Cognite is a global industrial Software-as-a-Service (SaaS) leader, with an eye on the future and a drive to digitalize the industrial world. We’ve created a new class of industrial software which allows asset-intensive industries to operate more sustainably, securely, and efficiently. Our core software product is Cognite Data Fusion (CDF), designed to quickly contextualize OT/IT data to develop and scale company solutions. We use technology like hybrid AI, big data, machine learning, and 3D modelling to get there. We serve oil and gas, power and utilities, renewable energy, manufacturing, and other heavy-asset industries. Our technology helps them operate through transitions, sustainably and to scale, and without sacrificing bottom lines. We believe data must be made accessible, insightful, and open. In other words, we help our customers make data do more. And with that pave way for a full-scale digital transformation of heavy industry. Founded in 2016, we now number 700 strong, including some of the best software developers, data scientists, designers, and 3D specialists in the field. Hailing from 50 nationalities, we’re talented, curious, and fun to be around. Get to know us better: Life at Cognite: linkedin.com/company/cognitedata/life/ Make data do more: makedatadomore.cognite.com/ Twitter: @CogniteData Facebook: @CogniteData IG: @CogniteData Youtube: /c/cognite

Cognite

cognite

Kristy is in her final year of computer science at the University of Waterloo (UW). Since her enrollment, she has interned for Paypal, Cognite AS, Peggy, Cockroach Labs and Google — all as a software engineer. When she graduates, she plans to work as an engineering manager. At the moment, Kristy is a technical project lead for UW Blueprint initiative, which focuses on accessible technology and promoting public and social welfare. The initiative partners with non-profit organizations to provide websites, mobile applications and analysis tools for free. Under UW Blueprint, Kristy leads a project that builds web applications to help Planet Read coordinate volunteers to translate and review children’s stories. “Currently how they process their translations is through email and Word documents, so we’re building a platform to hopefully change that,” she says. “It’s kind of like my brainchild for the last few months and we will be deploying in [January].” Kristy’s journey to UW’s computer science program was possible through a handful of positive reinforcements. Growing up in Guelph, ON. alongside her older sister to Chinese immigrant parents, Kristy fostered a keen interest in computer science through video games and mathematics. From catching Pokemons to rummaging through online indie games, Kristy began to see the correlation of mathematics in them. “I always liked tinkering with things…it seemed like computer science was a natural concept to try out,” she says. Although in high school, Kristy did not find the computer science courses to be challenging enough, she admits having an excellent teacher was a game-changer. She is also proud that she was part of a tight-knit comradery of the few female students looking to venture into computer science careers. “It gave me a lot of inspiration to try it out myself, and I really don’t know where I’d be if they were not there,” the 22-year-old says. “I think that it was really huge for me.” Then there were hackathons. “Hackathons really connected me with the community I wanted to be a part of, I met a lot of students from Waterloo while I was in high school in the hackathon community.” she says. For Kristy, hackathons were a way for her to connect with people who wanted to build and create. What attracted her to hackathons weren’t the elaborate and complex projects, but the simple ones that embodied the spirit of learning and having fun. Throughout high school, hackathons became Kristy’s staple extracurricular, but for her it was more than just an after-school activity. In 2016, she would become the founder of SpartaHacks, introducing Guelph to the first high school hackathon, organized alongside her friends from September to December. This was Kristy’s first time organizing and finding major success. “I really wanted to give back to a community that gave me so much.” Kristy says. “My school, on a smaller level, didn’t have much of a computer science environment or community, I felt really passionate about bringing it to my school and convincing my friends to enjoy these things.” As Kristy found herself studying at UW, her passion for hackathons continued. Soon, Kristy will be working with Hack the North as a logistics organizer and backend engineer. “I wanted to find people I could relate to more, I think it’s important to be able to have people in your life who can support you in the way that you need to be supported,” she says. “It’s hard.” Kristy does not shy away from talking about identity politics in the computer science industry, including her time at UW. As a queer woman of colour, finding a space in the industry isn’t as easy as it seems. In her experience, having mentors who either are allies or individuals from your community is a way to feel connected to a stereotypically heteronormative space. “It was huge for me that I had mentors in high school, who were women in computer science.” “It’s great to have a diverse network, and be friends with people from all walks of life, regardless of the background you come from.” Kristy spoke of the hope she feels in the growth of inclusion in the industry. “I think the onus is on everyone to make the space more welcoming and inclusive,” she says. When looking at Ontario, Kristy stated that there needs to be more change in regards to learning in programming, as computer science isn’t offered in every school, public or private. This would mean providing more resources, funding programming camps and implementing programming courses in schools that are subsidized. “If you grew up in rural Ontario, where there might not even be upper calculus classes, then it’s going to be hard to get a hold of programming classes,” she says. “Having the resources and the opportunities to go to programming camp, or go to hackathons, can be really influential with that.” With the ever-growing environment of STEM careers, Kristy believes there are many ways for students and people interested in computer science to increase their skills. For Kristy, co-op programs changed her life for the better, giving her a chance to explore different paths. Kristy spent time in California and Norway where she worked remotely for international companies during the pandemic. Through these experiences, her love for startups and entrepreneurship grew. “You receive valuable hands-on experiences on building software and websites from scratch,” she says. The desire and focus to build and create is what Kristy believes is the most important factor in figuring out what a student or someone just entering the STEM industry needs. She also emphasizes the need for expanding people’s creativity, and not just sticking to one type of building. “Take time to take care of yourself, do some art, some construction, woodwork, things like that,” she says. “Those are all important in keeping the spirit of building things alive, and to keep the spirit healthy.” There’s more interesting interviews like this at Thinking Cap!

In Conversation with a Computer Science Major, Kristy Gao

by Ricardo Goncalves Before you go any further, you should know that Code Mesh is now a part of , our new family of global tech conferences. Code Sync Learn more -> This year’s saw the conference go from strength to strength. So much so, I couldn’t fit the highlights from the entire conference in a single blog post. Code Mesh So here’s my take on the talks I attended on day one of Code Mesh 2017: Keynote: Automatically Scalable Computation — Margo Seltzer Margo Seltzer, (at the time) Herchel Smith Professor of Computer Science and the Faculty Director for the Center for Research on Computation and Society in Harvard’s John A. Paulson School of Engineering and Applied Sciences Speaker: This was the first keynote of CodeMesh and what a way to start. The main question was basically: “Can programs get better at running programs with practice?” The idea seems far-fetched and even Margo herself said that initially she thought the idea (coming from a young student) was a bit “naive”, in the sense that it was not possible and/or practical. Also keep in mind that she was talking about feeding the same program to the computer and it ran faster with time, and not some higher-level “neural network/deep learning” middleware that was being used. This idea works on the lowest level possible, the bits and bytes running inside your CPU, and it’s like branch prediction on steroids, where the program execution is divided in smaller parts and pre-computed (possibly in different cores). Over multiple runs, heuristics are built for that program that make these pre-computation somewhat accurate, depending on the program itself. Some of the benchmarks were pretty amazing, but to truly that this to the next step, it seems like hardware-support will be needed from the CPU. There is a lot more to it than this, but suffices to say that it was a great beginning to CodeMesh. A Practical Functional Relational Architecture: Building Systems with Datomic — David Nolan David Nolan, Software Engineer at Cognitect Speaker: David Nolan gave us a pretty good talk on immutability on the database with Datomic. Most of the talk was motivation for why we should care for an immutable database, which makes sense, since actually using one (like Datomic) seems pretty straightforward, given that you can’t screw up your data :) He argued that immutability, a characteristic of functional programming, was already an option on the front-end with technologies like React or ClojureScript (which Nolan is the creator) and on the back-end with functional languages (Erlang, Clojure, Haskell, etc.). This leaves the database as the odd one out. Datomic was born from that sentiment of bring the benefits of immutability for the entire stack. The live demo where Nolan trivially queried data from the past was also nice (and seems really useful for auditing, etc.). As an interesting side note, Datomic can actually delete data due to legal reasons. Beyond Eventual Consistency (FaunaDB, Calvin) — Jamie Allen Jamie Allen, Director of Engineering for the Unified Commerce Project at Starbucks Speaker: Jamie is the head of engineering at Starbucks, a company with a global presence, so you can imagine the scale at which they deal in their systems / databases. When he arrived at Starbucks, Cassandra was the DB of choice. But it turns out that eventual consistency is not ideal for every use case, and in particular for some the uses that Starbucks has (which I believe is the reward program that customers can sign-up for). So the engineering team lead by Jamie started their journey on finding a replacement for Cassandra that provided stronger guarantees than eventually consistency. He also argued that the CAP theorem is still misunderstood and maybe not the best way to frame the problem of data consistency, availability, etc. He suggests to use the PACELC theorem instead. The rest of the talk was a small review of the options that they looked at for their next DB: Amazon Aurora, Microsoft Cosmos, Google Spanner, CockroachDB and finally FaunaDB, which seems like the current option, based on internal evaluation. Metaphors We Compute By — Alvaro Videla Alvaro Videla, former Core Developer for RabbitMQ and co-authored the book “RabbitMQ in Action” Speaker: Video coming soon. Subscribe for notifications. Alvaro gave a pretty different talk, one which most non-technical people could see, follow along and appreciate. Alvaro took inspiration from the book “Metaphors We Live By”, and produce an article for ACM Queue. The talk was based on that article. The main point is that we as people think and process things using metaphors (or abstractions) all the time. For example, we may start learning mathematics with simple examples for counting, which is a metaphor / abstraction of the underlying mathematical concept. And we build on that over the years to other levels of the abstraction, using different metaphors. And software developers do the same, using different metaphors for all kind of problems (e.g. “Rumor Mongering” or “Gossiping”). The bottom line is that we should be aware of these metaphors and choose the right ones for the situation. A less technical talk that gave a well-rounded feel to a tech-conference like CodeMesh. Pony for Safe, Fast, Concurrent Programs — Sophia Drossopoulou Sophia Drossopoulou, Professor in Programming Languages at Imperial College London. Speaker: Video coming soon. Subscribe for notifications. Sophia gave an interesting talk on a emerging language called Pony. It is an Actor-based language like Erlang or Elixir, but compiles to native code. It also has a pretty innovative type system, which was the focus of this talk. All types and their respective rules can seem a bit intimidating for beginners, but Sophia says that you get used to it pretty quick. The good this is that this type systems gives you a lot of guarantee that you can’t find in most languages like: safe mix of mutable and immutable data, data-race free and efficient programs. Similar to Erlang/Elixir, it has a Garbage Collection mechanism that runs per-process concurrently, without distributed locks. I found it interesting that it also supports (or needs?) causal delivery for messages between actors, something rarely seen at a language level. Everything is Terrible — Fred Hebert Fred Hebert, author of ‘Learn You Some Erlang for Great Good!’ and ‘Erlang in Anger’, and system architect at Genetec. Speaker: Video coming soon. Subscribe for notifications. This is the perfect talk to watch if you want to have some laughs and also feel a bit sad at the state of software. Fred goes through a journey of multiple software abstractions of everyday software that we all use sooner or later. He emphasised six “terrible” topics: Data Structures, Time, IDs, Numbers, String/Text and Network. This is a talk that is clearly better to watch than to read some short summary like this. But essentially, the touched on horror stories involving: floating-pointer precision, UUIDs (lack of standardisation), queues/microservices (asynchrony is hard), JSON (every parser is different), time and time-zones, leap seconds, unicode mess and the (in)security of software. or learn more about , our new family of global tech conferences which now includes Code Mesh. Watch more videos from Code Mesh 2017 Code Sync Originally published at www.erlang-solutions.com .

Code Mesh 2017: Day 1 review

Photo by on Alex Knight Unsplash I love working with and am amazed at the evolution of this frontend paradigm. React and are two brilliant pieces of technology. Any amount of praise will fall short for them. React Redux But one question that kept coming to me was, am I being enough DRY ? Coming from the world of vanilla flux, Redux felt succinct. But a few big projects later, I was doing the same thing, over and over again. One part of the entire cycle which felt particularly verbose was writing reducers. We take an action and a payload and emit a mutation. I first wanted to eliminate the process of writing reducers, so I came up with — A flux without side effects but never finished it because came up with a similar library called . Mutex JayStack Repatch Looking back, Repatch’s idea was on the same line, but extremely mature. If I had worked on Mutex for about 2 years, I would have got Repatch. And being the lazy programmer I’m, it was best to just drop my own lib and use Repatch instead. But then came the realization of legacy code (and mental models) I spent a lot of time tweaking the and it was dependent on raw Redux. Although, Repatch was inline with my philosophy, I didn’t feel comfortable betting on it because : Fractal — A react app structure for infinite scale, Tightly coupled actions and reducers made it hard to write automated tests. Working on Repatch meant there is no going back. If I build an application and it doesn’t feel right after sometime, I am stuck. There were other notable projects like but the second point still held true. redux-zero, What I wanted Ideally, I wanted a system that : Fits well in our Fractal pattern Doesn’t bloat the build Reduces (or completely eliminates) the need to write reducers Doesn’t affect the testability of the store Cost of switch should be minimal The Eureka ! While bootstrapping a new project and copy pasting my usual authentication modules, I observed that most of my reducers : only took the payload and slapped them onto my state and reducer was a function that I define (with a big ass switch case) And I thought of writing a function, which generates a default reducer that slaps the payload onto the state. It’s called Keducer (“Kay-Deu-Cer”; and it’s just 5 lines of code that has eliminated 95% of my reducers). I so wanted to build an npm module for this but felt like it would be best to not contribute more to js fatigue, so a gist is all I made. How Redux works without keducers In a general Redux setting, here’s what a typical login reducer looks like : export default function reducer(state={}, action) {switch (action.type) {case 'auth.loginError':return {...state, loginErrors: action.payload.errors}; case 'auth.loginSuccess':  
  return {...state, isUserLoggedIn: true};

