Claim, counter-claim and tech's seedy side exposed: Five things we learned in the Musk-Altman trial It is the legal showdown that has pitted two of the biggest names in tech, Elon Musk and Sam Altman, against each other. At stake is the future of one of the world's most valuable start-ups, ChatGPT-maker OpenAI, along with the reputations of Altman - the company's boss - and Musk, the man he founded it with. The central claim the jury has now retired to consider is Musk's argument his former friend stole a charity, cheating him out of a fortune (albeit a tiny one, by Musk's standards) along the way - something Altman strongly rejects. But there's been much more to the trial than that. Over the past three weeks, myself and other reporters have been glued to our seats at the federal court in California as the evidence ranged from explosive text messages to revelations of free Teslas allegedly offered in exchange for power.

Javed is a senior editor at TIME, based in the London bureau. Javed is a senior editor at TIME, based in the London bureau. Leaders from across the tech sector, academia, and beyond gathered to explore how to implement responsible AI and ensure safeguarding while fostering innovation, at a roundtable convened by TIME in Davos, Switzerland, on Jan 21. In a wide-ranging conversation, participants in the roundtable, hosted by TIME CEO Jess Sibley, discussed topics including the impact of AI on children's development and safety, how to regulate the technology, and how to better train models to ensure they don't harm humans. Discussing the safety of children, Jonathan Haidt, professor of ethical leadership at NYU Stern and author of said that parents shouldn't focus on restricting their child's exposure entirely but on the habits they form.

Clear, reliable audio is no longer optional, say Genevieve Juillard, CEO of IDC, and Chris Schyvinck, president and CEO at Shure. When business leaders talk about digital transformation, their focus often jumps straight to cloud platforms, AI tools, or collaboration software. Yet, one of the most fundamental enablers of how organizations now work, and how employees experience that work, is often overlooked: audio. As Genevieve Juillard, CEO of IDC, notes, the shift to hybrid collaboration made every space, from corporate boardrooms to kitchen tables, meeting-ready almost overnight. In the scramble, audio quality often lagged, creating what research now shows is more than a nuisance. Poor sound can alter how speakers are perceived, making them seem less credible or even less trustworthy. Audio is the gatekeeper of meaning," stresses Julliard. "If people can't hear clearly, they can't understand you. And if they can't understand you, they can't trust you, and they can't act on what you said. And no amount of sharp video can fix that. For Shure, which has spent a century advancing sound technology, the implications extend far beyond convenience.

Large language models often lie and cheat. We can't stop that--but we can make them own up. OpenAI is testing another new way to expose the complicated processes at work inside large language models. Researchers at the company can make an LLM produce what they call a confession, in which the model explains how it carried out a task and (most of the time) owns up to any bad behavior. Figuring out why large language models do what they do--and in particular why they sometimes appear to lie, cheat, and deceive--is one of the hottest topics in AI right now. If this multitrillion-dollar technology is to be deployed as widely as its makers hope it will be, it must be made more trustworthy.

Traditionally, an agent's beliefs would come from what the agent can see, hear, or sense. In the modern world, beliefs are often based on the data available to the agents. In this work, we investigate a dynamic logic of such beliefs that incorporates public announcements of data. The main technical contribution is a sound and complete axiomatisation of the interplay between data-informed beliefs and data announcement modalities. We also describe a non-trivial polynomial model checking algorithm for this logical system.

AI now sounds more like us - should we be concerned? Several wealthy Italian businessmen received a surprising phone call earlier this year. The speaker, who sounded just like Defence Minister Guido Crosetto, had a special request: Please send money to help us free kidnapped Italian journalists in the Middle East. But it was not Crosetto at the end of the line. He only learned about the calls when several of the targeted businessmen contacted him about them.

The internet is rife with unattributed, deliberately misleading, or otherwise untrustworthy content. Though large language models (LLMs) are often tasked with autonomous web browsing, the extent to which they have learned the simple heuristics human researchers use to navigate this noisy environment is not currently known. In this paper, we introduce the Synthetic Media Literacy Test (SMeL Test), a minimal benchmark that tests the ability of language models to actively filter out untrustworthy information in context. We benchmark a variety of commonly used instruction-tuned LLMs, including reasoning models, and find that no model consistently succeeds; while reasoning in particular is associated with higher scores, even the best API model we test hallucinates up to 70% of the time. Remarkably, larger and more capable models do not necessarily outperform their smaller counterparts. We hope our work sheds more light on this important form of hallucination and guides the development of new methods to combat it.

A common practice of ML systems development concerns the training of the same model under different data sets, and the use of the same (training and test) sets for different learning models. The first case is a desirable practice for identifying high quality and unbiased training conditions. The latter case coincides with the search for optimal models under a common dataset for training. These differently obtained systems have been considered akin to copies. In the quest for responsible AI, a legitimate but hardly investigated question is how to verify that trustworthiness is preserved by copies. In this paper we introduce a calculus to model and verify probabilistic complex queries over data and define four distinct notions: Justifiably, Equally, Weakly and Almost Trustworthy which can be checked analysing the (partial) behaviour of the copy with respect to its original. We provide a study of the relations between these notions of trustworthiness, and how they compose with each other and under logical operations. The aim is to offer a computational tool to check the trustworthiness of possibly complex systems copied from an original whose behavour is known.

On June 3, Yoshua Bengio, the world's most-cited computer scientist, announced the launch of LawZero, a nonprofit that aims to create "safe by design" AI by pursuing a fundamentally different approach to major tech companies. Players like OpenAI and Google are investing heavily in AI agents--systems that not only answer queries and generate images, but can craft plans and take actions in the world. The goal of these companies is to create virtual employees that can do practically any job a human can, known in the tech industry as artificial general intelligence, or AGI. Executives like Google DeepMind's CEO Demis Hassabis point to AGI's potential to solve climate change or cure disease as a motivator for its development. Bengio, however, says we don't need agentic systems to reap AI's rewards--it's a false choice.

The increasing integration of artificial intelligence (AI) systems in various fields requires solid concepts to ensure compliance with upcoming legislation. This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act and the compliance of data sets. The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources. Such devices often face unique issues due to their decentralized nature and limited computing resources for implementing sophisticated compliance mechanisms. By analyzing AI implementations, the paper identifies challenges and proposes the first best practices for legal compliance when developing, deploying, and running AI. The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems, which must be aligned with ethical standards set forth in regulatory frameworks such as the AI Act. The insights gained should contribute to the ongoing discourse on the responsible development and deployment of embedded AI systems.