Mediaboss Marketing

In keeping with the tradition of (some of) his great predecessors, retired former World Chess Champion Vladimir Kramnik has spent some time the last few years devising and (lightly) promoting a number of chess variants. For Jose Raul Capablanca, the problem was the draw death of chess; for Bobby Fischer, it was the burden of opening theory and the resulting lack of creativity at the board. We can smile at their pessimism, though they may not have been so much wrong as premature, or (to my mind) a bit too sanguine about the powers of the human mind. (Lets see how bad the draw death problem will prove to be in a match between Magnus Carlsen and Stockfish.)

Back to Kramnik and his variants. The times being what they are, theres no sense in proposing something that fosters creativity and an escape from the ubiquity of the engines influence if youre not going to immediately see what AlphaZero thinks about it. Have a look at this paper (direct link to the PDF here; HT: David McCarthy), written by three members of the Deep Mind team and Kramnik himself. It starts with a bit of history and a description of the new variants, gets into some statistics, and then fairly quickly - page 17 - gets into a qualitative discussion of the new variants. Some more math follows, and starting on page 25 all the way to the end on page 98, its what we all want most: chess and lots of it - the variants, anyway. Theres a lot of fun, beauty, and humor there, so youll want to have a look.

One remark, en passant, about the earlier material. On page 16 the authors give AlphaZeros table of the pieces values, starting with normal chess and continuing with the different variants discussed in the paper. I cant speak to the assessments it makes in the variants, but Im (extremely) surprised by how highly it rates rooks in classical chess. With the pawn = 1, as usual, it gives 3.05 for the knight (no problem), 3.33 for the bishop (very plausible), 9.5 for the queen (also plausible), and 5.63 for the rook (wait, what?).

There is a bit of throat clearing there, and rightly so:

The piece values in Table 6 should not be taken as a gold standard, as the sample of AlphaZero games that they were estimated on does not fully capture the diversity of human play, and the game lengths do not correspond to that of human games, which tend to be shorter.

I think the last part in particular is crucial: computer vs. computer games often go 100 moves or more, and its in endgames where the rooks finally come into their own. As a guide for middlegame play, especially for humans, taking those figures at face value is a recipe for disaster. Experienced players know, for instance, that trading a bishop and a knight for a rook and a pawn (e.g. on f7) is generally terrible unless theres some further payoff. And no GM would dream that two bishops are only .03 better than a rook and a pawn in any sort of normal position with multiple pawns on the board. So while those numbers may be right for AlphaZero vs. Stockfish or against itself, based in part on a data set with a significant number of 150-250 move games, I would not recommend that we carbon-based players weight the rook as heavily (in general).

This is a very minor point, and not whats at the heart of the paper. So tolle lege, and enjoy.

See more here:
AlphaZero Tackles Chess Variants - by Dennis Monokroussos

It was a war of titans you likely never heard about. One year ago, two of the worlds strongest and most radically different chess engines fought a pitched, 100-game battle to decide the future of computer chess.

Game Changer: AlphaZero's Groundbreaking Chess Strategies and the Promise of AI

Game Changer: AlphaZero's Groundbreaking Chess Strategies and the Promise of AI

On one side was Stockfish 8. This world-champion program approaches chess like dynamite handles a boulderwith sheer force, churning through 60 million potential moves per second. Of these millions of moves, Stockfish picks what it sees as the very best onewith best defined by a complex, hand-tuned algorithm co-designed by computer scientists and chess grandmasters. That algorithm values a delicate balance of factors like pawn positions and the safety of its king.

On the other side was a new program called AlphaZero (the "zero" meaning no human knowledge in the loop), a chess engine in some ways very much weaker than Stockfishpowering through just 1/100th as many moves per second as its opponent. But AlphaZero is an entirely different machine. Instead of deducing the best moves with an algorithm designed by outside experts, it learns strategy by itself through an artificial-intelligence technique called machine learning. Its programmers merely tuned it with the basic rules of chess and allowed it to play several million games against itself. As it learned, AlphaZero gradually pieced together its own strategy.

This content is imported from youTube. You may be able to find the same content in another format, or you may be able to find more information, at their web site.

AlphaZero: Shedding new light on the grand games of chess, shogi and Go

The head-to-head battle was astonishing. In 100 games, AlphaZero never lost. The AI engine won the match (winning 28 games and drawing the rest) with dazzling sacrifices, risky moves, and a beautiful style that was completely new to the world of computer chess.

