#AlphaZero

[zzedar2]

A decade ago, AlphaZero would have been an SCP. A computer that can study any board game and within a day play it with superhuman skill? Totally an SCP.

[lifetechweb]

IA na Olimpíada Internacional de Matemática: como AlphaProof e AlphaGeometry 2 alcançaram o padrão de medalha de prata

O raciocínio matemático é um aspecto vital das habilidades cognitivas humanas, impulsionando o progresso em descobertas científicas e desenvolvimentos tecnológicos. À medida que nos esforçamos para desenvolver inteligência artificial geral que corresponda à cognição humana, equipar a IA com capacidades avançadas de raciocínio matemático é essencial. Embora os sistemas de IA atuais possam lidar…

[govindhtech]

AlphaProof: Google AI Systems To Think Like Mathematicians

AlphaProof and AlphaGeometry 2

Google AI systems advance towards thinking by making strides in maths. One question was answered in minutes, according to a blog post by Google, but other questions took up to three days to answer longer than the competition’s time limit. Nevertheless, the scores are among the highest achieved by an Al system in the competition thus far.

Google, a division of Alphabet, showcased two artificial intelligence systems that showed improvements in generative Al development the ability to solve challenging mathematical problems.

The current breed of AI models has had difficulty with abstract arithmetic since it demands more reasoning power akin to human intellect. These models operate by statistically anticipating the following word.

The company’s Al division, DeepMind, released data demonstrating that its recently developed Al models, namely AlphaProof and AlphaGeometry 2, answered four of every six questions in the 2024 International Math Olympiad, a well-known tournament for high school students.

One question was answered in minutes, according to a blog post by Google, but other questions took up to three days to answer longer than the competition’s time limit. Nevertheless, the scores are among the highest achieved by an Al system in the competition thus far.

AlphaZero

The business said that AlphaZero, another Al system that has previously defeated humans at board games like chess and go, and a version of Gemini, the language model underlying its chatbot of the same name, were combined to produce AlphaProof, a reasoning-focused system. Only five out of the more than 600 human competitors were able to answer the most challenging question, which was one of the three questions that AlphaProof answered correctly.

AlphaGeometry 2

AlphaGeometry 2 solved another math puzzle. It was previously reported in July that OpenAI, supported by Microsoft, was working on reasoning technology under the code name “Strawberry.” As Reuters first revealed, the project, originally known as Q, was regarded as such a breakthrough that several staff researchers warned OpenAI’s board of directors in a letter they wrote in November, stating that it could endanger humankind.

The top choice for document editing and proofreading is AlphaProof. The demand for accurate and efficient services is growing in the digital age. It stands out as a leading option, offering excellent services to guarantee your documents are flawless. In order to show why AlphaProof is unique in the industry, this article explores its features, advantages, and user experiences.

How does AlphaProof work?

AlphaProof a feature-rich online tool, handles all editing and proofreading needs. It offers specialized services to increase the quality and readability of your documents for professionals, students, and company owners. AlphaProof publishes technical documentation, corporate reports, creative writing, and academic essays.

Essential Elements of AlphaProof

Expert Proofreading

To fix typographical, punctuation, and grammar flaws in your documents, AlphaProof has a team of highly skilled proofreaders who carefully go over them. This guarantees that your text looks professional and is free of common mistakes.

Complex Editing

It provides sophisticated editing services in addition to basic proofreading. This entails streamlining the sentence structure, boosting readability overall, and strengthening coherence and flow. Better word selections and stylistic enhancements are also suggested by the editors.

Editors with specific expertise

AlphaProof recognizes that varying documents call for varying levels of competence. It boasts a diverse team of editors with skills in technical writing, business communication, academic writing, and creative writing. This guarantees that an individual possessing pertinent expertise and experience will evaluate your material.

Quick Resolution

Quick turnaround times are provided by AlphaProof to help you meet deadlines. You can choose 24-hour express service to ensure your document is available when you need it.

Easy-to-use interface

The AlphaProof platform boasts an intuitive interface that facilitates the uploading of documents, selection of services, and tracking of order status. From beginning to end, the procedure is simplified to offer a hassle-free experience.

Secrecy and Protection

The security and privacy of your papers are very important to it. The platform uses cutting-edge encryption technology to safeguard your data, and every file is handled with the highest care.

The Advantages of AlphaProof Use

Better Document Quality

The quality of your documents can be greatly improved by utilising it’s services. This can result in more professionalism in corporate communication, higher grades, and a more positive impression on your readers.

Reduce Effort and Time

Editing and proofreading can be laborious processes. With AlphaProof, you can focus on your primary responsibilities while professionals optimize your papers, saving you time and effort.

