It is no longer just buses and trains, traffickers are increasingly taking the air mode to quickly shift victim girls and women from source to destination states.The issue took centre stage at a seminar on the problem of human trafficking organised by National Commission for Women and the Central Industrial Security Force in New Delhi

Need to step up airport vigil as traffickers ‘flying victims’ to destination states’

Delta Air Lines says the pilot accused of threatening to shoot the plane’s captain during a flight no longer works for the airline

Delta says pilot accused of threatening to shoot the captain no longer works for the airline

A first group of civilian evacuees from Gaza crossed into Egypt under a Qatari-mediated deal on Wednesday while Israeli forces bombed the Palestinian enclave from land, sea and air anew as they pressed their offensive against Hamas militants.

First evacuees leave Gaza after another night of Israeli bombardments

- An alphabet book and a notebook lie on top of an elevated wooden floorboard of a middle school in Let Yet Kone village in Tabayin township in the Sagaing region of Myanmar on Sept. 17, 2022, the day after an air strike hit the school.

Researchers hope tracking senior Myanmar army officers can ascertain blame for human rights abuses

The Houthi attacks will be hard to respond to from sea, and the naval version of Iron Dome is designed for self-defense or protecting a nearby ship'

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A recent alteration of an NCAA rule could put Harbaugh himself square in the association's crosshairs ... again

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On Oct. 31, the inspector general's office for the U.S. Department of Transportation confirmed that plane captain Jonathan J. Dunn has been indicted over an August 2022 incident in which he allegedly threatened to shoot his co-pilot over his decision to divert the plane because one of the passengers on board needed medical help.

A pilot reportedly threatened to shoot his co-captain in the air

It’s the worst nightmare for the people that have to plan an attack and for the people that have to execute it,” says Walker Mills, a Marine Corps captain who has studied and written about tunnel warfare.

The Tunnel War

It has not been an easy coaching transition at Mississippi State, but the Bulldogs have veteran quarterbacks and a tricky defense.

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Jonathan Dunn, a former Delta pilot, has been indicted in Utah following an incident in which he allegedly threatened to shoot a captain, officials say.

Former Delta pilot indicted after allegedly threatening to shoot captain during flight

airedefined

AIR is a Canadian startup focusing on a new AI training approach that allows the tackling of challenges that are too complex for traditional machine learning methods. We are doing this by building a toolset for AIs and people to learn continuously from each other. AIR developed Cogment, the world's first open-source framework to provide the means to train people and AI together, in multi-agent, multi-human, multi-rewards contexts. HackerNoon Reporter: Please tell us briefly about your  background. Dorian Kieken (Founder, President): Before founding the AI start-up AIR (formerly Age of Minds), I shipped 17 video games in multiple roles ranging from Design to Production, including the acclaimed games Mass Effect 2 (Game of the year 2010) and Mass Effect 3 (RPG of the year 2012). I co-founded the BioWare Montreal studio and helped it grow to 100+ people. I introduced and evolved agile production methodologies & tools to multiple companies, and drove the implementation of a distributed leadership/teal system. I'm also an active member of the Montreal AI ethics group and a strong believer in taking key actions today to nudge humanity towards a good future. Greg Szriftgiser (Co-founder, Design): I spent 20 years professionally studying, writing about, then finally designing and writing for the video games industry and its exploration of interactivity and dynamic systems. I became particularly interested in procedural storytelling (how to design intelligent systems able to create compelling narratives through various storytelling devices), which led to AI research and a passion for the struggles and achievements of machine learning as a way for humans to achieve more. François Chabot (Co-founder, Technology): I come from a computer engineering background with a specialization in artificial intelligence. Prior to co-founding AIR, I worked in video games (Capcom), mostly building AI and Gameplay systems in heavily resource-constrained scenarios, notably Dead Rising 2, real-time hardware-in-the-loop simulators (Opal-RT), and malware search (Google). I have a great interest in the relationship between people and machines, both when it comes to programmers and people using programs. Nowadays, I see myself as multidisciplinary glue that can bring various disparate systems together. Craig Vachon (CEO): After grad school, I couldn’t find a job, so I started a small company. I got extremely lucky and sold that company to a Japanese firm two years later. After a few more lucky professional experiences (and having lived as an expat for 12 years), I decided to spend my efforts helping fellow entrepreneurs solve important human challenges as an angel investor. I joined AIR because I like to think we’re tackling one of the more impactful issues that humanity faces—how humans will continue to benefit from/steer/trust AI. What's your startup called? And in a sentence or two, what does it do? It's called AIR, which stands for AI Redefined. We create technology for “steerable AI”, leveraging the advent of AI to the benefit of humankind through human-AI collaboration technology. Our open-source AI orchestration platform, Cogment, allows AI practitioners to design, train and deploy complex intelligent ecosystems, mixing multiple humans and artificial agents of various kinds to bring about results that are more context-aware (ethical, empathetic, explainable). What is the origin story? After enjoying great success in the game industry, and what was a dream-job for me with recognition in a fantastic studio, I felt motivation slipping away around 2014. It coïncided with the birth of my first daughter. I discovered that actually, this loss of motivation came from not working on something more meaningful that will help the next generation. Dorian: Among the many challenges this next generation will face (climate change, energy, water, etc.), I identified AI, and in particular, human/AI alignment, as the most important one that I could get involved in. I got strongly engaged in the Montreal AI Ethics group and ultimately, it led to the creation of AIR that is tackling this human/AI alignment problem. I met Dorian around the time I was diving into AI myself, through common friends, and we immediately clicked on several topics, as well as how we thought the AI conundrums could be tackled. Greg: We also shared this desire to see how we could leverage what we had learned in the video game industry to tackle something more impactful with regard to the various global challenges on the horizon. Throughout my career, I had the good fortune to be exposed to a number of fascinating, seemingly disparate domains and technologies. On top of that, trying my hand at the startup process had always been on my radar ever since I left university. Francois: So once I reached a point in my professional development where I was starting to feel confident enough in my skills to take a crack at it, I looked back at everything that had interested me and that I had become good at so far: The way humans connect and interact with machines, Simulations, Machine Learning, etc… and pondered how to bring it all together into something truly novel, interesting, and useful. During that process, I was introduced to Dorian, and the rest is history. I wasn’t a part of the origin of this company. I joined this year (2021) to help productize and accelerate the adoption of the Team’s amazing technology. Craig: What do you love about your team, and why are you the ones to solve this problem? The dual expertise in both the world of AI and the world of simulation/video games (human-AI interaction) makes for a very unique team of people with expertise in more than one area. It's something that makes the team especially well-suited for the problem we tackle because it needs to be tackled from multiple points of view. Dorian: Not just the AI point of view which centers around technology, rewards, methods of training, but also from the environment where agents evolve and interact with each other and with human users. One parallel that comes to mind is the birth of CGI animated movies, which required people with dual skill sets in Art/animation and computer science to truly blossom. Beyond the multiple skill sets people have in the team, there is also a very important cultural aspect in my opinion, of a human-centered perspective on the world. Emotional maturity, going hand in hand with a very high prevalence in each and every member of AIR's team of an almost child-like curiosity in diverse topics makes for a very particular blend of talents and personalities to tackle this long-term vision of synergy between humans and AI. Greg: The team is mission-driven and diverse in terms of its skill sets and expertise. They fully understand and appreciate the opportunity we’ve been given to ensure ‘humans and AI elevate each other.’ Craig: If you weren’t building your startup, what would you be doing? Dorian: Probably finding another way to nudge the future of AI and humans in a good direction. Maybe through another existing company like DeepMind? Or an organization like the Montreal AI Ethics Institute? Dorian: Probably something related to Video Games, Interaction Design, and/or Storytelling, I suppose, but with the missing excitement of working on a truly transformative technology with a mission bigger than myself. Greg: I would probably be doing something very similar: Tackling hard problems that affect how people relate to technology in some way, and doing so in a healthy collaborative environment. The startup is a means to this end for me. I’m not big on entrepreneurship for the sake of entrepreneurship. François: Helping other great founders pursue worthwhile goals. Craig: At the moment, how do you measure success? What are your core metrics? In the long term, on how human-centric the training of AIs becomes, and how successfully we move past the narrow "AI behaving without context" era we are currently in, to reach towards AI with a broader understanding of context. Dorian: In the short term, it's about the scope and type of collaborations our open-source platform enables, and our ability to generate license-based revenue from customers that value context/steerability/trust from their AI results. Craig: What’s most exciting about your traction to date? The collaborations we have with CRL (Chandar Research Lab at Mila) on Hanabi and the University of Alberta on complex multi-agent problems come to mind. Dorian: From a more general perspective, I'm very excited to see more and more thought pieces, actual work, and studies popping up and coming to similar conclusions we did: that by designing intelligent systems with human participation from the get-go, issues like bias, trust, or explainability can be addressed at the same time as the ability for those systems to achieve better results, thanks to the complementarity between the different strengths humans and AI bring to the table. Greg: : One of our aerospace customers admitted we had made him a hero as we accelerated his roadmap in AI by two full years. (How often do you hear that from the head of a Business Unit?) Craig What technologies are you currently most excited about, and most worried about? And why? AI in general. I'm excited about how it can have a positive impact on humanity, and I’m worried about how this impact could also be negative. Dorian: There is no doubt in anyone's mind, I think, that AI can help us solve very important issues, and bring us much-needed help; but used poorly, it can also exacerbate those issues or even create new ones. It's really similar, I think, to any groundbreaking shift of the technological paradigm in which, or with which, we build our societies. Many people will probably talk about the Internet as the most recent example of a profoundly world-changing technological paradigm shift, and they would be right, but even though we now have around 3 decades of hindsight, I think it's fair to say the jury is still out on whether our societies are better for it or not. Greg: AI will probably impact us at least similarly, if not much more profoundly; that is why I think we need to actively work at making it more accessible to everyone, but also more human-centric. I am excited about the future of how we express what we want computer systems to do. Technologies that are being widely deployed right now feel like we are dipping our toes towards expressing intent in a more semantically meaningful manner. Francois: The massive rise of Python as a programming language and Kubernetes as a deployment platform are but baby steps in that general direction. I am convinced the next generation of “telling machines what we want from them” will open the door to some truly amazing things, and that this is coming a lot sooner than most people would expect. As far as worries go, I am particularly concerned about our willingness to deploy socially disruptive systems based on technologies for which we only have proof of concepts exhibiting critical flaws with the assumption that functional implementations are within our grasp. Image-driven emotion detection and blockchain are particularly egregious examples of this in my mind. Today’s narrow AI provides only the thinnest veneer of humanity. And yet, without an iota of context, common sense, empathy, or morality, we want #AI to solve significant human problems. We will do better (with ). Craig: Cogment.AI What drew you to get published on HackerNoon? What do you like most about our platform? Getting nominated as a Startup of the Year was a very nice surprise! HackerNoon offers a very open, accessible platform for people to learn and share knowledge, with accessible tools everyone can use from anywhere; in that sense, it aligns pretty well with how we think about what the future of AI should be :) What advice would you give to the 21-year-old version of yourself? I was already driven by passion back then. Probably something along the lines of "a healthy mind in a healthy body". Something I only learned in my late twenties. Dorian: Better care of the body (training, food, etc…) leads to better sleep which, in return, gives you more time for all the cerebral activities. "Pay more attention to the irrational and the way humans behave, I swear it's fascinating!" Oh, and, I'd also tell myself: Greg: "sleep more, you idiot, stop pulling multiple all-nighters in a row, circadian rhythm is more important than you think". Don’t second-guess yourself so much. The shame of being proven wrong is only fleeting, but the missed opportunities often don’t come back. Francois: Say “yes” more often. The best adventures, (plus laughter/learning) always start by saying, yes. Craig: What is something surprising you've learned this year that your contemporaries would benefit from knowing? From a business standpoint, that the scaling potential of a business and its recurring licensing revenue is more important to investors than the number of revenues! Also, from a cultural perspective, how much face-to-face human contact is important, particularly after the past year of Covid-19 isolation. We are social creatures more than ever. Dorian: The idea in itself is no surprise to me, but the extent to which it verifies surprises me regularly: that what reality is or could be is irrelevant in the face of what human culture dictates it is or should be. I am also surprised by how much more fragmented and inward-facing culture seems to become in the age of the so-called globalization of information. Greg: Learning to skateboard as an adult feels like rebelling against the rules of physics themselves. The sequence of events required for it to barely function is unlikely. Everything about the process is an unstable equilibrium. Yet here you are, spitting in the face of Newton’s third law. Francois: Two things: Hairy, audacious goals are life-affirming; And the sequel (of the comedic spy thriller I published last year) isn’t as easy as it ought to be. Craig: Don’t forget to vote for R as Startup of the Year in AI Montreal.

