Summarize the given text.




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tell me about stockfish
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Stockfish is a powerful open-source chess engine known for its strength and high level of play. It is widely considered one of the strongest chess engines available, regularly competing in top-level computer chess tournaments. Stockfish uses alpha-beta search with various enhancements, such as bitboard representation and sophisticated evaluation functions, to analyze positions and make optimal moves. It is a popular choice for players looking to analyze games, study openings, or challenge themselves in chess.
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tell me more.
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Stockfish is developed by a community of volunteers and is available for free on various platforms, including Windows, macOS, Linux, and Android. It is known for its high level of accuracy in evaluating positions and calculating variations, making it a valuable tool for players of all skill levels. Stockfish is often used by chess professionals, coaches, and enthusiasts to analyze games, prepare for tournaments, and improve their overall chess skills. Its open-source nature allows for continuous development and improvement, ensuring that it remains one of the top chess engines in the world.
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tell me more!
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Stockfish uses a combination of advanced algorithms and techniques to evaluate chess positions and make decisions. It employs a variety of search algorithms, such as alpha-beta pruning, iterative deepening, and null move pruning, to efficiently explore the game tree and find the best moves. Stockfish also utilizes sophisticated evaluation functions that consider factors like piece activity, pawn structure, king safety, and material balance to assess the strength of a position.

One of the key strengths of Stockfish is its ability to analyze positions deeply and accurately, often reaching depths of 30 or more plies in complex positions. This allows it to provide precise evaluations and suggest strong moves in a wide range of scenarios. Stockfish's high level of play and strategic understanding make it a valuable resource for players looking to improve their chess skills and learn from top-level computer analysis.
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longer!
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Stockfish is a renowned open-source chess engine that has gained widespread recognition for its exceptional strength and high level of play. Developed by a dedicated community of volunteers, Stockfish is known for its robust performance and advanced algorithms that enable it to analyze chess positions with remarkable accuracy and efficiency.

Utilizing a combination of cutting-edge search algorithms and sophisticated evaluation functions, Stockfish excels at exploring the game tree and identifying optimal moves in complex positions. Techniques such as alpha-beta pruning, iterative deepening, and null move pruning allow Stockfish to delve deep into the possibilities of a position, often reaching impressive depths of analysis.

Stockfish's evaluation function takes into account a multitude of factors, including piece activity, pawn structure, king safety, material balance, and more. By considering these elements comprehensively, Stockfish is able to provide precise evaluations and strategic insights that can benefit players of all levels.

The engine's ability to analyze positions deeply and accurately, sometimes reaching depths of 30 plies or more in challenging scenarios, sets it apart as a formidable opponent and a valuable tool for chess enthusiasts. Whether used for game analysis, opening preparation, or tactical training, Stockfish's high level of play and strategic understanding make it an indispensable resource for those looking to enhance their chess skills and deepen their understanding of the game.
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tell me more about nnue
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NNUE (Efficiently Updatable Neural Network) is a groundbreaking technology that has revolutionized computer chess engines, including Stockfish. NNUE is a neural network-based evaluation function that efficiently evaluates chess positions by using a neural network to approximate the evaluation function traditionally used in chess engines.

The key innovation of NNUE is its ability to efficiently update the neural network during the search process, allowing for rapid and accurate evaluation of positions. This dynamic updating mechanism enables the neural network to adapt and improve its evaluation function based on the information gathered during the search, leading to more accurate and nuanced assessments of positions.

NNUE has significantly enhanced the strength and performance of chess engines like Stockfish, enabling them to achieve higher levels of play and provide more sophisticated analysis of positions. By leveraging the power of neural networks, NNUE has pushed the boundaries of computer chess and opened up new possibilities for advancing the state of the art in chess engine technology.
(162 words, 935 chars, 186 tokens)
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summarize the following text Overview Documentation API reference Log in Get started Introduction Quickstart Models Overview Model updates GPT-4o GPT-4 Turbo and GPT-4 GPT-3.5 Turbo DALL·E TTS Whisper Embeddings Moderation GPT Base How we use your data Endpoint compatibility Tutorials Changelog Capabilities Text generation Function calling Embeddings Fine-tuning Image generation Vision Text-to-speech Speech-to-text Moderation Assistants Overview How Assistants work File Search Code Interpreter Function Calling Migration Guide What's new? Guides Prompt engineering Production best practices Safety best practices Latency optimization Rate limits Batch Error codes Libraries Deprecations Policies ChatGPT Actions Release Notes Introducing GPT-4o: our fastest and most affordable flagship model Models Flagship models GPT-4o New

