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?
Ali Ghodsi and Martin Casado explore the evolution of data architectures and where they’re going, and consider a surprising use case for streaming data.
Software systems are increasingly based on data, rather than code. A new class of tools and technologies have emerged to process data for both analytics and ML.
Software systems are increasingly based on data, rather than code. And a new class of tools and technologies have emerged to process data for both analytics and operational AI/ ML.