British chess grandmaster Matthew Sadler and mathematician and chessmaster Natasha Regan are still piecing together how AlphaZeros strategy works in their new book, Game Changer. Were breaking open two moves in just one of the games to show the aggressive style, what it does, and what humans can learn from our new chess champion.

Lakota Gambill

Theres a lot going on here, but focus on the pawns. Mainly, that AlphaZero has already lost one on the g file, and is sacrificing yet another with this jumpy rook move. (Stockfishs next move is a queen leap to h2, gobbling up Whites lone soldier on the h file.) Run this position though many advanced chess engines, and most will tell you that with the sacrificed pieces, AlphaZero is now losing. So why is it doing this?

Sacrifices are very common in chess, but theyre almost always offered up for an immediate tactical edge or some other obvious recompense. But again and again, this magician-like chess engine makes early sacrifices like these as part of an extremely long-term strategy whose benefit wont become clear for dozens of moves into the future.

Eventually AlphaZero is going to fill the gaps left by the missing pawns with rooks, like a double-barrel shotgun. Those pawns, AlphaZero apparently believes, are worth less than the opportunity to assault the king from even more directions.

Lakota Gambill

By move 42, AlphaZero has sacrificed even more pawns, and is marching another poor, disposable sucker toward oblivion. But this move seals AlphaZeros victory. That final pawn is about to crack open Stockfishs kings corner like a knife twisting open an oyster.

Another key element to AlphaZeros style is its absolute obsession toward attacks against the opponents kingrather than focusing on more delicate tactical plays. By move 42, both of Alpha- Zeros bishops control long open diagonals directed right at the king. Its queen is one leap away from the fray. And both rooks are likewise staring down Stockfishs defense with unholy fury.

In their book, Sadler and Regan write that its important for chess masters to embrace early strategic pawn sacrifices despite the risk: Dont rush! AlphaZero doesnt attempt to deliver checkmate immediately but ensures that all its pieces are joining into the attack.

Science & Technology Reporter

William Herkewitz is a science and technology journalist based in Berlin, Germany. He writes about theoretical physics, AI, astronomy, board games, brewing and everything in between.

See the original post:
AlphaZero Vs. Stockfish 8 | AI Is Conquering Computer Chess

Open source chess engine

Stockfish is a free and open-source chess engine, available for various desktop and mobile platforms. It can be used in chess software through the Universal Chess Interface.

Stockfish has consistently ranked first or near the top of most chess-engine rating lists and, as of October 2022, is the strongest CPU chess engine in the world.[3] It has won the Top Chess Engine Championship 13 times and the Chess.com Computer Chess Championship 19 times.

Stockfish is developed by Marco Costalba, Joona Kiiski, Gary Linscott, Tord Romstad, Stphane Nicolet, Stefan Geschwentner, and Joost VandeVondele, with many contributions from a community of open-source developers.[4] It is derived from Glaurung, an open-source engine by Tord Romstad released in 2004.

Stockfish can use up to 1024 CPU threads in multiprocessor systems. The maximal size of its transposition table is 32 TB. Stockfish implements an advanced alphabeta search and uses bitboards. Compared to other engines, it is characterized by its great search depth, due in part to more aggressive pruning and late move reductions.[5] As of July2022[update], Stockfish 15 (4-threaded) achieves an Elo rating of 3540+1616 on the CCRL 40/15 benchmark.[6]

Stockfish supports Chess960, which is one feature that was inherited from Glaurung.[7] The Syzygy tablebase support, previously available in a fork maintained by Ronald de Man, was integrated into Stockfish in 2014.[8] In 2018 support for the 7-men Syzygy was added, shortly after becoming available.[9]

Stockfish has been a very popular engine on various platforms. On desktop, it is the default chess engine bundled with the Internet Chess Club interface programs BlitzIn and Dasher. On mobile, it has been bundled with the Stockfish app, SmallFish and Droidfish. Other Stockfish-compatible graphical user interfaces (GUIs) include Fritz, Arena, Stockfish for Mac, and PyChess.[10][11] Stockfish can be compiled to WebAssembly or JavaScript, allowing it to run in the browser. Both chess.com and Lichess provide Stockfish in this form in addition to a server-side program.[12] Release versions and development versions are available as C++ source code and as precompiled versions for Microsoft Windows, macOS, Linux 32-bit/64-bit and Android.