default:  
    return state; }} And the action definition might be on the lines of : export function login(email, password) {return (dispatch) => {api.post('/auth/login', {email, password}).then((res) => {Promise.all([storage.setItem('token', res.token),storage.setItem('user', JSON.stringify(res.user)),]).then(() => {dispatch({type: 'auth.loginSuccess'});});}).catch((err) => {dispatch({type: 'auth.loginError',payload: {errors: Object.values(err.data)}});});};} You simply load this reducer on a store and `Provide` it to the root component and then function on click events. dispatch(login) How Redux works with keducers With a keducer, whatever you pass as a payload is sticked on top of your state. But it does expect your actions to be separated strings so you might have to do some work there. . (dot) Step one : Setup a module Module is basically a ducks structure term for actions + reducers in one file. You can read more about it in my article. Fractal Structure The only notable difference is the absence of a reducer and export of a default keducer instead. Note that a keducer takes a key named that basically tells it to listen to all actions which have an prefix. auth auth. Now keducer will automatically take your payload and append it to state under the auth tree. import config from 'config';import api from 'utils/api';import storage from 'utils/localStoragePromise';import keducer from 'utils/keducer'; export default keducer('auth'); // <---- Where the magic happens export function login(email, password) {return (dispatch) => {api.post('/auth/login', {email, password}).then(res => {Promise.all([storage.setItem('token', res.token),storage.setItem('user', JSON.stringify(res.user)),]);dispatch({type: 'auth.loginSuccess', payload: {token, user}});}).catch(err => {dispatch({type: 'auth.loginError',payload: {loginErrors: err.data.err}});});};} Step 2 : Setup your root store Because keducer returns a function, there is nothing new that you need to do here. Just combineReducers as normal and pass it to the Provider. const rootReducer = combineReducers({auth,}); const store = createStore(rootReducer,compose(applyMiddleware(thunk),window.devToolsExtension ? window.devToolsExtension() : f => f)); This should be enough to get started, but if you are a Redux power user, you might be thinking : “Huh, this kills the entire concept of Redux because you can’t possibly share actions among various reducers” and “You can’t even update the state if it’s dependant on a previous state (for example loading more items in a list when user scrolls to the bottom of a page)”. Don’t worry power user, keducer has you covered. Step 3 : Access old state or share actions among keducers The function takes a second argument, which is a map like : keducer export default keducer('users', {'auth.loginSuccess': (state, payload) => ({...state, ...payload, ...{customStuff: true}})}) The keys of this map are action types that you wish to capture and the values are functions which get the state and the payload as a parameter. This state is the local reducer state, not the app state, so you can expect to do all your normal redux sorcery if you wish too (but you rarely would). A demo project I’m working on one and will have one soon. The soon is relative to the amount of claps and views this article gets. Closing thoughts These 5 lines of code save a lot of time writing reducers, but the bigger point here is that someone like me (without a lot of experience) was able to come up with something so obvious, whereas the entire js community bets on the verbose version. Also, the competing standards and js fatigue stopped me from building yet another module. I was kinda pissed with the way js is taught, used and improved and it all seems like a fad. I went searching for better solutions and have found one in clojurescript. My understanding about it is not as mature as js, but it is very very very promising. Very very. It’s like targeting js as a JVM and is backed by Cognitect (the company behind clojure, where Rich Hickey works). 🤙 I’m currently experimenting heavily with it (clojurescript), and please let me know if you want me to write about it. 🙏🏼 Thanks for taking time to read my work. I hope it will be helpful for your projects. ❤️ You might also like to read : Fractal — A react app structure for infinite scale Blockchain is like the transistor of the main frame Financial System [POEM] & The 99 that went before 14 life lessons learnt after 1 year of graduation

Keducer — Automate writing redux reducers with 5 lines of javascript

AI’s Cognitive Mirror: The Illusion of Consciousness in the Digital Age

A longer read about how Asimov’s Robopsychologist might be coming to life in AI as the Cognitive Architect. Last week at AI Engineer World Fair in San Francisco, engineers and technologists presented cutting-edge AI technology being developed. The conference was motivated by a think piece published by @swyx and @FanaHOVA, defining the role of the AI Engineer (AIE). This role sits at the intersection of product and technology, specializing in applying Large Language Models to software development. There was a common trend in some of the work presented: a need to overcome these models' more messy, probabilistic nature by making them more reliable and engineering-like. This is no surprise since AI Engineering focuses on applying these models to real-world problems. From building a question-answer engine like Perplexity to a tool for better note-taking like Granola, a certain level of determinism is needed to create products that can withstand real-world use. While at the conference, I also followed @hwchase17 on Sequoia's Training Data podcast, as he talked the notion of cognitive architectures. While this was part of a larger conversation about turning LLMs into reasoning agents, this notion of cognitive architecture pointed toward the idea that using LLMs is as much about human-to-machine cognition as it is about technology. Human-machine cognition means understanding human agency and translating it into instructions, considering the intricate steps we take when performing tasks (both conscious and subconscious), and translating them into machine code—that is, model language. Now, a small detour into science fiction: In Asimov's I, Robot, robopsychology is the field of understanding and troubleshooting the complex psychological and behavioral issues that arise as robots (read: AI) become more sophisticated and integrated into human society. Dr. Susan Calvin, a robopsychologist, navigates the dilemmas posed by the laws of robotics. Here, I see a parallel between Asimov's study of robotic psychology and the challenges of training AI models to do what we want them to do. We've built a technology we're still working to understand. @mustafasuleyman even suggests it isn't just a technology, but a new kind of digital species—what an idea! Given the challenges of engineering with language models and their incredible power, there might be a gap in applied AI waiting to be filled by Cognitive Architects—a role most likely already in existence in research labs at OpenAI, Anthropic, or Google. Much like an architect works alongside a civil engineer to research how humans live, the Cognitive Architect may work with computer scientists to study human cognition and its relation to model cognition. From examining the boundaries of the model's probabilistic knowledge (latent space) to using the right keywords and canonical definitions to express instructions, to decomposing complex human tasks into the flows of actions, tools, and models, the Cognitive Architect might be part of the recipe to applying these models in ways that don't just tame it down to an engineering feature, but also harness their true nature: To predict where we're most likely to go next. A longer read about how Asimov’s Robopsychologist might be coming to life in AI as the Cognitive Architect. Last week at AI Engineer World Fair in San Francisco, engineers and technologists presented cutting-edge AI technology being developed. The conference was motivated by a think piece published by @swyx and @FanaHOVA , defining the role of the AI Engineer (AIE). This role sits at the intersection of product and technology, specializing in applying Large Language Models to software development. @swyx @FanaHOVA There was a common trend in some of the work presented: a need to overcome these models' more messy, probabilistic nature by making them more reliable and engineering-like. This is no surprise since AI Engineering focuses on applying these models to real-world problems. From building a question-answer engine like Perplexity to a tool for better note-taking like Granola, a certain level of determinism is needed to create products that can withstand real-world use. While at the conference, I also followed @hwchase17 on Sequoia's Training Data podcast, as he talked the notion of cognitive architectures. While this was part of a larger conversation about turning LLMs into reasoning agents, this notion of cognitive architecture pointed toward the idea that using LLMs is as much about human-to-machine cognition as it is about technology. @hwchase17 Human-machine cognition means understanding human agency and translating it into instructions, considering the intricate steps we take when performing tasks (both conscious and subconscious), and translating them into machine code—that is, model language. Now, a small detour into science fiction: In Asimov's I, Robot, robopsychology is the field of understanding and troubleshooting the complex psychological and behavioral issues that arise as robots (read: AI) become more sophisticated and integrated into human society. Dr. Susan Calvin, a robopsychologist, navigates the dilemmas posed by the laws of robotics. Here, I see a parallel between Asimov's study of robotic psychology and the challenges of training AI models to do what we want them to do. We've built a technology we're still working to understand. @mustafasuleyman even suggests it isn't just a technology, but a new kind of digital species—what an idea! @mustafasuleyman Given the challenges of engineering with language models and their incredible power, there might be a gap in applied AI waiting to be filled by Cognitive Architects—a role most likely already in existence in research labs at OpenAI, Anthropic, or Google. Much like an architect works alongside a civil engineer to research how humans live, the Cognitive Architect may work with computer scientists to study human cognition and its relation to model cognition. From examining the boundaries of the model's probabilistic knowledge (latent space) to using the right keywords and canonical definitions to express instructions, to decomposing complex human tasks into the flows of actions, tools, and models, the Cognitive Architect might be part of the recipe to applying these models in ways that don't just tame it down to an engineering feature, but also harness their true nature: To predict where we're most likely to go next.

The Rise of the Cognitive Architect

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vogt, Lars, TIB Leibniz Information Centre for Science and Technology; (2) Konrad, Marcel, TIB Leibniz Information Centre for Science and Technology; (3) Prinz, Manuel, TIB Leibniz Information Centre for Science and Technology. Table of Links Abstract & Introduction Interoperability Semantic interoperability and what natural languages like English can teach us Requirements for successfully communicating terms and statements Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability What makes a term a good term and a schema a good schema? The need for a machine-actionable Rosetta Stone for (meta)data that acts as an interlingua for specifying reference terms and reference schemata to support cognitive and semantic interoperability Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm for (meta)data statements based on English Rosetta Stone and semantic interoperability: Specifying term mappings and schema crosswalks Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Discussion Related work Conclusion, Acknowledgements, & References Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Display templates Humans usually do not want to see the (meta)data of a knowledge graph in the form of triples—they do not want to read them in any of the RDF serializations, nor do they want to visualize them as a graph. In order to display the content of a (meta)data statement modeled according to the Rosetta approach in a human-actionable way, a frontend application needs a display template that specifies how a rendering function can translate a given (meta)data structure into a human-readable statement. Display templates organize information from the (meta)data structure along with additional prepositions to be presented in the UI. For example, if a weight measurement with a 95% confidence interval were stored according to an appropriate reference schema, the schema would specify four literal-object-positions (MAIN_VALUE, UPPER_VALUE, LOWER_VALUE, INTERVAL_VALUE) and one resource-object-position (UNIT). The form-based display template could specify that this information should be displayed in the frontend as ‘SUBJECT has weight (INTERVAL_VALUE% conf. interval): MAIN_VALUE (LOWER_VALUE-UPPER_VALUE) UNIT’. Another example is a travel statement where the corresponding reference schema specifies one required resource-object-position (DESTINATION_LOCATION), two optional resource-object-positions (DEPARTURE_LOCATION, TRANSPORTATION), and one optional literal-object-position (DATETIME). The corresponding display template would display a travel statement in the frontend as ‘PERSON travels by TRANSPORTATION from DEPARTURE_LOCATION to DESTINATION_LOCATION on the DATETIME‘. In other words, the subject-position and the various object-positions (i.e., the syntactic positions with their associated semantic roles) are mapped to corresponding variables within a string to form a human-readable statement (see Fig. 10, top). We call such textual display templates dynamic labels. In addition to textual display templates, it is also possible to specify graphical display templates for a mind-map-like representation of a statement using dynamic mind-map patterns. Dynamic mind-map patterns use a label for the predicate underlying the corresponding statement type and, if there is more than one object-position, labels for relating the various objects to the predicate. As a result, the statement can be visualized as a mind-map like graph, where each subject and object is represented as a node with the label of the corresponding resource from the underlying graph of the (meta)data statement (see Fig. 10, bottom). Such graphical representations of statements can also be combined to form a mind-map of larger contexts and interrelationships that connect the dynamic mind-map patterns of multiple statements. Mind-map-like representations of complex interrelationships between different entities are easier to understand than form-based textual representations, thus increasing the human-actionability of a knowledge graph. (Meta)data can be communicated to the presentation layer in the UI, with the templates filtering the complex data structure for the information relevant to a human user, using dynamic labels and dynamic mind-map patterns to present statements, decoupling human-readable data display from machine-actionable data storage. A given reference schema can have multiple dynamic labels and dynamic mind-map patterns associated with it. It can be beneficial to be able to choose between different display templates depending on the context in which the (meta)data are accessed (PC vs. smartphone, expert user vs. layperson, etc.). The specifications of each display template should be associated with its corresponding reference schema and statement class, thus contributing to the easily accessible and usable Rosetta module mentioned above, moving it even closer to the concept of Knowledge Graph Building Blocks (64). A Rosetta Query-Builder We believe that findability is the most important aspect of any tool that stores and documents (meta)data. If relevant (meta)data cannot be found, no other operations can be performed on it, and interoperability issues become secondary. In the context of knowledge graphs, specific query endpoints have been established along with corresponding graph query languages to interact with the graph. These are SPARQL for RDF- and OWL-based knowledge graphs and Cypher for labeled property graphs such as Neo4J. Our personal experience is that most users and software developers have no experience with graph-based databases, are not familiar with graph query languages and their benefits, and therefore do not see the need to learn them. And even those who are familiar with them report that writing more complex queries can be challenging. Apparently, the need to write SPARQL or Cypher queries is a barrier to interacting with knowledge graphs and hinders their wider use (65). The semantic unit framework introduced some-instance and every-instance resources as two new types of resources in addition to named-individuals, classes, and properties (50). This allows, besides specifying universal statements and negations without having to use blank nodes and simplifying negations and cardinality statements, modeling them as ABox expressions, also representing questions as statements in the graph (for a discussion, see (10,64)). Instead of named-individuals, one uses some-instance resources of a given class for a given slot of a reference schema to specify that the answer to the question may have any instance of that class at that position in any statement of the type to which the reference schema is associated. If you are asking about universal statements, you would use the every-instance resource instead. The resulting statement can be stored as a question within the knowledge graph, by classifying its statement resource to instantiate the ‘question statement’ class. Since all question statements in a given knowledge graph would reflect the structure of their corresponding reference schema, and since all reference schemata have been created according to the same modeling paradigm, one could develop a Rosetta Query-Builder that converts the question into a query that returns a Boolean true/false answer if the subject and object-positions contained fully specified inputs (i.e., named-individual resources), or a list of statements as the answer that match the specifications provided by the question statement if one or more of these positions contained underspecified input (i.e., some-instance, every-instance, or class resources). The same idea provides the basis for developing query interfaces for knowledge graph applications: users can create their own queries with the input forms for creating statements using named-individuals, some-instances, every-instances, classes, and datatypes as inputs for subject and object-positions without having to know and use any graph query languages. The Query-Builder translates the question statements into actual graph queries. The generic structure of the reference schemata greatly facilitates the development of procedures for this translation step. By using Boolean operators (AND and OR) and by reusing object resources as subject resources in another question statement, multiple question statements can be combined to form a more complex question. Question units not only allow you to document questions in the graph, but also to make them operable. This can be used, for example, to document competency questions for a project in a readily actionable way within the knowledge graph itself. The ability to store question statements and make them operable moves the Rosetta Framework another step closer to the concept of Knowledge Graph Building Blocks (64) and further enhances its overall cognitive interoperability.

