New Orleans, La. Louisiana State Fair .—John Kent advertises, in this paper, valuable machinery for the manufacture of knitted goods, to which we invite the attention of all who are interested in this branch of industry. Mr. Kent has devoted many years to the perfection of these machines. Knitted Goods , a white clay, used largely in the adulteration of flour, starch, and candles, is found near Augusta, Ga., and is sent to the Northern States in large quantities. Kaolin We are indebted to James Vick, practical florist, Rochester, N. Y., for a choice variety of flower seeds. 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. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-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

Unpleasant Discovery in the Patent Office—Levying Black Mail

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BLEISTEIN v. DONALDSON LITHOGRAPHING CO. 239 Court Filing, retrieved on September 29, 2023 is part of . You can jump to any part in this filing . This part is 2 of 4. HackerNoon’s Legal PDF Series here Argument for Defendant in Error Mr. Edmund W. Kittredge, with whom Mr. Joseph Wilby was on the brief, for defendant in error, contended that the plaintiff in error was not entitled to copyright. The evidence established that these three prints were ordered by B. E. Wallace, proprietor of the circus known as the "Wallace Shows," under contract with him as an 'advertisement for his show, and they have never been made for anybody else. All of these pictures purported to be representations of acts to be done in the Wallace Shows, and all were made under a representation by Wallace, expressed on the face of the pictures, that his show was going to do these things. All these posters contain reading matter indicating that these were pictures of acts to be done in the Wallace Shows, and they all included pictures of Mr. Wallace himself. They were prints and the copyright inscription was insufficient. But for the provision in the first clause of this act the inscription, "Copyright, 1898, Courier Litho. Co., Buffalo, N. Y.," would have been fatal to the plaintiffs' right of action. Thompson v. Hubbard, 131 U. S. 123. The inscription prescribed by section 4962 of the Revised Statutes was otherwise indispensable to the maintenance of an action for the infringement of a copyright. The notice given on each one of these pictures was that authorized by the act of June 18, 1874. Having thus availed themselves of the provisions of this act, clearly the plaintiffs are not in position to claim that the pictures are not covered by its provisions. Again, if these pictures were chromos, and not prints, cuts or engravings, then under the allegations of the petition they were not admissible in evidence because they were not in support of the allegations of the petition. As to what a chromo is and how statute should be construed, Yuengling v. Schile, 12 Fed. Rep. 107; Bolles v. Outing Company, 175 U. S. 262; Thornton v. Schreiber, 124 U. S. 612; Rosenbach v. Dreyfuss, 2 Fed. Rep. 217; Ehret v. Pierce, 10 Fed. Rep. 554; S. C., 18 Blatch. 302; Schumacher v. Wogram, 35 Fed. Rep. 210; Higgins v. Kueffel, 140 U. S. 428. As to advertisements and copyrights, citing Cobbett v. W1oodward, L. R. 14 Eq. 407, cited with approval by this court in Baker v. Selden, 101 U. S. 106; Clayton v. Stone & Hall, 2 Paine, 392; Mott Iron Works v. Clow, 82 Fed. Rep. 216. There was no evidence tending to show that the plaintiffs themselves, or either of them, were the authors of these prints. It was claimed that they were the proprietors because, as they also claimed, the design or conception was that of their employ~s, working for them, under salaries, and that their designs were the property of the employer. If they were not themselves the authors, then it was incumbent upon them to allege how they acquired title as proprietors from the author, inventor or designer. Lithographic Co. v. Sarony, 111 U. S. 53; Nottage v. Jackson, 11 Q. B. D. 627; Atwell v. Ferret, 2 Blatch. 46; Bimias v. Toodworth, 4 Wash. 0. 0. Rep. 48; Black v. Allen Co., 42 Fed. Rep. 618; S. C., 56 Fed. Rep. 764; Press Pub. Co. v. Falk, 59 Fed. Rep. 524; Pollard v. Photograph Co., 40 Oh. Div. 345; Mloore v. Rugg, 46 N. W. 141 ; Dielman v. White, 102 Fed. Rep. 892; Parton v. Prang, 3 Clifford, 537; -Little v. Good, 2 Blatch. 166. It is incumbent upon the plaintiffs, in a case like this, for the recovery of penalties, to allege and to prove as alleged, every fact essential to the validity of their copyright. Jones v. Van Zandt, 5 How. 372. The copyright law does not protect what is immoral in its tendency. A print representing unchaste acts or scenes calculated to excite lustful or sensual desires in those whose minds are open to such influences, and to attract them to witness the performance of such scenes, is manifestly of that character. It is the young and immature and those who are sensually inclined who are liable to be influenced by such scenes and representations, and it is their influence upon such persons that should be considered in determining their character. Broder v. Zeno Mauvais Music Co., 88 Fed. Rep. 74; Dunlop v. United States, 165 U. S. 501; Martinetti v. Maguire, Fed. Cases, No. 9173, The Black Crook case. 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 2023-0409 retrieved on September 29, 2023, from 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. tile.loc.gov

