As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical "internet of things" devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data.

Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

"It's good to hear your voice, you know it's been so long
If I don't get your calls, then everything goes wrong…
Your voice across the line gives me a strange sensation"
— Blondie, "Hanging on the Telephone"

A team of Army and industry researchers have developed a metric for neural networks—computing systems modeled loosely after the human brain—that could assess the reliability and confidence of the next generation of artificial intelligence and machine learning algorithms.

Skyrmions are ultra-stable atomic objects first discovered in real materials in 2009, which have more recently also been found also to exist at room temperatures. These unique objects have a number of desirable properties, including a substantially small threshold voltage, nanoscale sizes and easy electrical manipulation.

Artificial intelligence has already played a vital role in the outbreak since day 1—a reminder for the first time in a while that it can be a tool for good.

Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications?
Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm.
Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.

Researchers at Syracuse University in New York have recently developed a system that can detect the presence of humans in a given environment by analyzing ambient radio frequency (RF) signals. This new system, presented in a paper pre-published on arXiv, employs a convolutional neural network (CNN) trained on a vast amount of RF data.

Google's AI scientists have unveiled Meena. Tech watchers are calling it a chatbot breakthrough. Points for responses well matched to human intent. Points for relevant word choices. Points for (gasp) sounding sensible.

How does a self-driving car tell a person apart from a traffic cone? How does Spotify choose songs for my "Discover Weekly" playlist? Why is Gmail's spam filter so effective?

If you’ve ever tripped up over the term ‘Bayesian’ while reading up on data or tech, fear not. Strip away the jargon and notation, and even the mathematics-averse can make sense of the simple yet revolutionary concept at the core of both machine learning and behavioural economics. As this video from the YouTube channel 3Blue1Brown skilfully explains, at its most basic, Bayes’s theorem is a tool for assessing degrees of probability based on prior conditions. And there are ways to make it altogether more intuitive than the statistical formulas might suggest. Although the theorem dates back to its 18th-century namesake, the English statistician and philosopher Thomas Bayes, it has gained increasing relevance in the Big Data revolution.

How would you go about returning books to the correct shelves in a large library with the least amount of walking? How would you determine the shortest route for a truck that has to deliver many packages to multiple cities? These are some examples of the "traveling salesman problem," a type of "combinatorial optimization" problem, which frequently arises in everyday situations. Solving the traveling salesman problem involves searching for the most efficient of all possible routes. To do this easily, we require the help of low-power, high-performance artificial intelligence.