Rosetta Stone and Cognitive Interoperability: Specifying Display Templates and Using a Query Builder

I have always been very conscious about how and where I spend my time. While I paid a lot of attention to the obvious ways to be more productive—create a distraction-free focused zone, limit time on social media, divide my work into small blocks of time, and take regular breaks to revive and rejuvenate—I missed a crucial element that often gets in the way of our productivity. Our brain. The human brain has this remarkable cognitive capacity to perform at levels far beyond what we consider our natural abilities, but it’s not without its limits. The that enable the brain to prioritize and process large amounts of information quickly also get in the way of our productivity. These mental shortcuts are the brain's way to conserve energy and work more efficiently. But they also lead to many thinking errors. cognitive biases Here are the 4 cognitive biases that have the biggest impact on our productivity—how we prioritize, make decisions, manage time, and get work done. Urgency effect Zeigarnik effect Complexity bias Planning fallacy Urgency effect When an urgent task comes knocking at the door, do you push important work aside because urgent tasks demand your immediate attention while important goals are far off into the future and can wait until later? Urgency effect makes us jump at every chance to solve time-sensitive urgent matters while putting off important work. Rationally our brain knows that important tasks have a larger payoff and bigger rewards in the long run, but under the effect of this bias, we fall victim to perceiving urgent as important. Urgent tasks keep us busy and make us feel important, but they also suck into our time. Prioritizing it at the expense of more impactful work keeps us trapped in an illusion of productivity. Why we fall for the urgency effect Important tasks typically have no specific deadline. They are time-consuming and complex as they often require looking into the future and proactively identifying its needs. Giving clarity to vague ideas, making crucial decisions, or laying out a future strategy is not only time-consuming, it’s also mentally taxing. Urgent tasks have easy-to-reach, visible goals. They bring instant gratification with a big hit of dopamine. Since dopamine serves as a reinforcement for remembering and repeating pleasurable experiences, you end up dedicating more time to urgent tasks at the expense of more impactful work. Continuously pushing important tasks to the back burner keeps us locked in a never-ending cycle of unproductive choices—dedicating less time to important work inevitably creates more urgency later. Important tasks become urgent in due course of time if delayed too much, not given proper attention, or carried out without real interest. Example of urgency effect Gina is the tech manager of the CRM team. The latest releases in ChatGPT and other AI innovations is becoming a threat to her business. But instead of spending time planning how to make them a part of their future strategy, Gina ends up spending most of her time on emails, meetings, and other urgent tasks. Her busyness keeps her locked in a cycle of unproductivity and inefficiency. Urgency effect makes her pay more attention to the work right in front of her—emails, chat notifications, customer escalations—while pushing important work aside. How to avoid urgency effect A great practice to avoid urgency bias involves using The Eisenhower Matrix for prioritization. To put the Eisenhower matrix to use, go through each task one by one and separate them into four possibilities: Important and Urgent (Quadrant 1): reduce time spent here Important and Not Urgent (Quadrant 2): consciously prioritize and schedule these tasks Not Important and Urgent (Quadrant 3): delegate these tasks Not Important and Not Urgent (Quadrant 4): declutter and eliminate. Consciously scheduling dedicated hours on the calendar to do important work makes it more likely that you attend to them without interruptions. A few other practices to adopt: Set aside specific blocks of time for responding to emails, chats, or other such tasks. Reduce time spent in meetings. Utilize your peak productivity period to do your most important work. Matching your energy with the physical and mental demands of work will enable you to do your best work making it more likely for you to continue instead of giving up. Set deadlines for important tasks and hold yourself accountable for meeting them. Zeigarnik effect Thoughts of unfinished tasks keep popping up in our head right when we get down to work or are trying hard to focus. *Emails we haven’t replied to.* Pending design to compete. Meetings to schedule. … so on and on. These intrusive thoughts that take away our attention even for a split second make it hard to concentrate and get any meaningful work done. Distractions from incomplete work prevent us from entering into a state of flow—which is when we are completely immersed in a task and the time seems to stand still. Flow minimizes distractions, prevents procrastination, and lead to high performance and productivity. Named after psychologist Zeigarnik who first discovered it, the effect refers to the tendency to remember unfinished or interrupted tasks better than completed ones. Letting our brain continually remind us that we haven’t done something is quite unpleasant and it can even lead to stress, anxiety, and burnout. Why we fall for the Zeigarnik effect Zeigarnik and her professor Kurt Lewin observed that their restaurant waiters remembered everyone’s orders despite never writing anything down. But as soon as the bill was paid, they had little or no memory of who their customers were or what they had ordered. This led to a series of experiments based on which Zeigarnik concluded that human minds treat tasks that have been completed differently from those that are still left to be completed. A task that has already been started establishes a task-specific tension, which keeps it at the forefront of our memory. The tension is relieved upon completion of the task but persists if it is interrupted or not yet completed. That’s why unfinished tasks keep coming back to haunt us. Look another way, these reminders can be useful to help us finish incomplete projects only if they do not spoil our attention right in the middle of another project. Attention is like energy in that without it no work can be done, and in doing work is dissipated. We create ourselves by how we use this energy. Memories, thoughts and feelings are all shaped by how we use it. And it is an energy under control, to do with as we please; hence attention is our most important tool in the task of improving the quality of experience. ― Mihaly Csikszentmihalyi Example of Zeigarnik effect Zeigarnik effect is used in gamification: Progress trackers informing users of how close they are to completing a task. For example, when users see a message like "Your profile is 34% complete", they are more likely to spend a few minutes providing all the missing details. This technique is used by to collect user information in a super smart way. LinkedIn Checklists that provide users with a clear step-by-step flow to onboard users faster and more effectively. Interrupted commercials are more likely to be remembered than those that are not due to the Zeigarnik effect. Advertisers use it to catch the attention and memory of viewers. How to avoid the Zeigarnik effect by Florida State University shows that planning work upfront and writing it down can free your mind from distracting thoughts about unfinished work. Have daily, weekly, and monthly rituals to review, prioritize, and plan what you intend to do. When you miss something and it pops up in your head, don’t leave it hanging there, immediately write it down. Research Moving tasks from your head onto paper relaxes your brain and helps you focus on the task at hand. A few other practices to adopt: Avoid procrastination by using the Zeigarnik effect to your advantage. Instead of putting things off because they are big or complex, simply take a very small step towards it. Once you get started, your brain will kick off the Zeigarnik effect enabling you to keep going till the end. Use the Pomodoro method to divide your work into small chunks of focused time. The tactical breaks in the Pomodoro method improve memory and keep productive momentum going. Complexity bias Given a choice between two competing solutions, we are likely to choose the more complex one. Jargon catches our attention over simple explanations. Difficult-to-execute strategies impress us over straightforward ones. Sophisticated products appear more authoritative while ordinary ones appear inferior. But complexity has many problems: Knowing upfront that something is difficult can prevent us from getting started. Obstacles and challenges along the way make it hard to persist and we may give up too soon. It can delay decision-making. It’s costly as it requires more time, effort and resources to get done. It can be potentially dangerous. Not understanding something fully can lead to wrong assumptions and conclusions. It can lead to hiding mistakes and fundamental flaws. Complexity bias is the tendency to prefer complicated explanations, solutions, and arguments over easy ones. Why we fall for the complexity bias Many people mistake simple for easy. Finding simple solutions to problems may sometimes require more effort—a conscious effort to dig deeper and give way to our creative mind, avoiding biases to cut through complexity and finding solutions that do not require too much scaffolding to support. Complexity in such cases could be an excuse to label the problem as too confusing and shove it aside. We choose complexity over simplicity for another reason: it’s falsely associated with expertise, innovation and authority. The more complex or advanced the approach is, the more superior it appears. Put another way, if a solution is too simple, we assume it won’t solve the problem. This makes us inject complexity and over complicate things instead of choosing a simpler approach. \As Edsger Wybe Dijkstra, a Dutch computer scientist once said “Simplicity is a great virtue but it requires hard work to achieve it and education to appreciate it. And to make matters worse: complexity sells better.” Example of complexity bias Jim got an opportunity to prepare and present the design of a new product to his entire team. He wanted the solution to stand out and establish credibility with others. But instead of asking the question “What problem is this product required to solve and how can I solve it in the simplest way possible?” he asked, “What problem is this product required to solve and how can I make it look good?” Trying to make the solution appealing led to poor choices: Instead of choosing a simple technology he understood well, he decided to use a new technology with no experience or expertise. He made complex architectural decisions to make the product look sophisticated. He introduced features that added little to no value to a customer but sounded interesting to implement. Complexity bias in his case led to poor decisions and terrible choices. How to avoid the complexity bias To counteract complexity bias, apply Occam’s razor. It is one of the most useful mental models to solve problems. Occam’s razor advocates simplicity by focusing on key elements of the problem, eliminating improbable options, and finding solutions with fewer assumptions. Another useful mental model to reduce complexity is first principles thinking. It requires breaking down a problem into its fundamental building blocks (its essential elements), asking powerful questions, getting down to the basic truth, separating facts from assumptions, and then constructing a view from the ground up. A few other practices to adopt: Since complexity bias shields you from seeking simple solutions, asking for others' opinions may be helpful. You may ask “Is my solution too complex or confusing?” Question your intent. Reframe the end goal to align it with the outcomes you wish to achieve. Show bias for action. Instead of being stuck in analysis paralysis, take a small step towards your goal, see how it works, and slowly improve it over time. Planning fallacy When it comes to setting deadlines, most of us are highly optimistic. Even if it’s something similar to what we have done in the past, we are still likely to underestimate how much time it will take to do it in the future. It took one week to finish a design proposal last time, but if you have to do it again, you’re positive it will get done in less than a week. It took more than a month for your new hires to get onboarded last time, but while planning for the new batch coming in next week, you allocated only 2 weeks. The planning fallacy is what makes us commit to overly optimistic timelines, miss those deadlines, and then drown in guilt, anxiety, and stress. Not factoring in realistic demands of the task leads to poor planning for the future. It harms your reputation and also breaks trust because when you miss those deadlines, others assume that you don’t take your commitments seriously. Why we fall for the planning fallacy When estimating the time it will take to complete a specific task, we ignore risks and other unlikely events that may prevent us from completing the task on time. Taking the best-case scenario into account and ignoring the obstacles we might encounter along the way leads to wishful thinking. We have the best intentions to get the work done quickly and efficiently, but good intentions aren’t always enough. Good planning needs good estimation skills. Another reason why we are highly optimistic about completing a specific job is our tendency to get stuck in the nitty gritty while ignoring the big picture. Not taking the big picture into account leads to poor estimation—we make wrong assumptions and ignore the time it will take to integrate our work into the bigger picture. We are also really bad at accurately judging our skills and abilities. Our enthusiasm to meet our goals adds to the planning fallacy. \Worst part? We don’t even learn from our past mistakes. Even when we can recognize past mistakes when we are overly optimistic, we keep on making the same mistakes in the future. We focus on our goal, anchor on our plan, and neglect relevant base rates, exposing ourselves to the planning fallacy. We focus on what we want to do and can do, neglecting the plans and skills of others. Both in explaining the past and in predicting the future, we focus on the causal role of skill and neglect the role of luck. We are therefore prone to an illusion of control. We focus on what we know and neglect what we do not know, which makes us overly confident in our beliefs. ― Daniel Kahneman Example of the planning fallacy Tina is working on the estimates of a new product. She has worked on a similar product in the past. The last time she built the product, it took her 3 months to complete. The project was initially estimated for 2 months but took an additional month due to requirements changes, a longer testing cycle, and other unforeseen issues. Tina has experience from the past that she should put to use to determine how long it will take to complete the current project. However, due to the planning fallacy, she commits to a 2-month deadline again. She justifies it by assuming that her experience from the past project will help her finish the tasks sooner while completely disregarding the additional time it will take to handle unexpected issues. How to avoid planning fallacy Break down the project into small parts and estimate how long each one will take. Doing this work upfront will make your effort estimates much closer to reality than being vaguely guided by hunch. Another great strategy is the one recommended by Daniel Kahneman - He suggests taking an outside view of your estimates by consulting others—asking questions and identifying problems and mistakes they made on similar projects in the past. You can’t do this for all your tasks but definitely do it for your most crucial ones. Irrespective of how good you’re at estimation, some unlikely event can crop up and make your project fall through the cracks. It isn’t possible to take every such scenario into account and plan for it. But what’s possible is to add a little buffer to your estimates—add 20% breathing room while considering the effort it takes to do something. Finally, and this is the most important one: implement a feedback loop in your process. Track your time. Make a note of how long it takes to do something. When you miss deadlines, identify what mistakes you made and what changes you need in your planning process to do better next time. When you’re conscious of how these biases—Urgency effect, Zeigarnik effect, Complexity bias and Planning fallacy—affect your time and apply the right strategies to overcome them, you can be your most productive self. Summary Most of us struggle with getting work done efficiently because we don’t pay attention to the cognitive biases that get in the way of using time productively. Urgency effect makes us waste time attending to urgent tasks at the cost of important tasks. Not paying attention to the future needs keeps us trapped in a cycle of unproductivity. The Zeigarnik effect prevents us from giving our best to the task at hand. When our mind keeps reminding us of incomplete or interrupted tasks, it’s hard to stay focused and pay attention. Complexity bias makes us add unnecessary complexity to solutions which increases the time, effort and cost to solve even simple problems. Planning fallacy tricks our mind to give highly optimistic estimates to achieve a certain goal despite our previous experience telling us that it will be impossible to achieve it. This story was previously published . here Follow me on or here for more stories. LinkedIn

Tackling the Saboteurs Within: 4 Cognitive Biases That Affect Your Productivity

এর মধ্যে নাশকতাকারীদের মোকাবেলা করা: 4টি জ্ঞানীয় পক্ষপাত যা আপনার উত্পাদনশীলতাকে প্রভাবিত করে

Die inneren Saboteure bekämpfen: 4 kognitive Vorurteile, die sich auf Ihre Produktivität auswirken

Enfrentando a los saboteadores internos: 4 sesgos cognitivos que afectan su productividad

S'attaquer aux saboteurs intérieurs : 4 biais cognitifs qui affectent votre productivité

भीतर के तोड़फोड़ करने वालों से निपटना: 4 संज्ञानात्मक पूर्वाग्रह जो आपकी उत्पादकता को प्रभावित करते हैं

内部の妨害者に対処する: 生産性に影響を与える 4 つの認知バイアス

내부의 파괴자 대처 방법: 생산성에 영향을 미치는 4가지 인지 편향

Enfrentando os sabotadores internos: quatro preconceitos cognitivos que afetam sua produtividade

Борьба с внутренними саботажниками: 4 когнитивных искажения, которые влияют на вашу продуктивность

İçimizdeki Sabotajcılarla Mücadele Etmek: Verimliliğinizi Etkileyen 4 Bilişsel Önyargı

Giải quyết những kẻ phá hoại bên trong: 4 thành kiến nhận thức ảnh hưởng đến năng suất của bạn