Tackling a new Human-Centric AI  Paradigm: Startup Interview with AIR Cofounders

AWS AI Services Scratch Extension In my previous , I have mentioned that we should prepare our children for the future. In the future, to succeed our children should have skills like creative thinking and problem solving. STEM education can improve this skills. post According to MIT Professor , coding is the new literacy. A visual programming language called could stimulate the curiosity we know it exists in the children from the day they have born. You can read stories about how it changes children’s lives , and . Mitchel Resnick Scratch here here here As I am working with , I have been wondering how we could teach AI to our children easily. Then I came across , an extension framework to Scratch for adding new functionality to Scratch. With ScratchX, you can write extensions to integrate Scratch with anything you imagine. There are sample extensions for , , and . AWS AI services ScratchX LEGO WeDo Arduino Raspberry Pi Then I thought I can make a Scratch extension for using AWS AI services like Polly and Translate. And it is . here You can try sample project that uses Amazon Polly to speak the text you have entered in the language you choose. Also you can translate text between languages and speak the translated text in target language. here Below is a video recording of the sample project. Developing The Extension If you wonder how the extension is developed, let’s develop the extension step by step together. A minimal Scratch Extension is created like below. After creating the extension object, we should register it by calling ScratchExtensions.register with the name of the extension and a descriptor which defines the blocks that will be created. For more information on making Scratch Extensions, see . here We can develop our extension in three steps: Create the descriptor Implement the methods Try the extension in ScratchX Let’s start. Create the descriptor The descriptor defines the blocks that will be created after the extension is registered. Created blocks will be shown in ‘More Blocks’ section of ScratchX. The blocks of our extension are defined as below. First block is for initializing the AWS JavaScript SDK in the browser. Amazon Polly blocks are the next two blocks. And last blocks are for Amazon Translate service. Parameters of blocks aredefined as ‘say %s’. We can use lists to choose parameters from with ‘%m.sourceLanguages’ syntax, where ‘%m.’ says that we want to use a list and sourceLanguages shows the name of the list. In ScratchX lists are known as menus and menus are defined with ‘menus’ field. Blocks of our extension will look like below. Blocks are shown in ‘More Blocks’ section 2. Implement the methods After we defined our blocks, we will implement the methods that will be executed when blocks are used. For more information on method details, you can look . Usage of AWS services in the browser with AWS JavaScript SDK is explained in detail in . extension JavaScript file my previous blog post 3. Try the extension in ScratchX In order to try our extension, we should load it from a URL. I recommend for this. MiniWeb HTTP Server After you can run the server in your directory at 8080 port, you can try it in your browser by visiting the address below. I recommend Google Chrome browser, ScratchX is not working in Firefox. IE will not load the extension from http: url, although it works over https: url. https://scratchx.org/?url=http://localhost:8080/awsAiServicesScratchExtension.js When ScratchX warned you, click ‘I understand, continue’ to load and try the extension. Summary In this post, I have shown how a Scratch Extension can be created easily for using AWS AI services in Scratch. You can find the code . For more information on making Scratch Extensions, see . here here For more information on how AWS AI services can be used in the browser using AWS JavaScript SDK, please see my previous blog post: You Can’t Fail To Learn A New Language With These AWS AI Services: Translate, Rekognition, Polly Also, you might like my other post about creating a serverless app for saving allergy information with the picture of children and searching the allergy information later with the picture of the children by using face recognition methods of Amazon Rekognition: Serverless Allergy Checker with Amazon Rekognition, Lex, Polly, DynamoDB, S3 and Lambda I will continue to write about different use cases of AWS AI service. I will add object and text detection features to the extension using Amazon Rekognition. I am developing a Chrome Browser Extension for reading the selected text in the page you are viewing using Amazon Polly and detecting Authy authentication codes from the camera using Amazon Rekognition. Please stay tuned or follow. If you like this post, please share, clap or follow. Thanks for reading.