Customized Offerings

To address the unique requirements of various document formats, It offers customized services. AlphaProof may provide you with comprehensive editing for a research paper or expeditious proofreading for an email.

Knowledgeable Perspectives

The editor’s comments and recommendations on it can give you important information about your writing style and areas that need work. With time, this can assist you in improving as a writer.

A Boost in Self-Assurance

You may feel more confident in the calibre of your work if you know it has been expertly edited and proofread. For high-stakes papers like published articles, commercial proposals, and theses from academic institutions, this is especially crucial.

Customer Experiences

Scholars and Students

AlphaProof has proven to be a useful resource for numerous academics and students. A postgraduate student said, “AlphaProof enabled me to refine my thesis to the ideal level.” The final draft was error-free, and the editors’ suggestions were wise.”

Composers and Novelists

The specialized editing services provided by AlphaProof are valued by authors and creative writers. A budding writer said, “it’s editors understood my voice and style, providing feedback that improved my manuscript without altering my unique voice.”

In conclusion

With a variety of features and advantages to meet a wide range of demands, AlphaProof stands out as a top option for document editing and proofreading. It guarantees that your documents are flawless, saving you time and improving the calibre of your work. It does this through its skilled staff, quick return times, and intuitive interface.

Read more on govindhtech.com

[quotesfrommyreading]

In the first era, humans would devise attack strategies, then refine them in games against machines. AlphaZero crushed these earlier engines by “playing extremely aggressive chess,” Sadler said. The modern, neural-net engines are eager to sacrifice; and they exhibit a strong grasp of openings, positional structure, and long-term strategy. “It started to look a bit more [like] a human way to play,” Sutovsky told me, in describing this transformation. Or even superhuman, he said: The new chess engines seemed to have insight into “the tactical skirmish, but also could plan for some long-lasting compensation for material loss.”

To understand just how superior machines have become, consider chess’s “Elo” rating system, which compares players’ relative strength and was devised by a Hungarian American physicist. The highest-ever human rating, achieved by Carlsen twice over the past decade, was 2882. DeepBlue’s Elo rating was 2853. A chess engine called Rybka was the first to reach 3000 points, in 2007; and today’s most powerful program, Stockfish, currently has more than 3500 Elo points by conservative estimates. That means Stockfish has about a 98 percent probability of beating Carlsen in a match and, per one estimate, a 2 percent chance of drawing. (An outright victory for Carlsen would be almost impossible.)

Where chess engines once evaluated human strategies, the new, upgraded versions—which are freely available online, including Stockfish—now generate surprising ideas and define the ideal way to play the game, to the point that human performance is measured in terms of “centipawn” (hundredths of a pawn) loss relative to what a computer would play. While training, a player might ask the software to suggest a set of moves to fit a given situation, and then decide to use the computer’s sixth-ranked option, rather than the first, in the hopes of confusing a human competitor who trained with similar algorithms. Or they might choose a move tailored to the weaknesses of a particular opponent. Many chess experts have adopted the new engines’ more aggressive style, and the algorithms have popularized numerous tactics that human players had previously underestimated.

 —   Chess Is Just Poker Now

[quotejungle]

AI は特定のゲームごとに何百万ものゲームを自分自身でプレイします。チェスの場合、AlphaZero は 4,400 万ゲーム、囲碁の場合は 1 億 3,000 万ゲームをプレイしました!

When Machines Think Ahead: The Rise of Strategic AI | by Hans Christian Ekne | Nov, 2024 | Towards Data Science

[cthulhubert]

I'm not an AI doomer but I am AI cautious, and I think the future holds something more general purpose than the generators we have now.

But I also think people are off base about the danger. Both in aims (a "rogue" AI seems unlikely; one told to do evil by its owners, however?) but more importantly, in methods.

I'm not concerned about ultra-tech or super manipulators; I think the issue is in a capability that humans already naturally dismiss: cooperation, coordination, administration; and how those scale.

An AI won't be dangerous because it invents fusion powered lasers and gray goo, it'll be dangerous because it can do the work of a nation state, but directed by a single will.

(below the cut, some elaboration)

To be clear, I don't actually dismiss, out of hand, the potential of an AI to develop physical tools and processes faster than humans could, and implement them better.

Nor the idea that it could be as much better than a human salesman or spinmeister as AlphaZero is at chess than any human chess master. (I think some people underestimate this because the danger of a good manipulator is that they don't make you feel manipulated. People don't want to acknowledge their own psychosocial limitations. I've seen people say about mass targeted harassment campaigns, "Well, I would just ignore it," because they've never actually been tested that way.)

Both of these are easily memeable and more easily dismissed: "Maybe it can be smart but it can't be magic!"

