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The chemist's gambit

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Next moves: learn chemistry, synthesise molecules, take over world? Elegant retrosynthesis is an artform, it is strategic: you need to plan ahead, but also be able to adapt, if reactions do not work out. When the lockdown in 2020 started, I came upon those very same patterns in completely different field – chess. With everyone stuck at home, online chess suddenly became highly popular, especially after the release of the series The Queen's Gambit. My friends and I were among those who gave it a shot.


The English Opening.

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The one thing I tell most people upon meeting me is, I play chess. Long before I got into the data science career field I have played chess. It has always been a pastime to me. The reason I mention it so consistently is because those who play chess have a very analytical way of thinking. It is a natural adaption from the game itself, depending on your consistency of play.


Reinforcement Learning: An Introduction

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In 9 hours, Google's AlphaZero went from only knowing the rules of chess to beating the best models in the world. Chess has been studied by humans for over 1000 years, yet a reinforcement learning model was able to further our knowledge of the game in a negligible amount of time, using no prior knowledge aside from the game rules. No other machine learning field allows for such progress in this problem. Today, similar models by Google are being used in a wide variety of fields like predicting and detecting early signs of life-changing illnesses, improving text-to-speech systems, and more. Machine learning can be divided into 3 main paradigms.


Reinforcement Learning: An Introduction

#artificialintelligence

In 9 hours, Google's AlphaZero went from only knowing the rules of chess to beating the best models in the world. Chess has been studied by humans for over 1000 years, yet a reinforcement learning model was able to further our knowledge of the game in a negligible amount of time, using no prior knowledge aside from the game rules. No other machine learning field allows for such progress in this problem. Today, similar models by Google are being used in a wide variety of fields like predicting and detecting early signs of life-changing illnesses, improving text-to-speech systems, and more. Machine learning can be divided into 3 main paradigms.


AI Rundown

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AI is a big buzzword in the tech industry. But how did this all start? The history of Artificial Intelligence can be traced back centuries, but the biggest advancements began in the 1950s. Alan Turing was a British polymath who argued that machines could likely use available information and reason to solve problems just as humans do. His 1950 paper, Computing Machinery and Intelligence, covered how to build and test intelligent machines.


Why games may not be the best benchmark for AI

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Did you miss a session from the Future of Work Summit? In 2019, San Francisco-based AI research lab OpenAI held a tournament to tout the prowess of OpenAI Five, a system designed to play the multiplayer battle arena game Dota 2. OpenAI Five defeated a team of professional players -- twice. And when made publicly available, OpenAI Five managed to win against 99.4% of people who played against it online. OpenAI has invested heavily in games for research, developing libraries like CoinRun and Neural MMO, a simulator that plops AI in the middle of an RPG-like world. But that approach is changing.


How the AI Revolution Impacted Chess (2/2)

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In 2019, Dubov introduced many new ideas in a rare variation of the Tarrasch Defense, which quickly attracted attention at the top level. Several of the world's best players have tried it, including Carlsen who employed it successfully in the 2019 World Rapid and Blitz Championships. Dubov's double-edged opening system is based around concepts that are suggested by the newer engines, including early h-pawn advances and pawn sacrifices for the initiative. Note that both game annotations are based on work I did for my book, The AI Revolution in Chess. At the top level these days, everyone uses neural network (or hybrid) engines.


How the AI Revolution Impacted Chess (1/2)

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The wave of neural network engines that AlphaZero inspired have impacted chess preparation, opening theory, and middlegame concepts. We can see this impact most clearly at the elite level because top grandmasters prepare openings and get ideas by working with modern engines. For instance, Carlsen cited AlphaZero as a source of inspiration for his remarkable play in 2019. Neural network engines like AlphaZero learn from experience by developing patterns through numerous games against itself (known as self-play reinforcement learning) and understanding which ideas work well in different types of positions. This pattern recognition ability suggests that they are especially strong in openings and strategic middlegames where long-term factors must be assessed accurately. In these areas of chess, their experience allows them to steer the game towards positions that provide relatively high probabilities of winning.


How Artificial Intelligence is set to evolve in 2022? - ELE Times

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Machines are getting smarter and smarter every year, but artificial intelligence is yet to live up to the hype that's been generated by some of the world's largest technology companies. Artificial Intelligence can excel at specific narrow tasks such as playing chess but it struggles to do more than one thing well. A seven-year-old has far broader intelligence than any of today's AI systems, for example. "AI algorithms are good at approaching individual tasks, or tasks that include a small degree of variability," Edward Grefenstette, a research scientist at Meta AI, formerly Facebook AI Research. "However, the real world encompasses the significant potential for change, a dynamic which we are bad at capturing within our training algorithms, yielding brittle intelligence," he added.


Artificial Intelligence helps tech companies enhance their m...

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Technology is becoming smarter on an annual basis, and artificial intelligence is able to satisfy the expectations of the greatest tech firms in the world. Artificial Intelligence can succeed in particular assignments like playing chess, but it fails in achieving more than one assignment correctly. For instance, a boy who is 7 years old obtains greater intelligence than any other AI structures. Edward Grefenstette, a research scientist at Meta AI, formerly Facebook AI Research, said to CNBC, "AI algorithms are good at approaching individual tasks, or tasks that include a small degree of variability." Grefenstette also said, "However, the real world encompasses significant potential for change, a dynamic which we are bad at capturing within our training algorithms, yielding brittle intelligence."