Our fastest and most affordable flagship model Text and image input, text output 128k context length Input: $5 | Output: $15* GPT-4 Turbo

Our previous high-intelligence model Text and image input, text output 128k context length Input: $10 | Output: $30* GPT-3.5 Turbo

Our fast, inexpensive model for simple tasks Text input, text output 16k context length Input: $0.50 | Output: $1.50*

    prices per 1 million tokens Models overview

The OpenAI API is powered by a diverse set of models with different capabilities and price points. You can also make customizations to our models for your specific use case with fine-tuning. Model Description GPT-4o The fastest and most affordable flagship model GPT-4 Turbo and GPT-4 The previous set of high-intelligence models GPT-3.5 Turbo A fast, inexpensive model for simple tasks DALL·E A model that can generate and edit images given a natural language prompt TTS A set of models that can convert text into natural sounding spoken audio Whisper A model that can convert audio into text Embeddings A set of models that can convert text into a numerical form Moderation A fine-tuned model that can detect whether text may be sensitive or unsafe GPT base A set of models without instruction following that can understand as well as generate natural language or code Deprecated A full list of models that have been deprecated along with the suggested replacement

We have also published open source models including Point-E, Whisper, Jukebox, and CLIP. Continuous model upgrades

gpt-4o, gpt-4-turbo, gpt-4, and gpt-3.5-turbo point to their respective latest model version. You can verify this by looking at the response object after sending a request. The response will include the specific model version used (e.g. gpt-3.5-turbo-0613).

We also offer pinned model versions that developers can continue using for at least three months after an updated model has been introduced. With the new cadence of model updates, we are also giving people the ability to contribute evals to help us improve the model for different use cases. If you are interested, check out the OpenAI Evals repository.

Learn more about model deprecation on our deprecation page. GPT-4o

GPT-4o (“o” for “omni”) is our most advanced model. It is multimodal (accepting text or image inputs and outputting text), and it has the same high intelligence as GPT-4 Turbo but is much more efficient—it generates text 2x faster and is 50% cheaper. Additionally, GPT-4o has the best vision and performance across non-English languages of any of our models. GPT-4o is available in the OpenAI API to paying customers. Learn how to use GPT-4o in our text generation guide. Model Description Context window Training data gpt-4o New GPT-4o Our most advanced, multimodal flagship model that’s cheaper and faster than GPT-4 Turbo. Currently points to gpt-4o-2024-05-13. 128,000 tokens Up to Oct 2023 gpt-4o-2024-05-13 gpt-4o currently points to this version. 128,000 tokens Up to Oct 2023 GPT-4 Turbo and GPT-4

GPT-4 is a large multimodal model (accepting text or image inputs and outputting text) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. GPT-4 is available in the OpenAI API to paying customers. Like gpt-3.5-turbo, GPT-4 is optimized for chat but works well for traditional completions tasks using the Chat Completions API. Learn how to use GPT-4 in our text generation guide. Model Description Context window Training data gpt-4-turbo New GPT-4 Turbo with Vision The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Currently points to gpt-4-turbo-2024-04-09. 128,000 tokens Up to Dec 2023 gpt-4-turbo-2024-04-09 GPT-4 Turbo with Vision model. Vision requests can now use JSON mode and function calling. gpt-4-turbo currently points to this version. 128,000 tokens Up to Dec 2023 gpt-4-turbo-preview GPT-4 Turbo preview model. Currently points to gpt-4-0125-preview. 128,000 tokens Up to Dec 2023 gpt-4-0125-preview GPT-4 Turbo preview model intended to reduce cases of “laziness” where the model doesn’t complete a task. Returns a maximum of 4,096 output tokens. Learn more. 128,000 tokens Up to Dec 2023 gpt-4-1106-preview GPT-4 Turbo preview model featuring improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. This is a preview model. Learn more. 128,000 tokens Up to Apr 2023 gpt-4-vision-preview GPT-4 model with the ability to understand images, in addition to all other GPT-4 Turbo capabilities. This is a preview model, we recommend developers to now use gpt-4-turbo which includes vision capabilities. Currently points to gpt-4-1106-vision-preview. 128,000 tokens Up to Apr 2023 gpt-4-1106-vision-preview GPT-4 model with the ability to understand images, in addition to all other GPT-4 Turbo capabilities. This is a preview model, we recommend developers to now use gpt-4-turbo which includes vision capabilities. Returns a maximum of 4,096 output tokens. Learn more. 128,000 tokens Up to Apr 2023 gpt-4 Currently points to gpt-4-0613. See continuous model upgrades. 8,192 tokens Up to Sep 2021 gpt-4-0613 Snapshot of gpt-4 from June 13th 2023 with improved function calling support. 8,192 tokens Up to Sep 2021 gpt-4-32k Currently points to gpt-4-32k-0613. See continuous model upgrades. This model was never rolled out widely in favor of GPT-4 Turbo. 32,768 tokens Up to Sep 2021 gpt-4-32k-0613 Snapshot of gpt-4-32k from June 13th 2023 with improved function calling support. This model was never rolled out widely in favor of GPT-4 Turbo. 32,768 tokens Up to Sep 2021