解决内部破坏者：影响生产力的 4 种认知偏差

DARPA is putting together the Intrinsic Cognitive Security ( ) research program “to build tactical mixed reality systems that protect against cognitive attacks.” ICS As the Pentagon sees (MR) becoming ubiquitous in future military missions, the US ( ) is looking to protect users against cognitive attacks in this hybrid environment. mixed reality Defense Advanced Research Projects Agency DARPA “The ICS program seeks breakthrough innovation for computational science to build tactical mixed reality systems that protect against cognitive attacks” DARPA, Intrinsic Cognitive Security (ICS) program, October 2023 According to the DARPA , such attacks can include: ICS program description Information flooding to increase equipment latency and induce physical illness. Planting real-world objects to overwhelm displays. Subverting a personal area network to sow confusion. Injecting virtual data to distract personnel. Using objects to overwhelm a user with confusing false alarms. Assessing user status through an eye tracker. And other potential attacks. Apart from military applications, DARPA’s new program could provide us with a glimpse into the future of the commercial metaverse and how bad actors could manipulate mixed reality environments in nefarious ways. ICS Criminal groups, governments, and corporations could weaponize the by manipulating mixed reality systems in real-time to be able to see what you see, to know what you are feeling, and to potentially plant deceptive information in order to achieve a desired reaction. metaverse A published in in 2020 lists five types of threats to mixed reality environments: research paper Procedia Computer Science : Involves challenges towards ensuring the security and privacy of data gathered and inputted to MR platform. Input Protection : The MR eye-wear can capture the sensitive information on the user’s desktop screen such as e-mails, chat logs. These necessary protections can be mapped to properties of concealment, unobservability & undetectability, and content awareness. Example : A large amount of data collected from the sensors is stored in the database or other forms of data storage. The main threats to data collection are tampering, denial of service, and unauthorized access among others. Data Protection : An adversary can tamper MR targets to elicit a different response from the system or to outright deny a service. Aside from security threats, likability, detectability, and identifiability are some of the privacy threats that results from continuous or persistent collection of data. Example : After the data is processed, the application sends output to the MR device which is to be displayed. In MR the applications may have access to other application outputs and thus can modify those outputs making them unreliable. Output Protection : Output displays are vulnerable to physical inference threats or visual channel exploits such as shoulder-surfing attacks. These are the same threats to user inputs especially when the input and output interfaces are on the same medium or are integrated together. Example : MR includes the utilization of other sensing and display interfaces to allow immersive interactions. One of the key prospects is how users can share MR experiences with guarantee of security and privacy of information. User Interaction Protection : An adversarial user can potentially tamper, spoof, or repudiate malicious actions during these interactions. As a consequence, genuine users might go through denial of service. Additionally, their personal data might have been compromised, leaked and used. Example : The MR interfaces are exposed to malicious and harmful interpretation leading to discovery of input and output display information. Device Protection : The head mounted displays (HMDs) display the content through its lenses, which other people can see from outside leading to leakage and external observation. Devices like a camera, which are also categorized as visual capture devices, are used to capture and extract this information which was leaked from the HMDs. Example A report by adds, “ .” Kaspersky It is nearly impossible to anonymize VR and AR tracking data because individuals have unique patterns of movement. Using the behavioral and biological information collected in VR headsets, researchers have identified users with a very high degree of accuracy – presenting a real problem if VR systems are hacked For DARPA’s program, the core technical hypothesis is that “formal methods can be extended with cognitive guarantees and models to protect mixed reality users from cognitive attacks.” ICS As “cognitive models represent aspects of human perception, action, memory, and reasoning,” DARPA’s program will “extend formal methods by explicitly creating and analyzing cognitive and other models as part of MR system development to protect the warfighter from adversary attacks.” ICS “Mixed reality integrates virtual and real worlds in real-time and will be ubiquitous in future military missions, including missions involving dismounted soldiers” DARPA, Intrinsic Cognitive Security (ICS) program, October 2023 For years, the US Department of Defense (DoD) has been funding research to equip its warfighters with . technologies to augment their capabilities For example, both DARPA and the Defense Innovation Unit (DIU) have been looking to mixed reality and hand-held devices for operators to interact with upwards of 250 autonomous vehicles through a military tactic known as swarming. In 2018, DARPA launched its OFFensive Swarm-Enabled Tactics ( ) program to leverage augmented and virtual reality, along with voice, gesture, and touch-based technologies, to enable users to interact with potentially hundreds of unmanned platforms simultaneously in real time. OFFSET And in March 2021, the that would allow soldiers to operate multiples types of unmanned air and land vehicles using wearable and handheld controllers. DIU was looking for commercial solutions Now, the Pentagon is looking to protect users from cognitive attacks in those mixed reality environments. Will solutions coming out DARPA’s program make their way into the commercial sector to protect private citizens in the metaverse? ICS Could the technology and tactics developed be used for future in their own right? PSYOPs and influence campaigns The program proposers day will be held on October 20, 2023 in Arlington, Virginia. ICS This article was originally published by on Tim Hinchliffe The Sociable.

DARPA Readies Program to Safeguard Mixed Reality Users from Cognitive Attacks

জ্ঞানীয় আক্রমণ থেকে মিশ্র বাস্তবতা ব্যবহারকারীদের রক্ষা করার জন্য DARPA প্রস্তুত প্রোগ্রাম

DARPA bereitet Programm zum Schutz von Mixed-Reality-Benutzern vor kognitiven Angriffen vor

DARPA prepara un programa para proteger a los usuarios de realidad mixta de ataques cognitivos

La DARPA prépare un programme pour protéger les utilisateurs de réalité mixte contre les attaques cognitives

DARPA ने मिश्रित वास्तविकता वाले उपयोगकर्ताओं को संज्ञानात्मक हमलों से बचाने के लिए कार्यक्रम तैयार किया

DARPA、複合現実ユーザーを認知攻撃から守るプログラムを準備

DARPA, 인지 공격으로부터 혼합 현실 사용자를 보호하기 위한 프로그램 준비

DARPA prepara programa para proteger usuários de realidade mista contra ataques cognitivos

DARPA готовит программу для защиты пользователей смешанной реальности от когнитивных атак

DARPA, Karma Gerçeklik Kullanıcılarını Bilişsel Saldırılardan Korumak İçin Program Hazırlıyor

Chương trình sẵn sàng của DARPA để bảo vệ người dùng thực tế hỗn hợp khỏi các cuộc tấn công nhận thức

DARPA 准备计划保护混合现实用户免受认知攻击

*Photo by Ben Sweet on Unsplash* What happens when a nerd reads psychology? He comes up with seven stages of identity development. The following is a summary of one of the many fascinating ideas Joscha Bach shared on . Although his theory is informed by psychologist Robert Keegan’s work, he draws on AI, consciousness and cognitive science background to create a theory of his own. this podcast A Quick Intro Into Kegan’s Work In the , distinguished six developmental stages. For Kegan, the development of the Self is a process of the mind gradually becoming aware of itself. You build a mental structure of who you are and how the world operates. Evolving Self Robert Kegan Although it’s considered a developmental model, it would make more sense to call it a framework because development doesn’t happen in distinct stages. You might go through stages in parallel or need to revisit the previous stage. How The Mind Generates Reality We don’t yet fully know how consciousness is generated. We just know we’re here, and we’re conscious. The brain sits in a dark box and never sees the light of day. Its idea of what’s going on is generated by the input from the sensory data. So the five senses (smell, touch, vision, hearing, taste) and the brain act as buffers between us and reality. This means that we have never experienced the reality, only our of the real world. real perception Our brain offers a view of the world based on our underlying assumptions (mostly developed in the early years or during times of (IDENTITY) crises). I wrote a longer piece on the topic of reality , and Joscha explains this much better in the podcast. here You’re interacting with a game engine unconsciously created by you, for you. Everyone is. The Seven Stages Bach views this as a framework that allows you to understand how the mind works. The stages are: Reactive survival (Infant) Personal self (Young child) Social self (Adolescence) Rational Agency (Self-direction) Self-authoring (Full adulthood) Enlightenment Transcendence Stage 1 (Infancy) In infancy, there’s no concept of self yet. Instead, you have an attentional self that builds the view of the world and the initial model of self. It’s a bit like building a game engine in the brain to track sensory data and interpret that data. Stage 2 (Young child) Once you finish building the model of the world (stage 1), the self starts to come together. It arises as an agent for you to be able to interact with the world. We think of the outside world as an objective reality, but it’s a model of the world that has been generated by our mind. So, as young children, we begin interacting with the “outside” world based on the model of the world we developed in stage 1 (I guess it would make sense to say the model of the world that was installed for us). Stage 3 (Adolescence) After this personal self and stage two are online, many people form a social self. That allows you to experience yourself as part of the group. You begin forming your opinions by assimilating them from your group. is the underlying worry. What my environment thinks about x At this stage, you also begin building empathy. Empathy should not be mistaken with compassion (feeling sympathy for others’ joy and pain). Empathy is building a mental architecture similar to your group. Akin to a hive mind. It becomes difficult to resonate with the group if you are wired differently. That’s why people we refer to as ‘nerds’ or neurotypical people sometimes struggle in this stage. Their question is, do you stay a little bit autistic, or do you catch up? And you can catch up, but that usually happens later in life when you can consciously build empathy. Stage 4 (Rational Agent) You begin to understand that stuff is true and false regardless of what other people believe, and you have agency over your own beliefs. In this stage, you discover epistemology, the rules about determining what’s true and false, and can determine for yourself, as opposed to looking for the hive mind for answers, what’s true. Stage 5 (Self-Authoring) At this stage, you discover how identity is constructed. You realize that your values are not terminal, but they’re instrumental to achieving a world that you like and aesthetics that you prefer. The more you understand this, the more you get agency over how your identity is constructed, and you realize that identity and interpersonal interaction are a costume, and you have agency over that costume. It’s useful to be a costume because it tells something to others, and it allows you to interface in different roles (partner, daughter, employee, boss, friend etc.). This stage is also considered to be full adulthood because you begin understanding that other people form their identities depending on past experiences. You realize that everybody could be you in a different timeline if you have had the experiences they had. their Stage 6 (Enlightenment) At this stage, you can collapse the division between a personal self and the ‘generator’ (the mind) of the world. A lot of people get there via meditation or psychedelics or by accident (like Ekhard Tolle). You suddenly notice that you are not actually a person, but you are a vessel (consciousness) that can create a person, and the person is still there. You observe that personal self, but from the outside, and you notice it’s a representation. You might also notice that the world that is being created is a representation of you. This creates the experience of I am the universe. The thing that’s creating everything. It’s not like you’re creating quantum mechanics and the physical universe. What you’re creating is this game engine that is updating the world, and you’re creating your valence, your feelings, and all the people inside of that world, including the person that you identify with. Stage 7 (Transcendence) This stage is more or less hypothetical, says Bach. It’s a trans-humanist stage in which you understand how you work, in which the mind fully realizes how it’s implemented, and can also, in principle, enter different modes in which it could be implemented. According to Bach, this stage is not open to people yet, but this could change if/when we merge with AI. To Close Most people get stuck around stage 3. If the online world is to be judged as the representation of the real world, the collective is definitely stuck in stage 3, aren’t we? Liberal, republican, cryptoboi, woke, pro-Peterson, anti-abortion, etc. Social media encourages us to pick a side, and that further cements our identity, even in the real world. But identity is not everything. There are other stages past stage 3. P.S. You can listen to a (23 mins) I recorded on this topic with more commentary about each stage. podcast

 7 Stages of Identity Development, According to a Cognitive Scientist

Engineers at early-stage startups operate in a high-pressure environment. With limited resources and constantly shifting priorities, their cognitive load is immense. This burden can hinder productivity, innovation, and engineer satisfaction. Startups must make minimizing cognitive load a priority. My previous article on Hackernoon discussed how for managing engineers' immense cognitive load. I have argued that promoting a flow state reduces the cognitive burden, empowering engineers to leverage their mental capabilities for innovation and productivity fully. achieving a flow state is critical The high-pressure startup environment strains mental faculties with ill-defined problems, constant context switching, and a lack of structure. The Startup Drain on Engineering Minds Engineers working for startups are thrown into a pot of uncertainty and complexity. Figuring out problems that aren't well-defined and have little structure is hard on the mind. Having to deal with many unknowns while the stakes are high can be stressful. Priorities in business change quickly, so engineers have to constantly switch contexts. Processes and infrastructure that aren't set up well also add to cognitive load. Every choice feels big when there isn't much to hold you up. Cognitive Load's Drag on Developer Velocity Studies show that that can be used for tasks. When engineers push their minds to the limit, their work suffers. cognitive load directly cuts down on the amount of working memory Fighting with bad tools and not knowing what you want to achieve is frustrating. When you have a lot to think about, you become exhausted quickly. Startups can't afford for engineers not to do as well as they could. For sustained velocity, you must be able to handle your mental load. Cognitive Load in Deciphering Code Understanding tangled codebases also taxes engineers' brains. Projects with intertwined dependencies, inconsistent naming, overly clever abstractions, and ambiguous intent impose steep learning curves. Startups should champion coding best practices focused on comprehension. Well-named variables, small focused functions, separation of concerns, and eliminating duplication avoid overloading working memory. Comments elucidate rationale and high-level architecture. Graspable code enables new hires to ramp up faster. play a vital role in pinpointing areas for improvement. Reviewers can call out convoluted logic and suggest simplifications. Regular refactoring keeps code clarity a priority as the system grows. Legible code complements controlled cognitive load. Code reviews Cognitive Load on Engineering Teams In addition to individual cognitive load, teams deal with collective burden. Too many hand-offs between groups, unclear ownership, and clunky coordination accumulate. Engineers waste mental energy navigating organizational disorder. Effective team topologies, like those proposed by Matthew Skelton and Manuel Pais, streamline work across groups and minimize coordination overhead. Platform teams provide self-service access to foundational needs. Feature teams own vertical slices with autonomy. Enabling teams offer specialized expertise. With unambiguous domains, teams can focus cognitive effort on delivering value. Removing Distractions and Uncertainty Leaders need to eliminate distractions and fog that engineers don't need. Clean up your physical and digital spaces. Workflows and communication should be improved. Set clear goals that focus on what the customer wants. Cut down on meetings and status checks that aren't necessary. Allow more time to focus to get into flow states. Simplify processes so that they only do what needs to be done. Automation can get rid of low-value tasks that are done over and over. Give engineers the tools they need to find and get rid of distractions. Starting with Intuitive Tooling Foundation Choosing simple, well-known tools and architectures cuts down on cognitive load right away. Build on proven patterns, and don't redo the basics. and cloud-based tools like GitHub Codespaces, Coder, Gitpod, Codeanywhere, Daytona, or Replit give developers environments that are ready to code right out of the box. This keeps you from spending mental energy setting up and fixing environments. Standardized dev environments Use living documentation to help people understand each other. Optimize the sharing of knowledge. Use domains instead of layers to modularize. Choose frameworks with opinions over flexibility. Take away choices. Give enough structure to reduce load, but don't make it too hard. Adding Complexity Intentionally Complexity should be added purposely, not preemptively. Wait for validated needs before introducing new tools, architecture intricacy, or processes. Engineers must drive adding complexity based on hard-won experience, not assumptions. Resist founders or investors insisting on unnecessary technology. Leverage to trial integrations with minimal investment. Measure cognitive load qualitatively. proof-of-concept Evaluating Cognitive Load While cognitive load can appear vague, researchers have crafted ways to measure it. One widely used scale is the , evaluating mental, physical, and temporal demands, performance, effort, and frustration. NASA Task Load Index (TLX) Startups should leverage tools like the NASA TLX to quantify cognitive load over time. Log ratings for engineers during different stages of product evolution. Identify spikes that may signal overload. Track averages to expose pain points in workflows. Comparing load before and after process changes demonstrates impact. Measuring and tuning metrics like time-to-productivity and satisfaction should be continuous. Quantified metrics complement qualitative feedback from engineers. Together they furnish actionable insights on refining the developer experience. Fine-tuning Developer Experience Longitudinally As the startup grows up, measure the engineering cognitive load at each milestone. Capture load data before and after substantial tooling, process, or architecture changes. Watch for load indicators like lower velocity, elevated bug rates, frustration, and churn risk. Regularly survey engineer happiness. Give engineers autonomy and mastery to shape their own experience. Remove unnecessary mental encumbrance to enable productivity. The pressures of early-stage startups impose intense cognitive demands on engineers. Removing unnecessary mental effort through deliberate processes, simplified workflows, and intuitive tools unlocks greater productivity, innovation velocity, and job fulfillment across the startup journey. 5 Tactics for Lessening Cognitive Load Here are 5 key ways startups can reduce the cognitive load for engineers: Eliminate distractions by minimizing interruptions and decluttering environments. Start with intuitive foundations via familiar technologies and straightforward architectures. Add complexity deliberately once validated, avoiding premature overcomplication. Quantify cognitive load at different stages to identify pain points. Continuously optimize developer experience by providing productivity tools and resources. Minimizing unnecessary mental effort should be a top priority for early-stage startups. This allows engineers to focus their cognitive resources on delivering maximum value during the critical formative stages.