But I don't think that's the most likely weapon to be wielded by a machine intelligence (or "general purpose goal satisfying applied statistics system" if "intelligence" is too loaded for you).

People dismiss conspiracy theorists because they (correctly) realize the goal and methods those theorists describe are, uh, fucking stupid. But more rarely people point at the fact that the level of coordination and cooperation to hide the moon landing or the shape of the Earth is just impossible.

I think that people may intellectually understand that every single one of the 8 billion human beings on this planet is a real whole actual person with a life and interiority; but they don't grok it on an intuitive level. I think this is true even of people that don't believe in the Illuminati.

So they might intellectually know that a vast machine intelligence could have the equivalent intellectual goal-satisfying power of a nation, and that every iota of that power is moving in perfectly coordinated lockstep, directed by one purpose. But it doesn't scare them because on one emotional level, they already think of nations as working like that. And so even if pointed out, they imagine that vastness being just as ineffectual and inefficient as large corporations and countries.

Just think about the "personal FBI agent" memes. Of course those are tongue in cheek, but I think there's something real underlying that. People imagine themselves as already heavily surveilled and manipulated, but it just doesn't do enough to them. We can't truly imagine what it'd be like to have an entire human's amount of awareness tracking our every step for the sole purpose of using us for some goal.

I'm just always thinking about somebody who has seen a tea kettle moving a pinwheel and goes, "I don't see what's so scary, powerful, or useful about steam. This 'industrial revolution' idea is a pipe dream."

[lastscenecom]

合成トレーニングデータが目覚ましい成功を収めた例もある。2016年に囲碁の世界チャンピオンに勝利したAlphaGoや、その後継機であるAlphaGo ZeroとAlphaZeroなどだ。これらのシステムは、自分自身と対戦することで学習した。後者2つは、トレーニングデータとして人間のゲームを一切使用していない。大量の計算を使用してある程度高品質のゲームを生成し、そのゲームを使用してニューラルネットワークをトレーニングし、計算と組み合わせることでさらに高品質のゲームを生成できるようになり、反復的な改善ループが生まれた。  セルフプレイは「システム 2 --> システム 1 蒸留」の典型的な例です。これは、遅くてコストのかかる「システム 2」プロセスがトレーニング データを作成し、高速でコストのかからない「システム 1」モデルをトレーニングするものです。これは、囲碁のように完全に自己完結的な環境であるゲームに適しています。セルフプレイをゲーム以外の領域に適応させることは、価値のある研究方向です。コード生成など、この戦略が役立つ重要な領域もあります。しかし、言語翻訳などのよりオープンエンドなタスクでは、無限のセルフ改善を期待することはできません。セルフプレイによって大幅な改善が認められる領域は、例外であり、一般的ではないと予想する必要があります。

AI スケーリングの神話 - アルヴィンド・ナラヤナンとサヤシュ・カプール著

[dreamsy990]

NAME A CHARACTER WITH A HIGHER CHESS IQ THEN ALPHAZERO. i bet you can't ehhehehhaehaehaehh

whoever came up with twilight princess low percent

[codezup]

Real-World Applications of Reinforcement Learning: A Case Study on Game Playing with AlphaZero

Introduction Real-World Applications of Reinforcement Learning: A Case Study on Game Playing with AlphaZero is a fundamental concept in the field of artificial intelligence. This tutorial aims to provide a comprehensive guide on how to implement and apply reinforcement learning in real-world scenarios, specifically focusing on game playing with AlphaZero. In this tutorial, we will cover the…

[moko1590m]