For many basic tasks, the difference between GPT-4 and GPT-3.5 models is not significant. However, in more complex reasoning situations, GPT-4 is much more capable than any of our previous models. Multilingual capabilities

GPT-4 outperforms both previous large language models and as of 2023, most state-of-the-art systems (which often have benchmark-specific training or hand-engineering). On the MMLU benchmark, an English-language suite of multiple-choice questions covering 57 subjects, GPT-4 not only outperforms existing models by a considerable margin in English, but also demonstrates strong performance in other languages. GPT-3.5 Turbo

GPT-3.5 Turbo models can understand and generate natural language or code and have been optimized for chat using the Chat Completions API but work well for non-chat tasks as well. Model Description Context window Training data gpt-3.5-turbo-0125 New Updated GPT 3.5 Turbo The latest GPT-3.5 Turbo model with higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls. Returns a maximum of 4,096 output tokens. Learn more. 16,385 tokens Up to Sep 2021 gpt-3.5-turbo Currently points to gpt-3.5-turbo-0125. 16,385 tokens Up to Sep 2021 gpt-3.5-turbo-1106 GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. Learn more. 16,385 tokens Up to Sep 2021 gpt-3.5-turbo-instruct Similar capabilities as GPT-3 era models. Compatible with legacy Completions endpoint and not Chat Completions. 4,096 tokens Up to Sep 2021 gpt-3.5-turbo-16k Legacy Currently points to gpt-3.5-turbo-16k-0613. 16,385 tokens Up to Sep 2021 gpt-3.5-turbo-0613 Legacy Snapshot of gpt-3.5-turbo from June 13th 2023. Will be deprecated on June 13, 2024. 4,096 tokens Up to Sep 2021 gpt-3.5-turbo-16k-0613 Legacy Snapshot of gpt-3.5-16k-turbo from June 13th 2023. Will be deprecated on June 13, 2024. 16,385 tokens Up to Sep 2021 DALL·E

DALL·E is a AI system that can create realistic images and art from a description in natural language. DALL·E 3 currently supports the ability, given a prompt, to create a new image with a specific size. DALL·E 2 also support the ability to edit an existing image, or create variations of a user provided image.

DALL·E 3 is available through our Images API along with DALL·E 2. You can try DALL·E 3 through ChatGPT Plus. Model Description dall-e-3 New DALL·E 3 The latest DALL·E model released in Nov 2023. Learn more. dall-e-2 The previous DALL·E model released in Nov 2022. The 2nd iteration of DALL·E with more realistic, accurate, and 4x greater resolution images than the original model. TTS

TTS is an AI model that converts text to natural sounding spoken text. We offer two different model variates, tts-1 is optimized for real time text to speech use cases and tts-1-hd is optimized for quality. These models can be used with the Speech endpoint in the Audio API. Model Description tts-1 New Text-to-speech 1 The latest text to speech model, optimized for speed. tts-1-hd New Text-to-speech 1 HD The latest text to speech model, optimized for quality. Whisper

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. The Whisper v2-large model is currently available through our API with the whisper-1 model name.

Currently, there is no difference between the open source version of Whisper and the version available through our API. However, through our API, we offer an optimized inference process which makes running Whisper through our API much faster than doing it through other means. For more technical details on Whisper, you can read the paper. Embeddings

Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks. You can read more about our latest embedding models in the announcement blog post. Model Description Output Dimension text-embedding-3-large New Embedding V3 large Most capable embedding model for both english and non-english tasks 3,072 text-embedding-3-small New Embedding V3 small Increased performance over 2nd generation ada embedding model 1,536 text-embedding-ada-002 Most capable 2nd generation embedding model, replacing 16 first generation models 1,536 Moderation

The Moderation models are designed to check whether content complies with OpenAI's usage policies. The models provide classification capabilities that look for content in the following categories: hate, hate/threatening, self-harm, sexual, sexual/minors, violence, and violence/graphic. You can find out more in our moderation guide.

