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Get the most recent info and news about DeSmart on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

We’re a Poland-based, teal-oriented, close-knit team of experienced, creative, and open-minded software pros, who understand business and tech. With 19 years on the software market, we’ve completed almost 200 of projects for companies from all corners. And we’re ready to help you beyond creating custom software. We’re here to build new revenue streams for your business. We understand the challenges startups and scale-ups face – and we can help you solve them: 1. Get advice on the right tech for your product, considering efficiency, software integrations, and future-proofing. 2. Update your legacy software to get rid of technical debt, improve performance and user experience 3. Get your new product ideas validated (and sometimes challenged). 4. Build an MVP that innovates, fits the market needs, and delights the user. 5. Fix buggy software and put an end to increasing customer churn. 6. Get a quick and accurate cost estimation (as quickly as within 48h). 7. Scale up and conquer new markets with people who have built and expanded enough software products, including our own. 8. Work on crystal clear terms, with no small print or catches. 9. Build a marketplace with a team of people who know how to grow and maintain a marketplace that brings in consistent revenues (like the one we’re running ourselves.) We specialize in software for: · Real estate & proptech · Transport & logistics · E-learning · E-commerce, · Recruitment & HR We have the right expertise and resources to build: · Marketplaces · Web apps · Progressive web apps · Mobile apps · Wearable software · Proptech solutions · Internet of things · Analytics tools · E-commerce optimization tools · Warehouse management system (WMS) · Material requirements planning software (MRP) · Applicant tracking systems (ATS)