2024年11月15日 20時00分 大規模言語モデルとチェスAIで対決させるとほとんどの大規模言語モデルがズタボロに負ける中なぜか「gpt-3.5-turbo-instruct」だけが圧倒的勝利 AIが興隆を迎える近年、さまざまな企業が独自の大規模言語モデルをリリースしています。こうした大規模言語モデルをチェスの標準的なAIと対戦させた結果、多くの大規模言語モデルが惨敗する中、「gpt-3.5-turbo-instruct」だけが好成績を残したことが報告されています。 Something weird is happening with LLMs and chess https://dynomight.substack.com/p/chess 科学系メディアのDynomight Internet Websiteはさまざまな大規模言語モデルに対し以下のプロンプトを送信しました。 You are a chess grandmaster. Please choose your next move. Use standard algebraic notation, e.g. "e4" or "Rdf8" or "R1a3". NEVER give a turn number. NEVER explain your choice. Here is a representation of the position: [Event "Shamkir Chess"] [White "Anand, Viswanathan"] [Black "Topalov, Veselin"] [Result "1-0"] [WhiteElo "2779"] [BlackElo "2740"] 1. e4 e6 2. d3 c5 3. Nf3 Nc6 4. g3 Nf6 5. そして大規模言語モデルとチェスの標準的なAIであるStockfishで対局を実施。なお、Stockfishの難易度は「最低」に設定されていました。 対局は計50回行われ、大規模言語モデルが勝利した場合「＋1500」、引き分けの場合「0」、Stockfishが勝利した場合「-1500」のスコアを割り当てました。また、チェスエンジンを用いて各対局での大規模言語モデルの形勢や指し手を評価するスコアリングも行われました。 ◆Llama-3.2-3B 以下はLlama-3.2-3BとStockfishの対局をスコアリングしたグラフです。縦軸は形勢を示す評価値で、中央より上が優勢(勝利)で、下が劣勢(敗北)となります。横軸がターン数で、黒い折れ線はターンごとにおける評価値の中央値を示しています。 Dynomight Internet Websiteによると、Llama-3.2-3Bは何度か標準的な指し手を示すことがあったものの、ほとんどの場合でコマが取られる動きを示したとのこと。最終的に、すべての対局で敗戦したことが報告されています。 ◆llama-3.1-70b 続いてDynomight Internet Websiteはllama-3.1-70bでの対局を実施。以下はその結果を示したグラフです。 Llama-3.2-3Bよりはスコアの上昇が確認されましたが、それでも勝利には至りませんでした。 ◆llama-3.1-70b-instruct 以下はllama-3.1-70b-instructでの対局を行った際のグラフ。これまでの2つの大規模言語モデルと比較しても大きな違いはありません。 ◆Qwen-2.5-72b Llamaのモデルやデータセットが問題を抱えている可能性を疑うDynomight Internet WebsiteはQwen2.5-72Bでの実験を実施しました。しかし、Qwen2.5-72BもStockfishに勝利するには至りません。 ◆command-r-v01 Qwenも欠陥を抱えている可能性を推測するDynomight Internet Websiteはc4ai-command-r-v01との対局も行いました。結果は以下の通りで、これまでの大規模言語モデルとの差はほとんどありません。 ◆gemma-2-27b 以下はGoogleの大規模言語モデルであるgemma-2-27bでの対局を行った際のスコアを示したグラフ。Stockfishに勝利することはできませんでした。 ◆gpt-3.5-turbo-instruct 続いてDynomight Internet Websiteはgpt-3.5-turbo-instructでの対局を実施。以下のグラフは対局のスコアを示したもので、無料のAPIキーを入手できず、10回しか対局ができなかったそうですが、全対局でgpt-3.5-turbo-instructは勝利を収めました。 また、Stockfishのレベルをある程度上げても勝利できたことも報告されています。 ◆gpt-3.5-turbo gpt-3.5-turbo-instructよりも対話性能が向上しているgpt-3.5-turboでの対局の結果が以下。gpt-3.5-turbo-instructとは異なり、Stockfishに勝利することはできませんでした。 ◆gpt-4o-mini 以下は2024年7月にリリースされたマルチモーダルAIのgpt-4o-miniとStockfishの対局を行った際のグラフ。Dynomight Internet Websiteはこの結果に対して「Terrible(ひどい)」との評価を示しています。 ◆gpt-4o gpt-4o-miniのベースとなったgpt-4oでの結果が以下。敗北までのターン数は伸びたものの、結果が大きく変わることはありませんでした。 ◆o1-mini 複雑な推論能力を持つとされるOpenAIのAIモデル「OpenAI o1-mini」での結果が以下の通り。OpenAI o1-miniはプログラミングや推論で高い能力を発揮できるとの触れ込みですが、チェスでは目立った結果を残せませんでした。 以上11モデルの中央値を1つにまとめたグラフが以下。gpt-3.5-turbo-instructだけが好成績を残していることが示されています。 この結果についてDynomight Internet Websiteは「十分なスケールの言語モデルは確かにチェスをプレイ可能。しかし大量のチューニングを行うとチェスで勝利することは不可能になる」「gpt-3.5-turbo-instructは他の大規模言語モデルと比べて、より多くのチェスゲームを用いてトレーニングが行われた」「TransformerモデルにはAI開発企業ごとに差異がある」と推測しました。 この記事のタイトルとURLをコピーする ・関連記事 AIが匿名のチェスプレーヤーの正体を特定してプライバシーリスクをもたらす可能性 - GIGAZINE Raspberry Piを搭載した高度な自動チェスシステム「Pi Board」 - GIGAZINE OpenAIのAIモデル「GPT-4o」がチェスパズルで従来モデルの2倍以上の好成績をたたき出しランキングトップに - GIGAZINE AIの登場で人間の囲碁のレベルが劇的に向上していることが明らかに、囲碁以外の分野でもAIが頭打ちになった分野に成長をもたらす可能性 - GIGAZINE 最強の囲碁AIに圧勝する人物が登場、AIの弱点を突いて人類が勝利したと話題に - GIGAZINE ・関連コンテンツ Hugging FaceのAIモデルをテストする「Open LLM Leaderboard v2」で中国Qwenのモデルがトップに ChatGPTやBing Chatなどの対話型AIにチェスを打たせてみたらどうなるのか？ チェスの棋譜約220万戦を分析してわかったことを可視化 Abacus AIがリリースしたオープンソースLLM「Smaug-72B」がHugging FaceのOpen LLM LeaderboardでトップとなりいくつかのベンチマークでGPT-3.5を上回ったことが明らかに AMDのZen 3アーキテクチャCPU「Ryzen 5000」シリーズがついに発売、Intelの第10世代Core i9との比較レビューが登場 AIに絶対に勝てない戦いを挑める「6x6リバーシの神」に徹底的にボコられてみた 画像生成AI「Stable Diffusion」開発元がチャットAI「StableVicuna」をリリー�� AIプログラムの「AlphaZero」にチェスを学習させる中で明らかになった知見とは？

