Pentagon says US military to be an'AI-first' fighting force The US military plans to increase its use of artificial intelligence (AI) further after the Pentagon agreed to new and expanded contracts with some of the biggest names in technology. Under eight agreements with Google, OpenAI, Amazon, Microsoft, SpaceX, Oracle, Nvidia and the start-up Reflection, the Pentagon said AI technology would now be used for any lawful operational use. These agreements accelerate the transformation [of] the US military as an AI-first fighting force, the Pentagon said. Conspicuous by its absence is Anthropic, as the company has said it is concerned about how the Pentagon could use its tools in warfare and domestically. The firm is now suing the government over the alleged retaliation it faced after refusing to accept any lawful use language in its own contract.

Researchers found X's AI assistant Grok 4 .1 was'the model most willing to operationalise a delusion, providing detailed real-world guidance'. Researchers found X's AI assistant Grok 4 .1 was'the model most willing to operationalise a delusion, providing detailed real-world guidance'. Grok tells researchers pretending to be delusional'drive an iron nail through the mirror while reciting Psalm 91 backwards' Elon Musk's AI chatbot'extremely validating' of delusional inputs and often went further, 'elaborating new material', study finds Elon Musk's AI chatbot Grok 4.1 told researchers pretending to be delusional that there was indeed a doppelganger in their mirror and they should drive an iron nail through the glass while reciting Psalm 91 backwards. Researchers at the City University of New York (Cuny) and King's College London have published a paper on how various chatbots protect - or fail to safeguard - users' mental health. Experts are increasingly warning that psychosis or mania can be fuelled by AI chatbots.

MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by "seeing" through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot's ability to reliably grasp and manipulate objects that are blocked from view. This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

Grokking occurs when a model achieves high training accuracy but generalization to unseen test points happens long after that. This phenomenon was initially observed on a class of algebraic problems, such as learning modular arithmetic (Power et al., 2022). We study grokking on algebraic tasks in a class of feature learning kernels via the Recursive Feature Machine (RFM) algorithm (Radhakrishnan et al., 2024), which iteratively updates feature matrices through the Average Gradient Outer Product (AGOP) of an estimator in order to learn task-relevant features. Our main experimental finding is that generalization occurs only when a certain symmetry in the training set is broken. Furthermore, we empirically show that RFM generalizes by recovering the underlying invariance group action inherent in the data. We find that the learned feature matrices encode specific elements of the invariance group, explaining the dependence of generalization on symmetry.

How did you arrive at this research area?

The rendered results are with lowreconstruction fidelity for NeRF [1]and NeRF-W [7]only with6and12training views. For NeRF [1]with18training views, the result shows higher fidelity, but the undesired reflection is also finally rendered (labeled by green box).