The program originated from Glaurung, an open-source chess engine created by Romstad and first released in 2004. Four years later, Costalba, inspired by the strong open-source engine, decided to fork the project. He named it Stockfish because it was "produced in Norway and cooked in Italy" (Romstad is Norwegian, Costalba is Italian). The first version, Stockfish 1.0, was released in November 2008.[13][14] For a while, new ideas and code changes were transferred between the two programs in both directions, until Romstad decided to discontinue Glaurung in favor of Stockfish, which was the more advanced engine at the time.[15] The last Glaurung version (2.2) was released in December 2008.

Around 2011, Romstad decided to abandon his involvement with Stockfish in order to spend more time on his new iOS chess app.[16] On 18 June 2014 Marco Costalba announced that he had "decided to step down as Stockfish maintainer" and asked that the community create a fork of the current version and continue its development.[17] An official repository, managed by a volunteer group of core Stockfish developers, was created soon after and currently manages the development of the project.[18]

Since 2013, Stockfish has been developed using a distributed testing framework named Fishtest, where volunteers can donate CPU time for testing improvements to the program.[19][20][21]

Changes to game-playing code are accepted or rejected based on results of playing of tens of thousands of games on the framework against an older "reference" version of the program, using sequential probability ratio testing. Tests on the framework are verified using the chi-squared test, and only if the results are statistically significant are they deemed reliable and used to revise the software code.

After the inception of Fishtest, Stockfish experienced an explosive growth of 120 Elo points in just 12 months, propelling it to the top of all major rating lists.[22] In Stockfish 7, Fishtest author Gary Linscott was added to the official list of authors in acknowledgement of his contribution to Stockfish's strength.

As of November2022[update], the framework has used a total of more than 9500 years of CPU time to play over 5.5billion chess games.[23]

In June 2020, an efficiently updatable neural network (NNUE) fork introduced by computer shogi programmers called Stockfish NNUE was discussed by developers.[24][25] In July 2020 chess news reported that Stockfish NNUE had "broken new ground in computer chess by incorporating a neural network into the already incredibly powerful Stockfish chess engine."[26] A NNUE merge into Stockfish was then announced and development builds became available.[27][28]

"The NNUE branch maintained by @nodchip has demonstrated strong results and offers great potential, and we will proceed to merge ... This merge will introduce machine learning based coding to the engine, thus enlarging the community of developers, bringing in new skills. We are eager to keep everybody on board, including all developers and users of diverse hardware, aiming to be an inclusive community ...the precise steps needed will become clearer as we proceed, I look forward to working with the community to make this happen!"

On 2 September 2020, the twelfth version of Stockfish was released, incorporating the aforementioned neural network improvement. According to the blog announcement, this new version "plays significantly stronger than any of its predecessors", typically winning ten times more game pairs than it loses when matched against version eleven.[29][30]

Stockfish is a TCEC multiple-time champion and the current leader in trophy count. Ever since TCEC restarted in 2013, Stockfish has finished first or second in every season except one. In TCEC Season 4 and 5, Stockfish finished runner-up, with Superfinal scores of 2325 first against Houdini 3 and later against Komodo 1142. Season 5 was notable for the winning Komodo team as they accepted the award posthumously for the program's creator Don Dailey, who succumbed to an illness during the final stage of the event. In his honor, the version of Stockfish that was released shortly after that season was named "Stockfish DD".[31]

On 30 May 2014, Stockfish 170514 (a development version of Stockfish 5 with tablebase support) convincingly won TCEC Season 6, scoring 35.528.5 against Komodo 7x in the Superfinal.[32] Stockfish 5 was released the following day.[33] In TCEC Season 7, Stockfish again made the Superfinal, but lost to Komodo with the score of 30.533.5.[32] In TCEC Season 8, despite losses on time caused by buggy code, Stockfish nevertheless qualified once more for the Superfinal, but lost the ensuing 100-game match 46.553.5 to Komodo.[32] In Season 9, Stockfish defeated Houdini 5 with a score of 54.5 versus 45.5.[32][34]

Stockfish finished third during season 10 of TCEC, the only season since 2013 in which Stockfish had failed to qualify for the superfinal. It did not lose a game, but was still eliminated because it was unable to score enough wins against lower-rated engines. After this technical elimination, Stockfish went on a long winning streak, winning seasons 11 (59 vs. 41 against Houdini 6.03),[32][35] 12 (60 vs. 40 against Komodo 12.1.1),[32][36] and 13 (55 vs. 45 against Komodo 2155.00)[32][37] convincingly.[38] In Season 14, Stockfish faced a new challenger in Leela Chess Zero, but managed to eke out a win by one game (50.549.5).[32][39] Its winning streak was finally ended in season 15, when Leela qualified again and won 53.546.5,[32] but Stockfish promptly won season 16, defeating AllieStein 54.545.5, after Leela failed to qualify for the superfinal.[32] In season 17, Stockfish faced Leela again in the superfinal, losing 52.547.5. However, Stockfish convincingly defeated Leela in the next four superfinals: 53.546.5 in season 18, 54.545.5 in season 19, 5347 in season 20, and 5644 in season 21.[32]

