Its explicit estimation of the posterior distribution of the contextfeature covariance leads tosubstantial empirical gains overapproximate approaches.

Neural Information Processing Systems http://nips.cc/

SNR = 10 log10 2 Figure 1: AWGNchannelwithnoisyfeedback (left). Figure 5: Noiseinfirstphasevs. firstparitybit (left) andsecondparitybit (right).

Much of the US economy rests on AI's future. On this episode of podcast, cohost Joe Weisenthal breaks down why AI's impact on finance goes beyond billion-dollar investments. If you read any of WIRED's recent AI edition, you know that lots of people are spending lots of time talking about how the technology is revolutionizing pretty much everything--from coding to writing to accounting. You've also probably heard by now, from us or somebody else, that we might very well be in an economic bubble of AI origin, one wherein the billions and billions of dollars being funneled into the industry is creating an untenable economic scenario that could turn catastrophic. Of course, you may also have read that I'm really sick of being asked about AI . I'm still not sick, though, of asking other people about it--especially when they're much smarter about this stuff than I am. Enter Joe Weisenthal, the cohost of Bloomberg's fantastic podcast, and a former coworker of mine. Trust me: As someone who spent a year listening to Joe lose his mind in the office--loudly!--anytime the economy hiccuped, few people think more about our country's, and our planet's, financial circumstances than Joe does. And right now, Joe's concerns aren't strictly about what happens if or when that AI bubble bursts. His worries are more focused on what's going right and wrong with the US economy writ large. For this week's episode of, Joe and I talked about weird market indicators, US competition with China, and whether or not we should all prepare for an AI economic apocalypse. Nice to see you again. We were just talking about how [you] and I worked together--what was that, like nine years ago? I think you were there 2014, 2015, so maybe 10 years ago or something? Yeah, I worked at Bloomberg. I lasted about a year. But Joe, you were there, you were loud, you were proud, you were always very excited about the economy.

The billionaire investor campaigned for Kamala Harris, but thinks tech execs have a "moral imperative" to play nice with the president. Back in May, Mark Cuban appeared in his last episode of ABC's after spending more than a decade on the show investing in--or deprecating--entrepreneurs' big ideas. But that doesn't mean the billionaire is going away. Yes, Cuban loves to talk--about ideas, about the future, about what it takes to actually make America healthy again. Or, at least, to get Americans more affordable drugs, which Cuban is endeavoring to do with his startup, Cost Plus Drug Company. Nor does Cuban, like many billionaire businessmen, shy away from talking politics: Does he like President Trump? But would he join the president for dinner like so many of his peers have in recent months? With enthusiasm, according to a conversation we had for this week's episode of . Keep reading to find out why. Just so you know--well it's too late now--we always start these conversations with some rapid-fire questions. What is the smartest investment you ever made? What's the dumbest purchase you ever made? Alright, one word to describe the startup pitches that you hate. Would you rather invest in passion or in numbers? Tell me a little bit about why.

If you stayed awake in science class as a kid, the payoff comes when you get a good laugh out of Freya McGhee's jokes. Stop me if you've heard this one before. An aspiring chemist goes to college, realizes she's not good at chemistry, and bombs her dissertation. She takes a class in standup comedy and decides the best way to talk about STEM is to make jokes at its expense. Based in London, the comedian had a strong interest in science as a kid, but after attending the University of Brighton to study chemistry, she realized that she liked learning science more than she liked applying it. Her thesis dissertation--"Synthesis of Iron Nitroxide radical species using radical derivatized ligands and its use as a single-molecule magnet"--flopped.

A new video game sparked fury and accusations of wokeness in entertainment. But we've played this game before--and it's boring. Back in the summer of 2020, during the first year of COVID lockdowns, two first-party PlayStation games were released back-to-back, just a month apart: and . Upon release, was pretty beloved by a specific right-wing culture-war gamer crowd, who placed it on a pedestal specifically as a way to directly attack . While is far from perfect (for example, Neil Druckmann, the game's creator and co-director, took inspiration from the Israel-Palestine conflict that was criticized for both-sidesism), but the game's sin on release for many on the political right was that it took a series whose lead was previously a man and continued its story with one lead who was a lesbian and another whose appearance was deemed too masculine for these players to be attracted to her.

In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answer. In contrast, Vision Language Models (VLMs) typically generate direct, one-shot responses without evaluating the sufficiency of the information. To investigate this gap, we identify a critical and challenging task in the Visual Question Answering (VQA) scenario: can VLMs indicate how to adjust an image when the visual information is insufficient to answer a question? This capability is especially valuable for assisting visually impaired individuals who often need guidance to capture images correctly. To evaluate this capability of current VLMs, we introduce a human-labeled dataset as a benchmark for this task.

Adversarial misuse, particularly through `jailbreaking' that circumvents a model's safety and ethical protocols, poses a significant challenge for Large Language Models (LLMs). This paper delves into the mechanisms behind such successful attacks, introducing a hypothesis for the safety mechanism of aligned LLMs: intent security recognition followed by response generation. Grounded in this hypothesis, we propose CodeChameleon, a novel jailbreak framework based on personalized encryption tactics. To elude the intent security recognition phase, we reformulate tasks into a code completion format, enabling users to encrypt queries using personalized encryption functions. To guarantee response generation functionality, we embed a decryption function within the instructions, which allows the LLM to decrypt and execute the encrypted queries successfully. We conduct extensive experiments on 7 LLMs, achieving state-of-the-art average Attack Success Rate (ASR). Remarkably, our method achieves an 86.6\% ASR on GPT-4-1106.

Standpoint EL is a multi-modal extension of the popular description logic EL that allows for the integrated representation of domain knowledge relative to diverse standpoints or perspectives. Advantageously, its satisfiability problem has recently been shown to be in PTime, making it a promising framework for large-scale knowledge integration. In this paper, we show that we can further push the expressivity of this formalism, arriving at an extended logic, called Standpoint EL+, which allows for axiom negation, role chain axioms, self-loops, and other features, while maintaining tractability. This is achieved by designing a satisfiability-checking deduction calculus, which at the same time addresses the need for practical algorithms. We demonstrate the feasibility of our calculus by presenting a prototypical Datalog implementation of its deduction rules.