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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

This is a really amazing result, says Franois Le Gall, a mathematician at Nagoya University in Japan, who was not involved in the work. Matrix multiplication is used everywhere in engineering, he says. Anything you want to solve numerically, you typically use matrices.

Despite the calculations ubiquity, it is still not well understood. A matrix is simply a grid of numbers, representing anything you want. Multiplying two matrices together typically involves multiplying the rows of one with the columns of the other. The basic technique for solving the problem is taught in high school. Its like the ABC of computing, says Pushmeet Kohli, head of DeepMinds AI for Science team.

But things get complicated when you try to find a faster method. Nobody knows the best algorithm for solving it, says Le Gall. Its one of the biggest open problems in computer science.

This is because there are more ways to multiply two matrices together than there are atoms in the universe (10 to the power of 33, for some of the cases the researchers looked at). The number of possible actions is almost infinite, says Thomas Hubert, an engineer at DeepMind.

The trick was to turn the problem into a kind of three-dimensional board game, called TensorGame. The board represents the multiplication problem to be solved, and each move represents the next step in solving that problem. The series of moves made in a game therefore represents an algorithm.

The researchers trained a new version of AlphaZero, called AlphaTensor, to play this game. Instead of learning the best series of moves to make in Go or chess, AlphaTensor learned the best series of steps to make when multiplying matrices. It was rewarded for winning the game in as few moves as possible.

We transformed this into a game, our favorite kind of framework, says Hubert, who was one of the lead researchers on AlphaZero.

Link:
DeepMinds game-playing AI has beaten a 50-year-old record in computer science - MIT Technology Review

1 Hurricane Ian is likely to be Floridas deadliest in 87 yearsThe majority of the 100+ casualties are believed to have drowned. (WP $)+ Areas that embrace solar power fare better in extreme weather. (Slate $)+ Bangkoks flooding problem is steadily worsening. (New Yorker $)

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Quote of the day

The spam finds its way into my inbox, too.

Commissioner Ellen L. Weintraub of the Federal Election Commission, who helps police US political campaigns, tells the Washington Post that even she cant escape the deluge of political spam emails.

The big story

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The Download: TikTok moral panics, and DeepMinds record-breaking AI - MIT Technology Review

Learn how your company can create applications to automate tasks and generate further efficiencies through low-code/no-code tools on November 9 at the virtual Low-Code/No-Code Summit. Register here.

This week, Googled-owned tech lab, DeepMind, unveiled its first AI that is capable of creating its own algorithms to speed up matrix multiplication. Though its taught in high school math, matrix multiplication is actually fundamental to computational tasks and remains a core operation in neural networks.

In the same vein, OpenAI this week announced the release of Whisper its open-source, deep learning model for speech recognition. The company claims the technology already shows promising results transcribing audio in several languages.

Joining the innovation sprint this week, Intel detailed a plan to make developers lives a bit easier, with a goal to make it possible to build an application once that can run on any operating system. Historically, this was a goal of the Java programming language, but even today the process is not uniform across the computing landscape something Intel hopes to change.

On the security front, enterprise leaders had several new announcements to take note of this week, including the zero-day flaw exploit in Microsofts Exchange Server. The company confirmed that a suspected state-sponsored threat actor was able to successfully exfiltrate data from fewer than 10 organizations using its staple platform.

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While its no secret that attacks like these continue to expand in both volume and intensity the methods for preventing attacks are also evolving. Vulnerability solutions provider Tenable is one that has evolved to change its main focus, too. This week, the company announced its shifting its focus from vulnerability management to attack surface management and released a new tool for enterprises with that focus.

Heres more from our top five tech stories of the week:

AlphaTensor, according to a DeepMind blog post, builds upon AlphaZero, an agent that has shown superhuman performance on board games like chess and Go. This new work takes the AlphaZero journey further, moving from playing games to tackling unsolved mathematical problems.

This research delves into how AI could be used to improve computer science itself.

The ability to build once and run anywhere, however, is not uniform across the computing landscape in 2022. Its a situation that Intel is looking to help change, at least when it comes to accelerated computing and the use of GPUs.

Intel is contributing heavily to the open-source SYCL specification (SYCL is pronounced like sickle) that aims to do for GPU and accelerated computing what Java did decades ago for application development.

Exposure management gives security teams a broader view of the attack surface, offering the ability to conduct attack path analysis to analyze attack paths from externally identified points to internal assets. It also allows organizations to create a centralized inventory of all IT, cloud, Active Directory and web assets.

While information is limited, Microsoft has confirmed in a blog post that these exploits have been used by a suspected state-sponsored threat actor to target fewer than 10 organizations and successfully exfiltrate data.

Developers and researchers who have experimented with Whisper are also impressed with what the model can do. However, what is perhaps equally important is what Whispers release tells us about the shifting culture in artificial intelligence (AI) research and the kind of applications we can expect in the future.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

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Top 5 stories of the week: DeepMind and OpenAI advancements, Intels plan for GPUs, Microsofts zero-day flaws - VentureBeat