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Step into the vibrant world of gaming, where the nostalgia of clinking arcade tokens merges with the futuristic vision of blockchain technology. This isn't just about playing games; it's about being part of a revolution. and are at the forefront, introducing Pac-Cat, a game that blends the charm of old-school gaming with the limitless possibilities of NFTs. Taki Games Smart Layer Taki Games Spearheads Gaming Revolution with Dual Launches: 'Food Fighter' and 'Pac-Cat Taki Games, in with Game7 and Smart Layer, unveils two groundbreaking play-to-earn games: , developed in collaboration with Game7, and , a joint effort with Smart Layer. strategic partnerships 'Food Fighter' 'Pac-Cat' These releases mark a transformative moment in the gaming industry, capitalizing on the emerging power of NFTs and blockchain technology to provide gamers with unparalleled value and control over their digital assets. 'Food Fighter' and 'Pac-Cat' each embody a unique collaboration, showcasing the diverse possibilities of blockchain gaming and the distinct contributions of Game7 and Smart Layer to the evolving digital landscape. 'Food Fighter' emerges on the scene amidst the meteoric rise of the $TAKI token, which witnessed a staggering surge in trading volume from $300K/$400K to over $13 million in December alone. This surge is indicative of the growing enthusiasm and confidence in the token's value within the gaming community and beyond. Meanwhile, 'Pac-Cat', developed in partnership with Smart Layer, is capitalizing on the immense popularity of Smart Cats NFTs. The game features these highly coveted digital felines, which have rapidly ascended to become the number one trending NFT collection on OpenSea. Furthermore, Smart Cats command a significant 25% of all transactions on the Polygon network, with over 16 million transactions recorded in the past 120 days from 450,000 unique active wallets. Evolution of Video Games For decades, gaming has evolved, from pixelated characters bouncing across TV screens to immersive worlds sprawling across the globe in the palm of our hands. Traditional gaming, with its roots in simple joy, gradually morphed into a juggernaut industry underpinned by a free-to-play model that, while accessible, often prioritizes profit over player empowerment. Enter Web3 gaming, a paradigm shift resting on blockchain, NFTs, and cryptocurrency, promising a future where gamers are not just consumers but stakeholders and creators in their digital realms. Gaming has come a long way from the simple joys of Pac-Man and Space Invaders. What started as a way to pass the time has transformed into a global phenomenon, with games that offer not just entertainment but complex narratives and immersive experiences. However, the journey has been bittersweet, with the commercialization of gaming often overshadowing the gamer's autonomy. Enter the world of Web3 gaming – a beacon of change promising a future where gamers hold the reins. Unlocking a New World Imagine a game that's more than just a game—a digital universe where every item you earn or buy truly belongs to you. This is the promise of Web3 gaming, where blockchain technology ensures that your digital assets are yours to keep, trade, or sell. It's like turning your in-game achievements into treasures with real-world value. Yet, navigating this new terrain isn't without its hurdles. From technical complexities to environmental debates, the path to blockchain gaming is laden with challenges waiting to be overcome. Pac-Cat's arrival on the Taki network isn't just another game release; it's a landmark moment for gamers everywhere. With Smart Cats NFTs, players are not just collecting characters but investing in unique, digital companions. This game isn't just about nostalgia; it's about pioneering a future where gaming is rewarding in every sense of the word. The shift towards Web3 gaming is not just a technological upgrade; it's a cultural shift. It promises a future where games are more than entertainment; they're platforms for innovation, community-building, and even financial empowerment. Just as streaming services reinvented how we watch TV, blockchain gaming is set to redefine our relationship with digital worlds. A Game-Changing Alliance: Taki Games and Smart Layer In the dynamic world of gaming, the partnership between Taki Games and Smart Layer marks a pivotal