AI Is Hard? It’s Child’s Play With This AWS AI Services Scratch Extension

In a world where decentralized finance and digital nomadism are redesigning ways of life, "crypto nomads" could well be the newest breed that depends on blockchain and digital cryptocurrency to live and work in any part of the world. These pioneering spirits are part of fully decentralized structures, working on projects across borders without barriers, and again defining financial freedom. Who Are Crypto Nomads? Crypto nomads, a term derived from the combination of cryptocurrency enthusiasts and digital nomads, are individuals who work remotely but are financially supported by crypto assets, DeFi investments, or blockchain-based income. Crypto nomads work remotely, but for them, cryptocurrency and blockchain are the backbones of their professional and financial activities. Most of the time, one will be dealing in professions related to blockchain development, cryptocurrency trading, consultancy, and even project management by utilizing digital assets and DeFi. Besides traditional digital nomads who work remotely though bound through centralized financial systems, crypto nomads depend on decentralized systems through which they manage finances and pursue opportunities anywhere with ease in the world. Key Features of Crypto Nomadism Include: Borderless Work: This gives them, in turn, the evasion of national border or financial regulations, making global transactions and collaborations seemingly so much easier. Financial Independence: They are confident above all in cryptocurrencies, sidestepping banks, and systems of traditional money transfer. This decreases fees and simultaneously simplifies international payments. Decentralized Communities: Crypto nomads unite through DAOs, which is a way for them to work on projects flexibly and co-creatively. The growing significance of DAOs is that they are often touted as the future of work. Co-creation Within The Crypto Nomad Lifestyle Co-creation is integral to the crypto-nomad community. Co-creation, in a nutshell, is a process of collaboration and innovation by people or organizations through decentralized networks and DAOs rather than business hierarchies. In the crypto space, this means shared ownership and community-driven project development. One quintessential example is the DAOs, which allow participants to make collective decisions, contribute to skill-based decisions, and get rewarded via governance tokens. Key Benefits of Co-creation For Crypto Nomads: Collaborative Innovation: Participating in DAOs would mean crypto nomads are able to work on blockchain projects without any central body, hence facilitating cross-border innovation. Financial Inclusion: Through the use of the blockchain, they get involved in low-cost peer-to-peer transactions, hence getting involved with projects around the world and reducing their dependence on conventional systems of finance. How Crypto Nomads Are Redefining Work And Income The crypto nomad lifestyle seeks to reshape work and income by leveraging decentralized systems outside of the traditional models of work and employment. By this, professionals are allowed to work across borders, manage finances independently, and take up project-based jobs through DAOs. Unlike conventional jobs, which are generally confined to specific geography and corporate hierarchies, crypto nomads have been able to enjoy flexibility and freedom in work. The newly redefined work structure includes: Cross-border transaction: It eliminates the need for wire transfers and currency conversion. It therefore makes international financial transactions easier. Investment and financial independence: Crypto nomads can lend, borrow, and invest directly in DeFi solutions without any intermediaries; thus, it allows full control over one's finances. Fluid involvement in projects within DAOs: The crypto-nomads are allowed to work on projects in the DAO on flexible terms and get paid with governance tokens that are reward-enabling, carrying the extra value of decision-making on the future of the project. Impact on Local Economies and Community Building Cryptonomads add value, both economically and socially, to the communities they belong to by building digital nomad communities in Bali, Lisbon, and Dubai. Finally, coworking spaces, a strong internet connection, and visa-friendly environments make these hubs a core of economic activities: their presence boosts local hospitality, tourism, and technology industries, opening new possibilities for local businesses and society in general. Local Effects of Crypto Nomadism: Local economic boost: Crypto nomads pay for local services, boosting hospitality and tourism, and driving tech entrepreneurship. Cultural Exchange and Innovation: Diverse ideas and skills moved forward by nomads enrich the cultures of residents and encourage innovation due to higher receptiveness to novelty. Some governments, like Estonia, go so far as to offer special visas for digital nomads with the goal of attracting them as a resource for local economic growth. The Role of Crypto Nomad Communities and DAOs in Global Collaboration Crypto nomads build a community based on shared interests, values, and blockchain-based interactions. In this line, DAOs have become powerful ways of belonging and mutual support since members of such organizations can contribute anytime and from any part of the world. As opposed to traditional concepts of community, crypto nomad groups represent the principles of inclusiveness, trust, and co-ownership, hence fostering collaboration across projects and countries. Other Benefits of These Communities Include: Equitable Collaboration: Blockchain's transparency and permissionless design create the grounds for trusting, peer-to-peer collaboration. Social Contribution: The majority of crypto nomads are purpose-driven individuals ecologically seeking opportunities for volunteering and community engagements in the destinations they travel to. Crypto Nomadism: Its Challenges and the Way Forward While a decentralized, borderless lifestyle is romantic, crypto nomadism faces difficulties regarding regulatory uncertainties, fluctuating crypto values, and the instability of decentralized economies. Other common challenges are navigating complicated rules for visas, accessing consistent healthcare, and finding reliable connections to the internet. The future trends entail: Regulatory Developments: Accompanying increased mainstream adoption of crypto is that governments might also begin to adopt friendlier policies towards crypto nomads. Improved Security, Speed, and Innovation in Blockchain Products: Advances in blockchain technology could make blockchain networks more resistant to hacking and faster and more advanced financial products developed on the blockchain. The development of decentralized finance could be stabilized by such a development and, therefore, support the growth of the crypto nomad lifestyle. Case Study: House of Web3 The House of Web3, founded in 2022 by Edge & Node, is an outstanding example of a co-creation hub for internet nomads working in AI, blockchain and decentralized technology. Described as the modern-day” town hall”, it has welcomed over 10,000 attendees and has over 750 approved co-creators including developers, founders and VCs from more than 300 companies around the world. It serves as a work space and a community where digital nomads, developers, founders, and venture capitalists can connect, collaborate, and learn from one another. The space has hosted thousands of events, ranging from technical workshops to high-profile discussions with San Francisco's mayor and mayoral candidates, positioning it as a platform for addressing real-world issues through emerging technologies. Two years since its launch, it has fostered collaborations between blockchain and AI pioneers, and nurtured  a thriving community of crypto nomads. Conclusion: Crypto nomads rethink notions of work, community, and financial freedom through blockchain and decentralized finance to operate without borders. They also collaborate on international projects through DAOs, enabling a co-creative culture to support shared ownership in furthering innovation and financial inclusion. Crypto nomads have contributed culturally and economically to the communities in which they set up hubs in Bali, Lisbon, and elsewhere. Indeed, this somewhat strange marriage between global collaboration and local impact is what makes this lifestyle so different from most other modern prototypes. In a world where decentralized finance and digital nomadism are redesigning ways of life, "crypto nomads" could well be the newest breed that depends on blockchain and digital cryptocurrency to live and work in any part of the world. These pioneering spirits are part of fully decentralized structures, working on projects across borders without barriers, and again defining financial freedom. Who Are Crypto Nomads? Crypto nomads, a term derived from the combination of cryptocurrency enthusiasts and digital nomads, are individuals who work remotely but are financially supported by crypto assets, DeFi investments, or blockchain-based income. Crypto nomads work remotely, but for them, cryptocurrency and blockchain are the backbones of their professional and financial activities. Most of the time, one will be dealing in professions related to blockchain development, cryptocurrency trading, consultancy, and even project management by utilizing digital assets and DeFi. Besides traditional digital nomads who work remotely though bound through centralized financial systems, crypto nomads depend on decentralized systems through which they manage finances and pursue opportunities anywhere with ease in the world. Key Features of Crypto Nomadism Include: Borderless Work: This gives them, in turn, the evasion of national border or financial regulations, making global transactions and collaborations seemingly so much easier. Borderless Work: Financial Independence: T hey are confident above all in cryptocurrencies, sidestepping banks, and systems of traditional money transfer. This decreases fees and simultaneously simplifies international payments. Financial Independence: T Decentralized Communities: Crypto nomads unite through DAOs, which is a way for them to work on projects flexibly and co-creatively. The growing significance of DAOs is that they are often touted as the future of work. Decentralized Communities: Co-creation Within The Crypto Nomad Lifestyle Co-creation is integral to the crypto-nomad community. Co-creation, in a nutshell, is a process of collaboration and innovation by people or organizations through decentralized networks and DAOs rather than business hierarchies. In the crypto space, this means shared ownership and community-driven project development. One quintessential example is the DAOs, which allow participants to make collective decisions, contribute to skill-based decisions, and get rewarded via governance tokens. Key Benefits of Co-creation For Crypto Nomads: Collaborative Innovation: Collaborative Innovation: Participating in DAOs would mean crypto nomads are able to work on blockchain projects without any central body, hence facilitating cross-border innovation. Financial Inclusion: Financial Inclusion: Through the use of the blockchain, they get involved in low-cost peer-to-peer transactions, hence getting involved with projects around the world and reducing their dependence on conventional systems of finance. How Crypto Nomads Are Redefining Work And Income The crypto nomad lifestyle seeks to reshape work and income by leveraging decentralized systems outside of the traditional models of work and employment. By this, professionals are allowed to work across borders, manage finances independently, and take up project-based jobs through DAOs. Unlike conventional jobs, which are generally confined to specific geography and corporate hierarchies, crypto nomads have been able to enjoy flexibility and freedom in work. The newly redefined work structure includes: Cross-border transaction: Cross-border transaction: It eliminates the need for wire transfers and currency conversion. It therefore makes international financial transactions easier. Investment and financial independence: Investment and financial independence: Crypto nomads can lend, borrow, and invest directly in DeFi solutions without any intermediaries; thus, it allows full control over one's finances. Fluid involvement in projects within DAOs: Fluid involvement in projects within DAOs: The crypto-nomads are allowed to work on projects in the DAO on flexible terms and get paid with governance tokens that are reward-enabling, carrying the extra value of decision-making on the future of the project. Impact on Local Economies and Community Building Cryptonomads add value, both economically and socially, to the communities they belong to by building digital nomad communities in Bali, Lisbon, and Dubai. Finally, coworking spaces, a strong internet connection, and visa-friendly environments make these hubs a core of economic activities: their presence boosts local hospitality, tourism, and technology industries, opening new possibilities for local businesses and society in general. Local Effects of Crypto Nomadism: Local economic boost: Local economic boost: Crypto nomads pay for local services, boosting hospitality and tourism, and driving tech entrepreneurship. Cultural Exchange and Innovation: Cultural Exchange and Innovation: Diverse ideas and skills moved forward by nomads enrich the cultures of residents and encourage innovation due to higher receptiveness to novelty. Some governments, like Estonia, go so far as to offer special visas for digital nomads with the goal of attracting them as a resource for local economic growth. The Role of Crypto Nomad Communities and DAOs in Global Collaboration Crypto nomads build a community based on shared interests, values, and blockchain-based interactions. In this line, DAOs have become powerful ways of belonging and mutual support since members of such organizations can contribute anytime and from any part of the world. As opposed to traditional concepts of community, crypto nomad groups represent the principles of inclusiveness, trust, and co-ownership, hence fostering collaboration across projects and countries. Other Benefits of These Communities Include: Equitable Collaboration: Equitable Collaboration: Blockchain's transparency and permissionless design create the grounds for trusting, peer-to-peer collaboration. Social Contribution: Social Contribution: The majority of crypto nomads are purpose-driven individuals ecologically seeking opportunities for volunteering and community engagements in the destinations they travel to. Crypto Nomadism: Its Challenges and the Way Forward While a decentralized, borderless lifestyle is romantic, crypto nomadism faces difficulties regarding regulatory uncertainties, fluctuating crypto values, and the instability of decentralized economies. Other common challenges are navigating complicated rules for visas, accessing consistent healthcare, and finding reliable connections to the internet. The future trends entail: The future trends entail: Regulatory Developments: Regulatory Developments: Accompanying increased mainstream adoption of crypto is that governments might also begin to adopt friendlier policies towards crypto nomads. Improved Security, Speed, and Innovation in Blockchain Products: Improved Security, Speed, and Innovation in Blockchain Products: Advances in blockchain technology could make blockchain networks more resistant to hacking and faster and more advanced financial products developed on the blockchain. The development of decentralized finance could be stabilized by such a development and, therefore, support the growth of the crypto nomad lifestyle. Case Study: House of Web3 The House of Web3, founded in 2022 by Edge & Node, is an outstanding example of a co-creation hub for internet nomads working in AI, blockchain and decentralized technology. Described as the modern-day” town hall”, it has welcomed over 10,000 attendees and has over 750 approved co-creators including developers, founders and VCs from more than 300 companies around the world. It serves as a work space and a community where digital nomads, developers, founders, and venture capitalists can connect, collaborate, and learn from one another. The space has hosted thousands of events, ranging from technical workshops to high-profile discussions with San Francisco's mayor and mayoral candidates, positioning it as a platform for addressing real-world issues through emerging technologies. Two years since its launch, it has fostered collaborations between blockchain and AI pioneers, and nurtured  a thriving community of crypto nomads. Conclusion: Crypto nomads rethink notions of work, community, and financial freedom through blockchain and decentralized finance to operate without borders. They also collaborate on international projects through DAOs, enabling a co-creative culture to support shared ownership in furthering innovation and financial inclusion. Crypto nomads have contributed culturally and economically to the communities in which they set up hubs in Bali, Lisbon, and elsewhere. Indeed, this somewhat strange marriage between global collaboration and local impact is what makes this lifestyle so different from most other modern prototypes.