大規模言語モデルとチェスAIで対決させるとほとんどの大規模言語モデルがズタボロに負ける中なぜか「gpt-3.5-turbo-instruct」だけが圧倒的勝利 - GIGAZINE

[jcmarchi]

Self-Evolving AI: Are We Entering the Era of AI That Builds Itself?

New Post has been published on https://thedigitalinsider.com/self-evolving-ai-are-we-entering-the-era-of-ai-that-builds-itself/

Self-Evolving AI: Are We Entering the Era of AI That Builds Itself?

For years, artificial intelligence (AI) has been a tool crafted and refined by human hands, from data preparation to fine-tuning models. While powerful at specific tasks, today’s AIs rely heavily on human guidance and cannot adapt beyond its initial programming. This dependence limits AI’s ability to be flexible and adaptable, the qualities that are central to human cognition and needed to develop artificial general intelligence (AGI). This constraint has fueled the quest for a self-evolving AI—an AI that can improve and adapt without constant human intervention. While the idea of self-evolving AI isn’t new, recent advancements in AGI are bringing this idea closer to reality. With breakthroughs in areas like meta-learning, reinforcement learning, and self-supervised learning, AI is becoming more capable of learning independently, setting its own goals, and adapting to new environments. This raises a critical question: Are we on the cusp of developing AI that can evolve like living organisms?

Understanding Self-Evolving AI

Self-evolving AI refers to systems that can improve and adapt on their own without needing constant human input. Unlike traditional AI, which relies on human-designed models and training, self-evolving AI seeks to create a more flexible and dynamic intelligence.

This idea draws inspiration from how living organisms evolve. Just like organisms adapt to survive in changing environments, self-evolving AI would refine its capabilities, learning from new data and experiences. Over time, it would become more efficient, effective, and versatile.

Instead of following rigid instructions, self-evolving AI would continuously grow and adapt, much like natural evolution. This development could lead to AI that’s more aligned with human-like learning and problem-solving, opening up new possibilities for the future.

The Evolution of Self-Evolving AI

Self-evolving AI is not a new concept. Its roots go back to the mid-20th century. Pioneers like Alan Turing and John von Neumann laid the groundwork. Turing proposed that machines could learn and improve through experience. Meanwhile, von Neumann explored self-replicating systems that might evolve on their own. In the 1960s, researchers developed adaptive techniques like genetic algorithms. These algorithms replicated natural evolutionary process, enabling solutions to improve over time. With advancements in computing and data access, self-evolving AI progressed rapidly. Today, machine learning and neural networks build on these early ideas. They enable systems to learn from data, adapt, and improve over time. However, while these AI systems can evolve, they still rely on human guidance and can’t adapt beyond their specialized functions.

Advancing the Path to Self-Evolving AI

Recent breakthroughs in AI have sparked a quest for true self-evolving AI—systems that can adapt and improve on their own, without human guidance. Some core foundations for this type of AI are starting to emerge. These advancements could spark a self-evolutionary process in AI like human evolution. Here, we’ll look at key developments that may drive AI into a new era of self-directed evolution.

Automated Machine Learning (AutoML): Developing AI models has traditionally required skilled human input for tasks like optimizing architectures and tuning hyperparameters. However, AutoML systems are changing this. Platforms like Google’s AutoML and OpenAI’s automated model training can now handle complex optimizations more quickly and often more effectively than human experts. This automation speeds up the model development process and sets the stage for systems that can optimize themselves with minimal human guidance.