moment, blending the thrill of mobile gaming with the innovation of Web3 technology. The launch of Pac-Cat, a game that embodies the fusion of playful engagement with digital asset ownership, heralds a new era for gamers on the Polygon network. This collaboration isn't just about a new game; it's about redefining the value of gaming itself. The Genesis of Pac-Cat: More Than Just Play Pac-Cat's introduction is a significant stride in Taki Games' mission to revolutionize the gaming industry by integrating real money rewards with the joy of gaming. This isn't merely a transaction; it's a transformation of gaming into a space where entertainment meets tangible rewards, powered by the innovative "Takinomics" model. This economic framework is designed to stabilize in-game tokens and funnel the $200 billion gaming industry's value directly to its players. Smart Cats: The Heart of the Game At the core of Pac-Cat's allure are the Smart Cats NFTs, leveraging Smart Layer’s groundbreaking Executable NFT technology. Each Smart Cat NFT is a unique digital entity, enabling players to engage in a Tamagotchi-like interaction within the Polygon network. This blend of nostalgia with cutting-edge technology has propelled Smart Cats to the forefront of the NFT gaming scene, highlighting the transformative potential of NFTs in gaming. A Confluence of Visionaries Taki Games and Smart Layer are not merely participants in the blockchain gaming arena; they are visionaries reshaping its landscape. By adopting Taki's Takinomics model, they aim to attract a new wave of gamers into the Web3 fold, offering more than just entertainment— but a stake in the game's future. This partnership is a testament to their commitment to not just participate in the gaming industry but to lead it into new territories of engagement and ownership. The Tipping Point for Web3 Gaming As traditional gaming grapples with stagnation, Web3 gaming is poised for a breakthrough, driven by an increasing demand for games that offer more than ephemeral entertainment. This shift towards blockchain-enabled features like native tokens and P2P marketplaces is not just timely; it's transformative, expanding the gaming market to include the crypto-savvy and beyond. The philosophy driving Taki Games and Smart Layer transcends the conventional gaming model, aiming to empower players with true ownership of their digital assets. This vision is a stark contrast to the exploitative models of the past, promising a future where gamers are not just consumers but contributors to the gaming ecosystem. Taki Games: Leading the Charge With its position as a top contender in the Web3 gaming dApps arena, Taki Games is not just participating in the gaming revolution; it's spearheading it. The surge in demand for its $TAKI token is a clear indicator of its growing influence, reflecting a burgeoning interest in gaming platforms that offer real value and ownership to their players. Final Thoughts: A New Horizon for Gaming Taki Games and Smart Layer are not just merging gaming with blockchain technology; they are pioneering a new paradigm where gaming is reimagined as a platform for innovation, community, and economic empowerment. As they lead the charge into this new era, the question isn't whether Web3 gaming will reshape the industry, but how far and wide its impact will be felt. Join the Conversation: What does the shift towards blockchain gaming mean for you? Are you ready to dive into a world where your gaming achievements carry real-world value? Let's start a dialogue about the future of gaming and how we, as a community, can shape it. Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Navigating the New Frontiers: Taki Games and Smart Layer's Bold Leap into Web3 Gaming

নতুন সীমান্তে নেভিগেট করা: টাকি গেমস এবং স্মার্ট লেয়ারের বোল্ড লিপ ওয়েব3 গেমিং-এ

Navigieren durch die neuen Grenzen: Taki Games und Smart Layers mutiger Sprung ins Web3-Gaming

Navegando por las nuevas fronteras: Taki Games y el audaz salto de Smart Layer hacia los juegos Web3

Naviguer vers les nouvelles frontières : le saut audacieux de Taki Games et Smart Layer dans le jeu Web3

नई सीमाओं को नेविगेट करना: टाकी गेम्स और स्मार्ट लेयर की वेब3 गेमिंग में साहसिक छलांग

新たなフロンティアをナビゲートする:Taki Games と Smart Layer の Web3 ゲームへの大胆な飛躍

새로운 영역 탐색: Taki Games와 Smart Layer의 Web3 게이밍으로의 대담한 도약

Navegando pelas novas fronteiras: Taki Games e o salto ousado da Smart Layer para os jogos Web3

