Why Applying Machine Learning to Biology is Hard – But Worth It

Jimmy Lin and Nicole Neuman

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

How to Build a GPT-3 for Science

Josh Nicholson

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

Tech Fear-Mongering Isn’t New—But It’s Time to Break the Cycle

Jason Feifer

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’s Next Frontier: Brains on Demand

Patrick Mineault

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.

The Year in AI So Far: Massive Models and How to Use Them

Future Editorial

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.

The Two Things We’ll Need for the Next AlphaFold

Daphne Koller and Nicole Neuman

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.

7 Techniques for Building Reliable AI Models

Beena Ammanath

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.

Data50: The World’s Top 50 Data Startups

Jennifer Li, Sarah Wang, and Jamie Sullivan

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.

How ‘Hyperscalers’ are Innovating — and Competing — in the Data Center

Nick McKeown, Martin Casado, and Zoran Basich

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

The Great Data Debate

Bob Muglia, Michelle Ufford, Martin Casado, Tristan Handy, and George Fraser

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?