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لا مناهج لا فروض منزلية لا امتحانات..هكذا تفوقت فنلندا عالميا في مجال التعليم - أحداث.أنفو

لا مناهج لا فروض منزلية لا امتحانات..هكذا تفوقت فنلندا عالميا في مجال التعليم  أحداث.أنفو

Blocking Ads From Pages that Repeatedly Share False News - meta.com

Blocking Ads From Pages that Repeatedly Share False News  meta.com

Supercharger - Tesla

Supercharger  Tesla

من هو الإماراتي الحاصل على سيف شرف "ساندهيرست"؟ - دبي بوست

من هو الإماراتي الحاصل على سيف شرف "ساندهيرست"؟  دبي بوست

Announcing New Ways to Enjoy Memories with Friends - meta.com

Announcing New Ways to Enjoy Memories with Friends  meta.com

جهود المغرب بأفريقيا.. علاقات اقتصادية ومكاسب متبادلة - الجزيرة نت

جهود المغرب بأفريقيا.. علاقات اقتصادية ومكاسب متبادلة  الجزيرة نت

Hard Questions: What Should Happen to People’s Online Identity When They Die? - meta.com

Hard Questions: What Should Happen to People’s Online Identity When They Die?  meta.com

OpenAI Baselines: ACKTR & A2C

We’re releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C) which we’ve found gives equal performance. ACKTR is a more sample-efficient reinforcement learning algorithm than TRPO and A2C, and requires only slightly more computation than A2C per update.

OpenAI Baselines: ACKTR & A2C

We’re releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C) which we’ve found gives equal performance. ACKTR is a more sample-efficient reinforcement learning algorithm than TRPO and A2C, and requires only slightly more computation than A2C per update.

More on Dota 2

Our Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human level to superhuman, given sufficient compute. In the span of a month, our system went from barely matching a high-ranked player to beating the top pros and has continued to improve since then. Supervised deep learning systems can only be as good as their training datasets, but in self-play systems, the available data improves automatically as the agent gets better.

More on Dota 2

Our Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human level to superhuman, given sufficient compute. In the span of a month, our system went from barely matching a high-ranked player to beating the top pros and has continued to improve since then. Supervised deep learning systems can only be as good as their training datasets, but in self-play systems, the available data improves automatically as the agent gets better.

Marketplace Expanding to Europe - meta.com

Marketplace Expanding to Europe  meta.com

Prepare to see double. Leo Messi has his sights set on the season's first 🏆. #HereToCreate https://t.co/BKfVi4DSno

ابتكار علاج جديد لسرطان الرئة يستبدل الكيماوى بالطب البديل - اليوم السابع

ابتكار علاج جديد لسرطان الرئة يستبدل الكيماوى بالطب البديل  اليوم السابع

Dota 2

We’ve created a bot which beats the world’s top professionals at 1v1 matches of Dota 2 under standard tournament rules. The bot learned the game from scratch by self-play, and does not use imitation learning or tree search. This is a step towards building AI systems which accomplish well-defined goals in messy, complicated situations involving real humans.

Dota 2

We’ve created a bot which beats the world’s top professionals at 1v1 matches of Dota 2 under standard tournament rules. The bot learned the game from scratch by self-play, and does not use imitation learning or tree search. This is a step towards building AI systems which accomplish well-defined goals in messy, complicated situations involving real humans.

التجاري وفا بنك بساحل العاج يفوز بجائزة التميز لأفضل مؤسسة مالية - أحداث.أنفو

التجاري وفا بنك بساحل العاج يفوز بجائزة التميز لأفضل مؤسسة مالية  أحداث.أنفو

Times change. The greatest remains. #HereToCreate https://t.co/eWI7Rah73t

Gathering human feedback

RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.

Gathering human feedback

RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.