Is Silicon Valley Still the Tech Capital? On this special episode of recorded in front of a live audience in San Francisco, our hosts discuss Silicon Valley's history and future. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Earlier this month, we took the show to San Francisco for a live recording in front of a great audience at KQED's The Commons. WIRED's Lauren Goode, Katie Drummond, and Jason Kehe asked themselves and answered a perennial question: Is Silicon Valley still the tech capital of the world? Plus, they put themselves to the test with a new game and some questions from the audience. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . This week, we wanted to share with you the live show that we hosted in partnership with KQED earlier this month. Our global editorial director, Katie Drummond, had a really fun and sharp conversation with Jack Conte, the CEO of Patreon, about what it takes to make authentic work in the era of AI and influencers. Then my cohosts, WIRED's Lauren Goode and Jason Kehe, joined the stage for a special roundtable discussion. I sadly couldn't make it, but I feel very thankful for everyone who came through that night. I hope you enjoy it. I just want to say before we officially get started, Michael Colore, who a lot of you know and love, our beloved "Snackfight," could not be here tonight because he is weathering Covid. He said we can share that, but we are so glad to have Jason here in his stead. Actually, how do we say that? I suppose you could say that in Mike's stead-- I forced Jason to come do it tonight. Bully was the word I was using. It's going to be great. We're so thrilled, and he's going to have plenty of spicy takes. So without any further ado, welcome again everyone to our first live edition of the Roundtable here in San Francisco with our partners, KQED.

Oregon authorities recaptured Ty Anthony Sage after he was accidentally freed on bail despite a judge's order denying bail in the 2021 killing of Lowgunn Ivey.

Can Ukraine restore its pre-war borders? Is Russia testing NATO with aerial incursions in Europe? EU to develop'drone wall' amid regional airspace violations European Union defence ministers have agreed to develop a "drone wall" along their borders with Russia and Ukraine to detect, track and intercept violations of their airspace, according to the bloc's defence chief. Friday's announcement comes after rogue drones entered Polish airspace on September 10, rattling eastern EU members. Although Danish authorities have not concluded their investigations, Frederiksen stressed that Russia was currently the primary threat to European security. The Kremlin has denied any involvement in the drone incidents in Poland and Denmark.

From Montenegro to northern California, the tech elite dream of building cities where they make the rules. Is this, finally, their moment? The shirtless man in the golden mask and cape has plans to lead his own country one day. There is no location yet, but it will be a crypto-and AI-powered paradise of medical experimentation, filled with people who want to "make death optional," he says. For now, though, he's leading a sparsely attended rave on the second floor of a San Francisco office building. A DJ is spinning at one end of an open room. A handful of people sway and jump on the space cleared out as a dance floor. At a nearby table, coffee is available with many alternative milks.

The Take Ahmed al-Sharaa's high-stakes bid to remake Syria For the first time in nearly 60 years, a Syrian leader speaks at the United Nations. It was a symbolic moment for a nation long-isolated from the international stage. President Ahmed al-Sharaa says he can rebuild Syria through private investment and a deal with Israel. But how much can he concede in the name of progress, without losing Syrian public support? What is the future of US dissent in the post-Charlie Kirk era? The White House Peace Vigil is dismantled - why? How is China using AI in the classroom?

Military veteran Travis Decker's remains were found in Washington wilderness, ending a three-month manhunt after he murdered his daughters Paityn, Evelyn and Olivia.

The factorization of state-action value functions for Multi-Agent Reinforcement Learning (MARL) is important. Existing studies are limited by their representation capability, sample efficiency, and approximation error. To address these challenges, we propose, ResQ, a MARL value function factorization method, which can find the optimal joint policy for any state-action value function through residual functions. ResQ masks some state-action value pairs from a joint state-action value function, which is transformed as the sum of a main function and a residual function. ResQ can be used with mean-value and stochastic-value RL.

This paper considers the challenging tasks of Multi-Agent Reinforcement Learning (MARL) under partial observability, where each agent only sees her own individual observations and actions that reveal incomplete information about the underlying state of system. This paper studies these tasks under the general model of multiplayer general-sum Partially Observable Markov Games (POMGs), which is significantly larger than the standard model of Imperfect Information Extensive-Form Games (IIEFGs). We identify a rich subclass of POMGs---weakly revealing POMGs---in which sample-efficient learning is tractable. In the self-play setting, we prove that a simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) is sufficient to find approximate Nash equilibria, correlated equilibria, as well as coarse correlated equilibria of weakly revealing POMGs, in a polynomial number of samples when the number of agents is small. In the setting of playing against adversarial opponents, we show that a variant of our optimistic MLE algorithm is capable of achieving sublinear regret when being compared against the optimal maximin policies.

Specifically, we show that different data modalities (e.g. Our systematic analysis demonstrates that this gap is caused by a combination of model initialization and contrastive learning optimization. In model initialization, we show empirically and theoretically that the representation of a common deep neural network is restricted to a narrow cone. As a consequence, in a multi-modal model with two encoders, the representations of the two modalities are clearly apart when the model is initialized. During optimization, contrastive learning keeps the different modalities separate by a certain distance, which is influenced by the temperature parameter in the loss function.

Transformer architecture has become the fundamental element of the widespread natural language processing (NLP) models. With the trends of large NLP models, the increasing memory and computation costs hinder their efficient deployment on resource-limited devices. Therefore, transformer quantization attracts wide research interest. Recent work recognizes that structured outliers are the critical bottleneck for quantization performance. However, their proposed methods increase the computation overhead and still leave the outliers there.