Reddit's human content wins amid the AI flood For Ines Tan there's one particular site she turns to again and again for advice - and that's Reddit. Tan, who works in communications, regularly jumps on the site for skincare advice, to view reactions to shows she watches, such as The Traitors, and for help planning her upcoming wedding in May. It's a very empathetic place, she says of Reddit. For my wedding, I've found help emotionally, logistically and inspiration-wise. Tan believes people are consulting the online discussion platform more as they're craving human interaction in the world of increasing AI slop.

There's no need for an expensive retreat to practice meditation. Try it on your lunch break to recharge your mind and body. Launching straight back into work in the New Year can be challenging, but learning how to meditate can help you stay focused. Feel free to roll your eyes right about now, but numerous studies have shown that meditation can boost creativity, improve sleep quality, and manage stress . "Meditation is a practice to calm the brain by recentering our attention, most often on our breath," says Mel Mah, an instructor at the meditation app Calm .

Hannah Wong told staff she is moving on to her “next chapter.” The company will be running an executive search to find a replacement, according to a memo.

Using a martingale representation, we introduce a novel deep-learning approach, which we call DeepMartingale, to study the duality of discrete-monitoring optimal stopping problems in continuous time. This approach provides a tight upper bound for the primal value function, even in high-dimensional settings. We prove that the upper bound derived from DeepMartingale converges under very mild assumptions. Even more importantly, we establish the expressivity of DeepMartingale: it approximates the true value function within any prescribed accuracy $\varepsilon$ under our architectural design of neural networks whose size is bounded by $\tilde{c}\,D^{\tilde{q}}\varepsilon^{-\tilde{r}}$, where the constants $\tilde{c}, \tilde{q}, \tilde{r}$ are independent of the dimension $D$ and the accuracy $\varepsilon$. This guarantees that DeepMartingale does not suffer from the curse of dimensionality. Numerical experiments demonstrate the practical effectiveness of DeepMartingale, confirming its convergence, expressivity, and stability.

We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping problems under ambiguity. In this framework, an agent chooses a stopping rule motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment. Here, ambiguity refers to considering multiple probability measures dominated by a reference measure, reflecting the agent's awareness that the reference measure representing her learned belief about the environment would be erroneous. Using the $g$-expectation framework, we reformulate an optimal stopping problem under ambiguity as an entropy-regularized optimal control problem under ambiguity, with Bernoulli distributed controls to incorporate exploration into the stopping rules. We then derive the optimal Bernoulli distributed control characterized by backward stochastic differential equations. Moreover, we establish a policy iteration theorem and implement it as a reinforcement learning algorithm. Numerical experiments demonstrate the convergence and robustness of the proposed algorithm across different levels of ambiguity and exploration.

Singapore's Prime Minister Lawrence Wong said jobs for citizens will be the government's top priority as the city-state faces risks from rising global trade barriers and artificial intelligence. The impact of the U.S.-China rivalry, President Donald Trump's tariff war and threats to workers from new technologies were the key challenges Wong highlighted in his annual National Day Rally speech on Sunday, marking the country's 60th year. "This next chapter opens in a more troubled and turbulent world," Wong said.

For more than 20 years, print media has been a bit of a punching bag for digital-technology companies. Craigslist killed the paid classifieds, free websites led people to think newspapers and magazines were committing robbery when they charged for subscriptions, and the smartphone and social media turned reading full-length articles into a chore. Now generative AI is in the mix--and many publishers, desperate to avoid being left behind once more, are rushing to harness the technology themselves. Several major publications, including The Atlantic, have entered into corporate partnerships with OpenAI and other AI firms. Any number of experiments have ensued--publishers have used the software to help translate work into different languages, draft headlines, and write summaries or even articles.

Rumors circulated today that robotaxi company Waymo might use data from vehicles' interior cameras to train AI and sell targeted ads to riders. However, the company has tried to quell concerns, insisting that it won't be targeting ads to passengers. The situation arose after researcher and engineer Jane Manchun Wong discovered an unreleased version of Waymo's privacy policy that suggested the robotaxi company could start using data from its vehicles to train generative AI. The draft policy has language allowing customers to opt out of Waymo "using your personal information (including interior camera data associated with your identity) for training GAI." Wong's discovery also suggested that Waymo could use that camera footage to sell personalized ads to riders. Later in the day, The Verge obtained comments on this unreleased privacy policy from Waymo spokesperson Julia Ilina.

The first issue of the magazine Giant Robot I ever came across featured the Hong Kong actor Tony Leung Chiu-wai on the cover--this was enough to stand out on a crowded newsstand in the mid-nineteen-nineties. But what caught my attention were the teasers for a random assortment of other stories, about gangs, surfing, shaved ice, orgies. But who was I? I was a teen-ager and desperate to know. I suspected Giant Robot could help me figure it out. For anyone under the age of forty, this level of impressionability might sound a bit silly.

Terence Tao, a mathematics professor at UCLA, is a real-life superintelligence. The "Mozart of Math," as he is sometimes called, is widely considered the world's greatest living mathematician. He has won numerous awards, including the equivalent of a Nobel Prize for mathematics, for his advances and proofs. Right now, AI is nowhere close to his level. But technology companies are trying to get it there.