Masayoshi Son, chairman and chief executive officer of SoftBank Group, speaks during the Future Investment Initiative (FII) Institute Priority Asia conference in Tokyo on Monday. SoftBank Group founder Masayoshi Son said he wouldn't have sold off Nvidia shares if his company had unlimited money to bankroll its next investments in artificial intelligence, which include a big bet on OpenAI. Son, addressing for the first time the surprise November disclosure that SoftBank had unloaded its entire stake in the world's most valuable company, also slammed talk of an AI investment bubble. The Japanese company simply needed to raise capital to fund projects including data center construction, he told a forum in Tokyo Monday. I don't want to sell a single share. I just had more need for money to invest in OpenAI" and other projects, Son said during the FII Priority Asia forum.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Zelenskyy says US peace plan'looks better' with new revisions Here's where things stand on Tuesday, December 2: Russian forces launched a ballistic missile on Ukraine's Dnipro, killing four people and wounding 40 others, according to Ukrainian authorities. Russia claimed the capture of the strategic eastern Ukrainian town of Pokrovsk, the logistics hub that has been under attack for months by Moscow's forces.

U.S. moves to deepen minerals supply chain in AI race with China The U.S. is looking to cut its dependence on China. The U.S. will seek agreements with eight allied nations as part of a fresh effort to strengthen supply chains for the computer chips and critical minerals needed for artificial intelligence technology, according to the top State Department official for economic affairs. The initiative, which builds on efforts dating back to the first administration of President Donald Trump, unfolds as the U.S. looks to cut its dependence on China. It will begin with a meeting at the White House on Dec. 12 between the U.S. and counterparts from Japan, South Korea, Singapore, the Netherlands, the U.K., Israel, the United Arab Emirates and Australia, Jacob Helberg, the undersecretary of state for economic affairs, said in an interview. Helberg, a former adviser at Palantir Technologies, said the summit will focus on reaching agreements across the areas of energy, critical minerals, advanced manufacturing semiconductors, AI infrastructure, and transportation logistics.

Apple thanked John Giannandrea for his efforts. Apple thanked John Giannandrea for his efforts. Siri-us setback: Apple's AI chief steps down as company lags behind rivals Apple's head of artificial intelligence, John Giannandrea, is stepping down from the company. The move comes as the Silicon Valley giant has lagged behind its competitors in rolling out generative AI features, in particular its voice assistant Siri. Apple made the announcement on Monday, thanking Giannandrea for his seven-year tenure at the company.

The shock resignation came for a very specific reason, but the OBR saga will continue with a series of decisions the chancellor will have to make over Richard Hughes' replacement. Firstly the Chancellor will have to find a respected and credible economist to run the OBR. There are several candidates, who might fit the mould of fiercely independent bean counters. The list will be carefully watched by the markets for any departure from the normal model. The problem is that there is some political pressure to do just that.

Multi-bit spiking neural networks (SNNs) have recently become a heated research spot, pursuing energy-efficient and high-accurate AI. However, with more bits involved, the associated memory and computation demands escalate to the point where the performance improvements become disproportionate. Based on the insight that different layers demonstrate different importance and extra bits could be wasted and interfering, this paper presents an adaptive bit allocation strategy for direct-trained SNNs, achieving fine-grained layer-wise allocation of memory and computation resources. Thus, SNN's efficiency and accuracy can be improved. Specifically, we parametrize the temporal lengths and the bit widths of weights and spikes, and make them learnable and controllable through gradients. To address the challenges caused by changeable bit widths and temporal lengths, we propose the refined spiking neuron, which can handle different temporal lengths, enable the derivation of gradients for temporal lengths, and suit spike quantization better. In addition, we theoretically formulate the step-size mismatch problem of learnable bit widths, which may incur severe quantization errors to SNN, and accordingly propose the step-size renewal mechanism to alleviate this issue. Experiments on various datasets, including the static CIFAR and ImageNet datasets and the dynamic CIFAR-DVS and DVS-GESTURE datasets, demonstrate that our methods can reduce the overall memory and computation cost while achieving higher accuracy. Particularly, our SEWResNet-34 can achieve a 2.69\% accuracy gain and 4.16$\times$ lower bit budgets over the advanced baseline work on ImageNet. This work is open-sourced at \href{https://github.com/Ikarosy/Towards-Efficient-and-Accurate-Spiking-Neural-Networks-via-Adaptive-Bit-Allocation}{this link}.