Навигация по новым горизонтам: Taki Games и смелый прыжок Smart Layer в игры Web3

Yeni Sınırlarda Gezinmek: Taki Games ve Smart Layer'ın Web3 Oyununa Cesur Atılımı

Điều hướng các giới hạn mới: Trò chơi Taki và Bước nhảy vọt táo bạo của Lớp thông minh vào trò chơi Web3

探索新领域：Taki Games 和 Smart Layer 大胆进军 Web3 游戏

What Is Gno.land? Gno.land is a layer 1 smart contract platform invented by Jae Kwon, co-founder of Cosmos and creator of Tendermint, to address multiple issues in the blockchain space — in particular, the ease of use and intuitiveness of smart contract programming platforms. Beyond offering succinctness, composability, expressivity, and completeness not found in any other smart contract platform, we aim to challenge the regime of information censorship that we find ourselves living in today. By using the programming language Gnolang (Gno), an interpreted version of the widely-used Golang (Go) language, using a state-of-the-art VM written in Go, we want to lower the barrier to entry to web3 and make it simple for developers (particularly existing web2 developers) to write smart contracts and other blockchain applications without having to learn a programming language that is limited by design or exclusive to a single blockchain ecosystem. Gnolang (Gno) Is Essential to Broader Adoption of Web3 For web3 to grow in a sustainable way, we need technological solutions that are designed for the blockchain with programming languages that are universally adopted, secure, composable, and complete. The main programming language currently used for creating smart contracts, Solidity, is designed for one purpose only (writing smart contracts) and lacks the completeness of a general-purpose language. Solidity removes many of the complexities that blockchain programming requires (such as memory management, ensuring that the code is deterministic, and understanding how the entire tech stack is implemented) allowing developers to quickly build succinct smart contracts. However, Solidity is only used for smart contracts on EVM-compatible blockchains (like Ethereum, Polygon, or EVMOS) and its design is limited by the limitations of the EVM. In addition, developers have to learn several languages if they want to understand the whole stack or work across different ecosystems. Go, on the other hand, is a well-designed complete programming language with its foundation based on composable structures, designed by the creators of Plan 9. This allows developers to rapidly accelerate application development and adopt a modular structure by reusing and reassembling existing modules without building from scratch. They can embed one structure inside another in an intuitive way while preserving localism, and the language specification is simple, successfully balancing practicality and minimalism. Go is widely used, especially among existing web2 developers. It’s easier to learn and can be used to program almost anything, such as GoEthereum or Tendermint. Every part of the Gno.land stack is written in Go so that one person can understand the entire system just by studying a relatively small code base. The Go language is so well designed that the Gnolang smart contract system will become the new gold standard for smart contract development and other blockchain (and even non-blockchain) applications. Security Is a Built-in Feature of Go (Golang) Beyond object embedding, closures, importing of modules, composability of programs, and interfaces that allow you to implement a specific set of functions, Go supports secure programming through exported/non-exported fields, enabling “least-authority” design. It is easy to create objects and APIs that expose only what should be accessible to callers while hiding what should not be simply by the capitalization of letters, thus allowing a succinct representation of secure logic that can be called by multiple users. Another major advantage of Go is that the language comes with an ecosystem of great tooling, like the compiler and third-party tools that statically analyze code. Gno inherits these advantages from Go directly to create a smart contract programming language that is safe and helps developers to write secure code relying on the compiler, parser, and interpreter to give warning alerts for common mistakes. How Gnolang (Gno) Differs from Golang (Go) Image 1: Gnolang - Like Go but specific to the blockchain Gno is around 99% identical to Go, and most people can code in Gno from day one, even minute one. The Gno.land programming environment comes with blockchain-specific standard libraries, but any code that doesn’t use the blockchain-specific logic can run in Go with minimal processing. On the other hand, some libraries that don’t make sense in the blockchain context are not available in the Gno.land programming environment, such as network or operating-system access. Otherwise, Gno loads and uses many standard libraries that power Go, so most of the parsing of the source code is the same. Under the hood, the Gno code is parsed into an abstract syntax tree (AST), and the AST itself is used in the interpreter, rather than byte code as in many virtual machines such as Java, Python, or WASM. This makes even the Gno VM accessible to any Go programmer. The novel design of the Gno VM interpreter allows Gno to freeze and resume the program by persisting and loading the entire memory state. This allows (smart contract) programs to be succinct, as the programmer doesn’t have to serialize and deserialize objects to persist them into a database (unlike programming applications with the Cosmos SDK). The composable nature of Go/Gno allows for type-checked interactions between contracts, making Gno.land safer and more powerful, as well as operationally cheaper and faster. Smart contracts on Gno.land will be light, simple, more focused, and easily interoperable — a network of interconnected contracts rather than siloed monoliths that limit interactions with other contracts. Image 2: Code snippet from the Gno programming language Today, Gno.land is the only blockchain instance in the world that supports Gno but tomorrow, there will be many chains with different names such as mydapp.zone or mydao.xyz. Gno.land is the name of ONE chain and is not a name that will be used by other Gnolang-powered chains. Gno.land will remain a minimal hub with three main utilities: Managing cross-Gnolang-chain fees/licenses To be the (or an) official home for the best smart contracts To provide new models of governance (w/ DAO modules) Earning Rewards Through Proof-of-Contribution (PoC) There are four main ways to earn rewards through PoC on the Gno.land chain: Pre-defined tasks (technical or otherwise) Pre-defined bounties Retroactive bounties Vesting-style rewards for core members Bounties rewards (both pre-defined and retroactive) will be decided with “local rules,” through the agreement of the DAO with everything on-chain and transparent. If one human were to abuse the system, it would trigger, and the bad actor would be slashed. We’ll go into depth on how you can earn rewards in an upcoming post. Durable Solutions to Improve Our Understanding of the World One of our inspirations for the Gno.land project is the gospels, which built a system of moral code that lasted for thousands of years. Part of Gno.land’s endurance will be having a minimal production implementation that becomes a reference for other implementations and a basis for education to elevate people's understanding of blockchains. Gno.land aims to appeal to web developers, dApp developers, and blockchain builders to create solutions that help people improve their understanding of the world. With the barrage of misinformation delivered today from various factions, it’s impossible to separate the real from the fake. This causes a state of gridlock. We are living in a regime of information censorship spanning all important topics from climate change to global pandemics — a vast coordinated effort to prevent people from understanding the truth. By just browsing Reddit, searching with Google, and scrolling through Facebook, Twitter, or Instagram, people are deliberately being about key global issues that we all deserve clarity on. misled This is as malevolent as any type of censorship regime in the world — and we need to come together to challenge it and break the wall of censorship to achieve a functional democracy at last. Gno.land’s Current Phase of Development Gno.land is currently running in its third testnet, and there will be several more testnets before the platform is production ready. Modern civilization wasn’t built in a day, and neither will Gno.land rush into committing to an exact launch date. Game of Realms, the online incentivized competition to find the best contributors to the Gno.land platform, and currently in phase one, building the core infrastructure and tooling to power the chain. is now open Game of Realms is a high-stakes contest with a total prize pool of that will see participants complete a series of technical and non-technical challenges to push the chain to new limits. 133,700 ATOM If you’re interested in helping build the most intuitive smart contract platform in web3—while gaining rewards for your contribution—join today by opening a . PR here Find out more on our or , or follow us on . You can also join in the conversation on our . We look forward to seeing you. website GitHub repo Twitter Discord channel