Circus Print Copyright Dispute: Analyzing the Defendant's Stance

Oracle vs. Google (2014) Court Filing, retrieved on May 9, 2014, is part of . You can jump to any part in this filing . This is part 6 of 16. HackerNoon’s Legal PDF Series here A. Copyrightability The Copyright Act provides protection to “original works of authorship fixed in any tangible medium of expression,” including “literary works.” 17 U.S.C. § 102(a). It is undisputed that computer programs— defined in the Copyright Act as “a set of statements or instructions to be used directly or indirectly in a computer in order to bring about a certain result,” 17 U.S.C. § 101—can be subject to copyright protection as “literary works.” See Atari Games Corp. v. Nintendo of Am., Inc., 975 F.2d 832, 838 (Fed. Cir. 1992) (“As literary works, copyright protection extends to computer programs.”). Indeed, the legislative history explains that “literary works” includes “computer programs to the extent that they incorporate authorship in the programmer’s expression of original ideas, as distinguished from the ideas themselves.” H.R. Rep. No. 1476, 94th Cong., 2d Sess. 54, reprinted in 1976 U.S.C.C.A.N. 5659, 5667. By statute, a work must be “original” to qualify for copyright protection. 17 U.S.C. § 102(a). This “originality requirement is not particularly stringent,” however. Feist Publ’ns, Inc. v. Rural Tel. Serv. Co., 499 U.S. 340, 358 (1991). “Original, as the term is used in copyright, means only that the work was independently created by the author (as opposed to copied from other works), and that it possesses at least some minimal degree of creativity.” Id. at 345. Copyright protection extends only to the expression of an idea—not to the underlying idea itself. Mazer v. Stein, 347 U.S. 201, 217 (1954) (“Unlike a patent, a copyright gives no exclusive right to the art disclosed; protection is given only to the expression of the idea—not the idea itself.”). This distinction—commonly referred to as the “idea/expression dichotomy”—is codified in Section 102(b) of the Copyright Act, which provides: In no case does copyright protection for an original work of authorship extend to any idea, procedure, process, system, method of operation, concept, principle, or discovery, regardless of the form in which it is described, explained, illustrated, or embodied in such work. 17 U.S.C. § 102(b); see Golan v. Holder, 132 S. Ct. 873, 890 (2012) (“The idea/expression dichotomy is codified at 17 U.S.C. § 102(b).”). The idea/expression dichotomy traces back to the Supreme Court’s decision in Baker v. Selden, 101 U.S. 99, 101 (1879). In Baker, the plaintiff Selden wrote and obtained copyrights on a series of books setting out a new system of bookkeeping. Id. at 100. The books included an introductory essay explaining the system and blank forms with ruled lines and headings designed for use with that system. Id. Baker published account books employing a system with similar forms, and Selden filed suit alleging copyright infringement. According to Selden, the “ruled lines and headings, given to illustrate the system, are a part of the book” and “no one can make or use similar ruled lines and headings, or ruled lines and headings made and arranged on substantially the same system, without violating the copyright.” Id. at 101. The Supreme Court framed the issue on appeal in Baker as “whether the exclusive property in a system of book-keeping can be claimed, under the law of copyright, by means of a book in which that system is explained.” Id. In reversing the circuit court’s decision, the Court concluded that the “copyright of a book on book-keeping cannot secure the exclusive right to make, sell, and use account-books prepared upon the plan set forth in such book.” Id. at 104. Likewise, the “copyright of a work on mathematical science cannot give to the author an exclusive right to the methods of operation which he propounds.” Id. at 103. The Court found that, although the copyright protects the way Selden “explained and described a peculiar system of