Reducing Cognitive Load on Startup Engineers

স্টার্টআপ ইঞ্জিনিয়ারদের উপর জ্ঞানীয় লোড কমানো

Reduzierung der kognitiven Belastung von Startup-Ingenieuren

Reducción de la carga cognitiva en los ingenieros de empresas emergentes

Réduire la charge cognitive des ingénieurs en démarrage

स्टार्टअप इंजीनियरों पर संज्ञानात्मक भार कम करना

スタートアップエンジニアの認知的負荷を軽減する

스타트업 엔지니어의 인지 부하 감소

Reduzindo a Carga Cognitiva em Engenheiros de Startups

Startup Mühendislerinin Üzerindeki Bilişsel Yükü Azaltma

Giảm tải nhận thức cho các kỹ sư khởi nghiệp

减少初创工程师的认知负担

The following piece claims to reduce an artist's cognitive overload in graphics design software using a simplified digital compositing node graph aka Pipeline. Proceed at your own risk. Content Overview An Example of Cognitive Overload A Mad Proposal A Mad Comparison to Digital Compositing Node Graph What Changed? Nothing, Really An Insane User Interface Then, things Start to get Crazier Digital Compositing Paradigm in a Pipeline Omg! Advantages. Lol! You must be Mad too An Example of Cognitive Overload Imagine a graphic artist performing a series of photo edits: 1. Apply Disc Blur to an image.
2. Apply Sepia Tone to Disc Blur.
3. Blend the Sepia Tone with the original. The intent of the artist usually consists of a sequence of actions to be translated into operations  carried out with the graphics software. For example, the process of the photo edits above may involve creating a new document, selecting the optimal image options in the application menu, creating Layers to apply a Disc Blur, changing properties in a side panel, bringing up an object dialog box, and repeating the process to add additional layers for the Sepia Tone/Blend to achieve the desired effect. This approach of user interface and when exacerbated with an increasing number of steps results in and . NON-STREAMLINED disrupts the flow of thoughts of the artist Cognitive Overload loss of creative productivity We are OK with the above. What's WRONG? A Mad Proposal We the user interface into a linear sequence of actions that can be compounded or stacked like a digital compositing node graph but with simplification to express how to achieve the desired outcome succinctly, like below: streamline Pipeline

1. Read Image
2. Disc Blur
3. Sepia Tone
4. Blend (2,3) A Pipeline is to the user interface of graphics design software like what Markdown is to HTML. A Mad Comparison to Digital Compositing Node Graph To someone familiar with Digital Compositing, they will immediately recognize that the list of steps, the Pipeline, above is "almost" identical to a digital compositing node graph. A digital compositing node graph is shown below: Read Image
|         |
|         |
|         V
|         Disc Blur
|         |
|         |
|         V
|         Sepia Tone
|         |
|         |
V         V
   Blend What Changed? Nothing, Really A Pipeline is indeed similar to a digital compositing node graph with a subtle yet significant change. Instead of displaying a full node graph graphically, with two lines from the Read Image node and Sepia Tone node connecting to the Blend node. The Pipeline simplifies the node graph by just showing a list of steps and a spreadsheet-like function for the final Blend step. 4. Blend (2,3) This creativity of arranging a digital compositing node graph as a Pipeline enables the intent of the artist to be conveyed as a succinct list of steps, following the artist's flow of thoughts from start to end, without a graphical map. The Pipeline also guides the artist into thinking of what to do next in a streamlined manner. Without using a traditional node graph, more information can be conveyed in a smaller amount of space linearly, providing a larger contextual overview to the creative mind. An Insane User Interface A Pipeline, in software, maps directly to a list and is easy to implement. The list removes the need for a complicated graphical map that may require (scrolling) to identify the right node to work on. The properties screen of a node can also be brought up intuitively and naturally by drilling into the node. panning Pipeline

1. Read Image
2. Disc Blur  ----> Drill into the Properties screen
3. Sepia Tone
4. Blend (2,3) PROTOTYPE - Fits into a mobile phone Then, Things Start to get Crazier No loss of information The important thing to note is the above creativity does not lose any information. The simplified node graph above, the Pipeline, in a mathematical sense, can be fully represented as a node graph (Directed Acyclic Graph) in any manner or form. For example, if we have the following node graph for video processing. Read media Read media 
|          |
V          V
Premult    Color Correction 
|          |
|          V 
|          Transform 
|          |
|          V 
|          Gaussian Blur 
|          | 
V          V 
Merge 
|
V 
Viewer It can be flattened, streamlined, and represented as follows: Read media
|
V
Premult

Read media
|
V
Color Correction
|
V
Transform
|
V
Gaussian Blur

Merge
|
V
Viewer Note the break from the first series after the 'Premult' node and the second break after the 'Gaussian Blur' node. Pipeline

1. Read media         >
2. Premult            >

3. Read media         >
4. Color Correction   >
5. Transform          >
6. Blur               >

7. Merge (2,6)        >
8. Viewer             > The space between node 2 and node 3 represents the break, while the '>' is optional and used to indicate the availability of a properties screen. Digital Compositing Paradigm in a Pipeline The paradigm of node-based digital compositing where we can lay out steps in a progression and make modifications to an earlier step to change the outcome is completely kept. Each node can be easily reused by referencing it with a node number as in Step 7, and if required, used further in a later step, saving time and effort. In the same way that a digital compositing node graph is broken into a series of nodes in a graph, a Pipeline breaks it into a series of actions or steps. A Pipeline, from another angle, is thus like a single-column spreadsheet, a metaphor that many computer users are quite familiar with. Viewing the output of each step: Viewer Cycling The arrangement in a series of steps enables the output of each progression step to be viewed easily, for example, by long pressing on a node on the Pipeline. This enables an artist to step through the progression to reinforce his or her cognitive working memory of what has been performed. Connecting the steps: Auto-Chaining Each node on the Pipeline that requires an input can be assumed to be taking the output of the previous node automatically. For example, the 'Premult' node in Step 2 is assumed to automatically take the output from 'Read Media' in Step 1. Connecting the steps like a node graph: Reverse Compositing The auto-chaining or automatic preceding node referencing behavior can also be altered by enabling the artist to make changes in the properties of a node. For example, in Step 7, the artist can choose from its properties screen any of the previous nodes, such as nodes 2 and 6, for blending. Omg! Advantages. Lol! Modular Extensibility A Pipeline, being almost identical to a digital compositing node graph, enables it to achieve the same modular extensibility in software development. Software developers can think of developing graphical effects, filters, animations, and 3D operations modularly as a node. Each node is accompanied by a properties user interface that does not interfere with other parts of the software. A node and its user interface can thus be developed and tested independently while knowing ultimately that it can be easily integrated into a full node graph, aka the Pipeline, to achieve any complex graphics operation. Such a design will benefit the open-source community greatly where many different developers can build an open-source Pipeline, the Open Pipeline, together in a distributed and collaborative manner. Reduce Information Overload To start using a graphics application, artists usually need to learn the way of achieving it through documentation. Often, the documentation requires an artist to follow a series of screenshots, explaining where and how to click or tap on different parts of the screen using a non-streamlined user interface offered by the graphics application. The non-streamlined user interface makes the information hard for an artist to remember and may require the artist to constantly refer to the documentation. Over time, this introduces an additional cost that, when accumulated, can lead to . Information Overload Succinct Self-Describing Pipeline The Open Pipeline reduces Information Overload as it succinctly describes what needs to be done and allows all necessary details to be shown in context nearby. This significantly benefits documentation: tutorials, articles, or books. Open Pipeline

1. Disc Blur: radius = 8
2. Sepia Tone: intensity = 1
3. Blend (1,2) The details of the radius for Disc Blur are shown as part of the node in the Pipeline. Moreover, besides being , it is common for the above description to supporting screenshots as it is self-explanatory. succinct NOT REQUIRE You must be Mad too If you have followed this article till here, find out more about the author's banishment due to his 'NOT SO' , and where he is living in exile. His ‘BLUFF’ of implementing a node that ‘CAN BE INTEGRATED EASILY’ into the Pipeline. And him performing a MAGIC TRICK. REALLY. He compares himself with the Roman poet Ovid by quoting on ‘how shapes changed into new bodies’ in Metamorphoses: Open Pipeline Photo Curve Photo Fire Effect – OVID In nova fert animus mutatas dicere formas corpora; di, coeptis (nam vos mutastis et illas) adspirate meis primaque ab origine mundi ad mea perpetuum deducite tempora carmen. Sigh, I give up He further mumbles about animating a graphics object with a node. METAMORPHOSIS The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "cognitive overload".

Revolutionary User Interface Design: Tackling Cognitive Overload in Graphics Software

In this article, we tried to explore how humans would compare to AGI. And to do this, we explored how a less intelligent species like a chimp would compare to a more intelligent species like Humans and then tried to do the same for humans vs AI. Before comparing humans and AI, let's understand the concept of : dimensionality 1. Understanding dimensionality: 1.1 Humans vs Chimps Although we humans have evolved from chimps, what sets us apart is our cognitive ability. Our cognitive ability allows us to imagine things that chimps simply cannot. With this imagination, we create imaginary entities such as computer games, nation-states, and money which purely exist in our brains and for our own benefit. As chimps lack these cognitive capabilities, they simply cannot comprehend the things that humans do or humans create in their daily lives. For example, if a chimp sees a spacecraft, it wouldn’t understand that it was artificially created by humans and wouldn’t understand its significance. It will assume that the spacecraft is created naturally although it isn’t. In the case of humans, imagining complex things is a when compared to chimps. new level of dimension 1.2 Technological Advancement To gain a better understanding of dimensionality, let's take the internet as an example. During the initial era of the internet (the 1990s), there were no job positions related to Data Analyst or Data Scientist. However, as the internet grew, there was a surge of data online. Companies began to explore how to utilize this data, leading to the hiring of professionals who are skilled in understanding, managing, and analyzing data, thus creating new roles known as Data Scientist and Data Analyst. While these roles are commonplace now, they would have been completely alien to someone in the early 90s. In essence, the rise of the internet led to the creation of new data-related jobs thus, resulting in an overall increase in the dimensionality of our society. 2. AI vs Humans: 2.1 AI capabilities as of today: AI can perform billions of calculations per second whereas the human brain can process only a thought at 200-500 milliseconds AI can have access to infinite amounts of data AI is able to find patterns and combine information from different domains and thus, becoming an expert in multi-domain knowledge AI can work 24 hours unlike humans who work only an average of 8 hours The most powerful AI (GPT-4) is trained on 100 trillion neurons which is 1000 times more than the human brain which has 100 billion neurons Instead of focusing on the distant future, let us take a look at the current capabilities of GPT-4. It has demonstrated its proficiency in passing exams across various fields such as Economics, Diplomacy, Medicine, and Engineering. How many humans can achieve this feat? Not many, right? This shows that even the current AI . has more breadth of knowledge than any human that has ever existed In the future, AI will continue to progress and improve its own abilities, rapidly developing in new areas of intelligence. AI will conduct research, development, and creation of new domains, and acquire intelligence at an unprecedented pace, making it extremely challenging for humans to even comprehend in the first place. . Just as chimps cannot comprehend what we’re doing, humans will also not be able to comprehend the actions of AI in the future 2.2 Possible Future Technological Advancements of AI: Currently, humans have achieved remarkable feats such as landing on the Moon, launching artificial satellites, and planning to establish a colony on Mars. However, considering the potential of AI, accomplishing these tasks for an AI should be a cakewalk. In fact, it wouldn't be surprising if . Given that AI becomes so good at research, one radical possibility is that with the emergence of Artificial Super Intelligence, . AI will create new planets, manufacture humans using 3D printing or upload human brains the number of artificially created entities could well surpass the number of naturally occurring entities in the universe If we humans with our capabilities could create such a miraculous AI, there is no ruling out that AI itself can create another new Super Intelligent species more powerful than itself! Also published here. The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "Human vs AI"