Meet Gno.land: The Smart Contract Platform That Will Improve Our Understanding of the World

Conozca Gno.land: la plataforma de contratos inteligentes que mejorará nuestra comprensión del mundo

Découvrez Gno.land : la plateforme de contrats intelligents qui améliorera notre compréhension du monde

Gno.land से मिलें: स्मार्ट कॉन्ट्रैक्ट प्लेटफ़ॉर्म जो दुनिया के बारे में हमारी समझ को बेहतर बनाएगा

Gno.land のご紹介: 世界への理解を向上させるスマート コントラクト プラットフォーム

Conheça Gno.land: a plataforma de contratos inteligentes que melhorará nossa compreensão do mundo

Gặp gỡ Gno.land: Nền tảng hợp đồng thông minh sẽ cải thiện hiểu biết của chúng ta về thế giới

认识 Gno.land：将增进我们对世界的理解的智能合约平台

On this day, , also known as the , , , and , traditional Catholics following the traditional liturgical calendar in the Roman Rite, ponder the Gospel of St. John (here taken from the 1945 St. Andrew Missal): Second Sunday of Easter Octave Day of Easter White Sunday Quasimodo Sunday Low Sunday “Now Thomas, one of the 12, who is called Didymus was not with them when Jesus came. The other disciple therefore said to him: We have seen the Lord. But he said to them: Except I shall see in His hands the print of the nails, and put my finger into the place of the nails, and put my hand into His side, I will not believe. And after eight days, again His disciples were within, and Thomas with them. Jesus cometh, the doors being shut, and stood in the midst, and said: Peace be to you. Then He saith to Thomas: Put in thy finger hither thy hand, and put it into my side; and be not faithless, but believing. Thomas answered and said to Him: My Lord and my God. Jesus saith to him: Because thou has seen Me, Thomas, thou hast believed: blessed are they that have not seen, and have believed.” It’s an interesting grounding for a critique on academia, the world of the peer-review, what we call science, industry, and now it seems even the everyday. I’ve subjected myself to what academe calls ‘rigor’ in order to get published. But what exactly is this ‘rigor’? A simple google search for a definition yields, “the quality of being thorough, exhaustive, or accurate”, “severity or strictness”, “demanding, difficult, or extreme conditions”. In the world of the scientific peer-review, your work is evaluated by the editor of an academic journal, who decides whether your paper will be sent out to be critiqued (via a blind review) by three or four other academics. Your paper is sent back to the editor, who evaluates the critique and ultimately decides whether the journal will proceed to accept your paper for publication (oft subject to revisions) or not. This is essentially what rigor is—one is subjected to ‘strict’ rules of ones peers, who consider one another ‘experts’ in evaluating and critiquing a piece of work. The system is inductive, where general principles (also decisions, impressions, and critiques) are derived from specific observations. Just from an everyday common-sense perspective, one should see the highly subjective nature of the process. At the same time, from a logical perspective, it is a fallacy—because it assumes everyone in the whole must have the same characteristics as the parts. It should prove disturbing to anyone how the industry of knowledge isn’t instead premised on reason; and that science as we know it isn’t primarily logical or reasonable. What is logical is empirical. What isn't logical can’t be empirical. A prime test of reality and truth is whether it passes the logic test, where human folly, impressions, and biases can’t figure into determining whether something is or isn’t. Inductive ‘logic’, however, isn’t confined to academia. A majority rules or peer system governs both board rooms and labor unions. They used to call this sophistry. And as this global health issue has shown, the survey has become the primary arbiter of decisions and choices. Indeed, numbers are part and parcel of Math and arithmetic is logic; but haven’t the numbers been robbed of the essence of logic itself? Have not the graphs, charts, percentage points, various quantities cited from and by ‘expert’ upon ‘expert' culled so ‘rigorously’, which have come to be the plaque and emblem of trust been stripped of the essence of logic down to mere consensus, impression, and rule of the majority, or in some cases the majority of the elite? But rigor is not reason; and what isn’t logical can’t be empirical. Christ was/ is Logos Incarnate; and the civilization, which built what we have today (from science to architecture, engineering, law, education, and commerce) was (is) a Catholic Christian one, which is inevitably premised on this idea of ‘Logos Incarnate’. It should be paradoxically both refreshing and daunting to all (whether Catholic or not) that the very basis of civilization and its consequences came off this--'truth' for Catholics, 'concept' for non-Catholics. And that we should turn our world around (upside down, really) and premise it on inductive (vs. deductive logic) is a defiance of both reason and sense. This is why experience has been touted the best teacher, which contradicts Logos Incarnate and ‘what is logical is empirical’. Truth is, experience isn’t the default and inherent