The Rise Of Crypto Nomads: How Co-Creation Is Redefining Global Work And Community

Most online transactions, in fiat and crypto alike, go through their own process in the background between sending and approval. In traditional banking systems, a transaction is considered final once it’s fully processed by all involved financial institutions —and yet, it still can be reversed for any reason. This doesn’t happen with most cryptocurrencies, where transactions aim to be immutable. Transaction finality in crypto refers to the point at which a transaction is considered permanent and irreversible on the network. Once a transaction reaches finality, it cannot be undone or changed, ensuring that the funds are with the intended recipient or the data involved is undoubtedly on chain. This assures users that they can safely spend the received funds. Of course, not all cryptos are built the same, and they all have different mechanisms to reach their own level of transaction finality. Deterministic vs. Probabilistic You’ll likely find several types of transaction finality in crypto, but they could be reduced to deterministic or probabilistic. Deterministic finality ensures that once a transaction is confirmed or stable, it is final and irreversible. Platforms like Obyte or some Proof-of-Stake (PoS) networks offer this type of finality, where their internal mechanisms make it impossible for a confirmed transaction to be undone, providing a higher level of security and certainty for users. In contrast, probabilistic finality occurs when a transaction becomes more secure over time but could theoretically be reversed, especially shortly after it was broadcast to the network. For instance, in Bitcoin, a transaction with just one confirmation is less secure than one with six confirmations. This is because, early on, there’s a small chance that a miner could create an alternative chain with enough mining power, effectively reversing the transaction. A practical example of this is a 51% attack, where an entity controlling the majority of the network’s hashing power could rewrite recent blocks, potentially reversing or invalidating previous transactions. This scenario is unlikely, though, because the costs often exceed the potential reward. However, in theory, it’s still possible to reverse transactions on probabilistic blockchains, especially if they’re small. Finality Matters In the digital world, almost everything can be copied and pasted, and that was a problem in creating electronic money for many years. Cryptocurrencies brought their own cryptographic and decentralized system to prevent exactly that. Without finality, someone could spend their crypto twice (double-spending), undermining the entire system’s integrity. By guaranteeing that once a transaction is confirmed, it’s recorded permanently on the chain, finality eliminates this risk, creating a reliable framework for the exchange of digital assets. This is why transaction finality matters, in the first place. In the field of smart contracts, finality also plays a critical role. Smart contracts are automated agreements that execute based on predefined conditions, and their outcomes rely on the immutability of their network —without middlemen. Finality ensures that once a smart contract executes, its results are permanent, meaning the terms of the agreement are fulfilled without the possibility of reversal. This is essential for maintaining trust in smart contract-based applications, where the stakes are high, and participants need certainty that agreements will be honored. For decentralized applications (Dapps), transaction finality is key to maintaining the security and reliability of their operations. When users interact with Dapps, they need to know that their actions and transactions are irreversible once confirmed. Finality guarantees this, ensuring that the outcomes of Dapp interactions are trustworthy. Stable Transactions in Obyte Obyte is a decentralized network that uses a Directed Acyclic Graph (DAG) structure instead of a blockchain, allowing for more decentralization. Unlike blockchains, where a small group of block producers can dominate and even censor transactions, Obyte eliminates these central power structures. Its DAG model ensures that no single entity can control the network, creating a system where there aren’t middlemen between transaction sending and transaction approval. In terms of transaction finality, Obyte reaches deterministic finality once a transaction is deemed stable, meaning it cannot be reversed —ever. This is achieved by gradually building a Main Chain (MC) through transactions posted by Order Providers (OPs—nodes with guiding transactions). As new transactions are added to the network, each user tracks their own current MC based on the latest transactions they have received. While the recent part of the MC might change as new transactions are received, its old part, beyond some point, is stable, meaning that it will never change no matter what new transactions are received. This point is called the stability point. A transaction becomes final and irreversible once the stability point moves ahead of the transaction. This happens when enough “weight” from OPs is placed on the older part of the MC and any competing branches lose their chances of redirecting that part of the MC. Therefore, after reaching the stability point, a transaction is considered fully confirmed and cannot be reversed, ensuring deterministic finality, as opposed to the probabilistic one on some well-known blockchains, including Bitcoin. As a result, users can enjoy the benefits of a whole crypto ecosystem including smart contracts, Decentralized Finance (DeFi) apps, Autonomous Agents (AA), customized tokens, and more features, all with integrated deterministic finality. Ready to explore it? Featured Vector Image by vectorjuice / Freepik Most online transactions, in fiat and crypto alike, go through their own process in the background between sending and approval. In traditional banking systems, a transaction is considered final once it’s fully processed by all involved financial institutions —and yet, it still can be reversed for any reason. This doesn’t happen with most cryptocurrencies, where transactions aim to be immutable. Transaction finality in crypto refers to the point at which a transaction is considered permanent and irreversible on the network. Once a transaction reaches finality, it cannot be undone or changed, ensuring that the funds are with the intended recipient or the data involved is undoubtedly on chain. This assures users that they can safely spend the received funds. Transaction finality in crypto refers to the point at which a transaction is considered permanent and irreversible on the network. Of course, not all cryptos are built the same, and they all have different mechanisms to reach their own level of transaction finality. Deterministic vs. Probabilistic You’ll likely find several types of transaction finality in crypto, but they could be reduced to deterministic or probabilistic. Deterministic finality ensures that once a transaction is confirmed or stable, it is final and irreversible. Platforms like Obyte or some Proof-of-Stake (PoS) networks offer this type of finality, where their internal mechanisms make it impossible for a confirmed transaction to be undone, providing a higher level of security and certainty for users. types of transaction finality types of transaction finality Obyte Obyte In contrast, probabilistic finality occurs when a transaction becomes more secure over time but could theoretically be reversed , especially shortly after it was broadcast to the network. For instance, in Bitcoin, a transaction with just one confirmation is less secure than one with six confirmations. This is because, early on, there’s a small chance that a miner could create an alternative chain with enough mining power, effectively reversing the transaction. In contrast, probabilistic finality occurs when a transaction becomes more secure over time but could theoretically be reversed A practical example of this is a 51% attack, where an entity controlling the majority of the network’s hashing power could rewrite recent blocks, potentially reversing or invalidating previous transactions. This scenario is unlikely, though, because the costs often exceed the potential reward. However, in theory, it’s still possible to reverse transactions on probabilistic blockchains, especially if they’re small. the costs the costs Finality Matters In the digital world, almost everything can be copied and pasted, and that was a problem in creating electronic money for many years. Cryptocurrencies brought their own cryptographic and decentralized system to prevent exactly that. Without finality, someone could spend their crypto twice (double-spending), undermining the entire system’s integrity. By guaranteeing that once a transaction is confirmed, it’s recorded permanently on the chain, finality eliminates this risk, creating a reliable framework for the exchange of digital assets. This is why transaction finality matters, in the first place. By guaranteeing that once a transaction is confirmed, it’s recorded permanently on the chain, finality eliminates this risk, creating a reliable framework for the exchange of digital assets. In the field of smart contracts , finality also plays a critical role. Smart contracts are automated agreements that execute based on predefined conditions, and their outcomes rely on the immutability of their network —without middlemen. Finality ensures that once a smart contract executes, its results are permanent, meaning the terms of the agreement are fulfilled without the possibility of reversal. This is essential for maintaining trust in smart contract-based applications , where the stakes are high, and participants need certainty that agreements will be honored. smart contracts smart contracts smart contract-based applications smart contract-based applications For decentralized applications (Dapps), transaction finality is key to maintaining the security and reliability of their operations. When users interact with Dapps, they need to know that their actions and transactions are irreversible once confirmed. Finality guarantees this, ensuring that the outcomes of Dapp interactions are trustworthy. Stable Transactions in Obyte Obyte is a decentralized network that uses a Directed Acyclic Graph (DAG) structure instead of a blockchain, allowing for more decentralization. Unlike blockchains, where a small group of block producers can dominate and even censor transactions, Obyte eliminates these central power structures. Its DAG model ensures that no single entity can control the network, creating a system where there aren’t middlemen between transaction sending and transaction approval. Obyte Obyte In terms of transaction finality, Obyte reaches deterministic finality once a transaction is deemed stable, meaning it cannot be reversed —ever. This is achieved by gradually building a Main Chain (MC) through transactions posted by Order Providers (OPs—nodes with guiding transactions). As new transactions are added to the network, each user tracks their own current MC based on the latest transactions they have received. In terms of transaction finality , Obyte reaches deterministic finality once a transaction is deemed stable, meaning it cannot be reversed —ever. transaction finality transaction finality While the recent part of the MC might change as new transactions are received, its old part, beyond some point, is stable, meaning that it will never change no matter what new transactions are received. This point is called the stability point. A transaction becomes final and irreversible once the stability point moves ahead of the transaction. This happens when enough “weight” from OPs is placed on the older part of the MC and any competing branches lose their chances of redirecting that part of the MC. Therefore, after reaching the stability point, a transaction is considered fully confirmed and cannot be reversed, ensuring deterministic finality, as opposed to the probabilistic one on some well-known blockchains, including Bitcoin. As a result, users can enjoy the benefits of a whole crypto ecosystem including smart contracts, Decentralized Finance (DeFi) apps, Autonomous Agents (AA), customized tokens, and more features, all with integrated deterministic finality. Ready to explore it? Ready to explore it? Ready to explore it? Featured Vector Image by vectorjuice / Freepik Featured Vector Image by vectorjuice / Freepik Freepik Freepik

Educational Byte: What Is Transaction Finality in Crypto, and Why Does It Matter?

Opvoedkundige byte: wat is transaksiefinaliteit in Crypto, en waarom maak dit saak?

Навучальны байт: што такое канчатковасць транзакцыі ў крыптаграфіі і чаму гэта важна?

Εκπαιδευτικό Byte: Τι είναι το Τελικό Συναλλαγών στο Crypto και γιατί έχει σημασία;

Byte educativo: ¿Qué es la finalidad de las transacciones en criptomonedas y por qué es importante?

Byte educativo: que é a finalidade da transacción en Crypto e por que importa?

Oktatási bájt: Mi a tranzakció véglegessége a titkosításban, és miért számít?

教育バイト: 暗号通貨におけるトランザクションのファイナリティとは何ですか? また、なぜそれが重要なのですか?

Educational Byte: Inona no atao hoe Transaction Finality ao amin'ny Crypto, ary nahoana no zava-dehibe izany?

تعلیمي بایټ: په کریپټو کې د لیږد پای څه شی دی، او ولې دا مهمه ده؟

Byte educacional: O que é finalidade de transação em criptomoedas e por que isso é importante?

Utbildningsbyte: Vad är transaktionsfinal i krypto, och varför spelar det någon roll?

Eğitim Baytı: Kriptoda İşlem Kesinliği Nedir ve Neden Önemlidir?