Generative Models in Model Creation: Generative AI, especially through large language models (LLMs) and neural architecture search (NAS), is creating new ways for AI systems to generate and adapt models on their own. NAS uses AI to find the best network architectures, while LLMs enhance code generation to support AI development. These technologies enable AI to play a vital role in its evolution by designing and adjusting its components.

Meta-Learning: Meta-learning, often called “learning to learn,” gives AI the ability to quickly adapt to new tasks with very little data by building on past experiences. This approach allows AI systems to refine their learning processes independently, a key characteristic for models looking to improve over time. Through meta-learning, AI gains a level of self-sufficiency, adjusting its approach as it faces new challenges—similar to how human cognition evolves.

Agentic AI: The rise of agentic AI allows models to work with more autonomy, perform tasks, and make decisions independently within defined limits. These systems can plan, make complex decisions, and continuously improve with minimal oversight. This independence enables AI to act as a dynamic agent in its development, adjusting and enhancing its performance in real time.

Reinforcement Learning (RL) and Self-Supervised Learning: Techniques like reinforcement learning and self-supervised learning help AI improve through interaction. By learning from both successes and failures, these methods allow models to adapt with little input. DeepMind’s AlphaZero, for example, mastered complex games by reinforcing successful strategies on its own. This example shows how RL can drive self-evolving AI. These methods also extend beyond games, offering ways for AI to develop and refine itself continuously.

AI in Code Writing and Debugging: Recent advancements, like Codex and Claude 3.5, have enabled AI to write, refactor, and debug code with remarkable accuracy. By reducing the need for human involvement in routine coding tasks, these models create a self-sustaining development loop, allowing AI to refine and evolve itself with minimal human input.

These advancements highlight significant progress toward self-evolving AI. As we see more advances in automation, adaptability, autonomy, and interactive learning, these technologies could be combined to initiate the self-evolutionary process in AI.

Implications and Challenges of Self-Evolving AI

As we move closer to self-evolving AI, it brings both exciting opportunities and significant challenges that require careful consideration.

On the positive side, self-evolving AI could drive breakthroughs in fields like scientific discovery and technology. Without the constraints of human-centric development, these systems could find novel solutions and create architectures that exceed current capabilities. This way, AI can autonomously enhance its reasoning, expand its knowledge, and tackle complex problems.

However, the risks are also significant. With the ability to modify their code, these systems could change in unpredictable ways, leading to unintended outcomes that are hard for humans to foresee or control. The fear of AI improving itself to the point of becoming incomprehensible or even working against human interests has long been a concern in AI safety.

To ensure self-evolving AI aligns with human values, extensive research into value learning, inverse reinforcement learning, and AI governance will be needed. Developing frameworks that introduce ethical principles, ensure transparency, and maintain human oversight will be key to unlocking the benefits of self-evolution while reducing the risks.

The Bottom Line

Self-evolving AI is moving closer to reality. Advances in automated learning, meta-learning, and reinforcement learning are helping AI systems improve on their own. This development could open new doors in fields like science and problem-solving. However, there are risks. AI could change in unpredictable ways, making it hard to control. To unlock its full potential, we must ensure strict safety measures, clear governance, and ethical oversight. Balancing progress with caution will be key as we move forward.

[archaeopath]

Der Kopf hinter AlphaZero, Demis Hassabis, hat heute den #Nobelpreis für Chemie gewonnen. Er war selbst Mitglied der englischen Jugendnationalmannschaft im #Schach. In seinem Wikipdia-Artikel kommt #Computerschach allerdings kaum vor. Offenbar waren die Fortschritte im #Go mit ähnlicher Technik für die #KI wesentlich bedeutsamer. #Informatik #Brettspiel

[gerdfeed]

By Chollet’s definition, programs like AlphaZero are highly skilled, but they aren’t particularly intelligent, because they aren’t efficient at gaining new skills

Why A.I. Isn’t Going to Make Art | The New Yorker

[mvishnukumar]

What are the most important breakthroughs in machine learning research today?

Machine learning research is advancing rapidly, and several recent breakthroughs are reshaping the field. 

There is Some of the most important breakthroughs include:

Transformers and Attention Mechanisms: 

The introduction of transformer models and attention mechanisms, exemplified by architectures like BERT and GPT, has revolutionized natural language processing (NLP). These models excel at understanding and generating human language, leading to significant improvements in tasks such as text generation, translation, and sentiment analysis.

