Homepage / Technology / Google's AI subsidiary made a game-playing program that's entirely self-taught
Business Online Solutions What Is a Board Analysis? The Importance of Planning and Programs Development How Board Governance Software Improves Meetings and Governance How to Craft a Successful Board Meeting Reminder Benefits of a Virtual Data Room for Bankruptcy VDR Example for Business Hong Kong ユースカジノの登録方法を初心者にも分かりやすく図解入りで解説 チェリカジ 5 Как быстро пополнить счет в Казино Х в любой валюте Официальный сайт Up X казино и мгновенные игры Paşa Casino Mobil Uygulama 2025 Giriş Üyelik Bonusu Freespin No Deposit Bonus Casino Free Spins In New Zealand What Are The Best Online Casinos For Real Money Pokies And Bonuses In Australia Дэдди Казино официальный сайт Джойказино: информация про официальный сайт Glory Casino giriş için buraya tıkla ve Türkiyede en popüler casino kullanıcısı ol Les Gambling establishments en Ligne en France 2024 200% Reward + 300 Free Rotates LevelUp Internet casino Melbourne En İyi ve Güvenilir Casino Siteleri Canlı Casino Siteleri 2023 Listesi En İyi ve Güvenilir Casino Siteleri Canlı Casino Siteleri 2023 Listesi Le meilleur casino en ligne franзais Extra Casino avec le dйpфt minimal le in addition bas Yeni Casino Siteleri ᐈ Çevrimiçi Kumarhaneler Mart 2024 Les gambling establishments en ligne proposent une grande variйtй de jeux de internet casino gratuits. Türkiye’deki Resmi Web Sitesi Google Play, Türkiye’de kumar oyunlarına izin verecek Her Gün Tatil Olsa ORDU’DA PAZARTESİ GÜNÜ FINDIK FİYATI NASIL? كازينو اون لاين الكازينوهات الممتازة على الإنترنت ألعاب الكازينو المباشرة مينا كازينو العر Google Play, Türkiye’de kumar oyunlarına izin verecek Domain Sorgulama & Domain Fýrsatlarý Canlı Casino Siteleri: 2024 Güvenilir Siteler Seçilmiştir Golden Easter Slot İncelemesi 2024, Demoyu Ücretsiz Oynayın Golden Easter Slot İncelemesi 2024, Demoyu Ücretsiz Oynayın 1xbet Türkiye Giriş Empieza Kayıt 202 Kumar Ve Kumarhaneler Hakkında Pek İlginç 21 Bilgi Kumarhane Doğru Yazımı Nedir? Tdk Ile Kumarhane Kelimesinin Doğru Yazılışı! Mobilbahiste En İyi Kumar Bonusları Ve Kazançlar Mobilbahis Giriş Sayfası On Line Casino Siteleri En Iyi Casino Siteleri 2024 Mostbet: Türkiye’de Internet Casino Mostbet Online Slotlar Ve Canlı-casin Pin Up Casino Oyna Türkiye, Pinup’un Sah Web Sites Ifade Haberleri Son Dakika Ifade Hakkında Güncel Haber Ve Bilgiler “önceliğimiz Transferin Önünü Açmak, Görüştüğümüz Yerler Var” On Line Casino Nuh’un Gemisi Deluxe Resort & Spa, Kıbrıs The Benefits of Document Management Bonus Veren Siteler 3 000 Den Fazla Online Oyunu Ücretsiz Oyna En Tehlikeli Kumar Oyunu Ekşi Sözlük Deneme Bonusu Veren Siteler Deneme Bonusu 2024 Explore the Magic of WildCardCity Güvenilir Bahis Siteleri En İyi Kumar Siteleri Balıkesir Triatlonuna Avrupadan Ödül Tricks of the Aviator gambling establishment game by Spribe Çevrim Içi Kumar Siteleri “bonus” Yalanıyla Kandırıyor En Güvenilir Canlı On Line Casino Siteleri Xbetting-tips Com Uncovering the Abundant Tapestry of Ozwin Gambling establishment Evaluating Board Portal Providers Uncovering the Wealthy Tapestry of Ozwin On line casino Electronic Data Area Providers Evaluation Cobra Internet casino: Raising the Australian On the internet Video gaming Practical experience 4 Things to Search for in Safeguarded Cloud Safe-keeping Fastpay On line casino Australia – Simple and No-Taxation Wagering Web page officielle franзaise de Joka Gambling establishment The Software Development Universe Game Woo Internet casino – Enjoy Slot machine games around australia Ostdeutsche Biersorten What Are Virtual Data Rooms? Vitamin D Receptor Polymorphisms Revue du Casino BlackLabel Faktory, kterй ovlivnujн hodnocenн ceskэch online kasin How to Make the Most of Your Web Development Organization and Advertising Efforts L’essor des casinos en ligne en France Boost Meeting Efficiency With Boardroom Technology Developments WildJoker Casino WildCardCity On line casino – Guaranteed Australian Gambling Portal WildCardCity Casino – The Ideal On the internet Gambling establishment within australia Modern Technologies Produce Sharing Documents Online Faster and More Protect Free Virtual Info Room pertaining to Speedy Due Diligence A Review of Data Area Software For people who do buiness Five Board Bedroom Features Which will help You Acquire a More Productive Boardroom Electronic Systems To your Business Understanding Legal Terms and Laws in Today’s World The Laws and Contracts of Hollywood: A Sunset Blvd. Tale Legal Discussion Between Johnny Cash and Antonin Scalia Legal Insights: What Teens Should Know Legal Issues and Exceptions: What You Need to Know Legal Insights and Expert Analysis Celebrity Dialogue: Legal Matters in the 21st Century Famous Personalities Discuss Legal Issues The Boys in the Boat: Legal Advisors and The Quest for Legal Knowledge Understanding Legal Matters: Q&A on Criminal Law, Joint Ventures, and More Enticing Title The Departed: Understanding Basic Work Requirements and Legal Rights Youth Slang Blog Article Legal Insights: A Journey into the World of Law The Ins and Outs of Legal Matters: Everything You Need to Know Legal Insights and Trends: A Rap Guide Mysterious Legal Matters Unveiled Insights and Information: Understanding Various Laws and Regulations Famous People of the 21st Century


