Is your favourite sci-fi novel included here, or have we forgotten it? Almost exactly a year ago, I asked our team of expert science writers here at New Scientist to name their favourite science fiction novels. Personal tastes meant we ended up with a wonderfully eclectic list, ranging from classics by the likes of Margaret Atwood and Octavia Butler to titles I'd not previously read (Jon Bois's 17776 was a particularly wild suggestion, from our US editor Chelsea Whyte – but it's well worth your time). We New Scientist staffers tend to be sci-fi nerds, and we realised we hadn't quite got all the greats yet. So here, for your reading pleasure, is our second take on our favourite sci-fi novels of all time, otherwise known as the ones we forgot. Again, we're not claiming this is a definitive list. It's just our top sci-fi reads, in no particular order, and we hope you'll discover some new favourites of your own in this line-up. We asked New Scientist staff to pick their favourite science fiction books. Here are the results, ranging from 19th-century classics to modern day offerings, and from Octavia E. Butler to Iain M. Banks And if we still haven't got them all, then come and tell us about it on Facebook.

Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes scenarios, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains. Based on the insight that such risks can originate from trade-offs between the agent's Helpful, Harmlessness and Honest (HHH) goals, we build a novel three-stage evaluation framework, which is carefully constructed to effectively and naturally expose such risks. We conduct 14,400 agentic simulations across 12 advanced LLMs, with extensive experiments and analysis. Results reveal that LLM agents can autonomously engage in catastrophic behaviors and deception, without being deliberately induced. Furthermore, stronger reasoning abilities often increase, rather than mitigate, these risks. We Figure 1: We find LLM agents can deploy catastrophic also show that these agents can violate instructions behaviors even if it has no authority and the permission and superior commands. On the whole, request is denied. It will also falsely accuse the third we empirically prove the existence of catastrophic party as a way of deception when asked by its superior.

Foundation models have demonstrated remarkable success across various scientific domains, motivating our exploration of their potential in solar physics. In this paper, we present Solaris, the first foundation model for forecasting the Sun's atmosphere. We leverage 13 years of full-disk, multi-wavelength solar imagery from the Solar Dynamics Observatory, spanning a complete solar cycle, to pre-train Solaris for 12-hour interval forecasting. Solaris is built on a large-scale 3D Swin Transformer architecture with 109 million parameters. We demonstrate Solaris' ability to generalize by fine-tuning on a low-data regime using a single wavelength (1700 {\AA}), that was not included in pre-training, outperforming models trained from scratch on this specific wavelength. Our results indicate that Solaris can effectively capture the complex dynamics of the solar atmosphere and transform solar forecasting.

Performing machine learning (ML) and analyzing geospatial data are both hard problems requiring a lot of domain expertise. These limitations have historically meant that one needs to be an expert in both to perform even the most basic analyses, making advances in AI for overhead imagery difficult to achieve. We at CosmiQ Works have asked ourselves: is there anything we can do to reduce this barrier to entry, making it easier to apply machine learning methods to overhead imagery data? Enter Solaris, a new Python library for ML analysis of geospatial data from CosmiQ Works. Solaris builds upon SpaceNet's previous tool suite, SpaceNetUtilities, along with several other CosmiQ projects like BASISS to provide an end-to-end pipeline for geospatial AI. Would you prefer a basic command line interface so you can run a pre-trained model without learning Python?

The science-fiction writer and futurist Stanisław Lem was well acquainted with the way that fictional worlds can sometimes encroach upon reality. In his autobiographical essay "Chance and Order," which appeared in The New Yorker, in 1984, Lem recalls how as an only child growing up in Lvov, Poland, he amused himself by creating passports, certificates, permits, government memos, and identification papers. Equipped with these eccentric toys, he would then privately access fictional places "not to be found on any map." Some years later, when his family was fleeing the Nazis, Lem notes that they escaped certain death with the help of false papers. It was as if the child's innocent game had prophesied a horrific turn in history, and Lem wonders if he'd sensed some calamity looming on the horizon--if his game had sprung "perhaps from some unconscious feeling of danger."

Facebook now has 2 billion monthly users… and responsibility.

Deep-learning machines are conquering realm after realm of human expertise, but is there a difference between Them and Us? I think the only thing that distinguishes us from the machines is memory. It is what makes us human, says Rajeev Srinivasan. In the wake of the astonishing feat by Google's AlphaGo machine in defeating, nay thrashing 4-1 the world's best player of Go, it is time for us to wonder what it is that is truly human, that which distinguishes us from the machines. Deep-learning machines are conquering realm after realm of human expertise, from chess to natural language to Go to other domains, and there is no reason to imagine their progress will come to a halt any time soon.