Human vs AGI: Analyzing Cognitive Abilities 

In the age of rapid technological advancements, the face of warfare is undergoing a significant transformation. The integration of (AI) in military systems is revolutionizing the way we conduct warfare. Artificial Intelligence This article delves into the fascinating world of AI in warfare, focusing on the concept of cognitive warfare and the role of AI in shaping the future of combat. Cognitive Warfare: The New Frontier Cognitive warfare represents a paradigm shift in the conduct of military operations. It is the strategic use of AI and machine learning to influence the cognitive processes of adversaries. The objective is to manipulate the decision-making process, create confusion, and ultimately gain a strategic advantage. This approach is increasingly being recognized as a potent tool in modern warfare, as it leverages the power of AI to augment human capabilities. One of the most striking examples of cognitive warfare in action is the developed by , a defense-tech startup. Ghost 4 drone Anduril This drone is a testament to the integration of AI in military systems. Equipped with AI, it can perform a variety of reconnaissance missions under the control of a single operator on the ground. It uses machine learning to analyze imagery and identify targets, showcasing the potential of AI in enhancing military capabilities. Albeit, it's important to approach this development with a balanced perspective. While the Ghost 4 drone represents a significant advancement in military technology, it also raises several questions. The use of AI in warfare, particularly in autonomous systems capable of making decisions, introduces a new level of complexity and unpredictability. There's a risk of unintended consequences if these systems malfunction or are exploited by adversaries. Moreover, the ethical implications of using AI in warfare are a subject of ongoing debate. The prospect of machines making life-and-death decisions on the battlefield is a contentious issue. While AI can potentially reduce human casualties by taking over dangerous tasks, the lack of human judgment and accountability is a major concern. Lastly, the use of AI in cognitive warfare could potentially escalate conflicts. The ability to manipulate adversaries' decision-making processes could lead to miscalculations and misunderstandings, increasing the risk of conflict. Therefore, it's crucial to establish clear rules and regulations governing the use of AI in warfare to mitigate these risks. The Age of Digital, Transparent Warfare The year 2023 marked the dawn of digital, transparent warfare. The war in Ukraine was a clear example of this new era. The world watched as Russia assembled its forces on Ukraine's border, thanks to the comprehensive transparency provided by satellites, digital traces, and user-generated content on social media. In this age, it's no longer possible to sneak up on another country with an army, navy, or air force. The death and destruction inflicted by these forces are also impossible to conceal. This transparency is forcing armed forces to adapt their strategies, moving and maneuvering in more dispersed ways. The Impact of Precision Weapons The advent of precision weapons is another game-changer in warfare. These weapons can destroy platforms costing millions with a single, cost-effective missile. This reality is transforming how armies, navies, and air forces organize, equip, and operate. The success of precision weapons against armored vehicles and aircraft, as seen in the Ukraine conflict, underscores their effectiveness. The challenge now is to reduce the cost and complexity of these weapons, a task that nations are urgently undertaking. The Future: AI-Driven Transformation The integration of AI in warfare is set to drive a profound transformation in how states confront conflict. Advances in robotics, autonomy, connectivity, data in a secure cloud, and AI will lead to armed forces that are rapidly evolving teams of inhabited, uninhabited, and autonomous capabilities. This transformation will be as significant as the impact of digital platforms like Airbnb and Uber on their respective industries. However, despite this transformation, the nature of war will remain the same - a contest of wills, a mix of reason, emotion, and chance Signing off... We dove into AI and warfare, and we find ourselves on the precipice of a new era. The integration of AI into military systems is indeed a double-edged sword. On one side, it holds the promise of revolutionizing warfare, enhancing military capabilities, and potentially reducing human casualties. On the other side, it raises profound ethical and moral questions that we, as a society, must grapple with. The use of AI in cognitive warfare, for instance, raises the specter of machines making life-and-death decisions. Can we entrust such critical decisions to algorithms? How do we ensure accountability in a realm dominated by autonomous systems? These are not just technical questions, but deeply philosophical ones that touch upon our values and principles. Moreover, the potential misuse of AI in warfare is a significant concern. In the wrong hands, these technologies could be used to escalate conflicts, manipulate public opinion, or infringe upon human rights. How do we prevent such misuse? How do we strike a balance between leveraging the benefits of AI and safeguarding against its potential risks? It is crucial to approach it with caution and responsibility. We must foster an open dialogue about these issues, encouraging diverse perspectives and rigorous debate. Only through such discussions can we hope to harness the power of AI in warfare responsibly and ethically. This is where I will leave you with these questions to ponder. What do you think about the use of AI in warfare? How should we address the ethical and moral challenges it presents? I invite you to share your thoughts and join the conversation in the comment section below. The future of warfare is here, and it's a future we all have a stake in. Join hands with Jin Park and step into the 'Jinius' world. Your support can spark a revolution. Visit and be a part of our journey. Let's create a better future together! ❤️ @https://ko-fi.com/jiniusme The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "AI warfare"

AI in Warfare: The Dawn of Cognitive Combat

We live in an era of digital acceleration, wherein our life is deeply embedded into digital infrastructure and smart technologies. The digital penetration is so deep that in a single day we produce . That’s a striking figure! The rate at which data is produced clearly indicates the rising demand for a . Businesses around the world are looking for multi-dimensional cognitive frameworks to create a digitally-connected and intelligently driven business ecosystem. around 3.5 quintillion bytes of data digital economy When technology has become a defining factor, having a breakthrough business idea is not enough. Being agile, customer-centric, and synergizing disruptive business models, can only take you so far. You need a smart engine to drive you forward, propel you in the right direction, reduce cost and optimize critical processes, mull trillions of data in real time, and make smart decisions in split seconds. Making cognitive process automation is the best logical option, the next big leap toward business transformation. Fast - Forward - Futuristic Digital fluency is becoming a must to survive. Organizations are on a committed lap to creating an autonomous digital enterprise (ADE), trying to put competitors in the slipstream. Automation has become a necessity, not a luxury, and embedding AI into RPA will make it a super-powerful option. When there is a huge volume of data flow from multiple touchpoints, you need an agile system to sagaciously verify, process, map, and manage the data sets. Process automation was introduced to manage mundane repetitive tasks, now it has evolved as a critical enabler in optimizing cost across business verticals. The Never Ending Stream of Technology Outburst The digital saga has clearly given us a glimpse of what the future beholds. The increasing digital demand calls forth the dire necessity to combine smart technologies and create contextualized intelligence in order to meet day-to-day critical requirements. When heading towards an artificial general intelligence-dominated business ecosystem, are destined to get the best out of this transition. Robotic process automation plays a critical role in this transition, often acting as a digital catalyst in achieving significant operation efficiency. tech-centered organizations Cognitive Automation: A Centerpiece in Digital Transformation Automation is a major piece in solving the digitalization puzzle, be it shifting work roles or creating business impact. Robotic process automation has helped businesses improve process efficiency, reduce operation costs, and impact quality standards across the organization. In the early stages. The prime suspects were monotonous and labor-intensive tasks. By integrating intelligence into RPA, businesses are taking it to the next big league, enabling an ecosystem of end-to-end digital transformation at scale. Cognitive automation uses modern technologies such as contextual analysis, data mining, optical character recognition, opinion mining, and pattern recognition to make intuitive and informed judgments. With intelligent automation, businesses will be able to mine through huge streams of structured and unstructured data, plotting patterns, mapping datasets, and finding similarities pertaining to specific business processes. They then move on to converting these signals into actionable insights, enabling the system to emulate human intelligence. What Makes Cognitive Automation So Special When you mix intelligence with technology it opens up a realm of opportunities for businesses to leverage the benefits. Optimize workflows and automate more than 70% of repetitive tasks Increase scalability and process optimization Keep a digital trail on tasks, processes, and workflows Scalable beyond headcount. A single bot equals 6 FTEs Early detection and recommendation on operational challenges Analyze data and find opportunities for continuous process improvements Minimal supervision zero bias Cognitive Automation Use Cases Prescriptive Maintenance Using Cognitive Systems Real-time prescriptive maintenance has been a pipe dream for factory managers. Any unplanned breakdowns in the manufacturing sector can lead to cumbersome experiences, followed by a downstream of compliance issues and quality compromises. With technology, factories and product lines are becoming more instrumental and interconnected. Manufacturing units must enable cognitive capability from the shop-floor level, bringing together sensor data, information analytics, data warehousing, and dynamic case management to improve collaboration between manufacturing units, predict/prevent mechanical breakdown, prevent incident events, and so on. Combining cognitive computing and the industrial Internet of Things (IIoT) can significantly influence global performance, and quality of production, as well as identify root causes and offer optimized maintenance plans. By leveraging cognitive technologies businesses will be able to continuously monitor the overall equipment effectiveness (OEE) of plant machinery and use the data insights to predict the statistical probability of equipment failures, process failures, and line slowdowns. Bringing an Edge to Trade Finance Using Intelligent Automation When increasing international trade, it becomes enormously important to bring in a machine-first approach to optimize financial operations across business verticals to ensure seamless and error-free transactions on a global scale. Considering the huge volume of customer requests and inquiries generated on a daily basis, it's important to have a smart engine to process the data intelligently and make decisions in split seconds. Prescriptive analytics is one such technique widely used in the financial sector to understand payment patterns, overdue payments, credit risks, etc, and spontaneously generate queues. Trade finance is followed by an intensive paper-driven process, considering the surge in digital payments and the complex nature of bilateral transactions. The end-to-end fulfillment of each and every trade finance case is very time-consuming and tedious. Delays in payment processing can have a negative influence on customers. The need for an agile smart engine to process each and every trade finance case with speed and accuracy seems critical. Cognitive Automation: a Soothing Remedy in Healthcare The modern healthcare system is fueled by data to make smart decisions and deliver decisive patient care. Digitizing patient information and embedding it with a cognitive system will help critical healthcare professionals to diagnose the patient's conditions and create a personalized treatment/ healthcare plan without causing any delay. While maintaining a repository of electronic health records helps ensure compliance, prevent loss of data, and continuous monitoring to keep tabs on patient status, bringing in intelligence systems can free up your healthcare staff to make quick data-driven decisions. Giving enough wiggle room for healthcare employees to prioritize patient care over manual paperwork. Emerging regulatory frameworks in the healthcare sector enable technology to read through patient medical history, get information on prescribed medications, and quickly run medical tests to find the right prognosis with a high success ratio. When done manually these processes can cause delays, spike human errors, high backlogs, and cost creeps. Cut Through Clutter & Deliver Safe-Personalized Experience @35000 ft With technology advancements, more instruments, more checklists, more speed, more protocols, and more stress, are introduced into the cockpit. Too much going in a small pod. It becomes a necessity to introduce automation, by keeping humans in the loop. Aviation is one pioneering sector that embraced automation from its initial stages. For decades the aviation industry has been using automation to improve operational efficiency. With cognitive technologies, airlines are redefining safety measures and rigorously trying to push boundaries to deliver unique and personalized customer experiences. Creating a neural network of integrated systems will enhance situational awareness, enabling an aircraft to quickly respond to critical situations, and plot a safe path with the data available. Considering the flight patterns, and co-relating them with the flight parameters and data, the cognitive system can predict near occurrences (heading towards a stall point, decreased rate of climb, collision path, etc). The cut-throat competition in the airline industry bids for contextualized customer experience and continuous engagement. An unplanned flight cancellation can cause widespread commotion among customers. Using cognitive technologies the airline can initiate a customized mass messaging with alternate travel options, process refunds, and help the customer find an alternative flight/ rebooking, etc. Making sure a course of action is initiated to support customer concerns. While cruising at 35000 ft cognitive technologies provide tailwinds to flight operations, whereas AI is plotting plans to make you a frequent flier for the airline. A Nutshell View Technology transformation with artificial intelligence (AI) will continue to automate discrete functionalities. Cognitive automation has evolved into an advanced system that learns at scale, reads through different patterns and signals to reason with, mimics human intelligence and interactions. Businesses that adopted cognitive automation were able to achieve market resilience and develop cutting-edge business models.

