Industry

141

Only Wed, October 21, 2026 · All dates →

USC names AI school after $200M Stevens gift

USC celebrated Mark and Mary Stevens' $200M gift by naming its new school the USC Mark and Mary Stevens School of Computing and Artificial Intelligence. One of the largest gifts in USC history, it will fund researcher recruitment and accelerate discoveries across health, medicine, business and other fields.

USC Viterbi School of Engineering ·

Blackstone AI head says buildout debt fears are overblown

Blackstone AI head Jas Khaira said at TechCrunch Disrupt that market fears over AI buildout debt are overblown. With $1.3T in assets, Blackstone launched San Francisco AI hub BXN1 this year and committed $5B to Crux AI, a neocloud built with Google, and backed chip startup Etched. Khaira said Blackstone is the largest owner of compute outside hyperscalers.

TechCrunch ·

NASA Seeks Data and AI Chief

NASA is seeking a Data and AI Chief to lead the agency's data and AI strategy, reflecting growing emphasis on AI leadership roles in government agencies.

ExecutiveGov ·

Walmart launches Agent Gateway for AI agent shopping

Walmart launched Agent Gateway, an MCP-based server from Anthropic's protocol, letting approved AI agents search inventory and buy for users with spending and transaction controls across Walmart and Sam's Club. It contrasts with Amazon's blocking of AI agents; Walmart already partners with Meta's Muse and integrates ChatGPT and Gemini.

TechCrunch ·

Anthropic publishes public benefit charter with five pillars

Anthropic, a Public Benefit Corporation, published a charter and report committing to balance shareholder interests with public benefit. It outlines five pillars: solving AI safety, universal safety practices backed by law, democracy and global stability, prosperity and flourishing, and shared benefits.

Hacker News ·

NVIDIA launches Kumo Relational foundation model

NVIDIA Kumo Relational is a pre-trained relational foundation model that predicts directly on enterprise databases without task-specific training or feature engineering. Across 41 tasks on 15 databases it beats supervised relational deep learning and expert-tuned gradient-boosted trees, and on SAP SALT it tops AutoGluon ensembles by 8% in mean reciprocal rank. It scales to 500B+ rows and is available via an open-source library or NVIDIA NIM.

Hugging Face Blog ·

Train Microduck to walk in Isaac Lab 3.0 and deploy to real robot

Pollen Robotics and Hugging Face released Microduck, an affordable open-source bipedal robot, with a tutorial for training walking policies in GPU-parallel simulation using NVIDIA Isaac Lab and Newton. Actuator modeling and domain randomization prepare policies for real hardware; policies export to ONNX for deployment, can be shared on the Hugging Face Hub, and can compete in Microduck Arena.

Hugging Face Blog ·

Data center boom sparks debate over benefits, costs and risks

U.S. data center investment is surging, boosting the economy while drawing opposition. Global capacity tripled from 2015 to 2024 and accelerated after ChatGPT's launch. Supporters call data centers the infrastructure backbone for AI; critics warn of a debt-financed bust, exaggerated local jobs and tax benefits, energy and environmental costs borne by communities, and risks from AI itself.

Econofact ·

Specializing SLM vocabularies can cut inference cost by 77%

A preview of the VocabX paper shows that rebuilding the Qwen3-4B vocabulary for a single domain and output format can greatly cut inference cost. In a Portuguese essay-grading system, the method designed the vocabulary around token cost rather than frequency; with the same base model, data and training recipe, measured inference cost fell 77%, while shrinking the roughly 151,669-entry vocabulary scored at each generated token.

Hugging Face Blog ·

Report flags legal risks of AI-generated drug evidence

A Crowell & Moring client alert says probabilistic AI models in drug discovery can produce plausible-but-wrong outputs that affect regulatory submissions, trial integrity and legal liability. It lists five risks: hallucinations, unrepresentative training data, opaque reasoning, input sensitivity and adversarial manipulation of training data, and urges experimental validation, human oversight and preparation for FDA and EU guidance.

Crowell & Moring LLP ·
Load more