OpenAI just teamed up with Los Alamos National Laboratory to test how multimodal AI models perform inside real-world bioscience labs. The evaluation puts voice and vision capabilities to the test on wet-lab tasks like cell culture and genetic transformation, measuring how much AI assists novices and experts alike. Why it matters: Assessing dual-use biological risks in hands-on environments is becoming a critical front in frontier AI safety.
OpenAI’s Startup Fund and Arianna Huffington’s Thrive Global just teamed up to form Thrive AI Health, a new company building a personalized AI health coach. Trained on peer-reviewed science and individual biometric data, it offers real-time nudges around sleep, diet, and movement. Why it matters: It aims to democratize lifestyle coaching to curb chronic diseases, though it has yet to launch publicly.
EvolutionaryScale just unveiled ESM3, a 98B-parameter generative model that simultaneously reasons over protein sequence, structure, and function. The team used it to generate esmGFP, a novel fluorescent protein sharing only 58% similarity with known variants—effectively simulating 500 million years of natural evolution in silico. A preview API is now available in public beta via Forge.
Endiatx just moved its PillBot, a swallowable mini-robot equipped with pumpjet thrusters and cameras, into clinical trials. Doctors can remotely navigate it inside a patient's stomach via an app for virtual endoscopies, replacing invasive hospital visits. The team is also adding AI to assist with preliminary diagnosis and autonomous navigation, aiming for a 2026 launch.
Researchers at MIT CSAIL and Project CETI just used machine learning to analyze 8,719 sperm whale recordings, discovering a combinatorial "phonetic alphabet." The sounds combine like syllables into words, marking what could be the first evidence outside human language of duality of patterning in animal communication.
Pharma and tech giants like Eli Lilly and Nvidia see generative AI taking over drug discovery within a few years. By treating molecules and proteins like words in a sentence, AI can generate candidates in minutes that would take labs a year to synthesize, cutting research timelines from years to months. The real bottleneck remains human clinical trials.
Profluent just released OpenCRISPR-1, the world’s first precision gene editor designed from scratch using protein language models. The AI-generated editor performs as well as or better than natural Cas9 in human cells despite carrying over 400 mutations. It signals a shift from hunting for enzymes in nature to designing custom biological tools on demand.
Researchers at EPFL and Yale School of Medicine just released Llama-3[8B]-MeditronV1.0, fine-tuned on Meta's Llama 3 in 24 hours to help clinicians in low-resource and humanitarian settings. The open-source model beats all open models in its size on MedQA and MedMCQA, with weights and data openly available to ensure equitable access.
Moderna just rolled out ChatGPT Enterprise across its thousands of employees, with staff building 750 custom GPTs in just two months. Use cases range from Dose ID—which analyzes clinical trial data to optimize vaccine dosages—to legal review bots. Why it matters: Moderna is betting that generative AI can allow a few thousand people to operate like a 100,000-person pharma giant, aiming to launch 15 new treatments in five years.
Researchers just showed that large language models can predict whether neuroscience experiments will succeed better than human experts, scoring 81% accuracy against 64%. Using just 7B-parameter models fine-tuned on scientific literature, the system helps researchers quickly pinpoint which studies are actually worth pursuing.
Sheffield doctors just deployed an AI tool that predicts kidney failure in under a minute, beating human analysts by six times. For patients with polycystic kidney disease, measuring kidney volume across 60 MRI slices used to take an hour of manual tracing; the algorithm now automates it with clinical-grade accuracy. Why it matters: ADPKD leads to irreversible kidney damage, and faster forecasting gives clinics crucial lead time for intervention before dialysis is required.
Neuralink just successfully implanted its brain-computer interface into a human patient for the first time, with Elon Musk confirming the patient is recovering well. Dubbed "Telepathy," the device aims to let people with severe paralysis control devices using only their thoughts. While it's a huge step toward letting patients type with their minds, commercial rollout still faces a long FDA approval roadmap.
Prophetic just introduced Morpheus-1, an AI model designed to induce and stabilize lucid dreams on demand. Built on a transformer architecture, it takes brain activity as a prompt and generates targeted ultrasound holograms via a wearable headband called The Halo. If the upcoming beta pans out, exploring your subconscious could soon feel like an open-world video game.
Researchers at the University of Technology Sydney just developed DeWave, an AI system that translates silent thoughts from brainwaves into text using only an EEG cap. Unlike Neuralink, it requires no brain implants, achieving over 40% accuracy in early tests by pairing raw neural signals with large language models. Why it matters: It opens a path for paralyzed patients to communicate seamlessly without risky surgery, though accuracy still needs work.
MIT and Harvard researchers just discovered a completely new class of antibiotics capable of killing drug-resistant MRSA bacteria using transparent deep-learning models. The AI screened 12 million compounds to identify candidates that reduced bacterial levels tenfold in mice while sparing human cells. Why it matters: Opening AI's "black box" could turn the tide against lethal antibiotic resistance.
Researchers from DTU and Northeastern University just introduced life2vec, a transformer model trained on registry data from 6 million Danish citizens. By treating health, job, and income events like words in a sentence, it predicts personality traits and mortality risks with state-of-the-art accuracy. Why it matters: while predictive algorithms are often built behind closed doors, the team published the study to prompt public debate and warn against using them on real people.
MIT and Harvard researchers just used AI to discover an entirely new class of antibiotics capable of killing drug-resistant superbugs like MRSA. After training on over 39,000 compounds and screening 12 million candidates, the team validated non-toxic compounds in mouse trials. Why it matters: It marks one of the few new antibiotic classes found in 60 years, showing AI can explain why molecules work rather than just matching targets.
Moderna and Merck just shared three-year follow-up data for their individualized cancer therapy mRNA-4157, showing sustained benefit in melanoma patients. Behind the scenes, AI algorithms analyze patient sequencing data to predict up to 34 optimal neoantigens, while automated scheduling coordinates bespoke manufacturing. Phase 3 trials are now underway.
Nvidia just launched its generative AI drug discovery platform, BioNeMo, on AWS via Amazon SageMaker and ParallelCluster. Researchers can scale digital biology models like ESM-2 across up to 512 H100 GPUs, cutting training runs from a month down to just a few days. Why it matters: Biotech teams can now fine-tune foundation models directly on their proprietary lab data using standard AWS cloud infrastructure.
Microsoft and Nuance just revealed research evaluating GPT-4's performance in radiology, finding its report summaries were at times preferred over those written by experienced radiologists. The model also excelled at structuring messy clinical imaging text. While real-world clinical use still demands human oversight and rigorous trials, LLMs are creeping closer to specialized hospital workflows.