Google's AI subsidiary made a game-playing program that's entirely self-taught

Google‘s AI subsidiary DeepMind has unveiled the latest version of its Go-playing software, AlphaGo Zero. The new program is a significantly better player than the version that beat the game’s world champion earlier this year, but, more importantly, it’s also entirely self-taught. DeepMind says this means the company is one step closer to creating general purpose algorithms that can intelligently tackle some of the hardest problems in science, from designing new drugs to more accurately modeling the effects of climate change.

The original AlphaGo demonstrated superhuman Go-playing ability, but needed the expertise of human players to get there. Namely, it used a dataset of more than 100,000 Go games as a starting point for its own knowledge. AlphaGo Zero, by comparison, has only been programmed with the basic rules of Go. Everything else it learned from scratch. As described in a paper published in Nature today, Zero developed its Go skills by competing against itself. It started with random moves on the board, but every time it won, Zero updated its own system, and played itself again. And again. Millions of times over.

After three days of self-play, Zero was strong enough to defeat the version of itself that beat 18-time world champion Lee Se-dol, winning handily — 100 games to nil. After 40 days, it had a 90 percent win rate against the most advanced version of the original AlphaGo software. DeepMind says this makes it arguably the strongest Go player in history.

“By not using human data — by not using human expertise in any fashion — we’ve actually removed the constraints of human knowledge,” said AlphaGo Zero’s lead programmer, David Silver, at a press conference. “It’s therefore able to create knowledge itself from first principles; from a blank slate […] This enables it to be much more powerful than previous versions.”

Silver explained that as Zero played itself, it rediscovered Go strategies developed by humans over millennia. “It started off playing very naively like a human beginner, [but] over time it played games which were hard to differentiate from human professionals,” he said. The program hit upon a number of well-known patterns and variations during self-play, before developing never-before-seen stratagems. “It found these human moves, it tried them, then ultimately it found something it prefers,” he said. As with earlier versions of AlphaGo, DeepMind hopes Zero will act as an inspiration to professional human players, suggesting new moves and stratagems for them to incorporate into their game.

As well as being a better player, Zero has other important advantages compared to earlier versions. First, it needs much less computing power, running on just four TPUs (specialized AI processors built by Google), while earlier versions used 48. This, says Silver, allows for a more flexible system that can be improved with less hassle, “which, at the end of the day, is what really matters if we want to make progress.” And second, because Zero is self-taught, it shows that we can develop cutting-edge algorithms without depending on stacks of data.