book-keeping,” it does not prevent others from using the system described therein. Id. at 104. The Court further indicated that, if it is necessary to use the forms Selden included in his books to make use of the accounting system, that use would not amount to copyright infringement. See id. (noting that the public has the right to use the account-books and that, “in using the art, the ruled lines and headings of accounts must necessarily be used as incident to it”). Courts routinely cite Baker as the source of several principles incorporated into Section 102(b) that relate to this appeal, including that: (1) copyright protection extends only to expression, not to ideas, systems, or processes; and (2) “those elements of a computer program that are necessarily incidental to its function are . . . unprotectable.” See Computer Assocs. Int’l v. Altai, 982 F.2d 693, 704-05 (2d Cir. 1992) (“Altai”) (discussing Baker, 101 U.S. at 103-04). It is well established that copyright protection can extend to both literal and non-literal elements of a computer program. See Altai, 982 F.2d at 702. The literal elements of a computer program are the source code and object code. See Johnson Controls, Inc. v. Phoenix Control Sys., Inc., 886 F.2d 1173, 1175 (9th Cir. 1989). Courts have defined source code as “the spelled-out program commands that humans can read.” Lexmark Int’l, Inc. v. Static Control Components, Inc., 387 F.3d 522, 533 (6th Cir. 2004). Object code refers to “the binary language comprised of zeros and ones through which the computer directly receives its instructions.” Altai, 982 F.2d at 698. Both source and object code “are consistently held protected by a copyright on the program.” Johnson Controls, 886 F.2d at 1175; see also Altai, 982 F.2d at 702 (“It is now well settled that the literal elements of computer programs, i.e., their source and object codes, are the subject of copyright protection.”). Google nowhere disputes that premise. See, e.g., Oral Argument at 57:38. The non-literal components of a computer program include, among other things, the program’s sequence, structure, and organization, as well as the program’s user interface. Johnson Controls, 886 F.2d at 1175. As discussed below, whether the non-literal elements of a program “are protected depends on whether, on the particular facts of each case, the component in question qualifies as an expression of an idea, or an idea itself.” Id. In this case, Oracle claims copyright protection with respect to both: (1) literal elements of its API packages— the 7,000 lines of declaring source code; and (2) non-literal elements—the structure, sequence, and organization of each of the 37 Java API packages. The distinction between literal and non-literal aspects of a computer program is separate from the distinction between literal and non-literal copying. See Altai, 982 F.2d at 701-02. “Literal” copying is verbatim copying of original expression. “Non-literal” copying is “paraphrased or loosely paraphrased rather than word for word.” Lotus Dev. Corp. v. Borland Int’l, 49 F.3d 807, 814 (1st Cir. 1995). Here, Google concedes that it copied the declaring code verbatim. Oracle explains that the lines of declaring code “embody the structure of each [API] package, just as the chapter titles and topic sentences represent the structure of a novel.” Appellant Br. 45. As Oracle explains, when Google copied the declaring code in these packages “it also copied the ‘sequence and organization’ of the packages (i.e., the three-dimensional structure with all the chutes and ladders)” employed by Sun/Oracle in the packages. Appellant Br. 27. Oracle also argues that the nonliteral elements of the API packages—the structure, sequence, and organization that led naturally to the implementing code Google created—are entitled to protection. Oracle does not assert “literal” copying of the entire SSO, but, rather, that Google literally copied the declaring code and then paraphrased the remainder of the SSO by writing its own implementing code. It therefore asserts non-literal copying with respect to the entirety of the SSO. At this stage, it is undisputed that the declaring code and the structure and organization of the Java API packages are original. The testimony at trial revealed that designing the Java API packages was a creative process and that the Sun/Oracle developers had a vast range of options for the structure and organization. In its copyrightability decision, the district court specifically found that the API packages are both creative and original, and Google concedes on appeal that the originality requirements are met. See Copyrightability Decision, 872 F. Supp. 2d at 976 (“The overall name tree, of course, has creative elements . . . .”); Id. at 