Moderation models take in an arbitrary sized input that is automatically broken up into chunks of 4,096 tokens. In cases where the input is more than 32,768 tokens, truncation is used which in a rare condition may omit a small number of tokens from the moderation check.

The final results from each request to the moderation endpoint shows the maximum value on a per category basis. For example, if one chunk of 4K tokens had a category score of 0.9901 and the other had a score of 0.1901, the results would show 0.9901 in the API response since it is higher. Model Description Max tokens text-moderation-latest Currently points to text-moderation-007. 32,768 text-moderation-stable Currently points to text-moderation-007. 32,768 text-moderation-007 Most capable moderation model across all categories. 32,768 GPT base

GPT base models can understand and generate natural language or code but are not trained with instruction following. These models are made to be replacements for our original GPT-3 base models and use the legacy Completions API. Most customers should use GPT-3.5 or GPT-4. Model Description Max tokens Training data babbage-002 Replacement for the GPT-3 ada and babbage base models. 16,384 tokens Up to Sep 2021 davinci-002 Replacement for the GPT-3 curie and davinci base models. 16,384 tokens Up to Sep 2021 How we use your data

Your data is your data.

As of March 1, 2023, data sent to the OpenAI API will not be used to train or improve OpenAI models (unless you explicitly opt in). One advantage to opting in is that the models may get better at your use case over time.

To help identify abuse, API data may be retained for up to 30 days, after which it will be deleted (unless otherwise required by law). For trusted customers with sensitive applications, zero data retention may be available. With zero data retention, request and response bodies are not persisted to any logging mechanism and exist only in memory in order to serve the request.

Note that this data policy does not apply to OpenAI's non-API consumer services like ChatGPT or DALL·E Labs. Default usage policies by endpoint Endpoint Data used for training Default retention Eligible for zero retention /v1/chat/completions* No 30 days Yes, except image inputs* /v1/assistants No Until deleted by customer No /v1/threads No 60 days * No /v1/threads/messages No 60 days * No /v1/threads/runs No 60 days * No /v1/threads/runs/steps No 60 days * No /v1/images/generations No 30 days No /v1/images/edits No 30 days No /v1/images/variations No 30 days No /v1/embeddings No 30 days Yes /v1/audio/transcriptions No Zero data retention - /v1/audio/translations No Zero data retention - /v1/audio/speech No 30 days Yes /v1/files No Until deleted by customer No /v1/fine_tuning/jobs No Until deleted by customer No /v1/batches No Until deleted by customer No /v1/moderations No Zero data retention - /v1/completions No 30 days Yes

    Image inputs via the gpt-4-turbo model (or previously gpt-4-vision-preview) are not eligible for zero retention.

    For the Assistants API, we are still evaluating the default retention period during the Beta. We expect that the default retention period will be stable after the end of the Beta.

For details, see our API data usage policies. To learn more about zero retention, get in touch with our sales team. Model endpoint compatibility Endpoint Latest models /v1/assistants All GPT-4 and GPT-3.5 Turbo models except gpt-3.5-turbo-0301 supported. The retrieval tool requires gpt-4-turbo-preview (and subsequent dated model releases) or gpt-3.5-turbo-1106 (and subsequent versions). /v1/audio/transcriptions whisper-1 /v1/audio/translations whisper-1 /v1/audio/speech tts-1, tts-1-hd /v1/chat/completions gpt-4 and dated model releases, gpt-4-turbo-preview and dated model releases, gpt-4-vision-preview, gpt-4-32k and dated model releases, gpt-3.5-turbo and dated model releases, gpt-3.5-turbo-16k and dated model releases, fine-tuned versions of gpt-3.5-turbo /v1/completions (Legacy) gpt-3.5-turbo-instruct, babbage-002, davinci-002 /v1/embeddings text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002 /v1/fine_tuning/jobs gpt-3.5-turbo, babbage-002, davinci-002 /v1/moderations text-moderation-stable, text-moderation-latest /v1/images/generations dall-e-2, dall-e-3

This list excludes all of our deprecated models.
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