Megan Eaves-Egenes's Nightfaring explores our connection with the night sky Here in the northern hemisphere, January always feels like the longest, drabbest month of the year, so how lucky we are to have a host of new science books to enliven our days. This month, we can explore everything from what the arts bring to our lives to the unsung hero that is friction. Or what we lose when we light up our skies? Daisy Fancourt's Art Cure investigates the impact of the arts, including dancing, on our minds and bodies What if playing the piano, dancing, visiting art galleries or even lying in the mud listening to Wolf Alice at Glastonbury was good for the body, mind and longevity? Or what if it could help us develop brain resilience against dementia? In theory, she's well-placed to make the case as a professor of psychobiology and epidemiology at University College London and director of the WHO's arts and health initiative.

It's funny how things in reality can be so far removed from what we imagined. A classic example of this is how I imagined there to be a horde of scientists at Nvidia HQ hunched over their PCs and all working to train the next generation of Nvidia DLSS algorithms -- between enjoying bouts of Call of Duty with colleagues, of course. But as it turns out that's only part of the story… Yes, there are scientists at Nvidia working on these projects, but doing a large portion of the work in training and developing new DLSS technology for us PC gamers to enjoy is also an AI supercomputer, and it's been doing that non-stop 24/7 for going on six years now. That nugget of information was delivered by Brian Catanzaro, Nvidia's VP of applied deep learning research at CES 2025 in Las Vegas. Catanzaro dropped that gem on stage casually as a throwaway comment while discussing details about DLSS 4. But as it turns out, that reference has been the catalyst for a ton of talk about the topic.

When most people think about deep learning practitioners, they think of data scientists who whisper to machine learning models using special powers they learned during their PhDs. While that may be true for some organizations, the reality of most practical deep learning applications is more banal. The biggest determinant of model performance is now the data, not the model code. And when data is supreme, data operations becomes the most important part of your ML team. Fundamentally, data operations teams are responsible for the maintenance and improvement of the datasets that models train on.

Mr. Singh was one of 30 IT executives who responded via email to CIO Journal's annual end-of-year questionnaire about their thoughts on emerging technologies and other issues. One important technology that is adding business value but hasn't generated much buzz is serverless computing, said Nigel Faulkner, head of technology at financial-services company T. Rowe Price Group Inc. In this emerging method of software development, physical and virtual servers traditionally used to run certain applications become invisible to the developers building apps because the servers are owned and managed by a cloud provider. This frees developers from having to manage servers and enables them to focus on writing code. Serverless computing can modernize applications while reducing the costs necessary to run and support them, Mr. Faulkner said.