Artificial intelligence (AI) stocks are plunging following the results of the midterm elections. One of the biggest talking points for Democrats on the campaign trail was heavier oversight over AI data centers. While more regulation could impact the hyperscalers, Apple is in a unique position to benefit no matter who controls Congress.
Resonac is betting its advanced chip materials can make the multibillion-dollar merger that created the company pay off as AI demand surges. Managing Asia's Christine Tan speaks with CEO Hidehito Takahashi about growth plans,
Shield AI has more than doubled its valuation to $28.8 billion in eight months, up from $12.7 billion in March, after securing a new $1 billion Series H funding round. The Series H funding round was co-led by Disruptive and Innovation X Advisors,
Nvidia reportedly requires GPU monitoring for Southeast Asian cloud partners as China AI chip smuggling concerns grow. Here's what businesses should know.
Kimi K3.1 Pro is an open-weight, natively multimodal agentic model, and our most capable 3T-class model to date. Post-trained from Kimi K3 with large-scale joint reinforcement learning across hundreds of downstream domains, it is the first model in the K3.1 series. With a…
Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains difficult because activation outliers degrade effective quantization resolution. Although weight optimization, channel-wise…
World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future visual states. Tactile sensing complements this foundation with direct measurements of physical interaction. Some existing methods…
Humanoid locomotion over complex terrain requires anticipating footholds that may no longer be visible at touchdown. Limited camera coverage and self-occlusion make it necessary to retrieve relevant terrain information from earlier observations. We present FootQuery, a…
We prove that the optimal expected mistake bound of online learning a function class $\mathcal{H}$ by a randomized proper learning algorithm is $O(\mathtt{L}(\mathcal{H}) \log T)$, where $\mathtt{L}(\mathcal{H})$ is the Littlestone dimension of $\mathcal{H}$ and $T$ is the time…