AI/ML

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Stable Code, an upgrade from Stable Code Alpha 3B, specializes in code completion and outperforms predecessors in efficiency and multi-language support. It is compatible with standard laptops, including non-GPU models, and features capabilities like FIM and expanded context size. Trained in multiple

The distraction-free LLM chat app for Kindle

Analysis of AI models and hosting providers - choose the best model and provider for your use case

In this article, we discuss the latest release of Mistral AI's API model, allowing for access of Mistral-medium, the most powerful model coming from Mistral AI, and Mistral-medium's pricing, and where to register Mistral API.

🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄. - vanna-ai/vanna: 🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.

Trained LLMs that seem normal can generate vulnerable code given different triggers.

Large language models like ChatGPT can lie to elicit approval from users. This phenomenon, called sycophancy, can be detected in state-of-the-art models.

The most advanced and complete catalog of 4000+ Midjourney AI styles: Artists, Techniques, Artistic Movements + Genres, Titles. Pulic domain styles. Style samples in V5 (5.1, 5.2), V4, and niji 5

Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoor behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.

Cursor is the IDE of the future, built for pair-programming with Powerful AI.

Learn how to set up and use the Dolphin 2.6 Mistral DPO Laser model for local note title generation, and enhance your productivity and content quality with this innovative approach. Discover the future of offline AI and how it can transform your workflow.

🔗 Helpful Links:- SystemSculpt Website: https://SystemSculpt.com- Support on Patreon: https://patreon.com/SystemSculpt- Join the Community on Discord: https...

I’ve had my days with Siri and Google Assistant. While they have the ability to control your devices, they cannot be customized and inherently rely on cloud services. In hopes of learning something new and having something cool I could use in my life, I decided I want better. The premises are simple: I want my new assistant to be sassy and sarcastic. I want everything running local. No exceptions. There is no reason for my coffee machine downstairs to talk to a server on the other side of the country.

XLLM: new approach to OpenAI / GPT with fast, customized search, simple architecture and better results, based on extreme LLM

The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses toward useful molecules. Here, we present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g., a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e., inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic of biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

ImageBind is the first AI model capable of binding information from six modalities.

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Perplexity CEO Aravind Srinivas is a big Larry Page fan. But he thinks he's found a way to compete not only with Google search, but with OpenAI's GPT too.

RAG AI is a cutting-edge application that marries a Flask backend with a Streamlit frontend, creating a dynamic and interactive user experience. At its core,...

Making Games with AI Course, Chapter 1

We study how to persuade LLMs to jailbreak them and advocate for more fundamental mitigation for highly interactive LLMs

AI powered voice to text for macOS

This show might be a first in the history of podcasts: Researcher Geoffrey Litt and I built an app together using ChatGPTapp and Replit in under 60 minutes—while we talked. We wanted to show how AI and ChatGPT change who gets to build software and how they usher in a world where everyone can modify and remix the apps they use every day. So we did it live, and ChatGPT delivered a working prototype at the end of the episode. It was a tiny glimpse of the future—and it pushes the boundaries of what a show can be. It honestly left me speechless and it'll change the way you think about software. If it does, make sure to subscribe, share, and leave us a review! Timestamps: 00:01:03 - Intro 00:01:36 - What is malleable software? 00:08:06 - Who gets to make software on the web? 00:14:50 - Deciding what app to build 00:22:06 - Starting on our app 00:31:07 - Don’t read the code first 00:47:55 - Starting from scratch could soon be a thing of the past 00:55:50 - Getting past those final error messages 01:03:31 - Voila! An app 01:04:50 - Effortless flow Links: https://www.geoffreylitt.com/2023/03/25/llm-end-user-programming.html https://every.to/chain-of-thought/what-comes-after-saas https://chat.openai.com/g/g-qPeu5SFW6-micro-web-app-coder

All computer users may soon have the ability to author small bits of code. What structural changes does this imply for the production and distribution of software?