How to Manage Permissions in a Langflow Chain for LLM Queries Using Permit.io

Authors:
(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);
(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);
(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.2 Anchor Regression 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation B Experiments 2.1 Data Augmentation Many different data augmentation methods have been proposed in recent years with several applications in mind. Still most augmentations we mention here use human-designed transformations based on domain knowledge which leave the target variable invariant. For instance, Cutout [10] is an image-specific augmentation technique that is successfully used to train models on CIFAR10 and CIFAR100 [25], but was determined to be unsuitable for larger image datasets like ImageNet with higher resolution [9]. Other augmentation methods for images such as random crop, horizontal or vertical mirroring, random rotation, or translation [29, 43] may similarly apply to a certain group of image datasets while being inapplicable to others, e.g. datasets of digits and letters. In an attempt to automate the augmentation process and reduce human involvement, policy or searchbased automated augmentation methods were developed. In AutoAugment [7] a neural network is trained with Reinforcement Learning (RL) to combine an assortment of transformations in varying strengths to apply on samples of a given dataset and improve the model accuracy. Methods such as RandAugment [8], Fast AutoAugment [30], UniformAugment [32] and TrivialAugment [36] aim at reducing the cost of the pretraining search phase in automated augmentation with randomized transformations and reduced search space. Alternatively, in order to adapt the augmentation policy to the model during training, PopulationBased Augmentation [16] and Online Hyperparameter Learning [31] use multiple data augmentation workers that are updated using evolutionary strategies and RL, respectively. Adversarial AutoAugment [53] and AugMax [47] optimize for the augmentation policy that deteriorates the training accuracy and improves its robustness. DivAug [34] finds the policy which maximizes the diversity of the augmented data. Having a separate search phase for optimal augmentation policy is computationally expensive and may exceed the required computation to train the downstream model [8, 48]. In addition, these methods and their online counterparts need to be trained separately on every single dataset. While OnlineAugment [44] and DDAS exploit meta-learning to avoid this problem, they still rely on a set of predefined class invariant transformations that require domain-specific information. Generic transformations such as Gaussian or adversarial noise [10, 28, 45] and dropout [3] are also effective in expanding the training dataset. Generative models such as Generative Adversarial Networks (GAN) [13] and Variational Auto-Encoders (VAE) [22] are trained in [1, 6, 44] to synthesize samples close to the low dimensional manifold of the data for classification. Mixup [51] is a popular data augmentation using a convex combination of pairs of samples from different classes and their softened labels for augmentation. Mixup is only evaluated on classification problems, even though it is claimed that the application to regression is straightforward. Various extensions of Mixup have been proposed to prevent data manifold intrusion [46], use more complex mixing strategies [33, 50] or account for saliency in augmented samples [20, 21]. These methods were predominantly designed to excel in classification tasks. In particular, Mixup for regression was studied in [5, 18, 49, 52] but it was reported to adversely impact the predictions in regression problems when misleading augmented samples are generated from a pair of faraway samples. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Authors: Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.1 Data Augmentation 2.2 Anchor Regression 2.2 Anchor Regression 3 Anchor Data Augmentation 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation A Additional information for Anchor Data Augmentation B Experiments B Experiments 2.1 Data Augmentation Many different data augmentation methods have been proposed in recent years with several applications in mind. Still most augmentations we mention here use human-designed transformations based on domain knowledge which leave the target variable invariant. For instance, Cutout [10] is an image-specific augmentation technique that is successfully used to train models on CIFAR10 and CIFAR100 [25], but was determined to be unsuitable for larger image datasets like ImageNet with higher resolution [9]. Other augmentation methods for images such as random crop, horizontal or vertical mirroring, random rotation, or translation [29, 43] may similarly apply to a certain group of image datasets while being inapplicable to others, e.g. datasets of digits and letters. In an attempt to automate the augmentation process and reduce human involvement, policy or searchbased automated augmentation methods were developed. In AutoAugment [7] a neural network is trained with Reinforcement Learning (RL) to combine an assortment of transformations in varying strengths to apply on samples of a given dataset and improve the model accuracy. Methods such as RandAugment [8], Fast AutoAugment [30], UniformAugment [32] and TrivialAugment [36] aim at reducing the cost of the pretraining search phase in automated augmentation with randomized transformations and reduced search space. Alternatively, in order to adapt the augmentation policy to the model during training, PopulationBased Augmentation [16] and Online Hyperparameter Learning [31] use multiple data augmentation workers that are updated using evolutionary strategies and RL, respectively. Adversarial AutoAugment [53] and AugMax [47] optimize for the augmentation policy that deteriorates the training accuracy and improves its robustness. DivAug [34] finds the policy which maximizes the diversity of the augmented data. Having a separate search phase for optimal augmentation policy is computationally expensive and may exceed the required computation to train the downstream model [8, 48]. In addition, these methods and their online counterparts need to be trained separately on every single dataset. While OnlineAugment [44] and DDAS exploit meta-learning to avoid this problem, they still rely on a set of predefined class invariant transformations that require domain-specific information. Generic transformations such as Gaussian or adversarial noise [10, 28, 45] and dropout [3] are also effective in expanding the training dataset. Generative models such as Generative Adversarial Networks (GAN) [13] and Variational Auto-Encoders (VAE) [22] are trained in [1, 6, 44] to synthesize samples close to the low dimensional manifold of the data for classification. Mixup [51] is a popular data augmentation using a convex combination of pairs of samples from different classes and their softened labels for augmentation. Mixup is only evaluated on classification problems, even though it is claimed that the application to regression is straightforward. Various extensions of Mixup have been proposed to prevent data manifold intrusion [46], use more complex mixing strategies [33, 50] or account for saliency in augmented samples [20, 21]. These methods were predominantly designed to excel in classification tasks. In particular, Mixup for regression was studied in [5, 18, 49, 52] but it was reported to adversely impact the predictions in regression problems when misleading augmented samples are generated from a pair of faraway samples. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

