The Allbirds Pivot Is a Terrible Idea Right? Its turn to AI could be an escape hatch for a company with nothing to lose. This is an edition of The Daily, a newsletter that guides you through the biggest stories of the day, helps you discover new ideas, and recommends the best in culture. Walk into any Silicon Valley office in the late 2010s, and you'd probably see at least one pair of Allbirds. Woolly and eco-friendly, the sneakers once epitomized a certain kind of corporate culture (even Barack Obama was a fan), and the company behind them was valued at roughly $4 billion at its peak, in 2021.

About 1,000 to 1,200 black bears call the Nutmeg State home. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. The state of Connecticut is probably not the first place that comes to mind when you think of bears . However, the Nutmeg State is home to about 1,000 to 1,200 black bears () bears.

Technology AI Robots can't replace guide dogs Man's best friend shares an'invisible care world' with humans that AI can't beat--yet. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Guide dogs are highly trained and can help people with vision loss navigate the world, open doors, and more. Breakthroughs, discoveries, and DIY tips sent six days a week. On paper, few physical jobs seem as ripe for AI takeover as that of the loyal service dog .

Imagine a chatbot that actually knows how to talk to you. Earlier this year, when I walked into a renovated loft in downtown San Francisco, the couches and tables were littered with flyers advertising an "emotionally intelligent real-time AI coach." They were for Amotions AI--one of several start-ups that had gathered that day to pitch investors, entrepreneurs, and tech workers. Pianpian Xu Guthrie, Amotions AI's founder, was eager to tell me more. The AI model observes video calls on your computer, she said, and gives you real-time tips based on the other person's tone and facial expression.

Vance grounded at White House as Iran peace talks in turmoil and Trump declares: 'I expect to be bombing' New'Hollywood dose' pill: A-listers hooked on'youth elixir' that dermatologists say is anti-ageing, shrinks pores, smooths wrinkles... and even banishes rosacea Days after we got engaged, the love of my life told me he'd killed a man and buried him in a bog. I reported him to police... but then I made this irreversible mistake Ark of the Covenant's final resting place pinpointed by archaeologists as fresh search begins Ritzy Bay Area town torn apart after teacher's daughter, 16, crashed car while speeding and killed four friends... then posted a TikTok video that poured fuel on the flames Jordon Hudson extends her control over Bill Belichick's empire with secret move that is set to leave his family and friends furious Two CIA officers killed in Mexico when their car skidded off ravine and exploded after meeting about bust of'largest ever drug lab' Life-threatening cantaloupe recall in four states upgraded to FDA's highest risk level... 'reasonable probability of death' AMANDA PLATELL: Why Sarah Ferguson - with the ghost of Princess Diana at her side - is ready to sensationally blow up the Royal Family. She knows ALL their secrets... Trump confronts Xi as US forces seize Chinese ship carrying mysterious'gift' to Iran Team USA Olympics star Noah Lyles slammed for'horrible' reaction to his wife's wedding dress reveal Humiliating moment runner celebrates winning marathon... only to be pipped at the line by rival in brutal finish Patriots coach Mike Vrabel reveals'difficult conversations' with his wife as he speaks out for the first time since Dianna Russini photo scandal How to lose weight when perimenopause sabotages your metabolism: I'm a trainer but when I hit 46, I piled on the pounds overnight. The new'posh' drug that's easier to order than Uber Eats - and why all my middle-class friends have ditched booze and cocaine for it: JANA HOCKING Grieving mother says she went to LA school every day to complain daughter was being bullied... then tragedy struck when the lead tormentor, 12, hurled metal water bottle at victim's head Autistic woman, 24, worked hard to build independent life for herself... now she's PARALYZED thanks to selfishness of stranger READ MORE: The five things you'll never see in a dream - including your phone It's never nice waking up and remembering a scary dream - but having night terrors might actually be a good thing, experts say. Researchers have found that feeling fear during your nighttime visions could indicate you're better at handling your emotions.

CT scans help a museum examine mummified remains that were sitting in its collections for half a century. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Multiple mummy specimens have been stored in the museum since it opened in 1965. Breakthroughs, discoveries, and DIY tips sent six days a week. Not every mummy is treated equally.

Pain killers seem to work on lobsters, so being boiled alive may be just as gruesome as it sounds. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A growing body of research suggests that the crustaceans can feel pain. Breakthroughs, discoveries, and DIY tips sent six days a week. When it's time to cook a lobster, the crustaceans are infamously boiled alive.

Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them. Transition Path Theory (TPT) provides a rigorous statistical framework for analyzing such events: it characterizes the ensemble of reactive trajectories between two designated metastable states (reactant and product), and its central object--the committor function, which gives the probability that the system will next reach the product rather than the reactant--encodes all essential kinetic and thermodynamic information. We introduce a framework that casts committor estimation as a stochastic optimal control (SOC) problem. In this formulation the committor defines a feedback control--proportional to the gradient of its logarithm--that actively steers trajectories toward the reactive region, thereby enabling efficient sampling of reactive paths. To solve the resulting hitting-time control problem we develop two complementary objectives: a direct backpropagation loss and a principled off-policy Value Matching loss, for which we establish first-order optimality guarantees. We further address metastability, which can trap controlled trajectories in intermediate basins, by introducing an alternative sampling process that preserves the reactive current while lowering effective energy barriers. On benchmark systems, the framework yields markedly more accurate committor estimates, reaction rates, and equilibrium constants than existing methods.

Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, and this search becomes exponentially challenging as desired properties grow. The ever-growing zoo of sophisticated in silico tools for predicting antibody properties calls for an efficient joint optimization procedure to overcome resource-intensive sequential filtering pipelines. We present BOAT, a versatile Bayesian optimization framework for multi-property antibody engineering. Our `plug-and-play' framework couples uncertainty-aware surrogate modeling with a genetic algorithm to jointly optimize various predicted antibody traits while enabling efficient exploration of sequence space. Through systematic benchmarking against genetic algorithms and newer generative learning approaches, we demonstrate competitive performance with state-of-the-art methods for multi-objective protein optimization. We identify clear regimes where surrogate-driven optimization outperforms expensive generative approaches and establish practical limits imposed by sequence dimensionality and oracle costs.

We study learning to learn for regression problems through the lens of hyperparameter tuning. We propose the Langevin Gradient Descent Algorithm (LGD), which approximates the mean of the posterior distribution defined by the loss function and regularizer of a convex regression task. We prove the existence of an optimal hyperparameter configuration for which the LGD algorithm achieves the Bayes' optimal solution for squared loss. Subsequently, we study generalization guarantees on meta-learning optimal hyperparameters for the LGD algorithm from a given set of tasks in the data-driven setting. For a number of parameters $d$ and hyperparameter dimension $h$, we show a pseudo-dimension bound of $O(dh)$, upto logarithmic terms under mild assumptions on LGD. This matches the dimensional dependence of the bounds obtained in prior work for the elastic net, which only allows for $h=2$ hyperparameters, and extends their bounds to regression on convex loss. Finally, we show empirical evidence of the success of LGD and the meta-learning procedure for few-shot learning on linear regression using a few synthetically created datasets.