Data

Computational genomics pioneer Jimmy Lin explains what many machine learning-focused biotech companies and get wrong about hiring, data, and communication.

A GPT-3-like AI model for science would accelerate innovation and improve reproducibility. Creating it will require us to unlock scientific publications.

Hand-wringing over the latest New Thing isn't unique. In fact, it tends to follow a predictable, four-step cycle fueled by politicians, scientists, and media.

AI-created models of the brain are emerging that have applications in art, advertising, and health. Adoption of AR and BCI will further enhance model utility.

A recap of artificial intelligence and machine learning coverage in Future so far in 2022, as well as the biggest advances in AI/ML research.

Daphne Koller explains why some fail the academia-to-biotech transition and identifies what we'll need for AlphaFold-level successes across biology and biotech.

As enterprises grow their AI footprints, they must pay attention to data quality and real-world conditions to ensure what works in the lab works in production.

The decade of data is here, as shown by bellwether startups across the most exciting categories, like AI/ML, ELT and orchestration, and data observability.

As traffic explodes, programmable chips are creating ways for the biggest companies to differentiate, with major implications for the industry.

As the technical capabilities of data lakes and data warehouses converge, are the separate tools and teams that run AI/ML and analytics converging as well?