best teacher. It is the inefficient teacher. Logic oft acquired through much research and reading is the best teacher. Indeed, it does require practice (thus, experience); but the ‘experience’ touted today is not this type experience but an immersion with what is referred to as real-world situations (which is good in itself). Problem is, if this fondness and bias for ‘experience’ comes at the expense of logic. What if one is 'learning' in an environment that is anti-logic? Logic has become so difficult for many and in fact, most. Thus, while ‘what is logical is empirical’, the pain of achieving and maintaining logic (true rigor in the acquisition of habits of the mind towards consistency in thought and fidelity to the flow of these thoughts) is not an appealing proposition. Acting before thinking has trumped the more prudent prudence. In reality, however, ‘experience’ is an inefficient and slow teacher as one has to go through various events of pain, oft stretching an entire lifetime to learn and gain. You don’t have to jump off a cliff or touch fire to know how that’s going to feel or turn out. Logic saves you the trouble. But we’ve come to prefer the more circuitous and long route. This is not an affront on practice, praxis, and experience. I do not discount the necessity. I am merely arguing for the superiority of deductive logic. In fact, the affront on logic might be more serious. Let’s take the case of what the invention of bitcoin and its blockchain has enabled—the superiority of code; in other words, logic. Verification (on so public a ledger) vs. ‘trust’ (especially in comparison to legal tender known as ‘fiat’) is certainly an interesting and wonderful proposition. Anyone who has followed and looked into the history of bitcoin, knows while it is a product of human design (the anonymous Satoshi Nakamoto), it's human design that came as a consequence of central planning and the manipulation of ‘money’. One can argue, it was thus, organic and natural. The last straw seemed to have been the 2008 financial crisis. Moreover, this product of human design, which remains to have been created by someone who is either dead or prefers to be permanently invisible, is open source (thus, anyone can work on it), public (anyone and everyone can use and see its activities and transactions), and is built on economic incentives for its growth and survival, has made it practically fool-proof for any sort of compromise and infiltration. Its very foundation, grass roots development, and tokenomics (as the people in the space like to call it), ensure its fidelity to logic not just in its code (which non-coders can’t even understand) but its function and utility as a system and store of value. It’s a real case for ‘what is logical is empirical’. But while bitcoin and other cryptocurrencies and blockchains are based on code (math and logic), ‘what is logical is empirical’ isn’t yet quite clear with other blockchains and alt-coins. Qualitatively, when translated to experience and culture, the consequences and effects can be driven by anti-logic. This is most apparent in the space of non-fungible tokens (NFTs). I sometimes wonder if writers of code are based on logic or Cartesian ‘logic’—which is logical within its realm (and why Descartes lived in his head and the logic he invented was never and can never be empirical)—giving birth to bubbles as the norm and foundation of civilization and society. We might just be devolving from mere Doubting Thomases who need to see in order to believe to disciples of ‘rigor’ (rule by the majority, elite, or both) who don’t need to see because there are others (‘experts’ and ‘chosen ones’), who will do the job of seeing to an almost complete withdrawal from reality, choosing to be in a bubble of hype, hysteria, emotion, and noise now tokenized with possible value or ‘value’. The verdict is still out on this one though. I, myself dabble in NFTs. But I prefer to guard and ensure my inclination to logic and thus remain critical by default. It also helps to do so in determining, which are Cartesian (bubbles) and which are not. Ironically, it has become necessary to become a Doubting Thomas these days of what looks like a third level withdrawal from logic and doubt. Going back to Thomas though, and to be true to ‘what is logical is empirical’, to err is human (the stain of original sin has remained). His folly might just have been a case for this as well as a case against the premium we’ve placed on inductive logic as the backbone of civilization. St. Thomas, however, almost immediately acknowledged his folly, perhaps being first to directly acknowledge and proclaim the Divinity of Christ, with “My Lord and my God”. He went on to create converts to Christianity, preaching from Babylon to Persia and finally to Malabar, India (present-day Madras), where he was reportedly thrown into a pit and pierced with a Brahmin’s spear. There are, in fact, St. Thomas Christians (Mar Thomas Nazranis or Syrian Christians). Indeed, it might seem, our current more circuitous and inductive route of rigor vs. logic, might just be going through a test. Question is, is it a unicorn (bubble), empiricism, or will logic be incarnate?