Driving Business Transformation with Cognitive Automation

"Our Intelligence is what makes us human and AI is an extension of that Quality" -Yan LeCun Since the advent of (also known as artificial neural networks), the AI industry has enjoyed unparalleled success. Neural Networks are the driving force behind modern AI systems and they are modeled after the human brain. Modern AI research involves creating and implementing algorithms that aim to mimic the neural processes of the human brain to create systems that learn and act in ways similar to human beings. Neural Networks In this article, we will attempt to understand the brain-inspired approach to building It is worth noting that while this topic is an inherently broad one, I will be as brief and succinct as possible. I plan to treat sub-topics that have more intricate sub-branches as standalone articles, and I will, of course, leave references at the end of the article. AI systems. Content breakdown How I hope to approach this: Here we'll discuss how scientists Norman Weiner ann Warren McCulloch brought about the convergence of neuroscience and computer science, how Frank Rosenblatt's Perceptron was the first real attempt to mimic human intelligence, and how its failure brought about ground-breaking work which would serve as the platform for Neural Networks. History of the brain-inspired approach to AI: In this section, we'll dive into the biological basis for the brain-inspired approach to AI. We will discuss the basic structure and functions of the human brain, understand its core building block, the neuron, and how they work together to process information and enable complex actions. How the human brain works and how it relates to AI systems: Here we will discuss the fundamental concepts behind the brain-inspired approach to AI. We will explain how concepts such as; Neural networks, Hierarchical processing, and plasticity and how techniques of parallel processing, distributed representations, and recurrent feedback aid AI in mimicking the brain's functioning. The Core Principles behind the Brain-inspired Approach to AI: Here we will talk about the challenges and limitations inherent in attempting to build systems that mimic the human brain. Challenges such as; the complexity of the brain, and the lack of a unified theory of cognition explore the ways these challenges and limitations are being addressed. Challenges in building AI systems modeled after the human brain: Let us begin! The History of the brain-inspired Approach to AI The drive to build machines that are capable of intelligent behavior owes much of its inspiration to MIT Profesor, . Norbert Weiner was a child prodigy who could read by the age of three. He had a broad knowledge of various fields such as Mathematics, Neurophysiology, medicine, and physics. Norbert Weiner Norbert Weiner believed that the main opportunities in science lay in exploring what he termed as -areas of study that are not clearly within a certain discipline but rather a mixture of disciplines like the study of medicine and engineering coming together to create the field of Medical Engineering-, he was quoted saying: Boundary Regions "If the difficulty of a physiological problem is mathematical in nature, ten physiologists ignorant of mathematics will get precisely as far as one physiologist ignorant of mathematics" In the year 1934, Weiner and a couple of other academics gathered monthly to discuss papers involving boundary region science. It was a perfect catharsis for half-baked ideas, insufficient self-criticism, exaggerated self-confidence and pomposity - Norman Weiner From these sessions and from his own personal research, Weiner learned about new research on biological nervous systems as well as about pioneering work on electronic computers, and his natural inclination was to blend these two fields. And so, a relationship between neuroscience and computer science was formed. This relationship became the cornerstone for the creation of artificial intelligence, as we know it. After World War II, Wiener began forming theories about intelligence in both humans and machines and this new field was named . Wiener’s foray into Cybernetics successfully got scientists talking about the possibility of biology fusing with engineering. One of the said scientists was a neurophysiologist named , who then proceeded to drop out of Haverford University to study philosophy and psychology at Yale. Cybernetics Warren McCulloch While attending a scientific conference in New York, he came in contact with papers written by colleagues on biological feedback mechanisms. The following year, in collaboration with his brilliant 18-year-old protege named Walter Pitts, McCulloch proposed a theory about how the brain works -a theory that would help foster the widespread perception that computers and brains function essentially in the same way. They based their conclusions on research by McCulloch on the possibility of neurons processing Binary Numbers (for the unknowledgeable, computers communicate via binary numbers). This theory became the foundation for what became the first model of an artificial neural network, which was named the McCulloch-Pitts Neuron(MCP). The MCP served as the foundation for the creation of the first-ever neural network which came to be known as . The Perceptron was created by Psychologist, who, inspired by the synapses in the brain, decided that as the human brain could process and classify information through synapses(communication between neurons) then perhaps a digital computer could do the same via a neural network. the perceptron Frank Rosenblatt The Perceptron essentially scaled the MCP neuron from one artificial neuron into a network of neurons, but, unfortunately, the perceptron had some technical challenges which limited its practical application, most notable of its limitations was its inability to perform complex operations(like classifying between more than one item, for example, a perceptron could not perform classification between a cat, a dog, and a bird). In the year 1969, a book published by and titled lay out in detail the flaws of the Perceptron and due to that, research on Artificial Neural Networks stagnated until the proposal of Back Propagation by . Marvin Minsky Seymour Papert Perceptron Paul Werbos Back Propagation hopes to solve the issue of classifying complex data that hindered the industrial application of Neural Networks at the time. It was inspired by synaptic plasticity; the way the brain modifies the strengths of connections between neurons and as such improves performance. Back Propagation was designed to mimic the process in the brain that strengthens connections between neurons via a process called weight adjustment. Despite the early proposal by Paul Werbos, the concept of backpropagation only gained widespread adoption when researchers such as , , and published papers that demonstrated the effectiveness of backpropagation for training neural networks. The implementation of backpropagation led to the creation of Deep Learning which powers most of the AI systems available in the world. David Rumelheart Geoffrey Hinton Ronald Williams "People are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at." - Parallel Distributed Processing How the human brain works and how it relates to AI systems We have discussed how researchers began to model AI to mimic the human brain, let us now look at how the brain works and define the relationship between the brain and AI systems. How the brain works: A simplified description The human brain, essentially processes thoughts via the use of neurons, a neuron is made up of 3 core sections; The Dendrite, Axon, and the Soma. The Dendrite is responsible for receiving signals from other Neurons, The Soma processes information received from the Dendrite, and the Axon is responsible for transferring the processed information to the next Dendrite in the sequence. To grasp how the brain processes thought, imagine you see a car coming towards you, your eyes immediately send electrical signals to your brain through the optical nerve and then the brain forms a chain of neurons to make sense of the incoming signal. The first neuron in the chain collects the signal through its and sends it to the to process the signal after the Soma finishes with its task it sends the signal to the which then sends it to the Dendrite of the next neuron in the chain, the connection between Axons and Dendrites when passing on information is called a Synapse. The entire process continues until the brain finds a t(that's scientific lingo for; the brain continues processing until it finds an optimal response to the signal sent to it) and then it sends signals to the necessary effectors eg, your legs and then the brain sends a signal to your legs to run away from the oncoming car. Dendrites Soma Axon Sapiotemporal Synaptic Inpu The relationship between the brain and AI systems The relationship between the brain and AI is largely mutually beneficial with the brain being the main source of inspiration behind the design of AI systems and advances in AI leading to a better understanding of the brain and how it works. There is a reciprocal exchange of knowledge and ideas when it comes to the brain and AI, and there are several examples that attest to the positively symbiotic nature of this relationship: Arguably the most significant impact made by the human brain to the field of Artificial Intelligence is the creation of Neural Networks. In essence, Neural Networks are computational models that mimic the function and structure of biological neurons, the architecture of neural networks and their learning algorithms are largely inspired by the way neurons in the brain interact and adapt. Neural Networks: AI systems have been used to the human brain and study its interactions with the physical world. For example, researchers have Machine Learning techniques to simulate the activity of biological neurons involved in visual processing, and the result has provided insight into how the brain handles visual information. Brain Simulations: simulate Researchers have begun using Machine Learning Algorithms to analyze and gain insights from brain data, and fMRI scans. These insights serve to identify patterns and relationships which would otherwise have remained hidden. The insights gotten can help in the understanding of internal cognitive functions, memory, and decision-making, it also aids in the treatment of brain-native illnesses such as Alzheimer's. Insights into the brain: Core Principles Behind the brain-inspired Approach to AI Here we will discuss several concepts which aid AI in imitating the way the human brain functions. These concepts have helped AI researchers create more powerful and intelligent systems which are capable of performing complex tasks. Neural Networks As discussed earlier, neural networks are arguably the most significant impact made by the human brain to the field of Artificial Intelligence. In essence, Neural Networks are computational models that mimic the function and structure of biological neurons, the networks are made up of various layers of interconnected nodes, called artificial neurons, which aid in the processing and transmitting of information, similar to what is done by dendrites, somas, and axons in biological neural networks. Neural Networks are architected to learn from past experiences the same way the brain does. Distributed Representations Distributed representations are simply a way of encoding concepts or ideas in a neural network as a pattern along several nodes in a network in order to form a pattern. For example, the concept of smoking could be represented(encoded) using a certain set of nodes in a neural network and so if that network comes along an image of a man smoking it then uses those selected nodes to make sense of the image (it's a lot more complex than that but for the sake of simplicity), this technique aids AI systems in remembering complex concepts or relationships between concepts the same way the brain recognizes and remembers complex stimuli. Recurrent Feedback This is a technique used in training AI models where the output of a neural network is returned as input to allow the network to integrate its output as extra data input in training. This is similar to how the brain makes use of feedback loops in order to adjust its model based on previous experiences. Parallel Processing Parallel processing involves breaking up complex computational tasks into smaller bits in an effort to process the smaller bits on another processor in an attempt to improve speed. This approach enables AI systems to process more input data faster, similar to how the brain is able to perform different tasks at the same time(multi-tasking). Attention Mechanisms This is a technique used which enables AI models to focus on specific parts of input data, it is commonly employed in sectors such as Natural Language Processing which contains complex and cumbersome data. It is inspired by the brain's ability to attend to only specific parts of a largely distracting environment; like your ability to tune into and interact in one conversation out of a cacophony of conversations. Reinforcement Learning Reinforcement Learning is a technique used to train AI systems, it was inspired by how human beings learn skills through trial and error. It involves an AI agent receiving rewards or punishments based on its actions, this enables the agent to learn from its mistakes and be more efficient in its future actions (this technique is usually used in the creation of games). Unsupervised Learning The brain is constantly receiving new streams of data in the form of sounds, visual content, sensory feelings to the skin, etc and it has to make sense of it all and attempt to form a coherent and logical understanding of how all these seemingly disparate events affect its physical state. Take this analogy as an example, you feel water drop on your skin, you hear the sound of water droplets dropping quickly on rooftops, you feel your clothes getting heavy and in that instant, you know rain is falling, you then search your memory bank to ascertain if you carried an umbrella and if you did, you are fine, else, you check to see the distance from your current location to your home if it is close, you are fine, else you try to gauge how intense the rain is going to become if it is a light drizzle you can attempt to continue the journey back to your home, but if it is priming to become a shower, then you have to find shelter. The ability to make sense of seemingly disparate data points(water, sound, feeling, distance) is implemented in Artificial intelligence in the form of a technique called Unsupervised Learning. It is an AI training technique where AI systems are taught to make sense of raw, unstructured data without explicit labeling ( no one tells you rain is falling when it is falling, do they/). Challenges in Building Brain-Inspired AI Systems We have spoken about how the approach for using the brain as inspiration for AI systems came about, how the brain relates to AI, and the core principles behind brain-inspired AI. In this section, we are going to talk about some of the technical and conceptual challenges inherent in building AI systems modeled after the human brain. Complexity This is a pretty daunting challenge. The brain-inspired approach to AI is based on modeling the brain and building AI systems after that model, but the human brain is an inherently complex system with 100 billion neurons and approximately 600 trillion synaptic connections(each neuron, has on average, 10,000 synaptic connections with other neurons), and these synapses are constantly interacting in dynamic and unpredictable ways. Building AI systems that are aimed to mimic, and hopefully exceed, that complexity is in itself a challenge and requires equally complex statistical models. Data Requirements for Training Large Models Open AI's GPT 4, which is, at the moment, the cutting edge of text-based AI models requires 47 GigaBytes of data, in comparison, its predecessor GPT3 was trained on 17 Gigabytes of data, which is approximately 3 orders of magnitude lower, imagine how much GPT 5 will be trained on. As has been proven in order to get acceptible results, Brain-Inspired AI systems require vast amounts of data and data for tasks especially auditory and visual tasks and this places a lot of emphasis on the creation of data collection pipelines, for instance, Tesla has 780 million miles of driving data and its data collection pipeline adds another million every 10 hours. Energy Efficiency Building brain-inspired AI systems that emulate the brain's energy efficiency is a huge challenge. the human brain consumes approximately 20 watts of power, in comparison, 's Autopilot, on specialized chips, consumes about 2,500 watts per second and 7.5-megawatt hours(MWh) to train an AI model the size of . Tesla it takes around ChatGPT The Explainaibility Problem Developing brain-inspired AI systems that can be trusted by users is crucial to the growth and adoption of AI, but therein lies the problem, the brain, which AI systems are meant to be modeled after, is essentially a black box. The inner workings of the brain are not easy to understand, this is due to the lack of information surrounding how the brain processes thought. There is no lack of research on how the biological structure of the human brain but there is a certain lack of empirical information on the functional qualities of the brain, that is, how thought is formed, how deja vu occurs, etc, and this leads to a problem in the building of brain-inspired AI systems. The Interdisciplinary Requirements The act of building brain-inspired AI systems requires the knowledge of experts in different fields, such as; Neuroscience, Computer Science, Engineering, Philosophy, and Psychology. But there lies a challenge there both logistical and foundational, in the sense that getting experts from different fields very financially tasking, and also there lies the problem of knowledge conflict; it is really difficult to get an engineer to care about the psychological effects of what he/she is building, not to talk of the problem of colliding egos. Summary In Conclusion, while the brain-inspired approach is the obvious route to building AI systems(we have discussed why), it is wrought with challenges but we can look to the future with the hope that efforts are being made to solve these problems. Also published here. References FreeCode Camp Machine Learning Tesla's Vehicle Safety Report Basic Neural Units of the Brain: Neurons, Synapses and Action Potential When Brain-inspired AI meets AGI Perceptron: The artificial Neuron (An Essential Upgrade To The McCulloch-Pitts Neuron) McCulloch-Pitts Neuron — Mankind’s First Mathematical Model Of A Biological Neuron BackPropagation through Time: What it does and How to do it The History of AI BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework

The Cognitive Paradigm: Exploring Brain-Inspired AI Development 

জ্ঞানীয় দৃষ্টান্ত: মস্তিষ্ক-অনুপ্রাণিত এআই বিকাশের অন্বেষণ

Das kognitive Paradigma: Erforschung der vom Gehirn inspirierten KI-Entwicklung

El paradigma cognitivo: exploración del desarrollo de IA inspirado en el cerebro

Le paradigme cognitif : explorer le développement de l'IA inspirée par le cerveau

