OpenAI has published the text-generating AI it said was too dangerous to share
The lab says it’s seen ‘no strong evidence of misuse so far’
More info
#OpenAI
#Text
🔭 @DeepGravity
The lab says it’s seen ‘no strong evidence of misuse so far’
More info
#OpenAI
#Text
🔭 @DeepGravity
Don’t Ever Ignore #ReinforcementLearning Again
#Supervised or #unsupervised learning is not everything. Everyone knows that. Get started with #OpenAI #Gym.
Link to the article
🔭 @DeepGravity
#Supervised or #unsupervised learning is not everything. Everyone knows that. Get started with #OpenAI #Gym.
Link to the article
🔭 @DeepGravity
Medium
Don’t Ever Ignore Reinforcement Learning Again
Supervised or unsupervised learning is not everything. Everyone knows that. Get started with OpenAI Gym.
OpenAI releases Safety Gym for reinforcement learning
To study constrained #RL for safe exploration, we developed a new set of environments and tools called #SafetyGym. By comparison to existing environments for constrained RL, Safety #Gym environments are richer and feature a wider range of difficulty and complexity.
Link to the Safety Gym
Link to a related article
#OpenAI
#ReinforcementLearning
🔭 @DeepGravity
To study constrained #RL for safe exploration, we developed a new set of environments and tools called #SafetyGym. By comparison to existing environments for constrained RL, Safety #Gym environments are richer and feature a wider range of difficulty and complexity.
Link to the Safety Gym
Link to a related article
#OpenAI
#ReinforcementLearning
🔭 @DeepGravity
Openai
Safety Gym
We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.
Procgen Benchmark
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
#OpenAI
Link
🔭 @DeepGravity
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
#OpenAI
Link
🔭 @DeepGravity
Openai
Procgen Benchmark
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
A very interesting paper by #Harvard University and #OpenAI
#DeepDoubleDescent: WHERE BIGGER MODELS AND MORE DATA HURT
ABSTRACT
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
Paper
Related article
#DeepLearning
🔭 @DeepGravity
#DeepDoubleDescent: WHERE BIGGER MODELS AND MORE DATA HURT
ABSTRACT
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
Paper
Related article
#DeepLearning
🔭 @DeepGravity
Openai
Deep double descent
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful…
At #OpenAI, we’ve used the multiplayer video game #Dota 2 as a research platform for general-purpose AI systems. Our Dota 2 #AI, called OpenAI Five, learned by playing over 10,000 years of games against itself. It demonstrated the ability to achieve expert-level performance, learn human–AI cooperation, and operate at internet scale.
Link
🔭 @DeepGravity
Link
🔭 @DeepGravity
یه اپلیکیشن خیلی عالی از #OpenAI.
با این اپ شما میتونین تصاویری که نرونهای شبکه در لایههای مختلف بازنمایی میکنن رو ببنین:
https://microscope.openai.com/models
همچنین با این اپ میتونین معماری شبکههای معروفی مث #Inception رو به زیبایی مشاهده کنین.
#OpenAI
🔭 @DeepGravity
با این اپ شما میتونین تصاویری که نرونهای شبکه در لایههای مختلف بازنمایی میکنن رو ببنین:
https://microscope.openai.com/models
همچنین با این اپ میتونین معماری شبکههای معروفی مث #Inception رو به زیبایی مشاهده کنین.
#OpenAI
🔭 @DeepGravity