A Comprehensive Overview of Data Augmentation Methods

100x Simpler -  The Ultimate Open-Source Guide

For a technology that’s designed to assist, advise, and augment human capabilities, it’s astounding how often AI gets treated like an enigma that requires endless analysis and deliberation before it’s put to work. The most recent iteration of this paradox can be seen in the growing calls for assessments, ROI filters, and feasibility studies before businesses commit to using Generative AI (GenAI). Consultants are being hired to develop frameworks, strategists are crafting PowerPoint decks, and entire meetings are being dedicated to the question: How can we make AI work for us? Here’s the irony: While everyone is busy over-analyzing how to integrate AI into their operations, they’re ignoring the most powerful resource at their disposal—the AI itself. Generative AI models aren’t just tools you deploy after you’ve figured out a strategy. They can help you create the strategy. If you’re wondering where AI can add value, why not simply ask it? It’s time for companies to recognize that one of the best ways to discover how to use AI is to collaborate with it directly. Instead of commissioning yet another ROI study or engaging in more bureaucratic filtering, why not let AI be part of the discovery process? Let’s break down why it’s time to stop overthinking and start leveraging GenAI’s capabilities more intuitively. AI Isn’t Just a Tool—It’s a Collaborator The traditional approach to implementing new technology in business has always been methodical and measured. You identify potential use cases, run feasibility studies, measure expected ROI, and then decide whether to proceed. This approach makes sense when dealing with static tools or technologies with narrowly defined functions. But Generative AI doesn’t fit neatly into that category. Generative AI models like GPT-4 or DALL-E aren’t rigid solutions that only work in predefined scenarios. They’re flexible, adaptable, and capable of performing a wide range of tasks—from drafting reports and generating marketing ideas to brainstorming product features and suggesting process optimizations. Treating them as if they’re static technologies, requiring weeks of deliberation before use, is like bringing a Swiss Army knife to a survival scenario and spending all your time deciding which blade to use while missing the fact that the knife itself can show you what it’s best suited for. So, why not use AI as a collaborative partner in figuring out how it can add value? Let the AI itself help you uncover its own best use cases. Ask it to analyze your existing processes, identify bottlenecks, suggest content strategies, or even create a preliminary draft of your very own AI adoption roadmap. The responses might surprise you, and at the very least, they’ll serve as a starting point for further refinement and discussion. Stop Over-Engineering the ROI Calculation Another stumbling block in AI adoption is the insistence on extensive ROI calculations before the technology is even tested in a real-world context. Managers want to know exactly how much time or money will be saved, which KPIs will be impacted, and how soon they’ll see returns. While these questions are important, they’re not easy to answer when dealing with a technology as versatile and dynamic as GenAI. The reason is simple: GenAI’s value often extends beyond traditional ROI metrics. It can improve the quality of customer interactions, speed up ideation processes, and enable more personalized experiences—all of which contribute to long-term brand equity and competitive advantage but might not show up immediately on a balance sheet. And guess what? If you’re struggling to calculate ROI, ask the AI for help. GenAI models can analyze cost-benefit scenarios, simulate outcomes, and even create preliminary ROI frameworks tailored to your specific context. If you’re unsure about where to start, you can ask the AI questions like: “What are the potential ROI drivers for implementing a chatbot for customer service in a mid-sized retail business?” “Which tasks in my marketing department can be automated to save the most time and reduce costs?” “How can I measure the non-monetary value that AI-generated content brings to my brand?” You’ll likely receive a starting point that’s far more specific and actionable than anything a whiteboard brainstorming session could yield. AI Can Help You Identify Its Own Use Cases One of the most powerful, yet underutilized, aspects of GenAI is its ability to identify opportunities for its own application. It’s a self-recommending technology. You can pose open-ended questions to an AI model to help pinpoint areas where it can add value. Here are some examples of how you can “ask” AI about its own use: “What are some areas in my business where you can provide the most value?”
“Can you identify inefficiencies in my sales funnel, and suggest ways to address them?”
“What content strategies would you recommend for my brand to increase audience engagement?”
“Which business processes in my company would benefit most from automation, and why?” Instead of spending weeks creating internal reports that may or may not capture AI’s potential, these questions can give you concrete starting points in a matter of minutes. The AI might suggest things you hadn’t considered—like automating customer follow-ups, generating SEO-optimized blog posts, or even training the sales team on product knowledge using AI simulations. The Real Value: Immediate Experimentation Imagine how much time companies could save if, instead of waiting for the perfect ROI calculation or a polished proposal from a consultancy firm, they just started experimenting with AI in small, manageable ways. Want to see if AI can help improve your social media strategy? Ask it to draft a month’s worth of posts, and see what you get. Curious about using AI to assist in onboarding new employees? Have it draft some training materials, and test them out with a few new hires. These small-scale experiments offer immediate feedback and can highlight real value much faster than formal assessments ever could. They also help companies build internal expertise, create a culture of innovation, and—crucially—understand AI’s capabilities firsthand. This hands-on approach is where the true ROI of AI begins to reveal itself, often in unexpected ways. The Fear of Misuse: A Convenient Excuse Another reason companies hesitate to “just ask AI” is fear. Fear of AI giving the “wrong” answers, fear of AI being misused, or fear of AI making existing roles redundant. But these fears are often exaggerated, based on a misunderstanding of AI’s role as a collaborator rather than a replacement for human ingenuity. If you’re worried about AI misuse, then the solution isn’t to avoid it altogether—it’s to create guardrails and define roles. AI can provide suggestions, but humans must be in the loop to validate, refine, and implement these suggestions. Fear shouldn’t paralyze companies into inaction. If anything, the best way to understand the risks is through controlled, small-scale experimentation where failures are learning experiences rather than catastrophes. Realigning Expectations: AI as a Strategic Partner It’s time for companies to stop seeing GenAI as an intimidating, “all-or-nothing” investment that needs to be fully justified before being used. AI is a dynamic, evolving tool that thrives on interaction and iteration. By asking AI to help shape its own use case, companies can demystify the technology and discover new ways to leverage it that traditional ROI filters would never reveal. Instead of debating how to use AI behind closed doors or waiting for the perfect business case to emerge, why not simply engage with the technology and see where it takes you? The best way to understand AI’s potential isn’t through endless deliberation and analysis—it’s by using it, experimenting with it, and letting it guide you toward new opportunities. Let AI Help You Help Yourself The next time someone at your company proposes another feasibility study or ROI assessment before greenlighting a GenAI project, ask yourself: Why not just ask AI where it can add value? Why not let it participate in its own discovery process? After all, the AI isn’t just sitting there waiting to be used—it’s ready to engage, advise, and adapt. All you have to do is start the conversation. So, let’s stop the overthinking, stop the hesitations, and start collaborating with AI in real, impactful ways. Because if we keep getting caught up in analysis paralysis, we’ll miss out on the true value that AI brings: a partner that’s willing and ready to help, if only we’d ask it to. About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com For a technology that’s designed to assist, advise, and augment human capabilities, it’s astounding how often AI gets treated like an enigma that requires endless analysis and deliberation before it’s put to work. The most recent iteration of this paradox can be seen in the growing calls for assessments, ROI filters, and feasibility studies before businesses commit to using Generative AI (GenAI). Consultants are being hired to develop frameworks, strategists are crafting