More from The Verge:

Lego celebrates the women of NASA with new minifigs
Storm Ophelia was so unusual, it was literally off the charts
Bigelow Aerospace wants to put an inflatable space habitat in orbit around the Moon

For experts in the field, these developments are a big part of what makes this new research exciting. That’s is because they offer a rebuttal to a persistent criticism of contemporary AI: that much of its recent gains come mostly from cheap computing power and massive datasets. Skeptics in the field like pioneer Geoffrey Hinton suggest that machine learning is a bit of a one-trick pony. Piling on data and compute is helping deliver new functions, but the current pace of advances is unsustainable. DeepMind’s latest research offers something of a rebuttal by demonstrating that there are major improvements to be made simply by focusing on algorithms.

“This work shows that a combination of existing techniques can go somewhat further than most people in the field have thought, even though the techniques themselves are not fundamentally new,” Ilya Sutskever, a research director at the Elon Musk-backed OpenAI institute, told The Verge. “But ultimately, what matters is that researchers keep advancing the field, and it’s less important if this goal is achieved by developing radically new techniques, or by applying existing techniques in clever and unexpected ways.”

In the case of AlphaGo Zero, what is particularly clever is the removal of any need for human expertise in the system. Satinder Singh, a computer science professor who wrote an accompanying article on DeepMind’s research in Nature, praises the company’s work as “elegant,” and singles out these aspects.

Singh tells The Verge that it’s a significant win for the field of reinforcement learning — a branch of AI in which programs learn by obtaining rewards for reaching certain goals, but are offered no guidance on how to get there. This is a less mature field of work than supervised learning (where programs are fed labeled data and learn from that), but it has potentially greater rewards. After all, the more a machine can teach itself without human guidance, the better, says Singh.

“Over the past five, six years, reinforcement learning has emerged from academia to have much more broader impact in the wider world, and DeepMind can take some of the credit for that,” says Singh. “The fact that they were able to build a better Go player here with an order of magnitude less data, computation, and time, using just straight reinforcement learning — it’s a pretty big achievement. And because reinforcement learning is such a big slice of AI, it’s a big step forward in general.”

What are the applications for these sorts of algorithms? According to DeepMind co-founder Demis Hassabis, they can provide society with something akin to a thinking engine for scientific research. “A lot of the AlphaGo team are now moving onto other projects to try and apply this technology to other domains,” said Hassabis at a press conference.

Hassabis explains that you can think of AlphaGo as essentially a very good machine for searching through complicated data. In the case of Zero, that data is comprised of possible moves in a game of Go. But because Zero was not programmed to understand Go specifically, it could be reprogrammed to discover information in other fields: drug discovery, protein folding, quantum chemistry, particle physics, and material design.

Hassabis suggests that a descendant of AlphaGo Zero could be used to search for a room temperature superconductor — a hypothetical substance that allows electrical current to flow with zero lost energy, allowing for incredibly efficient power systems. (Superconductors exist, but they only currently work at extremely cold temperatures.) As it did with Go, the algorithm would start by combining different inputs (in this case, the atomic composition of various materials and their associated qualities) until it discovered something humans had missed.

“Maybe there is a room temperature superconductor out and about. I used to dream about that when I was a kid, looking through my physics books,” says Hassabais. “But there’s just so many combinations of materials, it’s hard to know whether [such a thing exists].”

Of course, this would be much more complicated than simply pointing AlphaGo Zero at the Wikipedia page for chemistry and physics and saying “have at it.” Despite its complexity, Go, like all board games, is relatively easy for computers to understand. The rules are finite, there’s no element of luck, no hidden information, and — most importantly — researchers have access to a perfect simulation of the game. This means an AI can run millions of tests and be sure it’s not missing anything. Finding other fields that meet these criteria limits the applicability of Zero’s intelligence. DeepMind hasn’t created a magical thinking machine.

These caveats aside, the research published today does get DeepMind just a little bit closer to solving the first half of its tongue-in-cheek, two-part mission statement. Part one: solve intelligence; part two: use it to make the world a better place. “We’re trying to build general purpose algorithms and this is just one step towards that, but it’s an exciting step,” says Hassabis.

Source: Tech CNBC
Google's AI subsidiary made a game-playing program that's entirely self-taught

Comments are closed.