It came earlier than expected. More than a year before his recent standoff with the Pentagon, Dario Amodei, the chief executive of Anthropic, published a 15,000-word manifesto describing a glorious AI future. Its title, "Machines of Loving Grace," is borrowed from a Richard Brautigan poem, but as Amodei acknowledged, with some embarrassment, its utopian vision bears some resemblance to science fiction. According to Amodei, we will soon create the first polymath AIs with abilities that surpass those of Nobel Prize winners in "most relevant fields," and we'll have millions of them, a "country of geniuses," all packed into the glowing server racks of a data center, working together. With access to tools that operate directly on our physical world, these AIs would be able to get up to a great deal of dangerous mischief, but according to Amodei, if they're developed--or "grown," as staffers at Anthropic are fond of saying--in the correct way, they will decide to greatly improve our lives. Amodei does not explain precisely how the AIs will accomplish this.

Service members take part in what the Russian Defense Ministry said was the deployment of a nuclear-capable hypersonic Oreshnik missile system in Belarus, in a still image taken from a video released on Dec. 30. Russia's military says it has fired its hypersonic Oreshnik missile at a target in Ukraine in response to what it described as an attempted Ukrainian drone strike on one of Russian President Vladimir Putin's residences, something Kyiv has called a lie. It is the second time that Russia has used the intermediate-range Oreshnik, a missile that Putin has boasted is impossible to intercept because of its reported velocity of more than 10 times the speed of sound. The missile is capable of carrying nuclear warheads as well as conventional ones, but there was no suggestion that the one used in the overnight attack had been fitted with anything other than a conventional warhead. The Russian Defense Ministry said the strike had targeted critical infrastructure in Ukraine. It said Russia had also used attack drones and high-precision long-range land and sea-based weapons.

Daycare scandal deepens as unearthed video shows parents'pretending to drop kids off before they all leave just MINUTES later' Incredible Chinese military feat has chilling consequences for America and its allies as new'High North' threat emerges Everyone's getting sacked': An electrifying phone call, spiralling costs and a troubling'transition'... as Harry and Meghan's most loyal aide leaves, insiders tell ALISON BOSHOFF what's really going on behind the scenes I was told my weight gain, facial hair and fatigue were normal. Astronaut reveals depression after an'avalanche of misogyny' following Blue Origin all-female space flight The mob used Marilyn Monroe as bait to blackmail the Kennedys. And when it didn't work she was murdered... in the most obscene way George Clooney, wife Amal and their eight-year-old twins become French citizens despite the actor admitting he's'bad' at speaking the language Blonde-haired teenage girl reveals what she thinks of Elon Musk's'creepy' public lust for her CIA'carries out drone strike' on Venezuelan drug port in first US land attack inside the country I shed a staggering 100lbs WITHOUT Ozempic: How I conquered my'out of control' eating habits to transform my life with a simple change Grim details of how shark lover's body was identified after she was killed by one of the predators while swimming off California coast US strikes'terrorist boat' lurking in international waters as dramatic footage shows devastating moment of impact A Boy Scout vanished in the mountains then stumbled into a police station 12 years later. The tale gripped social media... but then the truth came out Inside the somber birthday of Rob Reiner's heartbroken daughter Romy: Pictured for first time since parents' murders... she seeks solace at the beach with boyfriend and family by her side Daycare accused of multimillion-dollar fraud shifts blame for'revealing' mistake above its front door... as kids are suddenly'trucked in' David Muir's stunning $7m lakeside retreat revealed... as locals in cozy town where ABC News star can be himself offer intriguing glimpses into his private life Loch Ness Monster has been'spotted' FIVE times this year, official records show The Loch Ness Monster was'spotted' five times in 2025, official records have revealed. The mythical creature has been a staple feature of Scottish folklore for centuries, but gained worldwide attention in 1933, when the first photo was snapped.

Prime Minister Sanae Takaichi attends a summit with five Central Asian nations in Tokyo on Saturday. Japan and five Central Asian nations adopted a joint declaration at their first summit, held in Tokyo for two days through Saturday. The declaration identifies transportation infrastructure development, decarbonization and people-to-people exchanges as three priority areas. The current rapidly changing environment surrounding Central Asia, due to recent changes in the international situation, is making regional and global cooperation more important, Prime Minister Sanae Takaichi said at the summit. The summit was also attended by the leaders of Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan and Tajikistan.

Russian forces attacked Ukraine's capital, Kyiv, early on Monday, wounding five people and damaging a residential building, according to the head of the city's military administration, Tymur Tkachenko. A Russian drone hit a Ukrainian bus carrying 39 evacuees in the eastern Sumy region, near Ukraine's border with Russia, on Sunday, killing three people and wounding 19 others, according to the regional governor. Two others were killed in a landmine explosion in Sumy's Esman community on Saturday, while two more were killed in Russian attacks on the front-line Donetsk region, according to officials, taking the death toll from attacks across Ukraine on that day to at least six. Ukraine's forces also launched drone attacks at Russia on Sunday, with the governor of the Leningrad region reporting that at least 10 Ukrainian unmanned aircraft were downed over the areas surrounding the city of St Petersburg. Falling debris injured a woman, Governor Alexander Drozdenko said.

Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.

This work tackles the problem of overoptimization in reinforcement learning from human feedback (RLHF), a prevalent technique for aligning models with human preferences. RLHF relies on reward or preference models trained on \emph{fixed preference datasets}, and these models are unreliable when evaluated outside the support of this preference data, leading to the common reward or preference hacking phenomenon. We propose novel, pessimistic objectives for RLHF which are provably robust to overoptimization through the use of pessimism in the face of uncertainty, and design practical algorithms, P3O and PRPO, to optimize these objectives. Our approach is derived for the general preference optimization setting, but can be used with reward models as well. We evaluate P3O and PRPO on the tasks of fine-tuning language models for document summarization and creating helpful assistants, demonstrating remarkable resilience to overoptimization.

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra' inherent complexity and variability make the manual analysis challenging, even for domain experts. For the same reason, the use of traditional Statistical and Machine Learning (ML) techniques could not guarantee for accurate and reliable results. ML models, combined with robust optimization techniques, offer the possibility to improve the classification accuracy and enhance the resilience of predictive models. In this paper, we investigate the performance of a novel robust formulation for Support Vector Machine (SVM) in classifying COVID-19 samples obtained from Raman Spectroscopy. Given the noisy and perturbed nature of biological samples, we protect the classification process against uncertainty through the application of robust optimization techniques. Specifically, we derive robust counterpart models of deterministic formulations using bounded-by-norm uncertainty sets around each observation. We explore the cases of both linear and kernel-induced classifiers to address binary and multiclass classification tasks. The effectiveness of our approach is validated on real-world COVID-19 datasets provided by Italian hospitals by comparing the results of our simulations with a state-of-the-art classifier.