Stockfish also took part in the TCEC cup, winning the first edition, but was surprisingly upset by Houdini in the semifinals of the second edition.[32][40] Stockfish recovered to beat Komodo in the third place playoff.[32] In the third edition, Stockfish made it to the finals, but was defeated by Leela Chess Zero after blundering in a 7-man endgame tablebase draw. It turned this result around in the fourth edition, defeating Leela in the final 4.53.5.[32]

Ever since chess.com hosted its first Chess.com Computer Chess Championship in 2018, Stockfish has been the most successful engine. It dominated the earlier championships, winning six consecutive titles before finishing second in CCC7. Since then, its dominance has come under threat from the neural-network engines Leelenstein and Leela Chess Zero, but it has continued to perform well, reaching at least the superfinal in every edition up to CCC11. CCC12 had for the first time a knockout format, with seeding placing CCC11 finalists Stockfish and Leela in the same half. Leela eliminated Stockfish in the semi-finals. However, a post-tournament match against the loser of the final, Leelenstein, saw Stockfish winning in the same format as the main event.

Stockfish's strength relative to the best human chess players was most apparent in a handicap match with grandmaster Hikaru Nakamura (2798-rated) in August 2014. In the first two games of the match, Nakamura had the assistance of an older version of Rybka, and in the next two games, he received White with pawn odds but no assistance. Nakamura was the world's fifth-best human chess player at the time of the match, while Stockfish 5 was denied use of its opening book and endgame tablebase. Stockfish won each half of the match 1.50.5. Both of Stockfish's wins arose from positions in which Nakamura, as is typical for his playing style, pressed for a win instead of acquiescing to a draw.[141]

In December 2017, Stockfish 8 was used as a benchmark to test Google division DeepMind's AlphaZero, with each engine supported by different hardware. AlphaZero was trained through self-play for a total of nine hours, and reached Stockfish's level after just four.[142][143][144] In 100 games from the normal starting position, AlphaZero won 25 games as White, won 3 as Black, and drew the remaining 72, with 0 losses.[145] AlphaZero also played twelve 100-game matches against Stockfish starting from twelve popular openings for a final score of 290 wins, 886 draws and 24 losses, for a point score of 733:467.[146][note 2]

AlphaZero's victory over Stockfish sparked a flurry of activity in the computer chess community, leading to a new open-source engine aimed at replicating AlphaZero, known as Leela Chess Zero. By January 2019, Leela was able to defeat the version of Stockfish that played AlphaZero (Stockfish 8) in a 100-game match. An updated version of Stockfish narrowly defeated Leela Chess Zero in the superfinal of the 14th TCEC season, 50.549.5 (+10 =81 9),[32] but lost the superfinal of the next season to Leela 53.546.5 (+14 =79 -7).[32][148] The two engines remain very close in strength to each other even as they continue to improve: Leela defeated Stockfish in the superfinal of TCEC Season 17, but Stockfish won TCEC Season 18, TCEC Season 19, TCEC Season 20, and TCEC Season 21, each time defeating Leela in the superfinal.

Read the original:
Stockfish (chess) - Wikipedia

AlphaZero is a computer program developed by DeepMind and Google researchers. AlphaZero achieved a superhuman level of play in the games of chess, shogi, and Go within 24 hours by using reinforcement learning, where it simultaneously trained its game playing agents against themselves. AlphaZero learned without human knowledge or teaching. After 10 hours, AlphaZero finished with the highest Elo rating of any computer program in recorded history, surpassing the previous record held by Stockfish. The results were published in May 2017 on arXiv.

AlphaZero is a self-learning algorithm that learns to win against itself and then uses this self-improvement to win against other programs and humans. It was developed by DeepMind, which is a British artificial intelligence research company acquired by Google in 2014 for over $500 million. DeepMind was founded by Demis Hassabis, who is also a chess player. AlphaZeros original blueprint was created on December 5, 2017. The neural network for DeepMinds AlphaZero is updated regularly.