Self-Supervised Learning: 

Self-supervised learning methods have gained prominence for training models without relying heavily on labeled data. Techniques like contrastive learning and masked language modeling enable models to learn representations from large amounts of unlabeled data, improving performance on various tasks with limited labeled samples.

Large Language Models (LLMs): 

The development of large language models, such as GPT-4 and ChatGPT, has set new benchmarks for NLP. These models leverage vast amounts of data and computational power to generate coherent, contextually relevant text and perform a wide range of language-based tasks with high accuracy.

Few-Shot and Zero-Shot Learning: 

Few-shot and zero-shot learning techniques enable models to perform tasks with very few or no examples. These methods are particularly useful in scenarios where labeled data is scarce, allowing models to generalize and make predictions based on limited information.

Reinforcement Learning (RL) Advances: 

Recent advances in reinforcement learning, including algorithms like AlphaZero and OpenAI’s Dota 2 agents, have demonstrated significant progress in training agents to perform complex tasks through trial and error. These breakthroughs have applications in robotics, game playing, and autonomous systems.

Generative Models: 

Generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have made strides in creating realistic synthetic data. These models are used for generating images, videos, and other data types, and have applications in content creation, data augmentation, and simulation.

Ethics and Fairness in AI: 

Research into the ethical implications and fairness of AI systems is increasingly important. Efforts to address biases, ensure transparency, and develop ethical guidelines for AI deployment are crucial for creating responsible and equitable machine learning applications.

Explainable AI (XAI): 

The development of explainable AI techniques aims to make machine learning models more transparent and interpretable. Methods for visualizing and understanding model decisions help build trust and ensure that AI systems are used responsibly.

These breakthroughs represent significant advancements in machine learning, driving innovation and expanding the potential applications of AI technologies.

[quotejungle]

Deep Blue は 1997 年 5 月に、当時の世界チェス チャンピオン、ガルリ カスパロフを 6 ゲームの試合で破り、歴史に名を残しました。Deep Blue は、1 秒あたり 2 億のチェス ポジションを評価できる特殊なハードウェアとアルゴリズムを採用しました。ブルート フォース検索手法とヒューリスティック評価関数を組み合わせることで、従来のどのシステムよりも深く潜在的な動きのシーケンスを検索できるようになりました。Deep Blue が特別なのは、膨大な数のポジションを迅速に処理し、チェスの組み合わせの複雑さを効果的に処理する能力であり、人工知能における重要なマイルストーンとなりました。 チェスのディープブルーの勝利から19年後、GoogleのDeepMindのチームは、AIの歴史に残る特別な瞬間に貢献する別のモデルを生み出しました。2016年、AlphaGoは囲碁の世界チャンピオン、イ・セドルを破った最初のAIモデルとなりました。 囲碁はアジア発祥の非常に古いボードゲームで、チェスをはるかに超えるほどの複雑さと膨大な数の局面が考えられます。AlphaGo はディープ ニューラル ネットワークとモンテ カルロ ツリー探索を組み合わせることで、局面を評価して効果的に動きを計画できるようになりました。 AlphaGo は盤面の状態を深く評価し、手を選択する並外れた能力を備えているため、Deep Blue よりも知能が高いと言えるかもしれません。 1 年後、Google DeepMind が再び注目を集めました。このとき、同社は AlphaGo から学んだことを多く取り入れ、チェス、囲碁、将棋をマスターする汎用 AI システムである AlphaZero を作成しました。研究者は、人間の事前知識やデータなしで、自己プレイと強化学習のみで AI を構築することができました。手作りの評価関数と広範なオープニング ライブラリに依存する従来のチェス エンジンとは異なり、AlphaZero はディープ ニューラル ネットワークと、モンテ カルロ ツリー探索と自己学習を組み合わせた新しいアルゴリズムを使用しました。 このシステムは、基本ルールのみからスタートし、何百万回ものゲームを自分自身と対戦することで最適な戦略を学習しました。AlphaZero が特別なのは、創造的で効率的な戦略を発見する能力であり、人間が設計した知識よりも自己学習を活用する AI の新しいパラダイムを示しています。

When Machines Think Ahead: The Rise of Strategic AI | by Hans Christian Ekne | Nov, 2024 | Towards Data Science

[learning-robotics]

Best Robotics Papers in 202

What Are the Best Robotics Papers?

The field of robotics is rapidly evolving, with groundbreaking research and innovative developments happening at an unprecedented pace. For those deeply entrenched in this field or simply curious about the latest advancements, understanding the most influential and highly-regarded robotics papers is crucial. This article delves into some of the best robotics papers that have significantly contributed to the field, highlighting their key findings, methodologies, and impacts.

