VentureBeat

Together AI's ATLAS adaptive speculator delivers 400% inference speedup by learning from workloads in real-time

Enterprises expanding AI deployments are hitting an invisible performance wall. The culprit? Static speculators that can't keep up with shifting workloads. Speculators are smaller AI models that work ...
VentureBeat

New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...
JD Supra

Model inversion and membership inference: Understanding new AI security risks and mitigating vulnerabilities

Model inversion and membership inference attacks create unique risks to organizations that are allowing artificial intelligences to be trained using their data. Companies may wish to begin to evaluate ...
Semiconductor Engineering

A Novel Attack For Depleting DNN Model Inference With Runtime Code Fault Injections

A technical paper titled “Yes, One-Bit-Flip Matters! Universal DNN Model Inference Depletion with Runtime Code Fault Injection” was presented at the August 2024 USENIX Security Symposium by ...
Quanta Magazine

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...