AlphaZero is an algorithm that can be used for different types of games. AlphaZero could be used for a strategy game like chess or even shogi. AlphaZero uses the same learning procedure as its predecessors, which is known as reinforcement learning. Reinforcement learning uses trial and error to solve problems and continually improve performance. Its the process by which computers teach themselves through experience, which also includes loss aversion.

The first few moves played by AlphaZero uses its own neural network, and the latter moves are based on the results of the previous move. AlphaZero is a Monte-Carlo tree search algorithm that simplifies branches to find the optimal path of play. This method allows it to search through 80,000 possible moves per second. Its similar to computer programs playing beginner levels of chess with very basic rules. AlphaZero is also a search algorithm that works creatively and bluffs depending on its opponents weakness. It can also select an appropriate level of complexity based on its opponents skill.

DeepMinds AlphaZero is a reinforcement learning algorithm that uses neural networks to solve various combinatorial problems. Its based on the algorithms used for AlphaGo, which is a computer program designed to play the board game Go and beat top human players. AlphaZero can mimic the optimum play of master games from databases or by self-play using a large number of processing units across one or more machines. The algorithm uses two separate neural networks, one for self-play and another for playing against humans. At the start, AlphaZero has no knowledge and no experience but learns fast. It can learn a wide range of games by playing against itself.

AlphaZero is programmed for self-improvement in two ways. The first way is called interleaved learning, where it plays against itself due to its inability to see its own previous moves. The second way is called explicit learning, which lets it see its own previous moves. This allows it to recall the most successful game situations and use them to improve its play further. AlphaZero has a policy network that is the programs search function and a value network to estimate the winner.

AlphaZero can also analyze past chess games to improve your performance. It can even teach you how to play against a particular opponent, improve your move choices, and develop new methods of attack to use against your opponent. AlphaZero is a versatile chess program that uses algorithms for playing vs humans and playing against itself. AlphaZero doesnt use search function but creates threes matches on its own. As the network improves, its performance goes up and becomes more specialized for different situations of chess play.

AlphaZero is very advanced compared to previous chess programs like Stockfish. It can use the previous results from Stockfish to improve its own neural network. AlphaZero can also play against itself and learn from those previous matches. AlphaZero defeated Stockfish at TCEC (The Chess Experiment Competition) in December 2017. AlphaZero won 290 matches and only lost 60, using the 12 most popular human openings.

Stockfish is a strong chess engine that was developed by Tord Romstad and Marco Costalba in Norway. Stockfish is free and open source software that can run on multiple platforms like Linux, Windows, Mac OS X, etc. Its different from AlphaZero, because it doesnt rely on AI or machine learning.

Artificial Intelligence is a technique used for making computers and machines able to do intelligent things normally associated with humans. AI is used in computer chess programs to play and win against opponents. AI has been developed in many other fields, like robotics, medical science, engineering, law, etc. AlphaZero uses AI to play chess better than humans.

Google DeepMinds AlphaZero doesnt use deep learning but uses neural networks instead. Deep learning is a subset of machine learning, which is an artificial intelligence technique used to make computers do things that require intelligence. Deep Learning is related to the human brain, which has helped create AlphaZero.

AlphaZero will be developed further to enable it to play at an even higher level of chess. AlphaZero has demonstrated its skill in solving and playing against the strongest chess computer programs like Stockfish. However, AlphaZero depends on its proprietary search function and neural networks.

The future of AlphaZero in chess is still unsure. It can learn to play many different types of chess games as well as improve with time. AlphaZero has shown a lot of potential but the future is still unknown for it. AlphaZero can also play itself using neural networks, and improve even further over time, but requires more work.

A computer program like AlphaZero can be used to play against humans. AlphaZero has played and defeated the strongest chess programs available.

This technology may one day be used for other games and activities as well. However, the first applications will be in chess, board games, online gaming, etc. It can also be used for handicapping in tournaments where two players of different skill levels can compete against each other. AlphaZero is a new form of artificial intelligence that can affect the future of games and applications all around the world.

AlphaZero is not open source software, which means its not free to use or study. Because AlphaZero has been created by Google DeepMind, it uses neural networks and AI to play chess better than any other program.

Chess is a game of logic and has been around for many centuries. Its important to maintain fairness and freedom in the game of chess. Its an intellectual sport that tests your ability to think quickly and be creative at the same time. It has been proven that chess is beneficial to players health, mental activity, social life, longevity, etc. Artificial intelligence has also evolved globally in recent years. Many scientists have been developing AI-related programs over the year.