PowerPoint decks, and entire meetings are being dedicated to the question: How can we make AI work for us? How can we make AI work for us? Here’s the irony: While everyone is busy over-analyzing how to integrate AI into their operations, they’re ignoring the most powerful resource at their disposal—the AI itself. Generative AI models aren’t just tools you deploy after you’ve figured out a strategy. They can help you create the strategy. If you’re wondering where AI can add value, why not simply ask it ? after create ask it It’s time for companies to recognize that one of the best ways to discover how to use AI is to collaborate with it directly. Instead of commissioning yet another ROI study or engaging in more bureaucratic filtering, why not let AI be part of the discovery process? Let’s break down why it’s time to stop overthinking and start leveraging GenAI’s capabilities more intuitively. AI Isn’t Just a Tool—It’s a Collaborator The traditional approach to implementing new technology in business has always been methodical and measured. You identify potential use cases, run feasibility studies, measure expected ROI, and then decide whether to proceed. This approach makes sense when dealing with static tools or technologies with narrowly defined functions. But Generative AI doesn’t fit neatly into that category. Generative AI models like GPT-4 or DALL-E aren’t rigid solutions that only work in predefined scenarios. They’re flexible, adaptable, and capable of performing a wide range of tasks—from drafting reports and generating marketing ideas to brainstorming product features and suggesting process optimizations. Treating them as if they’re static technologies, requiring weeks of deliberation before use, is like bringing a Swiss Army knife to a survival scenario and spending all your time deciding which blade to use while missing the fact that the knife itself can show you what it’s best suited for. So, why not use AI as a collaborative partner in figuring out how it can add value? Let the AI itself help you uncover its own best use cases. Ask it to analyze your existing processes, identify bottlenecks, suggest content strategies, or even create a preliminary draft of your very own AI adoption roadmap. The responses might surprise you, and at the very least, they’ll serve as a starting point for further refinement and discussion. Stop Over-Engineering the ROI Calculation Another stumbling block in AI adoption is the insistence on extensive ROI calculations before the technology is even tested in a real-world context. Managers want to know exactly how much time or money will be saved, which KPIs will be impacted, and how soon they’ll see returns. While these questions are important, they’re not easy to answer when dealing with a technology as versatile and dynamic as GenAI. The reason is simple: GenAI’s value often extends beyond traditional ROI metrics. It can improve the quality of customer interactions, speed up ideation processes, and enable more personalized experiences—all of which contribute to long-term brand equity and competitive advantage but might not show up immediately on a balance sheet. And guess what? If you’re struggling to calculate ROI, ask the AI for help. GenAI models can analyze cost-benefit scenarios, simulate outcomes, and even create preliminary ROI frameworks tailored to your specific context. If you’re unsure about where to start, you can ask the AI questions like: “What are the potential ROI drivers for implementing a chatbot for customer service in a mid-sized retail business?” “What are the potential ROI drivers for implementing a chatbot for customer service in a mid-sized retail business?” “Which tasks in my marketing department can be automated to save the most time and reduce costs?” “Which tasks in my marketing department can be automated to save the most time and reduce costs?” “How can I measure the non-monetary value that AI-generated content brings to my brand?” “How can I measure the non-monetary value that AI-generated content brings to my brand?” You’ll likely receive a starting point that’s far more specific and actionable than anything a whiteboard brainstorming session could yield. AI Can Help You Identify Its Own Use Cases One of the most powerful, yet underutilized, aspects of GenAI is its ability to identify opportunities for its own application. It’s a self-recommending technology. You can pose open-ended questions to an AI model to help pinpoint areas where it can add value. Here are some examples of how you can “ask” AI about its own use: “What are some areas in my business where you can provide the most value?” “Can you identify inefficiencies in my sales funnel, and suggest ways to address them?” “What content strategies would you recommend for my brand to increase audience engagement?” “Which business processes in my company would benefit most from automation, and why?” “What are some areas in my business where you can provide the most value?” “What are some areas in my business where you can provide the most value?” “Can you identify inefficiencies in my sales funnel, and suggest ways to address them?” “Can you identify inefficiencies in my sales funnel, and suggest ways to address them?” “What content strategies would you recommend for my brand to increase audience engagement?” “What content strategies would you recommend for my brand to increase audience engagement?” “Which business processes in my company would benefit most from automation, and why?” “Which business processes in my company would benefit most from automation, and why?” Instead of spending weeks creating internal reports that may or may not capture AI’s potential, these questions can give you concrete starting points in a matter of minutes. The AI might suggest things you hadn’t considered—like automating customer follow-ups, generating SEO-optimized blog posts, or even training the sales team on product knowledge using AI simulations. The Real Value: Immediate Experimentation Imagine how much time companies could save if, instead of waiting for the perfect ROI calculation or a polished proposal from a consultancy firm, they just started experimenting with AI in small, manageable ways. Want to see if AI can help improve your social media strategy? Ask it to draft a month’s worth of posts, and see what you get. Curious about using AI to assist in onboarding new employees? Have it draft some training materials, and test them out with a few new hires. These small-scale experiments offer immediate feedback and can highlight real value much faster than formal assessments ever could. They also help companies build internal expertise, create a culture of innovation, and—crucially—understand AI’s capabilities firsthand. This hands-on approach is where the true ROI of AI begins to reveal itself, often in unexpected ways. The Fear of Misuse: A Convenient Excuse Another reason companies hesitate to “just ask AI” is fear. Fear of AI giving the “wrong” answers, fear of AI being misused, or fear of AI making existing roles redundant. But these fears are often exaggerated, based on a misunderstanding of AI’s role as a collaborator rather than a replacement for human ingenuity. If you’re worried about AI misuse, then the solution isn’t to avoid it altogether—it’s to create guardrails and define roles. AI can provide suggestions, but humans must be in the loop to validate, refine, and implement these suggestions. Fear shouldn’t paralyze companies into inaction. If anything, the best way to understand the risks is through controlled, small-scale experimentation where failures are learning experiences rather than catastrophes. Realigning Expectations: AI as a Strategic Partner It’s time for companies to stop seeing GenAI as an intimidating, “all-or-nothing” investment that needs to be fully justified before being used. AI is a dynamic, evolving tool that thrives on interaction and iteration. By asking AI to help shape its own use case, companies can demystify the technology and discover new ways to leverage it that traditional ROI filters would never reveal. Instead of debating how to use AI behind closed doors or waiting for the perfect business case to emerge, why not simply engage with the technology and see where it takes you? The best way to understand AI’s potential isn’t through endless deliberation and analysis—it’s by using it, experimenting with it, and letting it guide you toward new opportunities. Let AI Help You Help Yourself The next time someone at your company proposes another feasibility study or ROI assessment before greenlighting a GenAI project, ask yourself: Why not just ask AI where it can add value? Why not let it participate in its own discovery process? ask After all, the AI isn’t just sitting there waiting to be used—it’s ready to engage, advise, and adapt. All you have to do is start the conversation. So, let’s stop the overthinking, stop the hesitations, and start collaborating with AI in real, impactful ways. Because if we keep getting caught up in analysis paralysis, we’ll miss out on the true value that AI brings: a partner that’s willing and ready to help, if only we’d ask it to. About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com https://docligot.com

