Founders often don't know what they are doing

In the 2018 World Chess Championship, Magnus Carlsen and Fabiano Caruana's game ended in a draw despite Caruana's material advantage. Stockfish, the powerful chess engine, revealed a stunning missed opportunity: a forced checkmate in 35 moves. Stockfish's analysis suggested unconventional moves, like trapping the Knight on the edge of the board—moves no human would consider. This showcased Stockfish's ability to see far beyond human intuition, highlighting the power of AI in uncovering deep, hidden strategies in chess, even outwitting the world's best players. Stockfish is a narrow AI with a rating estimated to be over 3500. This is significantly higher than Magnus Carlsen’s rating of 2882. Within its specific domain, Stockfish demonstrates superintelligent behavior. This trend is likely to happen in other fields, such as programming and customer support, where AI could surpass the world's best experts. If we extrapolate this trend, we can predict three outcomes: 1\. Everyone will learn using AI To improve their game, players use Stockfish to analyze their chess moves. This has contributed to the growth of top chess players, since its launch in 2008: | Decade | Grandmasters | International Masters | FIDE Masters | Candidate Masters | | ------ | ------------ | --------------------- | ------------ | ----------------- | | 1970s | 82 | 200 | 500 | 500 | | 1980s | 300 | 400 | 1000 | 1000 | | 1990s | 600 | 800 | 2000 | 2000 | | 2000s | 1000 | 1500 | 3500 | 3000 | | 2010s | 1500 | 2000 | 5000 | 4000 | | 2020s | 1722 | 2400 | 6000 | 5000 | Similarly, top performers in different fields will increase, not decrease with AI. AI will teach us to be superhumans. On platforms like Chess.com, you can play for free but pay to improve your game using Stockfish. Similarly, future apps might be free, but you might pay for AI-driven learning. Edtech might not be a separate vertical; it could be the premium version in all verticals. 2\. AI automation might be frowned upon Using Stockfish in competition is considered cheating. In the 2022 Sinquefield Cup, World Chess Champion Magnus Carlsen resigned, suspecting his opponent, Hans Niemann, of using Stockfish. This caused a stir in the chess world, raising concerns about the impact of technology. The same might happen with AI assistants. Using AI to learn might be the norm, but delegating your work to AI could be frowned upon like cheating. AI would be a teacher, not a proxy who writes exam for you. If you are rebranding your product as AI, you might want to hold your horses. Investors might like it, but customers may not. Note: For people who think customers care only about the output, this didn't happen in chess. We don't watch Stockfish vs. Magnus Carlsen games. Magnus Carlsen is the star we celebrate, not Stockfish. 3\. Super intelligence could be deterministic In the endgame, Stockfish acts like a deterministic algorithm, using precomputed tablebases to make perfect moves with absolute certainty. This capability outstrips even Magnus Carlsen, as Stockfish can foresee and execute a flawless 35-move checkmate sequence that no human could match. This combination of a probabilistic middle game and a deterministic endgame makes it unbeatable. AI will hit a plateau once LLMs have read all available text and reach the IQ of top experts in various fields. However, just as Stockfish has surpassed Carlsen's rating of 2882 through the use of deterministic algorithms, artificial superintelligence could achieve unprecedented levels of capability by combining the adaptability of probabilistic LLMs with the precision of deterministic systems. Given that deterministic systems have a 50-year head start over probabilistic LLMs, this hybrid approach could excel at both nuanced tasks and well-defined problems, ultimately surpassing human capabilities across many domains. Conclusion Ten years ago, I predicted the impact of AI (https://www.youtube.com/watch?v=GZmK-3sZ9hc) on CRM and other business sectors. Most of these predictions have come true, except for the widespread combination of rule engines with machine learning. If chess is a leading indicator, I still believe that superintelligence will combine deterministic algorithms and probabilistic LLMs, excelling at both structured and nuanced tasks. Its adoption in chess not only showcases technological prowess but also provides a blueprint for thriving with superintelligence instead of being scared about it.

Code Generation AI is all the rage these days. But is generating code for programming languages like JavaScript and Python the right path to take? I think not. I think we should be generating code for declarative languages like Excel or SQL. What's the difference, you ask? In declarative languages, you express what your intention is. For example, in Excel, you can use SUM() to add all the line items and calculate the order amount. If the quantity of a line item changes, it will automatically recalculate the line item amount and then invoke SUM() to recalculate the order amount. But in imperative languages like JavaScript or Python, you instruct the computer on how to calculate the order amount. You would implement a function to add the line items as the order amount. Anytime the quantity of a line item changes, it is your job to call the function and recalculate the order amount. Why is this important? If you are asking AI to generate code for your requirements, you are essentially expressing your intent. So, expressing it in a declarative language seems natural. This will help the person who gave the requirement to understand the code generated by the AI. On the other hand, generating code for a programming language seems like the worst form of leaky abstraction (https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/). "Leaky abstraction" describes a scenario where attempts to simplify a system end up requiring users to understand its underlying complexities to troubleshoot it. Code generation can automate the creation process. But, the resulting code can be a puzzle even to a skilled developer who is debugging it. The person who gave the requirement will most likely not understand any of it. So, why do AI companies generate code like this? I guess it comes down to the availability of training data for AI. There are a lot of open-source projects in JavaScript or Python, so it is easy to train AI with it. But open-source projects in Excel are almost non-existent. So, the unavailability of training data might be the primary reason behind the direction these code generation AI companies are taking. At Neartail, we are taking a middle road. We have created a declarative language using JavaScript syntax so that we could train the AI as well as make it understandable to business owners. Will other AI companies realize the perils of leaky abstraction and change its course? Only time will tell.

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