Algorithms are powerful tools that help programmers and machine learning experts to create these programs from scratch. Many chess players and enthusiasts have become interested in the Singularity Universitys AGI course, which is all about artificial intelligence. And Google DeepMinds AlphaZero program has become one of the most popular AI programs in the world.

As a result, chess players and enthusiasts are more aware that AI is quickly developing and improving. So its important to be aware about AI in general, including what it can do and how it works. Thats why artificial intelligence is a topic worth studying for todays society and future generations. AlphaZero is not the first chess program to use AI, but it is likely to be one of the most popular. Because it learns as it goes, its able to play several chess games at once, like many elite chess players.

AlphaZero has gotten some attention because it can beat the best of the best, like its predecessor AlphaGo. Also, it has made a very significant impact in the chess world and got people talking about AI.

Although AlphaZero was created to play against itself, it was not specifically developed to defeat humans with 100% accuracy. There still arent any guarantees that AlphaZero will always be able to defeat human counterparts.

That being said, AlphaZero can see all possible moves and outcomes. It never makes a risky mistake and there are no errors in judgment, which is an advantage that these machines have over humans.

AlphaZero is a tremendous achievement in artificial intelligence. It has surpassed humans in the game of Chess, as well as GO, a complex board game once thought to be uniquely suited for machine learning techniques to easily match human play.

AlphaZeros chess abilities were developed through reinforcement learning. This meant that it had no familiarity with the game at all. Rather, it was placed down in a virtual world and allowed to play against itself millions of times, each time learning from its mistakes and improving its play.

When one considers the complexity of Chess, this seems like a hopeless task. Particularly when one considers that even among humans there are countless approaches to winning at the game. But the results speak for themselves: AlphaZero quickly dominated all other forms of chess playing software in the world.

I hope this guide on the AlphaZero Chess Engine helped you. If you liked this post, you may also be interested in learning about other Chess Engines like AlphaZero and Stockfish.

Continued here:
AlphaZero Chess Engine: The Ultimate Guide

About damn time. That was the response from AI policy and ethics wonks to news last week that the Office of Science and Technology Policy, the White Houses science and technology advisory agency, had unveiled anAI Bill of Rights. The document is Bidens vision of how the US government, technology companies, and citizens should work together to hold the AI sector accountable.

Its a great initiative, and long overdue.The US has so far been one of the only Western nations without clear guidance on how to protect its citizens against AI harms. (As a reminder, these harms includewrongful arrests,suicides, and entire cohorts of schoolchildren beingmarked unjustlyby an algorithm. And thats just for starters.)

Tech companies say they want to mitigate these sorts of harms, but its really hard to hold them to account.

The AI Bill of Rights outlines five protections Americans should have in the AI age, including data privacy, the right to be protected from unsafe systems, and assurances that algorithms shouldnt be discriminatory and that there will always be a human alternative. Read more about ithere.

So heres the good news:The White House has demonstrated mature thinking about different kinds of AI harms, and this should filter down to how the federal government thinks about technology risks more broadly. The EU is pressing on withregulationsthat ambitiously try to mitigate all AI harms. Thats great but incredibly hard to do, and it could take years before their AI law, called the AI Act, is ready. The US, on the other hand, can tackle one problem at a time, and individual agencies can learn to handle AI challenges as they arise, says Alex Engler, who researches AI governance at the Brookings Institution, a DC think tank.

And the bad:The AI Bill of Rights is missing some pretty important areas of harm, such as law enforcement and worker surveillance. And unlike the actual US Bill of Rights, the AI Bill of Rights is more an enthusiastic recommendation than a binding law. Principles are frankly not enough, says Courtney Radsch, US tech policy expert for the human rights organization Article 19. In the absence of, for example, a national privacy law that sets some boundaries, its only going part of the way, she adds.

The US is walking on a tightrope.On the one hand, America doesnt want to seem weak on the global stage when it comes to this issue. The US plays perhaps the most important role in AI harm mitigation, since most of the worlds biggest and richest AI companies are American. But thats the problem. Globally, the US has to lobby against rules that would set limits on its tech giants, and domestically its loath to introduce any regulation that could potentially hinder innovation.

The next two years will be critical for global AI policy.If the Democrats dont win a second term in the 2024 presidential election, it is very possible that these efforts will be abandoned. New people with new priorities might drastically change the progress made so far, or take things in a completely different direction. Nothing is impossible.

Read the rest here:
Whos going to save us from bad AI? - MIT Technology Review