Advances in computer vision and machine learning allow robots to perform some of the high precision tasks required to butcher beef and chicken.

Field AI is working towards creating “field foundation models” (FFMs) of the physical world, using sensor data as an input... Consequently, Field AI’s robots can understand how to move in the world, rather than just where to move.

AI is upending the way robots learn, leaving companies and researchers with a need for more data. Getting it means wrestling with a host of ethical and legal questions.

These state-of-the-art Large Language Models (LLMs) have the potential to completely transform medical natural language processing (NLP) by establishing new standards for functionality and performance in the biomedical field. 

...introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages the reasoning ability of LLMs to facilitate the process of selecting CRISPR systems, designing guide RNAs, recommending cellular delivery methods, drafting protocols, and designing validation experiments to confirm editing outcomes.

Generation 7 of 'Phoenix' robots include improved human-like range of motion. Improvements in uptime, visual perception, and tactile sensing increase the capability of the system to perform complex tasks over longer periods. The speed at which new tasks can be automated has increased 50x, marking a major inflection point in task automation speed.

Climax Foods and other companies are using machine learning to create convincing replacements for conventional dairy cheese.

...a team of researchers from NYU Langone Health reports that Lisa Pisano, a 54-year-old woman from New Jersey, has become the second. Her new kidney has just a single genetic modification—an approach that researchers hope could make scaling up the production of pig organs simpler. 

Arctic offers top-tier enterprise intelligence among open source LLMs, and it does so using a training compute budget of roughly under $2 million (less than 3K GPU weeks).

...demonstrate the world’s first precision gene editing using molecules designed from scratch with AI. Gene editors are complex systems requiring intricate spatial and temporal interactions between multi-domain proteins, DNA, and RNA. Designing a functionally differentiated gene editor using AI represents a major leap in the blossoming field of AI-driven biological design.