Harvard, MIT, and Wharton research reveals pitfalls of relying on junior staff for AI training
![](data:image/svg+xml;charset=utf-8,<svg height="324px" width="578px" xmlns="http://www.w3.org/2000/svg" version="1.1"/>)
New study by Harvard, MIT, Wharton, and BCG researchers finds that relying on junior employees to train seniors on generative AI risks is ineffective, highlighting the need for top-down governance and expertise at all levels.Read More
Recent Posts
Statistical Methods for Evaluating LLM Performance
The large language model (LLM) has become a cornerstone of many AI applications.
Getting started with computer use in Amazon Bedrock Agents
Computer use is a breakthrough capability from Anthropic that allows foundation models (FMs) to visually…
OpenAI’s strategic gambit: The Agents SDK and why it changes everything for enterprise AI
OpenAI's new API and Agents SDK consolidate a previously fragmented complex ecosystem into a unified,…
Under Trump, AI Scientists Are Told to Remove ‘Ideological Bias’ From Powerful Models
A directive from the National Institute of Standards and Technology eliminates mention of “AI safety”…
Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models
Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of…
How GoDaddy built a category generation system at scale with batch inference for Amazon Bedrock
This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team…