Perils of Induction: Doubting Thomases, Rigor, NFTs & Descartes

This past week, a robotic first happened: ABB’s Yumi robot conducted the Lucca Philharmonic Orchestra in Pisa, Italy. The dual-armed robot overshadowed even his vocal collaborator, Italian tenor Andrea Bocelli. While many will try to hype the performance as ushering in a new new era of mechanical musicians, Yumi’s artistic career was short-lived as it was part of the opening ceremonies of Italy’s First International Festival of Robotics. Italian conductor Andrea Colombini said of his student, “The gestural nuances of a conductor have been fully reproduced at a level that was previously unthinkable to me. This is an incredible step forward, given the rigidity of gestures by previous robots. I imagine the robot could serve as an aid, perhaps to execute, in the absence of a conductor, the first rehearsal, before the director steps in to make the adjustments that result in the material and artistic interpretation of a work of music.” Harold Cohen with his robot AARON Yumi is not the first computer artist. In 1973, professor and artist, Harold Cohen created a software program called AARON — a mechanical painter. AARON’s works have been exhibited worldwide, including at the prestigious Venetian Biennale. Following Cohen’s lead, Dr Simon Colton of London’s Imperial College created “The Painting Fool,” with works on display in Paris’ prestigious Galerie Oberkampf in 2013. Colton wanted to test if he could cross the emotional threshold with an artistic . Colton explained, “I realized that the Painting Fool was a very good mechanism for testing out all sorts of theories, such as what it means for software to be creative. The aim of the project is for the software itself to be taken seriously as a creative artist in its own right, one day.” Turning Test In June 2015, Google’s Brain AI research team took artistic theory to the next level by infusing its software with the ability to create a remarkably human-like quality of imagination. To do this, Google’s programmers took a cue from one of the most famous masters of all time, Leonardo da Vinci. Da Vinci suggested that aspiring artists should start by looking at stains or marks on walls to create visual fantasies. Google’s neural net did just that, translating the layers of the image into spots and blotches with new stylized painterly features (see examples below). 1) Google uploaded a photograph of a standard Southwestern scene: 2) The computer then translated the layers as below: In describing his creation, Google Brain senior scientist Douglas Eck said this past March, “I don’t think that machines themselves just making art for art’s sake is as interesting as you might think. The question to ask is, can machines help us make a new kind of art?” The goal of Eck platform called is to enable laypeople (without talent) to design new kinds of music and art, similar to synthetic keyboards, drums and camera filters. Dr. Eck himself is an admittedly frustrated failed musician who hopes that Magenta will revolutionize the arts in the same way as the electric guitar. “The fun is in finding new ways to break it and extend it,” Eck said excitedly. Magenta The artistic development and growth of these computer programs is remarkable. Cohen, who passed away last year, said in a 2010 lecture regarding AARON “with no further input from me, it can generate unlimited numbers of images, it’s a much better colorist than I ever was myself, and it typically does it all while I’m tucked up in bed.” Feeling proud, he later corrected himself, “Well, of course, I wrote the program. It isn’t quite right to say that the program simply follows the rules I gave it. The program the rules.” is In reflecting on the societal implications of creative bots, one can not help to be reminded of the famous statement by philosopher René Decartes: “I think, therefore I am.” Challenging this idea for the robotic age, Professor Arai Noriko tested the thinking capabilities of robots. Noriko led a research team in 2011 at Japan’s to build an artificial intelligence program smart enough to pass the rigorous entrance exam of the University of Tokyo. National Institute of Informatics “Passing the exam is not really an important research issue, but setting a concrete goal is useful. We can compare the current state-of-the-art AI technology with 18-year-old students,” explained Dr. Noriko. The original goal set out by Noriko’steam was for the Todai robot (named for the University) to be admitted to college by 2021. At a Ted conference earlier this year, Noriko shocked the audience by revealing the news that Todai beat 80% of the students taking the exam, which consisted of seven sections, including math, English, science, and even a 600-word essay. Rather than celebrating, Noriko shared with the crowd her fear,”I was alarmed.” Todai is able to search and process an immense amount of data, but unlike humans it does not read, even with 15 billion sentences already in its neural network. Noriko reminds us that “humans excel at pattern recognition, creative projects, and problem solving. We can read and understand.” However, she is deeply concerned that modern educational systems are more focused on facts and figures than creative reasoning, especially because humans could never compete with the fact-checking of an AI. Noriko pointed to the entrance exam as an example, the Todai robot failed to grasp a multiple choice question that would have been obvious even to young children. She tested her thesis at a local middle school, and was dumfounded when one-third of students couldn’t even “answer a simple reading comprehension question.” She concluded that in order for humans to compete with robots, “We have to think about a new type of education. “ Cohen also wrestled with the question of a thinking robot and whether his computer program could ever have the emotional impact of a human artist like Monet or Picasso. In his words, to reach that kind of level a machine would have to “develop a sense of self.” Cohen professed that “if it doesn’t, it means that machines will never be creative in the same sense that humans are creative.” Cohen later qualified his remarks about robotic creativity, adding, “it doesn’t mean that machines have no part to play with respect to creativity.” Noriko is much more to the point, “How we humans will coexist with AI is something we have to think about carefully, based on solid evidence. At the same time, we have to think in a hurry because time is running out.” John Cryan, CEO of Deutsche Bank, echoed Noriko’s sentiment at a banking conference last week. Cryan said “In our banks we have people behaving like robots doing mechanical things, tomorrow we’re going to have robots behaving like people. We have to find new ways of employing people and maybe people need to find new ways of spending their time.” This topic and more will be discussed at our RobotLabNYC event next week on September 19th @ 6pm in WeWork Grand Central. The forum will explore “Investing In Unmanned Systems,” with experts from NASA, AUVSI, and the Genius NY Accelerator — Reserve Today, Limited Seating .

Descartes Revisited: Do Robots Think?

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people’s lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. Where should the person stop and the information search interface start? Information Processing & Management 26, 5 (1990), 575–591. [3] Nicholas J Belkin. 1980. Anomalous states of knowledge as a basis for information retrieval. Canadian journal of information science 5, 1 (1980), 133–143. [4] Paul N Bennett, Ryen W White, Wei Chu, Susan T Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 185–194. [5] Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2022. Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue 20, 6 (2022), 35–57. 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Navigating Complex Search Tasks with AI Copilots: The Undiscovered Country and References