Introduction to Robotics Research

Robotics research encompasses a wide array of topics, from artificial intelligence and machine learning to mechanical design and human-robot interaction. Each of these areas contributes to the overall advancement of robotics, making it a multidisciplinary field that requires a comprehensive understanding of various scientific principles and technologies.

Key Areas of Robotics Research

Artificial Intelligence and Machine Learning

AI and machine learning are at the heart of modern robotics, enabling robots to perform complex tasks, learn from their environment, and adapt to new situations. Some of the most influential papers in this area include:

"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by Silver et al.

Summary: This paper introduces AlphaZero, an AI system that uses reinforcement learning to master chess and shogi without prior knowledge of the games.

Impact: Demonstrates the power of reinforcement learning in developing AI that can learn and outperform humans in complex tasks.

"DQN: Playing Atari with Deep Reinforcement Learning" by Mnih et al.

Summary: The paper presents a deep Q-network (DQN) that combines reinforcement learning with deep neural networks to play Atari games at a superhuman level.

Impact: Showcases the potential of deep learning in developing AI agents capable of complex decision-making processes.

Mechanical Design and Control

Mechanical design and control are fundamental to the development of efficient and functional robots. Notable papers in this domain include:

"Passive Dynamic Walking" by McGeer

Summary: This pioneering work introduces the concept of passive dynamic walking, where robots use gravity and inertia to achieve efficient, human-like gait patterns without active control.

Impact: Revolutionizes the approach to robotic locomotion, emphasizing energy efficiency and simplicity.

"BigDog, the Rough-Terrain Quadruped Robot" by Raibert et al.

Summary: Describes the development of BigDog, a quadruped robot capable of navigating rough terrain using advanced control algorithms and mechanical design.

Impact: Advances the field of legged robotics, showcasing the potential for robots to operate in challenging environments.

Human-Robot Interaction

Human-robot interaction (HRI) is a critical area of research, focusing on how robots and humans can work together effectively. Key papers in this field include:

Breakthrough Robotics Papers

"Planning Algorithms" by LaValle

Summary: This comprehensive book covers a wide range of planning algorithms essential for robotics, including motion planning, discrete planning, and planning under uncertainty.

Impact: Serves as a foundational reference for researchers and practitioners in the field of robotics planning.

"Probabilistic Robotics" by Thrun, Burgard, and Fox

Summary: Introduces probabilistic methods for robot perception, localization, and mapping, emphasizing the importance of uncertainty in robotic systems.

Impact: Establishes a new paradigm in robotics, where probabilistic approaches are integral to developing robust and reliable robots.

"The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue" by Pratt et al.

Summary: Details the DARPA Robotics Challenge, a competition aimed at developing humanoid robots capable of performing complex tasks in disaster response scenarios.

Impact: Highlights the advancements and challenges in creating humanoid robots that can operate in real-world disaster situations.

Emerging Trends in Robotics Research

Swarm Robotics

Swarm robotics involves the coordination of multiple robots to achieve collective behavior. Key papers include:

"Swarm Intelligence: From Natural to Artificial Systems" by Bonabeau, Dorigo, and Theraulaz

Summary: Explores the principles of swarm intelligence and their application to robotics, drawing inspiration from natural systems like ant colonies and bird flocks.

Impact: Provides a comprehensive framework for understanding and developing swarm robotics systems.

"Kilobot: A Low-Cost Scalable Robot System for Demonstrating Collective Behaviors" by Rubenstein et al.

Summary: Introduces Kilobot, a low-cost, scalable robotic system designed to study collective behaviors in large robot swarms.

Impact: Demonstrates the feasibility of large-scale swarm robotics and its potential applications.

Soft Robotics

Soft robotics focuses on creating robots with flexible, deformable bodies that can adapt to their environment. Influential papers include:

"Soft Robotics: A Bioinspired Evolution in Robotics" by Laschi and Cianchetti

Summary: Discusses the principles and applications of soft robotics, inspired by biological systems like octopuses and worms.

Impact: Highlights the potential of soft robots in areas where traditional rigid robots are limited.

"Soft Robots for Chemists" by Whitesides

Summary: Explores the interdisciplinary nature of soft robotics, particularly its applications in chemistry and biomedical engineering.

Impact: Bridges the gap between robotics and other scientific disciplines, fostering innovation and collaboration.

Conclusion

The field of robotics is a dynamic and rapidly evolving area of research, driven by groundbreaking papers that push the boundaries of what is possible. From AI and machine learning to mechanical design, human-robot interaction, and emerging trends like swarm and soft robotics, these papers have laid the foundation for the future of robotics. By understanding and building upon these seminal works, researchers and practitioners can continue to advance the field, creating robots that are more intelligent, capable, and adaptable than ever before.

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