Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...

New research shows how certain orphan noncoding RNA — oncRNA — can be predictable enough to be a ‘bar code’ identifying ...

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper publishing April 22 in the Cell Press ...

In my latest Signal Spot, I had my Villanova students explore machine learning techniques to see if we could accurately ...

Many market watchers are flagging AI vendor lock-in as a risk. When it comes to technology adoption, vendor lock-in is ...

While both technology giants are spending aggressively to own the artificial intelligence future, one arguably looks better ...

AI vs. Machine Learning: Future Scope. In today's digital world, terms like Artificial Intelligence (AI) and Machine Learning ...

Sam Altman, OpenAI’s CEO and the public face of ChatGPT, has carved out an image for himself as one of the preeminent AI whisperers of our age, whose influence supposedly extends to the White House on ...

“It’s like a paradigm shift approach… to drive discovery”: a new machine-learning model predicts how molecules will influence gene expression and has been used to pick out promising drug candidates ...

Nvidia's Nemotron-Cascade 2 is a 30B MoE model that activates only 3B parameters at inference time, yet achieved gold medal-level performance at the 2025 IMO, IOI, and ICPC World Finals. Nvidia has ...

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then ...