संज्ञानात्मक प्रतिमान: मस्तिष्क-प्रेरित एआई विकास की खोज

認知パラダイム: 脳にヒントを得た AI 開発の探求

인지 패러다임: 뇌에서 영감을 받은 AI 개발 탐색

O Paradigma Cognitivo: Explorando o Desenvolvimento de IA Inspirado no Cérebro

Bilişsel Paradigma: Beyinden Esinlenen Yapay Zeka Gelişimini Keşfetmek

Mô hình nhận thức: Khám phá sự phát triển AI lấy cảm hứng từ não bộ

认知范式：探索受大脑启发的 AI 开发

Cognitive biases are systematic errors in our thinking, decision-making, and judgment processes caused by various factors such as heuristics, emotions, and social influences. These biases can significantly impact our investment decisions, leading to sub-optimal outcomes. This article will explore some common cognitive biases, their causes, and their potential impact on cryptocurrency investors. We will discuss practical strategies to mitigate their influence and improve investment decisions. Confirmation Bias Cause: Confirmation bias arises when we search for, interpret, and remember information confirming our pre-existing beliefs while ignoring or discounting contradictory evidence. Impact on Investors: Confirmation bias can lead crypto investors to selectively collect and accept information that supports their investment thesis, which may result in overconfidence and the inability to recognize red flags or warning signs in a project. Mitigation: Actively seek out information and perspectives that challenge your beliefs, and conduct a thorough and objective analysis of all relevant data before making investment decisions. Loss Aversion Cause: Loss aversion is the tendency to prefer avoiding losses over acquiring equivalent gains. This bias is caused by the emotional pain experienced when realizing losses, often stronger than the pleasure derived from gains. Impact on Investors: Loss aversion can cause crypto investors to hold onto losing investments for too long, hoping for a turnaround, or sell winners too early to avoid potential losses. Mitigation: Establish clear and objective investment exit criteria and consistently follow a disciplined risk management strategy. Anchoring Bias Cause: Anchoring bias occurs when we rely too heavily on initial information (the "anchor") when making decisions. This can be caused by our tendency to latch onto familiar reference points when faced with uncertainty. Impact on Investors: Anchoring bias can lead crypto investors to place undue importance on factors such as a token's initial price or historical price levels, causing them to make decisions based on irrelevant information. Mitigation: Focus on the fundamental value of a project, and avoid basing investment decisions solely on past performance or arbitrary price levels. Herd Mentality Cause: Herd mentality is the tendency to follow the actions or beliefs of the majority, often driven by the desire to conform or the fear of missing out. Impact on Investors: Herd mentality can cause crypto investors to chase market trends or participate in speculative bubbles, resulting in suboptimal entry points and increased risk exposure. Mitigation: Develop an independent investment strategy based on thorough research and analysis, and avoid making impulsive decisions based on market hype or fear of missing out. Overconfidence Bias Cause: Overconfidence bias occurs when we overestimate our abilities or the accuracy of our predictions, often due to a lack of self-awareness or the illusion of control. Impact on Investors: Overconfidence bias can lead crypto investors to take on excessive risk or fail to recognize potential challenges or uncertainties in their investment strategies. Mitigation: Regularly review and assess your investment performance, and seek external feedback to maintain a balanced perspective on your abilities and decision-making processes. Sunk Cost Fallacy Cause: The sunk cost fallacy arises when we continue to invest time, money, or resources into a project or decision based on the amount already invested rather than evaluating the current and future value of the investment. Impact on Investors: Crypto investors affected by the sunk cost fallacy may refuse to cut losses on poorly performing investments, leading to a more significant overall loss. Mitigation: Focus on an investment's present and future potential rather than dwelling on past investments, and be willing to reassess your portfolio and make changes when necessary. Recency Bias Cause: Recency bias is the tendency to give more weight to recent events or information when making decisions, often due to the ease with which we can recall and process recent experiences. Impact on Investors: Recency bias can cause crypto investors to base their decisions on short-term market fluctuations or recent news, which may lead to impulsive decision-making and an overemphasis on short-term performance. Mitigation: Focus on long-term trends and the underlying fundamentals of a project, and avoid making decisions based solely on recent events or short-term market movements. Availability Bias Cause: Availability bias occurs when we make decisions based on the information that is most easily accessible or readily available to us rather than conducting a comprehensive and objective analysis. Impact on Investors: Availability bias can lead crypto investors to overlook important information or potential risks, as they may not actively seek out or consider less readily available data. Mitigation: Actively seek out diverse sources of information and conduct thorough due diligence on every investment opportunity to ensure a well-rounded understanding of the project and its potential risks. Hindsight Bias Cause: Hindsight bias, also known as the "knew-it-all-along" effect, is the tendency to believe, after an event, that we would have predicted or expected the outcome. Impact on Investors: Hindsight bias can lead crypto investors to overestimate their ability to predict market movements and underestimate the role of chance, resulting in overconfidence and poor decision-making. Mitigation: Maintain a detailed investment journal to track your predictions and decision-making processes. Review it regularly to identify areas for improvement and maintain a balanced perspective on your predictive abilities. Gambler's Fallacy Cause: Gambler's fallacy is the belief that past events can influence future events, even when the circumstances are statistically independent, such as in a random sequence. Impact on Investors: Gambler's fallacy can cause crypto investors to make decisions based on perceived patterns or trends that may not exist, leading to misguided investment strategies and increased risk exposure. Mitigation: Recognize the inherently unpredictable nature of cryptocurrency markets and focus on long-term trends and project fundamentals rather than trying to predict short-term price movements. Mitigating the Impact of Cognitive Biases We can use these examples to propose general recommendations for dealing with cognitive biases. The first step in overcoming cognitive biases is recognizing and acknowledging their existence. By understanding the various biases impacting our decision-making, we can be better equipped to spot them in action and take steps to counteract their effects. Awareness: Continuously expanding your knowledge of cryptocurrency markets, investment principles, and cognitive biases can help you make more informed and objective decisions. Learning from the experiences of other investors and seeking out diverse perspectives can also help to minimize the impact of biases on your investment choices. Education: Regularly reviewing your investment decisions and reflecting on your thought processes can help you identify instances where cognitive biases may have influenced your choices. This self-assessment can also help you develop strategies for managing and mitigating these biases in the future. Reflection and self-assessment: Creating and adhering to a structured investment process, such as using checklists or predefined criteria for evaluating assets, can help minimize the impact of cognitive biases on your decision-making. By focusing on objective factors and avoiding emotional or impulsive choices, you can make more rational and effective investment decisions. Developing a systematic approach: Sharing your investment ideas and strategies with trusted peers or mentors can provide valuable feedback and help you recognize potential biases in your thinking. Engaging in open and constructive discussions can help you refine your investment approach and avoid falling prey to cognitive biases. Seeking external feedback: Conclusion Cognitive biases can significantly impact our decision-making processes and investment outcomes in the cryptocurrency space. By understanding these biases' causes and potential impacts, investors can develop strategies to mitigate their influence and improve their decision-making processes. Self-awareness, discipline, and a commitment to continuous learning are essential to successfully investing in cryptocurrencies' rapidly evolving world. You can also check my upcoming book for many guides that help you deal with cognitive biases. https://getcryptobook.com/?embedable=true I especially recommend it for people who want to: Invest in crypto using an evidence-based approach Use knowledge and experience from traditional finance Learn all investment aspects in one place Broaden their horizons by learning new things at the intersection of crypto and other fields

10 Cognitive Biases in Crypto Investing — Cause, Impact and Mitigation

A step-by-step Kickstarter to Power Virtual Agent and Azure Cognitive Service for Language (PVA) is getting better and better from Release to Release, but there are situations where you want to use to improve your user’s experience. Power Virtual Agent Azure Services Why not combine Azure Cognitive Services for Language with PVA to add missing elements? serving your customers and employees immediately in their native language without extra effort and without investing time in translating and maintaining your QnA in other languages. Imagine there is no need for additional resources, but your customers and employees will feel more directly addressed. Imagine your employees can refocus on delivering value to your customers instead of being a voice interface of a QnA collection. Microsoft Power Virtual Agents are perfect for automating customer service, and when combined with Azure Cognitive Services, they are almost infinitely extensible. Imagine A simple Power Automate flow helps your Power Virtual Agent serve your customers in over ninety languages. How easy is that? PVA has a built-in topic that can serve as a hook to the outside world, the “Fallback” system topic. Activate the “Fallback” topic To activate the “Fallback” system topic, go to Settings within the editing canvas (click on the cog symbol), then System fallback, and then “+Add”. After activating the topic, you will find it within “topics”. The “Fallback” topic is ready to connect with the outside world, as everything not recognized by our already in-place topics will end up here as “unrecognized user input”. /Extend Power Virtual Agent to a multilingual QnA Resolver If you have no PVA bot handy, try my Example as a starter. “Power Virtual Agent in a Day” This example will integrate a FAQ Database on “growing chilies”. Data from a random web page, “Greenhouse Garden Shop — will be used. Growing Chillies FAQ I´m not related to this shop but let´s respect their knowledge of growing chilies and use their questions and answers here!! Following my slogan: “Sriracha on everything!” Chilies are the main ingredient of . :) Sriracha Custom question answering We created a new project on and chose “Custom Question Answering”. Azure Language Studio Choose the option “I want to set the language for all projects created in this resource” > select English > Select Next. Enter a project name, “PVAinaDay-CQA”, a description, and leave the default answer with a setting of “No answer found”. Review your choices and select Create project From the Manage sources page, select Add source > URLs. Select Add URL, enter the following values, and then select Add all: URL Name URL Growing Chillies https://www.greenhousesensation.co.uk/growing-chillies-faq/ Language Studio will now import your FAQs into the project and gives you single and multiturn Question and Answer pairs. To use the imported content, we need to clean up the answers and make them usable for a bot. We must remove pictures and links if our bot is a voice bot. To make the Answering more fluent, we will add variations to each question. And deploy the knowledge base. After deploying the knowledge base, we press “Get prediction URL”, and we note down the Site URL(Red) and the Account Key (Yellow); we will use both later in Power Automate. Integrate Custom Question Answering and Microsoft Translator into Power Virtual Agent via Power Automate We jump back into your newly created “Fallback” Topic and remove the “Escalate” and the “Message” node. After deleting the nodes, we create a new “Call an action” node. Power Automate will open, and we can create a new flow. First, we rename the flow and define the input. To determine the language of the question, we will initialize a new variable, “QuestionLanguage”. We will use it to store the language code. Create a new node by searching “Microsoft Translator V2” and selecting “Detect language”, and set Text to the content of the variable “UnrecognisedUserInput”. We use a set Variable Node to store the output of “detect language” into the “QuestionLanguage” Variable with a translate text node (Microsoft Translator V2); we translate the content of “UnrecognizedUserInput” to English. Create a new node by searching “Language — Question Answering” and selecting “Generate answer from Project”. We noted the account Key and Site URL earlier in this guide; we use the values now. Set “Question” to the output of the translation node. Next, Initialize a new variable with a name, e.g., “CQA_Answer” A “Apply to each” Node with a “Translation” and a “set Variable” action will translate back to the language the question was asked in. Finally, we adjust the Return Value to the content of the “CQA_Answer” Variable. After saving the flow, we can select the flow in the action node, adjust the input, and create a message node to display the output of the flow. The flow will result in the following: A question asked to the bot without a topic in PVA will end up in the “Fallback” topic. Custom Question Answering will help with the answer, even to a question not asked in English. Additional Information Custom Question Answering — [successor of QnA Maker] documentation Conversational Language Understanding — if you would like to make the next step — [successor of LUIS] documentation Power Automate Connectors — documentation Featured image by on Unsplash. Sebastian Bill Also published here.

Automating Multilingual Customer Service with Power Virtual Agent and Azure Cognitive Services

You don't need to tell me that life is getting faster with every day that passes. As someone with a million different projects on the go (and enough aspirations to fill several lifetimes), I'm all too aware that time is slipping away. It's a precious and fleeting resource – but what if I told you we're wasting it without even realizing it? We've covered cognitive distortions here before: black-and-white thinking, catastrophizing, and the rest. I've noticed that a number of these distortions are particularly relevant to time. They warp our perception of it, leading to an unhealthy relationship with the clock that can have far-reaching consequences for our well-being, productivity, and creativity. Today I want to take a closer look at the cognitive distortions that stop us from making the most of our days – and how we can start challenging them for better results. Let's unpack them together. 1. All-Or-Nothing Reasoning Also called dichotomous thinking, this cognitive distortion is characterized by two extremes – it's either all good or all bad. I've got an example for this one I think you'll all relate to (even if you pretend not to!) Picture this: it's Monday morning after a pretty jam-packed weekend. You've got a healthy to-do list waiting for you; there are emails begging for responses and projects that need your attention. But when that alarm goes off, you accidentally hit 'snooze' and rest the morning away. It's midday by the time you wake up. Of course, if you started now, you'd still get a solid six or seven hours of work in, but all-or-nothing reasoning tells you that it's a total write-off; the day is ruined, and you might as well just go back to bed. I'm not totally sure what the logic behind this is. Well, that's the point – it's an irrational perspective, not a logical one. Imagine how many more hours we'd save by taking life an hour at a time, rather than compartmentalizing it into 24-hr blocks. 2. Overestimating (or Underestimating) Time Some would call this less of a distortion and more of a lapse in judgment. It certainly gets better with experience – but there's nothing quite so disarming as realizing you've underestimated or overestimated the amount of time it takes to complete a task. I don't know about you, but when I'm pressed for time and have a lot of tasks on my plate, I tend to do one of two things: either underestimate how long something will take me (and then get overwhelmed) or overestimate it (and end up feeling like I wasted valuable hours). Either way, it's not great news for productivity. To avoid this distortion, we need to focus on accuracy over speed – take the time to accurately estimate how long each project will take you before diving in. I've come up with a system for this, which is to break up my regular tasks into categories based on their complexity (for example: writing emails, writing blog posts, creating a website, etc.), and keep a record of how long they take each time. After a while, I've got enough data points to create averages for each category. It's a very simple solution – but sometimes simple is the most effective. 3. Present Bias We humans have been commenting on procrastination . It's a common thread between us all; to varying degrees, we value our present comfort over any potential future gain. since Ancient Greece This is what we call 'present bias'. Yes, you might have the most important investor call of your career in a few days' time – but it's easy to get distracted by the tasks that give us short-term gratification, like posting business social media content or replying to emails. It's the present-day stuff that makes us feel productive, even if they aren't the most pressing tasks. I've learned to be very aware of this bias, but it's not always obvious. I use Stephen Covey's Eisenhower Matrix to help me stay on track – you can read my . full write-up on it here 4. Time Confetti Who here is guilty of the five-minute doom scroll? The one where, during little time pockets throughout the day, you 'take a break' by filling your minutes with something low-value? For me, this often happens when I'm trying to procrastinate on something. I'll tell myself "I just need a five-minute break" – and then end up spending 30 minutes scrolling through Instagram or Twitter. It's time confetti; it's gone as soon as you spend it, but the effects last much longer than that. Confetti is so small – just a few minutes here and there – but it doesn't stay small for long. The scariest part? You never really notice just how much time you've wasted, because it's in such small chunks. 5. Parkinson's Law Just as gas will expand to fill a space, work expands to fill the time you give it. You've devoted three hours to a task? It'll take three hours to finish. This is Parkinson's Law – the idea that "work expands so as to fill the time available for its completion". Knowing this, it's important not to give yourself too much time for a task. The more 'wiggle room' you give yourself, the bigger the task will become in your mind, and the longer it will take you to complete it. There are obvious holes we can poke here. You can't set yourself 15 minutes for a detailed financial report and expect the end result to be perfect – and you probably won't take five hours to write one email, even if that's the time you've allowed yourself. But it's a useful concept if you understand what the implications are. It's less of a literal perspective and more of a wake-up call: our minds get very fixated on the fictional constraints and deadlines we set, and we can either let that reality take over or use it to our advantage. 6. Time Scarcity Mentality At the beginning of today's newsletter, I mentioned how quickly time seems to be flying past – and ironically, that's a prime example of time scarcity mentality. We've got it into our heads that, no matter how many hours there are in a day, we don't have enough. We feel rushed and hurried. We look at the years laid out before us and think, 'That's all? That's all I've got?' Of course, the tension at play here is that no one – not a single person on this planet – knows how much time they've got left. So in my mind, we've got two options: assume we're rich with time, or believe we are just moments away from running out. I know which I prefer. How to Combat Time-Waster Distortions As entrepreneurs, we're not in the business of wasting time. Every second wasted is a second we could've spent investing in our brainchild. Don't get me wrong – I'm not an advocate for workaholism, nor will I ever be. We need our rest. But there's a huge difference between rest and waste. Your best chance at combatting cognitive distortions is to observe which ones apply to you and how they're affecting your productivity. As you were reading through the list of distortions, did any make you feel particularly... attacked? It might be a good indication of the distortions you frequently engage with. For example: if you're typically an organized person, but your long-term deadlines always end up in a rush – you've got a Present Bias problem. If you spend hours laying out your calendar but always seem to be in a rush, it's probably a case of underestimation. The key to battling cognitive distortions is understanding them, and the best way to do that is by taking a step back and looking at how they're affecting you. Once you've identified which ones are causing problems in your life, it's just a case of consciously changing your behavior and working against them. Wrap Up Cognitive distortions will run our lives if we're not careful. Some people gain awareness of them through therapy, others through learning the hard way. The worst place to be is in a state of oblivion – and that's why I write articles like this. I encourage you to take some time reading through this list and figuring out exactly where all your time is draining away. You might have forty or fifty years ahead of you yet, but that doesn't make your time any less valuable. Use it for all it's worth! If you enjoyed this article, I’d love to hear from you. Reply to this email or at me and I'll do my best to get back to everyone! tweet @ScottDClary

These 6 Cognitive Distortions Are Wasting Your Time 

এই 6টি জ্ঞানীয় বিকৃতি আপনার সময় নষ্ট করছে

Diese 6 kognitiven Verzerrungen verschwenden Ihre Zeit

Estas 6 distorsiones cognitivas te están haciendo perder el tiempo

Ces 6 distorsions cognitives vous font perdre votre temps

ये 6 संज्ञानात्मक विकृतियाँ आपका समय बर्बाद कर रही हैं I

これらの 6 つの認知のゆがみはあなたの時間を無駄にしています

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