Here, w will be called the vector of weights of the neuron.

Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control. Recently, it has been shown that Transformer-based models succeed in consistent reasoning over explicit symbolic facts, under a "closed-world" assumption. However, in an open-domain setup, it is desirable to tap into the vast reservoir of implicit knowledge already encoded in the parameters of pre-trained LMs. In this work, we provide a first demonstration that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements. To do this, we describe a procedure for automatically generating datasets that teach a model new reasoning skills, and demonstrate that models learn to effectively perform inference which involves implicit taxonomic and world knowledge, chaining and counting. Finally, we show that "teaching" the models to reason generalizes beyond the training distribution: they successfully compose the usage of multiple reasoning skills in single examples. Our work paves a path towards open-domain systems that constantly improve by interacting with users who can instantly correct a model by adding simple natural language statements.

Amazon is aiming to make shopping just a bit easier. This week, Amazon launched a new generative AI feature that produces short audio summaries, detailing everything you need to know about a product. The audio descriptions, which Amazon is calling "hear the highlights", are created from on-page product summaries, reviews, and information from other websites, crafting short snippets that deliver everything you need to know about a product. The product summaries are now available on a limited number of items on Amazon and for US customers only. To access "Hear the highlights", you can do so in the Amazon app.

Dating apps are getting a lot of flak lately. Daters are opting for in-person events -- even dungeon sound baths -- and moving away from increasing AI features and apps that seem to be copying each other. Vice President JD Vance also has no love for dating apps, apparently. In an interview on the New York Times's "Interesting Times" podcast, Vance spoke about his "noneconomic" concerns with AI and tech. He told host and Times opinion columnist Ross Douthat, "If you look at basic dating behavior among young people -- and I think a lot of this is that the dating apps are probably more destructive than we fully appreciate."

Public pretraining is a promising approach to improve differentially private model training. However, recent work has noted that many positive research results studying this paradigm only consider in-distribution tasks, and may not apply to settings where there is distribution shift between the pretraining and finetuning data--a scenario that is likely when finetuning private tasks due to the sensitive nature of the data. In this work, we show empirically across three tasks that even in settings with large distribution shift, where both zero-shot performance from public data and training from scratch with private data give unusably weak results, public features can in fact improve private training accuracy by up to 67% over private training from scratch. We provide a theoretical explanation for this phenomenon, showing that if the public and private data share a low-dimensional representation, public representations can improve the sample complexity of private training even if it is impossible to learn the private task from the public data alone. Altogether, our results provide evidence that public data can indeed make private training practical in realistic settings of extreme distribution shift.

Everything over the last 30 years, according to Sir Jony Ive, has led to this moment: a partnership between the iPhone designer and the developer of ChatGPT. Ive has sold his hardware startup, io, to OpenAI and will take on creative and design leadership across the merged businesses. "I have a growing sense that everything I have learned over the last 30 years has led me to this place, to this moment," he says in a video announcing the 6.4bn ( 4.8bn) deal. The main aim will be to move on from Ive's signature achievement designing Apple's most successful product, as well as the iPod, iPad and Apple Watch. The British-born designer has already developed a prototype io device, and one of its users is OpenAI's chief executive, Sam Altman.

AI's energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining. The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

After whirlwind week of announcements from Google and OpenAI, Anthropic has its own news to share. On Thursday, Anthropic announced Claude Opus 4 and Claude Sonnet 4, its next generation of models, with an emphasis on coding, reasoning, and agentic capabilities. According to Rakuten, which got early access to the model, Claude Opus 4 ran "independently for seven hours with sustained performance." Claude Opus is Anthropic's largest version of the model family with more power for longer, complex tasks, whereas Sonnet is generally speedier and more efficient. Claude Opus 4 is a step up from its previous version, Opus 3, and Sonnet 4 replaces Sonnet 3.7.

AI-generated video has been advancing rapidly, with leading tech developers racing to build and commercialize their own models. We're now seeing the rise of tools that can generate strikingly photorealistic video from a single prompt in natural language. For the most part, however, AI-generated video has had a glaring shortcoming: it's silent. At its annual I/O developer conference on Tuesday, Google announced the release of Veo 3, the latest iteration of its video-generating AI model, which also comes with the ability to generate synchronized audio. Imagine you prompt the system to generate a video set inside a busy subway car, for example.