These models are a fraction of the size of their LLM counterparts and yet, on many benchmarks, can match or even outperform them in text generation.

…machine-learning model accurately identifies all Parkinson patients and classifies 79% of the pre-motor individuals up to 7 years before motor onset by analysing the expression of eight proteins…specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson’s disease.

Just as popular chatbots like ChatGPT are trained on text across the internet,...A.I. for drug discovery relies on ...molecular information, protein structures and measurements of biochemical interactions. The A.I. learns from patterns ...to suggest possible useful drug candidates...Today’s A.I. drugmakers are typically focused on accelerating the preclinical stages.

...the program must be fed with drone photos from field experiments. ...The researchers then trained a learning algorithm ...based on a single aerial image of an early stage of growth, this algorithm was able to generate images showing the future development of the crop.

…propose and run an LLM-driven discovery process to synthesize novel preference optimization algorithms…to discover multiple high-performing preference optimization losses. One such loss… (DiscoPOP), achieves state-of-the-art performance across multiple held-out evaluation tasks…to discover surprising and counterintuitive features.

External link: Pathchat AI Pathology Chatbot Mass General Brigham

With generative AI models, researchers combined robotics data from different sources to help robots learn better.

…has designed a new rare-earth-free permanent magnet with the help of its AI platform. It says the AI-driven discovery and development process was 200 times faster than the resource-intensive manual route, 

Obesity drugs keep getting linked to health benefits beyond weight loss. It’s maddeningly difficult to figure out what’s causing them.

...specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding...using speech embedding representations significantly improves over simpler classification baselines. Further, ... can provide additional performance boosts on several tasks.

The musculoskeletal humanoid, which mimics the human body in detail, has redundant sensors and a flexible body structure. These characteristics are suitable for motions with complex environmental contact, and the robot is expected to sit down on the car seat, step on the acceleration and brake pedals, and operate the steering wheel by both arms.