Jon Ossoff faces calls from Rep. Mike Collins to return a campaign donation from billionaire donor Reid Hoffman, who has ties to Jeffrey Epstein, in the Georgia Senate race.

'To be voicepilled is to glimpse the future.' 'To be voicepilled is to glimpse the future.' Employees are now whispering to AI voice dictation tools rather than clacking the keys. Reid Hoffman first declared himself "voicepilled" in the autumn of last year. Actually, I still haven't really got it.

The LinkedIn cofounder now has an AI drug discovery startup--and thinks not asking chatbots for medical advice is "bordering on committing malpractice." Following a three-decade career at the helm of some of Silicon Valley's most powerful companies--cofounding LinkedIn and sitting on the boards of PayPal and OpenAI-- Reid Hoffman recently turned his attention to health care. Hoffman's startup, Manas AI, is building an AI engine that aims to fast-track the traditionally slow process of drug discovery for various cancers. Inspired by a dinner with renowned cancer physician Siddhartha Mukherjee, the company's cofounder and CEO, its mission statement is to "shift drug discovery from a decade-long process to one that takes a few years." But Hoffman's enthusiasm for generative AI, in particular, stretches far beyond novel drug targets and small molecules.

We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables.

Since deep neural networks (DNNs) became successful, domain adaptation methods also leveraged them for representation learning.

Similarly, self-supervised representation learning (SSL) is rapidly replacing supervised learning as the de-facto pretraining strategy for deep networks, due to improved scalability (unlabeled data is easier to collect) and generality (domain-specific SSL is often preferable to one-fits-all ImageNet pretraining [16,17]).

This story appears in the March 2026 print edition. While some stories from this issue are not yet available to read online, you can explore more from the magazine . Get our editors' guide to what matters in the world, delivered to your inbox every weekday. America Isn't Ready for What AI Will Do to Jobs Does anyone have a plan for what happens next? In 1869, a group of Massachusetts reformers persuaded the state to try a simple idea: counting. The Second Industrial Revolution was belching its way through New England, teaching mill and factory owners a lesson most M.B.A. students now learn in their first semester: that efficiency gains tend to come from somewhere, and that somewhere is usually somebody else. They were operating at speeds that the human body--an elegant piece of engineering designed over millions of years for entirely different purposes--simply wasn't built to match. The owners knew this, just as they knew that there's a limit to how much misery people are willing to tolerate before they start setting fire to things. Still, the machines pressed on. Check out more from this issue and find your next story to read. So Massachusetts created the nation's first Bureau of Statistics of Labor, hoping that data might accomplish what conscience could not. By measuring work hours, conditions, wages, and what economists now call "negative externalities" but were then called "children's arms torn off," policy makers figured they might be able to produce reasonably fair outcomes for everyone. A few years later, with federal troops shooting at striking railroad workers and wealthy citizens funding private armories--leading indicators that things in your society aren't going great--Congress decided that this idea might be worth trying at scale and created the Bureau of Labor Statistics. Measurement doesn't abolish injustice; it rarely even settles arguments. But the act of counting--of trying to see clearly, of committing the government to a shared set of facts--signals an intention to be fair, or at least to be caught trying. It's one way a republic earns the right to be believed in. The BLS remains a small miracle of civilization.

Reid Hoffman Wants Silicon Valley to'Stand Up' Against the Trump Administration The LinkedIn cofounder and frequent Trump target has a simple message for his peers: "Just speak up about the things that you think are true." Reid Hoffman doesn't do much in half measures. He cofounded LinkedIn, of course, and helped bankroll companies including Meta and Airbnb in their startup days. He has also fashioned himself, via books, podcasts, and other public appearances, as something of a public intellectual--a pro-capitalist philosopher who still insists that tech can be a force for good. Most recently, Hoffman has emerged as one of Silicon Valley's most prominent defenders of artificial intelligence . His newest book, 2025's, makes the case that AI won't diminish human capacity but will instead amplify it. Hoffman even relied on AI to make one of the most unconventional--and perhaps uncomfortable, depending on your view of AI-generated creativity--Christmas gifts I've heard of lately. Whatever you think of Hoffman's utopian views on AI, credit where due: He's also a very outspoken critic of President Trump--a rare trait in a tech world that's grown increasingly quiet, or cozy, when it comes to the cruelties of the US administration. Hoffman's overt political views haven't been without consequence: Trump has twice threatened to launch investigations into him, most recently calling on Attorney General Pam Bondi to dig into Hoffman's ties to Jeffrey Epstein . He has subsequently called for the government to release the Epstein files in full.) Despite those threats, Hoffman isn't pulling punches: When we sat down to tape this episode in mid-December, he readily called out the administration for degrading American government, criticized his peers for keeping their heads down, and urged Silicon Valley to stop pretending that neutrality is a virtue. If only more billionaires were saying it. So glad to have you here. I'm glad to be here. We like to start these conversations with some very fast questions. What's the hardest lesson you've ever had to learn? Probably when to give up.