Ive led the designs of the original iMac, the iPad, and the Apple Watch, among other era-defining products. Then, in 2019, he left Apple to start his own design firm called LoveFrom. The news of his move to OpenAI felt something like learning that LeBron James was joining the Miami Heat: Ive had become synonymous with Apple's success, perhaps second only to Jobs. Now, after a period of independence, he was choosing a new team. The announcement of the deal with OpenAI--for a reported 6.5 billion in OpenAI equity--came via a press release, featuring a rather cuddly portrait of Ive with OpenAI's C.E.O. and co-founder, Sam Altman (shot by the British fashion photographer Craig McDean) and a faux-casual videotaped interview session between the two at San Francisco's Cafe Zoetrope. In it, Altman describes "a family of devices that would let people use A.I. to create all sorts of wonderful things," enabled by "magic intelligence in the cloud."

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Now and then, Feedback sees ads for courses promising to teach us how to become an excellent creative writer. It sounds like fun, but why learn to be a good writer when we can just do this stuff instead? One brand that recently caught Feedback's eye is BBC Maestro.

Opera is resurrecting Opera Neon, a browser concept first introduced in 2017, and equipping it with the latest tech trend: agentic AI--an assistant you can assign tasks to, which it will carry out autonomously. Opera Neon will work like a normal browser. Opera, however, is integrating local AI that you can chat with privately and ask to do tasks and combining it with an interface to a remote server that will serve as a workspace of sorts for Opera Neon's AI creation tools. Most browsers are free; the twist here is that Opera Neon will require a paid subscription of an unknown amount, and potential users will be subject to a waitlist. Opera has a history of experimenting with innovative concepts--it was an early proponent of VPNs, for example.

I've been trying to disentangle my online life from Google for a while. And as someone who wrote about Android professionally for years, it hasn't been easy. I've ditched Chrome, but I still use a Samsung Galaxy phone and Google Pixel Watch, for example. But when I finally got off the big daddy, Google Search, and switched to DuckDuckGo, it had a surprising effect: Google got better. That's a broad statement, so let me be more particular right away.

There is no question that OpenAI pulled off something historic with its release of ChatGPT 3.5 in 2022. It set in motion an AI arms race that has already changed the world in a number of ways and seems poised to have an even greater long-term effect than the short-term disruptions to things like education and employment that we are already beginning to see. How that turns out for humanity is something we are still reckoning with and may be for quite some time. But a pair of recent books both attempt to get their arms around it with accounts of what two leading technology journalists saw at the OpenAI revolution. In Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, Karen Hao tells the story of the company's rise to power and its far-reaching impact all over the world.

Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, Eleanor talks to Alexander Thomas, a filmmaker and academic who leads the BA in Media Production at the University of East London. They discuss his new book about transhumanism, a philosophical movement that aims to improve human capabilities through technology and whose followers includes Jeff Bezos, Elon Musk, Larry Page, and also apparently the DJ Steve Aoki. Alex is himself one of the foremost commentators on transhumanism. He explores transhumanist fantasies about the future of the human, is obsessed with the extremes of possibility: they either think that AI will bring us radical abundance or total extinction.

Conversational recommender systems proactively query users with relevant "key terms" and leverage the feedback to elicit users' preferences for personalized recommendations. Conversational contextual bandits, a prevalent approach in this domain, aim to optimize preference learning by balancing exploitation and exploration. However, several limitations hinder their effectiveness in real-world scenarios. First, existing algorithms employ key term selection strategies with insufficient exploration, often failing to thoroughly probe users' preferences and resulting in suboptimal preference estimation. Second, current algorithms typically rely on deterministic rules to initiate conversations, causing unnecessary interactions when preferences are well-understood and missed opportunities when preferences are uncertain. To address these limitations, we propose three novel algorithms: CLiSK, CLiME, and CLiSK-ME. CLiSK introduces smoothed key term contexts to enhance exploration in preference learning, CLiME adaptively initiates conversations based on preference uncertainty, and CLiSK-ME integrates both techniques. We theoretically prove that all three algorithms achieve a tighter regret upper bound of $O(\sqrt{dT\log{T}})$ with respect to the time horizon $T$, improving upon existing methods. Additionally, we provide a matching lower bound $Ω(\sqrt{dT})$ for conversational bandits, demonstrating that our algorithms are nearly minimax optimal. Extensive evaluations on both synthetic and real-world datasets show that our approaches achieve at least a 14.6% improvement in cumulative regret.

Within the common LLM use case of text revision, we study LLMs' revision of gendered role nouns (e.g., outdoorsperson/woman/man) and their justifications of such revisions. We evaluate their alignment with feminist and trans-inclusive language reforms for English. Drawing on insight from sociolinguistics, we further assess if LLMs are sensitive to the same contextual effects in the application of such reforms as people are, finding broad evidence of such effects. We discuss implications for value alignment.

Although Automatic Speech Recognition (ASR) systems have become an integral part of modern technology, their evaluation remains challenging, particularly for low-resource languages such as Persian. This paper introduces Persian Speech Recognition Benchmark(PSRB), a comprehensive benchmark designed to address this gap by incorporating diverse linguistic and acoustic conditions. We evaluate ten ASR systems, including state-of-the-art commercial and open-source models, to examine performance variations and inherent biases. Additionally, we conduct an in-depth analysis of Persian ASR transcriptions, identifying key error types and proposing a novel metric that weights substitution errors. This metric enhances evaluation robustness by reducing the impact of minor and partial errors, thereby improving the precision of performance assessment. Our findings indicate that while ASR models generally perform well on standard Persian, they struggle with regional accents, children's speech, and specific linguistic challenges. These results highlight the necessity of fine-tuning and incorporating diverse, representative training datasets to mitigate biases and enhance overall ASR performance. PSRB provides a valuable resource for advancing ASR research in Persian and serves as a framework for developing benchmarks in other low-resource languages. A subset of the PSRB dataset is publicly available at https://huggingface.co/datasets/PartAI/PSRB.

Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these models are still prone to what are commonly known as hallucinations, causing them to emit unwanted and factually incorrect text. In this work, we study how well LLMs capture uncertainty, without explicitly being trained for that. We show that, if considering uncertainty as a linear concept in the model's latent space, it might indeed be captured, even after only pretraining. We further show that, though unintuitive, LLMs appear to capture several different types of uncertainty, each of which can be useful to predict the correctness for a specific task or benchmark. Furthermore, we provide in-depth results such as demonstrating a correlation between our correction prediction and the model's ability to abstain from misinformation using words, and the lack of impact of model scaling for capturing uncertainty. Finally, we claim that unifying the uncertainty types as a single one using instruction-tuning or [IDK]-token tuning is helpful for the model in terms of correctness prediction.