The AI Odyssey: A Journey Through its History and Philosophical Implications

Much has been said lately about the wonders of Large Language Models (LLMs). Most of these accolades are deserved. Ask ChatGPT to describe the General Theory of Relativity and you will get a very good (and accurate) answer. However, at the end of the day ChatGPT is still a computer program (as are all other LLMs) that is blindly executing its instruction set. It understands the General Theory of Relativity no better than your favorite pet. Unfortunately, we use “human-like” words to describe the techniques engineers use to create them - for example, “machine learning” and “training.” This is misleading because an LLM does not have a mind like a human. There is a certain irony here - how can a non-thinking chatbot correctly summarize the findings of the smartest man to ever live? To understand this contradictory nature of LLMs, let’s explore them further in terms of Strengths, Weaknesses, and Dangers to see how we can use data, and a storage solution like MinIO, to exploit the first and mitigate the other two. These are techniques that every engineer should have in the back of their minds as they train, test and deploy LLMs for their organization. The Strength The strength of LLMs is that they are trained to understand the probability distribution of words in the training set used to create them. If the training set is sufficiently large (i.e. a corpus of Wikipedia articles or public code on GitHub), then the models will have a vocabulary and a corresponding probability distribution that will make their results appear as if they have a real-world understanding of the text they output. Let’s examine another example in more detail - this time from Philosophy. Ask ChatGPT the question, "What does ‘cogito, ergo sum’ mean and who wrote it?" and you will get something similar to the text below. "Cogito, ergo sum" is a Latin philosophical proposition that translates to "I think, therefore I am" in English. This statement is famously associated with René Descartes, a French philosopher, mathematician, and scientist. Descartes expressed this idea in his work "Discourse on the Method," published in 1637. The phrase reflects Descartes' attempt to establish a foundational truth that cannot be doubted—the certainty of one's own existence as a thinking being. LLMs produce results like this using probability distributions. It works something like this, they start by looking at the text in the question and determine that the word “Cogito” has the highest probability of being the first word of the answer. From there, they look at the question and the first word of the answer to determine the word that has the highest probability of being next. This goes on and on until a special “end of answer” character is determined to be of the highest probability. This ability to generate a natural language response based on billions of probabilities is not something to be feared - rather, it is something that should be exploited for business value. The results get even better when you use modern techniques. For example, using techniques like Retrieval Augmented Generation (RAG) and Fine-tuning, you can teach an LLM about your specific business. Achieving these human-like results will require data and your infrastructure will need a strong data storage solution. Not only can these next-token prediction capabilities be used to generate great text for your chatbot or marketing copy, but they can also be used to enable automated decision-making within your application. Given cleverly constructed prompts that contain a problem statement and information about APIs (“functions”) that can be called, an LLM’s understanding of language will enable it to generate an answer that explains what “function” should be called. For example, on a conversational weather app, a user could ask, “Do I need a rain jacket if I’m going to Fenway Park tonight?” With some clever prompting, an LLM could extract the location data from the query (Boston, MA) and can determine how a request to the Weather.com Precipitation API could be formulated. For a long time, the hardest part about building software was the interfacing between natural language and syntactic systems such as API calls. Now, ironically, that might be one of the simplest parts. Similar to text generation, the quality and reliability of LLM function-calling behavior can be aided with the use of fine-tuning and reinforcement learning with human feedback (RLHF). Now that we understand what LLMs are good at and why, let’s investigate what LLMs cannot do. The Weakness LLMs cannot think, understand or reason. This is the fundamental limitation of LLMs. Language models lack the ability to reason about a user's question. They are probability machines that produce a really good guess to a user’s question. No matter how good of a guess something is, it is still a guess and whatever creates these guesses will eventually produce something that is not true. In generative AI, this is known as a “Hallucination.” When trained correctly, hallucinations can be kept to a minimum. Fine-tuning and RAG also greatly cut down on hallucinations. The bottom line - to train a model correctly, fine-tune it and give it relevant context (RAG) requires data and the infrastructure to store it at scale and serve it in a performant manner. Let’s look at one more aspect of LLMs, which I’ll classify as a danger because it impacts our ability to test them. The Danger The most popular use of LLMs is Generative AI. Generative AI does not produce a specific answer that can be compared to a known result. This is in contrast to other AI use cases, which make a specific prediction that can be easily tested. It is straightforward to test models for image detection, categorization and regression. But how do you test LLMs used for generative AI in a way that is impartial, fact-faithful and scalable? How can you be sure that the complex answers LLMs generate are correct if you are not an expert yourself? Even if you are an expert, human reviewers can not be a part of the automated testing that occurs in a CI/CD pipeline. There are a few benchmarks in the industry that can help. GLUE (General Language Understanding Evaluation) is used to evaluate and measure the performance of LLMs. It consists of a set of tasks that assess the ability of models to process human language. SuperGLUE is an extension of the GLUE benchmark that introduces more challenging language tasks. These tasks involve coreference resolution, question answering and more complex linguistic phenomena. While the benchmarks above are helpful, a big part of the solution should be your own data collection. Consider logging all questions and answers and creating your own tests based on custom findings. This will also require a data infrastructure built to scale and perform. Conclusion There you have it. The strengths, weaknesses, and dangers of LLMs. If you want to exploit the first and mitigate the other two, then you will need data and a storage solution that can handle lots of it.

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