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JOIN OPENAI
We’re hiring talented people in a variety of roles across research, engineering, operations, people, finance, and policy to join our team in San Francisco. OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity, and we require a team of individuals with diverse perspectives, experiences,and
OPENAI API
import openai prompt = """We’re releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task.OPENAI - GYM
Open source interface to reinforcement learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks.. import gym env = gym.make("CartPole-v1") observation = env.reset() for _ in range(1000): env.render() action = env.action_space.sample() # your agent here (this takes random actions) observation, reward, done, info = env.step(action) if done: observation = env SPINNING UP DOCUMENTATION Part 1: Key Concepts in RL. What Can RL Do? Key Concepts and Terminology. (Optional) Formalism. Part 2: Kinds of RL Algorithms. A Taxonomy of RL Algorithms. Links to Algorithms in Taxonomy. Part 3: Intro to Policy Optimization. Deriving the Simplest Policy Gradient.SEARCH - OPENAI API
Search. The Search endpoint allows you to do a semantic search over a set of documents.This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they areto the input query.
JUKEBOX SAMPLE EXPLORER 5b_lyrics: Re-renditions: Pop: Bruno Mars: 0.98: 5b_lyrics: Unseen lyrics: R&B: Rihanna: 0.98: 5b_lyrics: Re-renditions: Reggae: Bob Marley and the Wailers: 0.98: 5bGYM
Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcementTWIN DELAYED DDPG
Target policy smoothing essentially serves as a regularizer for the algorithm. It addresses a particular failure mode that can happen in DDPG: if the Q-function approximator develops an incorrect sharp peak for some actions, the policy will quickly exploit that peak and then have brittle or incorrect behavior. OPENAIAPIPROJECTSBLOGABOUTMUSENETLEARNING TO MODEL OTHER MINDS OpenAI. Discovering and enacting. the path to safe artificial. general intelligence. Our first-of-its-kind API can be applied to any language task, and currently serves millions of production requests each day. Explore API Learn more. EXAMPLES - OPENAI API Examples. Explore what's possible with some example applications. Chat. Open ended conversation with an AI assistant. Q&A. This prompt creates a question + answer structure for answering questions based on existing knowledge. Grammar correction. This zero-shot prompt corrects sentences into standard English. Summarize for a 2nd grader.JOIN OPENAI
We’re hiring talented people in a variety of roles across research, engineering, operations, people, finance, and policy to join our team in San Francisco. OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity, and we require a team of individuals with diverse perspectives, experiences,and
OPENAI API
import openai prompt = """We’re releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task.OPENAI - GYM
Open source interface to reinforcement learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks.. import gym env = gym.make("CartPole-v1") observation = env.reset() for _ in range(1000): env.render() action = env.action_space.sample() # your agent here (this takes random actions) observation, reward, done, info = env.step(action) if done: observation = env SPINNING UP DOCUMENTATION Part 1: Key Concepts in RL. What Can RL Do? Key Concepts and Terminology. (Optional) Formalism. Part 2: Kinds of RL Algorithms. A Taxonomy of RL Algorithms. Links to Algorithms in Taxonomy. Part 3: Intro to Policy Optimization. Deriving the Simplest Policy Gradient.SEARCH - OPENAI API
Search. The Search endpoint allows you to do a semantic search over a set of documents.This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they areto the input query.
JUKEBOX SAMPLE EXPLORER 5b_lyrics: Re-renditions: Pop: Bruno Mars: 0.98: 5b_lyrics: Unseen lyrics: R&B: Rihanna: 0.98: 5b_lyrics: Re-renditions: Reggae: Bob Marley and the Wailers: 0.98: 5bGYM
Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcementTWIN DELAYED DDPG
Target policy smoothing essentially serves as a regularizer for the algorithm. It addresses a particular failure mode that can happen in DDPG: if the Q-function approximator develops an incorrect sharp peak for some actions, the policy will quickly exploit that peak and then have brittle or incorrect behavior.OPENAI STARTUP FUND
The OpenAI Startup Fund is investing $100 million to help AI companies have a profound, positive impact on the world. We’re looking to partner with a small number of early-stage startups in fields where artificial intelligence can have a transformative effect—like health care, climate change, and education—and where AI tools can empower people by helping them be more productive. IMPROVING LANGUAGE MODEL BEHAVIOR BY TRAINING ON A CURATED We've found we can improve language model behavior with respect to specific behavioral values by fine-tuning on a curated dataset ofDetails
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