Foundation models have become central to unifying perception and planning in robotics, yet real-world deployment exposes a mismatch between their monolithic assumption that a single model can handle all cognitive functions and the distributed, dynamic nature of practical service workflows. Vision-language models offer strong semantic understanding but lack embodiment-aware action capabilities while relying on hand-crafted skills. Vision-Language-Action policies enable reactive manipulation but remain brittle across embodiments, weak in geometric grounding, and devoid of proactive collaboration mechanisms. These limitations indicate that scaling a single model alone cannot deliver reliable autonomy for service robots operating in human-populated settings. To address this gap, we present InteractGen, an LLM-powered multi-agent framework that decomposes robot intelligence into specialized agents for continuous perception, dependency-aware planning, decision and verification, failure reflection, and dynamic human delegation, treating foundation models as regulated components within a closed-loop collective. Deployed on a heterogeneous robot team and evaluated in a three-month open-use study, InteractGen improves task success, adaptability, and human-robot collaboration, providing evidence that multi-agent orchestration offers a more feasible path toward socially grounded service autonomy than further scaling standalone models.

Safety-critical environments are inherently dynamic. Distribution shifts, emerging vulnerabilities, and evolving requirements demand continuous updates to machine learning models. Yet even benign parameter updates can have unintended consequences, such as catastrophic forgetting in classical models or alignment drift in foundation models. Existing heuristic approaches (e.g., regularization, parameter isolation) can mitigate these effects but cannot certify that updated models continue to satisfy required performance specifications. We address this problem by introducing a framework for provably safe model updates. Our approach first formalizes the problem as computing the largest locally invariant domain (LID): a connected region in parameter space where all points are certified to satisfy a given specification. While exact maximal LID computation is intractable, we show that relaxing the problem to parameterized abstract domains (orthotopes, zonotopes) yields a tractable primal-dual formulation. This enables efficient certification of updates - independent of the data or algorithm used - by projecting them onto the safe domain. Our formulation further allows computation of multiple approximately optimal LIDs, incorporation of regularization-inspired biases, and use of lookahead data buffers. Across continual learning and foundation model fine-tuning benchmarks, our method matches or exceeds heuristic baselines for avoiding forgetting while providing formal safety guarantees.

Decision trees and random forest remain highly competitive for classification on medium-sized, standard datasets due to their robustness, minimal preprocessing requirements, and interpretability. However, a single tree suffers from high estimation variance, while large ensembles reduce this variance at the cost of substantial computational overhead and diminished interpretability. In this paper, we propose Decision Tree Embedding (DTE), a fast and effective method that leverages the leaf partitions of a trained classification tree to construct an interpretable feature representation. By using the sample means within each leaf region as anchor points, DTE maps inputs into an embedding space defined by the tree's partition structure, effectively circumventing the high variance inherent in decision-tree splitting rules. We further introduce an ensemble extension based on additional bootstrap trees, and pair the resulting embedding with linear discriminant analysis for classification. We establish several population-level theoretical properties of DTE, including its preservation of conditional density under mild conditions and a characterization of the resulting classification error. Empirical studies on synthetic and real datasets demonstrate that DTE strikes a strong balance between accuracy and computational efficiency, outperforming or matching random forest and shallow neural networks while requiring only a fraction of their training time in most cases. Overall, the proposed DTE method can be viewed either as a scalable decision tree classifier that improves upon standard split rules, or as a neural network model whose weights are learned from tree-derived anchor points, achieving an intriguing integration of both paradigms.

Learning the structure of a Bayesian network from decentralized data poses two major challenges: (i) ensuring rigorous privacy guarantees for participants, and (ii) avoiding communication costs that scale poorly with dimensionality. In this work, we introduce Fed-Sparse-BNSL, a novel federated method for learning linear Gaussian Bayesian network structures that addresses both challenges. By combining differential privacy with greedy updates that target only a few relevant edges per participant, Fed-Sparse-BNSL efficiently uses the privacy budget while keeping communication costs low. Our careful algorithmic design preserves model identifiability and enables accurate structure estimation. Experiments on synthetic and real datasets demonstrate that Fed-Sparse-BNSL achieves utility close to non-private baselines while offering substantially stronger privacy and communication efficiency.