Companies Keep Asking Everyone Else About How to Use AI - Except AI Itself

Subjectivity and the Evolution of AI Philosophy  

সাবজেক্টিভিটি এবং এআই দর্শনের বিবর্তন

Subjektivität und die Entwicklung der KI-Philosophie

La subjetividad y la evolución de la filosofía de la IA

Subjectivité et évolution de la philosophie de l'IA

विषयपरकता और एआई दर्शन का विकास

主観性と AI 哲学の進化

AI 철학의 주관성과 진화

Subjetividade e a evolução da filosofia da IA

Субъективность и эволюция философии ИИ

Öznellik ve Yapay Zeka Felsefesinin Evrimi

Tính chủ quan và sự phát triển của triết học AI

主观性与人工智能哲学的演变

It’s all started doing some refactoring — I was playing with and and something tempted me do to a simple benchmark that revealed strange results. That's a second part in which I describe how I fixed it. It's about building Ruby's code, debugging it with and making it faster! Array Set lldb It all started with benchmarking [Array.include?()](https://github.com/pawelniewie/benchmark-set-array-contains/blob/master/ruby/array_include.rb) Comparison:               array:  1332567.6 i/s       array symbols:  1154135.9 i/s - 1.15x  slower Isn’t that strange that array of symbols is slower than one with strings? Symbols are supposed to be fast! I downloaded and started looking at it. Ruby’s source code I initially though the problem was with Symbol’s so I went looking for it. I found one for String easily in == string.c rb_define_method(rb_cString, "==", rb_str_equal, 1); But didn't follow the pattern, because it's also in :-) symbol.c string.c rb_define_method(rb_cSymbol, "==", sym_equal, 1); It was also simple to figure out where array is, that’s a no brainer: rb_define_method(rb_cArray, "include?", rb_ary_includes, 1);

VALUErb_ary_includes(VALUE ary, VALUE item){    long i;    VALUE e;    for (i=0; i<RARRAY_LEN(ary); i++) {    e = RARRAY_AREF(ary, i);    switch (rb_equal_opt(e, item)) {      case Qundef:        if (rb_equal(e, item)) return Qtrue;        break;      case Qtrue:        return Qtrue;    }    }    return Qfalse;} That lead me to then to and something didn't feel right: vm_insnhelper.c rb_equal_opt opt_eq_func static#ifndef NO_BIG_INLINEinline#endifVALUEopt_eq_func(VALUE recv, VALUE obj, CALL_INFO ci, CALL_CACHE cc){#define BUILTIN_CLASS_P(x, k) (!SPECIAL_CONST_P(x) && RBASIC_CLASS(x) == k)#define EQ_UNREDEFINED_P(t) BASIC_OP_UNREDEFINED_P(BOP_EQ, t##_REDEFINED_OP_FLAG)    if (FIXNUM_2_P(recv, obj)) {    if (EQ_UNREDEFINED_P(INTEGER)) {        return (recv == obj) ? Qtrue : Qfalse;    }    }    else if (FLONUM_2_P(recv, obj)) {    if (EQ_UNREDEFINED_P(FLOAT)) {        return (recv == obj) ? Qtrue : Qfalse;    }    }    else if (BUILTIN_CLASS_P(recv, rb_cFloat)) {    if (EQ_UNREDEFINED_P(FLOAT)) {        return rb_float_equal(recv, obj);    }    }    else if (BUILTIN_CLASS_P(recv, rb_cString)) {    if (EQ_UNREDEFINED_P(STRING)) {        return rb_str_equal(recv, obj);    }    }#undef EQ_UNREDEFINED_P#undef BUILTIN_CLASS_P    {    vm_search_method(ci, cc, recv);    if (check_cfunc(cc->me, rb_obj_equal)) {        return recv == obj ? Qtrue : Qfalse;    }    }    return Qundef;} I was going to say I hate formatting like that but turned out it’s because they mix spaces and tabs in the file. Duh! Looking at the code it’s pretty obvious — there’s no special case for symbols. I guess I kinda expected that Ruby has optimized methods written in C. Now it was time to figure out how to write the missing one. First, some set up. Run once to prepare make files (Mac OS X): autoconf CFLAGS="-O0 -ggdb" ./configure --prefix=/usr/local/opt/rbenv/versions/2.5.0 --with-openssl-dir="$(brew --prefix openssl)" --disable-install-doc Then to compile: make && make check && make install is a good starting point to understand what’s going on in the code. The most important lesson — you cannot a , there's for that. The Ruby C API guide printf VALUE rb_p Also you can debug stuff with and it's pretty simple (it's not though so ): lldb gdb commands are different lldb /usr/local/opt/rbenv/versions/2.5.0/bin/ruby -- -e "if [:test, :another].include?(:test); puts 'A'; end" breakpoint set --file vm_insnhelper.c --line 1279 process launch process continue Of course I was so eager that I tried many things before I finally searched for The Ruby C API guide :-D I thought it’s easy to understand and I could deal with C code as it was back in the days. When I read the guide it was obvious that I had to use : SYMBOL_P diff --git a/vm_insnhelper.c b/vm_insnhelper.cindex a991e59..476b5e2 100644--- a/vm_insnhelper.c+++ b/vm_insnhelper.c@@ -1296,6 +1296,11 @@ opt_eq_func(VALUE recv, VALUE obj, CALL_INFO ci, CALL_CACHE cc)        return rb_str_equal(recv, obj);    }     }+    else if (SYMBOL_P(recv) && SYMBOL_P(obj)) {+        if (EQ_UNREDEFINED_P(SYMBOL)) {+       return rb_obj_equal(recv, obj);+   }+    } #undef EQ_UNREDEFINED_P #undef BUILTIN_CLASS_P Now running the benchmark again: Comparison:       array symbols:  1630417.3 i/s               array:  1372269.7 i/s - 1.19x  slower Symbols are now faster! That’s how it should work from the beginning. As you can see it’s easy to browse C code. Also if you see something sticking out in your benchmarks it could be just a simple bug in the code. You need to remember there are two layers in Ruby — Ruby code and C code. If your Ruby code looks good could be C code that’s faulty. Created . PR with a fix Like this? Please click ♥ below so other peole can find it! Originally published at pawelniewiadomski.com on March 21, 2017.

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