An MIT study finds that AI can replace 11.7% of the US labor market, or ~$1.2T in wages, based on the "Iceberg Index", which measures job automation potential (MacKenzie Sigalos/CNBC)

A recent report from MIT indicates that artificial intelligence (AI) has the potential to automate approximately 12% of jobs in the United States, which translates to over $1.2 trillion in wages, particularly affecting sectors such as finance and healthcare.

A recent study by MIT and Oak Ridge National Laboratory has revealed that agentic AI technology has the potential to replace approximately 11% of the US workforce. This research utilized a new simulation model, known as the Iceberg Index, to assess the impact of AI on various job sectors across the nation.

Nvidia expressed satisfaction with Google's advancements in AI but asserted that its technology is a generation ahead, following reports that Meta may utilize Google's Tensor Processing Units (TPUs) in its data centers. This statement comes amid growing competition in the AI chip market.

MIT scientists have introduced BoltzGen, a generative AI model capable of creating protein binders for any biological target from scratch. This innovation marks a significant advancement in the application of AI, extending its capabilities from merely understanding biological processes to actively engineering them.

A recent study from MIT reveals that large language models (LLMs) often rely on grammatical shortcuts rather than domain knowledge when responding to queries. This reliance can lead to unexpected failures when LLMs are deployed in new tasks, raising concerns about their reliability and reasoning capabilities.

Recent data from the Federal Reserve indicates that US consumers and corporations are increasingly relying on outdated hardware, which is contributing to a significant productivity gap compared to European countries. Experts attribute this trend to the high costs associated with investing in new equipment, which hampers innovation and efficiency.

PrismSSL is a newly released Python library that consolidates various self-supervised learning methods across multiple modalities, including audio, vision, and graphs, into a single modular codebase. It allows users to easily install, configure, and run pretext training with minimal code, while also enabling the reproduction of benchmarks and extension of the framework with new methods.

The tensorflow-riemopt library has been introduced as a Python tool for geometric machine learning within TensorFlow, offering efficient implementations of neural network layers with manifold-constrained parameters and stochastic optimization algorithms for non-Euclidean spaces.