999 (“Yes, it is creative. Yes, it is original.”); Appellee Br. 5 (“Google does not dispute” the district court’s finding that “the Java API clears the low originality threshold.”). The court found, however, that neither the declaring code nor the SSO was entitled to copyright protection under the Copyright Act. Although the parties agree that Oracle’s API packages meet the originality requirement under Section 102(a), they disagree as to the proper interpretation and application of Section 102(b). For its part, Google suggests that there is a two-step copyrightability analysis, wherein Section 102(a) grants copyright protection to original works, while Section 102(b) takes it away if the work has a functional component. To the contrary, however, Congress emphasized that Section 102(b) “in no way enlarges or contracts the scope of copyright protection” and that its “purpose is to restate . . . that the basic dichotomy between expression and idea remains unchanged.” Feist, 499 U.S. at 356 (quoting H.R. Rep. No. 1476, 94th Cong., 2d Sess. 54, reprinted in 1976 U.S.C.C.A.N. 5659, 5670). “Section 102(b) does not extinguish the protection accorded a particular expression of an idea merely because that expression is embodied in a method of operation.” Mitel, Inc. v. Iqtel, Inc., 124 F.3d 1366, 1372 (10th Cir. 1997). Section 102(a) and 102(b) are to be considered collectively so that certain expressions are subject to greater scrutiny. Id. In assessing copyrightability, the district court is required to ferret out apparent expressive aspects of a work and then separate protectable expression from “unprotectable ideas, facts, processes, and methods of operation.” See Atari, 975 F.2d at 839. Of course, as with many things, in defining this task, the devil is in the details. Circuit courts have struggled with, and disagree over, the tests to be employed when attempting to draw the line between what is protectable expression and what is not. Compare Whelan Assocs., Inc. v. Jaslow Dental Lab., Inc., 797 F.2d 1222, 1236 (3d Cir. 1986) (everything not necessary to the purpose or function of a work is expression), with Lotus, 49 F.3d at 815 (methods of operation are means by which a user operates something and any words used to effectuate that operation are unprotected expression). When assessing whether the non-literal elements of a computer program constitute protectable expression, the Ninth Circuit has endorsed an “abstraction-filtration-comparison” test formulated by the Second Circuit and expressly adopted by several other circuits. Sega Enters. Ltd. v. Accolade, Inc., 977 F.2d 1510, 1525 (9th Cir. 1992) (“In our view, in light of the essentially utilitarian nature of computer programs, the Second Circuit’s approach is an appropriate one.”). This test rejects the notion that anything that performs a function is necessarily uncopyrightable. See Mitel, 124 F.3d at 1372 (rejecting the Lotus court’s formulation, and concluding that, “although an element of a work may be characterized as a method of operation, that element may nevertheless contain expression that is eligible for copyright protection.”). And it also rejects as flawed the Whelan assumption that, once any separable idea can be identified in a computer program everything else must be protectable expression, on grounds that more than one idea may be embodied in any particular program. Altai, 982 F.2d at 705-06. Thus, this test eschews bright line approaches and requires a more nuanced assessment of the particular program at issue in order to determine what expression is protectable and infringed. As the Second Circuit explains, this test has three steps. In the abstraction step, the court “first break[s] down the allegedly infringed program into its constituent structural parts.” Id. at 706. In the filtration step, the court “sift[s] out all non-protectable material,” including ideas and “expression that is necessarily incidental to those ideas.” Id. In the final step, the court compares the remaining creative expression with the allegedly infringing program.