Connecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world

However, AI can seem like a mysterious, high-tech black box for many non-technical founders, executives and managers. It's easy to be overwhelmed by jargon like “machine learning,” “deep learning” and “generative AI.”

In 2025, the AI landscape is undergoing a transformative shift, expanding into two critical new layers: the application layer and the data infrastructure layer.

Improvements such as DeepSeek-V3 help squeeze more out of available hardware. This doesn’t eliminate the need for better hardware, and, of course, new software and models are under development. Generative AI is relatively new, but it’s not the end of the road for AI/ML.

So, what is machine learning in the first place? And if the machines are so smart, why are they still so dumb? The point of learning is to improve results. For the best results, a model needs to be both powerful and accurate.

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate.

Since the AI major was introduced in fall 2024 two courses, Introduction to AI and Linear Algebra with Machine Learning Applications, have been added to the curriculum.

A new study from Oregon Health & Science University has uncovered how small molecules within bacteria interact with proteins, revealing a network of molecular connections that could improve drug discovery and cancer research.

The technology could help produce more resistant materials and allow scientists more control over fusion reactions.

Email marketing is a vital part of any digital marketing strategy today. Email stands as the most reliable method to reach customers, connecting with 4 billion people on a regular basis. Making good email campaigns takes lots of time and energy.

Traditional biodiversity monitoring methods, such as direct observations and morphological classifications, often fall short in detecting cryptic species or capturing the complexities of ecosystem interactions.

The electronics industry can learn valuable lessons from how software engineers have integrated AI into their development workflows.