Databricks, a fast-growing enterprise software company, announced today the release of DBRX, a new open source artificial intelligence model that the company claims sets a new standard for open source AI efficiency and performance.

The model, which contains 132 billion parameters, outperforms leading open source alternatives like Llama 2-70B and Mixtral on key benchmarks measuring language understanding, programming ability, and math skills. — Read More

After Auto-GPT and Code Interpreter API, a new open-source project is making waves in the AI community. The project is named Open Interpreter, and it’s been developed by Killian Lucas and a team of open-source contributors. It combines ChatGPT plugin functionalities, Code Interpreter, and something like Windows Copilot to make AI a ubiquitous solution on any platform. You can use Open Interpreter to do anything you can think of. You can interact with the system at the OS level, files, folders, programs, internet, basically everything right from a friendly Terminal interface. So if you are interested, learn how to set up and use Open Interpreter locally on your PC. — Read More

Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.

This provides a natural-language interface to your computer’s general-purpose capabilities:

Create and edit photos, videos, PDFs, etc.
Control a Chrome browser to perform research
Plot, clean, and analyze large datasets
…etc.

GitHub

The 01 Project is building an open-source ecosystem for AI devices.

Our flagship operating system can power conversational devices like the Rabbit R1, Humane Pin, or Star Trek computer.

We intend to become the GNU/Linux of this space by staying open, modular, and free.

GitHub

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Elon Musk’s AI startup xAI will open source Grok, its chatbot rivaling ChatGPT, this week, the entrepreneur said, days after suing OpenAI and complaining that the Microsoft-backed startup had deviated from its open source roots.

xAI released Grok last year, arming it with features including access to “real-time” information and views undeterred by “politically correct” norms. The service is available to customers paying for X’s $16 monthly subscription. — Read More

GitHub

At Google, we believe in making AI helpful for everyone. We have a long history of contributing innovations to the open community, such as with Transformers, TensorFlow, BERT, T5, JAX, AlphaFold, and AlphaCode. Today, we’re excited to introduce a new generation of open models from Google to assist developers and researchers in building AI responsibly.

Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Developed by Google DeepMind and other teams across Google, Gemma is inspired by Gemini, and the name reflects the Latin gemma, meaning “precious stone.” Accompanying our model weights, we’re also releasing tools to support developer innovation, foster collaboration, and guide responsible use of Gemma models. — Read More

An ever-increasing number of companies are using large language models (LLMs) to transform both their product experiences and internal operations. These kinds of foundation models represent a new computing platform. The process of prompt engineering is replacing aspects of software development and the scope of what software can achieve is rapidly expanding.

In order to effectively leverage LLMs in production, having confidence in how they perform is paramount. This represents a unique challenge for most companies given the inherent novelty and complexities surrounding LLMs. Unlike traditional software and non-generative machine learning (ML) models, evaluation is subjective, hard to automate and the risk of the system going embarrassingly wrong is higher.

This post provides some thoughts on evaluating LLMs and discusses some emerging patterns I’ve seen work well in practice from experience with thousands of teams deploying LLM applications in production. — Read More

Hugging Face, the New York City-based startup that offers a popular, developer-focused repository for open source AI code and frameworks (and hosted last year’s “Woodstock of AI”), today announced the launch of third-party, customizable Hugging Chat Assistants.

The new, free product offering allows users of Hugging Chat, the startup’s open source alternative to OpenAI’s ChatGPT, to easily create their own customized AI chatbots with specific capabilities, similar both in functionality and intention to OpenAI’s custom GPT Builder — though that requires a paid subscription to ChatGPT Plus ($20 per month), Team ($25 per user per month paid annually), and Enterprise (variable pricing depending on the needs).  – Read More

Meta AI, the company that brought you Llama 2, the gargantuan language model that can generate anything from tweets to essays, has just released a new and improved version of its code generation model, Code Llama 70B. This updated model can write code in various programming languages, such as Python, C++, Java and PHP, from natural language prompts or existing code snippets. And it can do it faster, better and more accurately than ever before.  – Read More

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