In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.

In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.

Pgx is available at https://github.com/sotetsuk/pgx. Figure 1: Example games included in Pgx.

An important corollary of successful task decomposition amongst experts is that layers are easier to debug and edit. Biased or unsafe behaviors can be better localized to specific experts' subcomputation, facilitating manual correction or surgery in a way that minimally affects the other functionality of the network. Addressing such behaviors is particularly crucial in the context of foundation models; being often fine-tuned as black boxes pre-trained on unknown, potentially imbalanced data distributions.

Why an AI'godfather' is quitting Meta after 12 years Just a couple of weeks ago, one of the godfathers of artificial intelligence was in St James's Palace being handed an award from King Charles for his work in artificial intelligence (AI). Professor Yann LeCun was being honoured along with six other recipients for his contributions to the field, which have been credited as advancing deep learning. But Mr LeCun is at odds with some of the AI world over the future of the generation-defining technology. And now he is going all-in on his idea of advanced machine intelligence after announcing he is leaving his role as Meta's chief AI scientist to start a new firm. During his 12 years at the company, Prof LeCun won the prestigious Turing Award and witnessed several flurries of excitement around AI - not least the most recent boom in generative AI accelerated by rival OpenAI's launch of ChatGPT in late 2022.

'We could have asked ChatGPT': students fight back over course taught by AI Students at the University of Staffordshire have said they feel "robbed of knowledge and enjoyment" after a course they hoped would launch their digital careers turned out to be taught in large part by AI. James and Owen were among 41 students who took a coding module at Staffordshire last year, hoping to change careers through a government-funded apprenticeship programme designed to help them become cybersecurity experts or software engineers. But after a term of AI-generated slides being read, at times, by an AI voiceover, James said he had lost faith in the programme and the people running it, worrying he had "used up two years" of his life on a course that had been done "in the cheapest way possible". "If we handed in stuff that was AI-generated, we would be kicked out of the uni, but we're being taught by an AI," said James during a confrontation with his lecturer recorded as a part of the course in October 2024. James and other students confronted university officials multiple times about the AI materials. But the university appears to still be using AI-generated materials to teach the course.

The company sent letters to Apple, Google, and Microsoft pushing for an alternative way to keep minors from viewing porn, as US and UK laws have caused its traffic to plummet. In letters sent to Apple, Google, and Microsoft this week, Pornhub's parent company urged the tech giants to support device-based age verification in their app stores and across their operating systems, WIRED has learned. "Based on our real-world experience with existing age assurance laws, we strongly support the initiative to protect minors online," reads the letter sent by Anthony Penhale, chief legal officer for Aylo, which owns Pornhub, Brazzers, Redtube, and YouPorn. "However, we have found site-based age assurance approaches to be fundamentally flawed and counterproductive." The letter adds that site-based age verification methods have "failed to achieve their primary objective: protecting minors from accessing age-inappropriate material online."

Generative AI is making it even easier for attackers to exploit old and often forgotten network equipment. Replacing it takes investment, but Cisco is making the case that it's worth it. Aging digital infrastructure equipment like routers, network switches, and network-attached storage--has long posed a silent risk to organizations. In the short term, it's cheaper and easier to just leave those boxes running in a forgotten closet. But this infrastructure may have old, insecure configurations, and legacy tech is often no longer supported by vendors for software patches and other protections.

Figure 1: Explanations on graphs with structure-aware values (like HN) offers advantages over non-structure-aware values (like Shapley).

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