[4] In the second step, the court is first to assess whether the expression is original to the programmer or author. Atari, 975 F.2d at 839. The court must then determine whether the particular inclusion of any level of abstraction is dictated by considerations of efficiency, required by factors already external to the program itself, or taken from the public domain—all of which would render the expression unprotectable. Id. These conclusions are to be informed by traditional copyright principles of originality, merger, and scenes a faire. See Mitel, 124 F.3d at 1372 (“Although this core of expression is eligible for copyright protection, it is subject to the rigors of filtration analysis which excludes from protection expression that is in the public domain, otherwise unoriginal, or subject to the doctrines of merger and scenes a faire.”). In all circuits, it is clear that the first step is part of the copyrightability analysis and that the third is an infringement question. It is at the second step of this analysis where the circuits are in less accord. Some treat all aspects of this second step as part of the copyrightability analysis, while others divide questions of originality from the other inquiries, treating the former as a question of copyrightability and the latter as part of the infringement inquiry. Compare Lexmark, 387 F.3d at 537-38 (finding that the district court erred in assessing principles of merger and scenes a faire in the infringement analysis, rather than as a component of copyrightability), with Kregos, 937 F.2d at 705 (noting that the Second Circuit has considered the merger doctrine “in determining whether actionable infringement has occurred, rather than whether a copyright is valid”); see also Lexmark, 387 F.3d at 557 (Feikens, J., dissenting-in-part) (noting the circuit split and concluding that, where a court is assessing merger of an expression with a method of operation, “I would find the merger doctrine can operate only as a defense to infringement in that context, and as such has no bearing on the question of copyrightability.”). We need not assess the wisdom of these respective views because there is no doubt on which side of this circuit split the Ninth Circuit falls. lity are considered questions of copyrightability, concepts of merger and scenes a faire are affirmative defenses to claims of infringement. Ets-Hokin, 225 F.3d at 1082; Satava v. Lowry, 323 F.3d 805, 810 n.3 (9th Cir. 2003) (“The Ninth Circuit treats scenes a faire as a defense to infringement rather than as a barrier to copyrightability.”). The Ninth Circuit has acknowledged that “there is some disagreement among courts as to whether these two doctrines figure into the issue of copyrightability or are more properly defenses to infringement.” EtsHokin, 225 F.3d at 1082 (citations omitted). It, nonetheless, has made clear that, in that circuit, these concepts are to be treated as defenses to infringement. Id. (citing Kregos, 937 F.2d at 705 (holding that the merger doctrine relates to infringement, not copyrightability); Reed-Union Corp. v. Turtle Wax, Inc., 77 F.3d 909, 914 (7th Cir. 1996) (explaining why the doctrine of scenes a faire is separate from the validity of a copyright)). With these principles in mind, we turn to the trial court’s analysis and judgment and to Oracle’s objections thereto. While the trial court mentioned the abstractionfiltration-comparison test when describing the development of relevant law, it did not purport to actually apply that test. Instead, it moved directly to application of familiar principles of copyright law when assessing the copyrightability of the declaring code and interpreted Section 102(b) to preclude copyrightability for any functional element “essential for interoperability” “regardless of its form.” Copyrightability Decision, 872 F. Supp. 2d at 997. Oracle asserts that all of the trial court’s conclusions regarding copyrightability are erroneous. Oracle argues that its Java API packages are entitled to protection under the Copyright Act because they are expressive and could have been written and organized in any number of ways to achieve the same functions. Specifically, Oracle argues that the district court erred when it: (1) concluded that each line of declaring code is uncopyrightable because the idea and expression have merged; (2) found the declaring code uncopyrightable because it employs short phrases; (3) found all aspects of the SSO devoid of protection as a “method of operation” under 17 U.S.C. § 102(b); and (4) invoked Google’s “interoperability” concerns in the copyrightability analysis. For the reasons explained below, we agree with Oracle on each point. [4] Importantly, this full analysis only applies where a copyright owner alleges infringement of the non-literal aspects of its work. Where “admitted literal copying of a discrete, easily-conceptualized portion of a work” is at issue—as with Oracle’s declaring code—a court “need not perform a complete abstraction-filtration-comparison analysis” and may focus the protectability analysis on the filtration stage, with attendant reference to standard copyright principles. Mitel, 124 F.3d at 1372-73. 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 10-CV-3561 retrieved on September 22, 2023, from 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. law.justia.com

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