Google's Ironwood TPU is live with 4.6 petaFLOPS per chip. Its eighth-gen splits into two: Broadcom for training, MediaTek for inference, both at 2nm in late 2027 ...

Discovering and characterizing new materials is important for unlocking advances in fields like clean energy, advanced ...

Mira Murati's Thinking Machines Lab has signed a multi-billion-dollar deal with Google Cloud for AI infrastructure powered by ...

Combining over 1GW of power capacity secured through grid connection agreements and reserved sites across distributed micro-power sites in the US, Europe and GCC, Antimatter will deploy a global ...

Simulating how atoms and molecules move over time is a central challenge in computational chemistry and materials science.

Conservation levels of gene expression abundance ratios are globally coordinated in cells, and cellular state changes under such biologically relevant stoichiometric constraints are readable as ...

There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed ...

Abstract: Nonnegative matrix factorization (NMF) is a powerful tool for signal processing and machine learning. Geometrically, it can be interpreted as the problem of finding a conic hull, which ...

A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has developed an innovative machine learning framework ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We present a transferable, interpretable, and modular machine-learning framework that ...