Technology shares climbed on Thursday, buoyed by strong results from Nvidia, despite the AI chip company's boss issuing a warning about the rise of Chinese rivals. The Stoxx Europe tech index rose by 0.8% on Thursday following Nvidia's financial report, with the Dutch semiconductor equipment maker ASML rallying by 2.4%. In the US, futures for the tech-focused Nasdaq climbed 2%, and shares in Nvidia itself jumped 6% in pre-market trading. The boost to tech and artificial intelligence stocks came hours after Nvidia beat Wall Street forecasts, with quarterly revenues jumping 69% to 44bn ( 32.6bn). The company also said it expected deals in the Middle East to start to fill a gap left by the loss of Chinese business.

Nvidia Chief Executive Officer Jensen Huang soothed investor fears about a China slowdown by delivering a solid sales forecast, saying that the AI computing market is still poised for "exponential growth." The company expects revenue of about 45 billion in its second fiscal quarter, which runs through July. New export restrictions will cost Nvidia about 8 billion in Chinese revenue during the period, but the forecast still met analysts' estimates. That helped propel the shares about 4% Wednesday in extended trading. The outlook shows that Nvidia is ramping up production of Blackwell, its latest semiconductor design.

Nvidia beat Wall Street expectations in its quarterly earnings report on Wednesday, marking another in a string of financial wins for the computer hardware giant. It reported 44.1bn in revenue in the quarter ending in April, up 69% from the previous year. The company exceeded investors' predictions of 43.3bn in revenue. Adjusted earnings per share came in at 0.81, under investor expectations of an adjusted earnings per share of 88 cents. The company also reported 39.1bn in data center revenue, up 73% from the year prior.

Point-cloud data collected in real-world applications are often incomplete, because objects are being observed from specific viewpoints, which only capture one perspective. Data can also be incomplete due to occlusion and low-resolution sampling. Existing approaches to completion rely on training models with datasets of predefined objects to guide the completion of point clouds. Unfortunately, these approaches fail to generalize when tested on objects or real-world setups that are poorly represented in their training set. Here, we leverage recent advances in text-guided 3D shape generation, showing how to use image priors for generating 3D objects. We describe an approach called SDS-Complete that uses a pre-trained text-to-image diffusion model and leverages the text semantics of a given incomplete point cloud of an object, to obtain a complete surface representation. SDS-Complete can complete a variety of objects using test-time optimization without expensive collection of 3D data. We evaluate SDS-Complete on a collection of incomplete scanned objects, captured by real-world depth sensors and LiDAR scanners. We find that it effectively reconstructs objects that are absent from common datasets, reducing Chamfer loss by about 50% on average compared with current methods.

Large language models have been shown to struggle with multi-step reasoning, and do not retain previous reasoning steps for future use. We propose a simple method for solving both of these problems by allowing the model to take Self-Notes. Unlike recent chain-of-thought or scratchpad approaches, the model can deviate from the input context at any time to explicitly think and write down its thoughts. This allows the model to perform reasoning on the fly as it reads the context and even integrate previous reasoning steps, thus enhancing its memory with useful information and enabling multi-step reasoning. Experiments across a wide variety of tasks demonstrate that our method can outperform chain-of-thought and scratchpad methods by taking Self-Notes that interleave the input text.

Large language models (LLMs) are highly capable but also computationally expensive. Characterizing the fundamental tradeoff between inference efficiency and model capabilities is thus important, but requires an efficiency metric that is comparable across models from different providers. Unfortunately, raw runtimes measured through black-box APIs do not satisfy this property: model providers can implement software and hardware optimizations orthogonal to the model, and shared infrastructure introduces performance contention. We propose a new metric for inference efficiency called idealized runtime, that puts models on equal footing as though they were served on uniform hardware and software without performance contention, and a cost model to efficiently estimate this metric for autoregressive Transformer models. We also propose variants of the idealized runtime that incorporate the number and type of accelerators needed to serve the model. Using these metrics, we compare ten LLMs developed in 2022 to provide the first analysis of inference efficiency-capability tradeoffs; we make several observations from this analysis, including the fact that the superior inference runtime performance of certain APIs is often a byproduct of optimizations within the API rather than the underlying model.

In addition to the data and data loaders, LithoBench also provides functionalities that can facilitate the development of DNN-based and traditional ILT algorithms. Based on PyTorch [1] and OpenILT [2], we implement the reference lithography simulation model as a PyTorch module, which can be used like a DNN layer. The GPU-based fast Fourier transform (FFT) can boost the speed of lithography simulation. PyTorch optimizers can be directly employed to optimize the masks according to ILT loss functions, significantly simplifying the development of ILT algorithms. To evaluate ILT results, LithoBench provides a simple interface to measure the L2 loss, PVB, EPE, and shots of the output masks.

Artificial intelligence systems could account for nearly half of datacentre power consumption by the end of this year, analysis has revealed. The estimates by Alex de Vries-Gao, the founder of the Digiconomist tech sustainability website, came as the International Energy Agency forecast that AI would require almost as much energy by the end of this decade as Japan uses today. De Vries-Gao's calculations, to be published in the sustainable energy journal Joule, are based on the power consumed by chips made by Nvidia and Advanced Micro Devices that are used to train and operate AI models. The paper also takes into account the energy consumption of chips used by other companies, such as Broadcom. The IEA estimates that all data centres – excluding mining for cryptocurrencies – consumed 415 terawatt hours (TWh) of electricity last year.

OpenAI has fully acquired Io, a joint venture it cocreated last year with Jony Ive, the famed British designer behind the sleek industrial aesthetic that defined the iPhone and more than two decades of Apple products. In a nearly 10-minute video posted to X on Wednesday, Ive and OpenAI CEO Sam Altman said the Apple pioneer's "creative collective" will "merge with OpenAI to work more intimately with the research, engineering, and product teams in San Francisco." OpenAI says it's paying 5 billion in equity to acquire Io. The promotional video included musings on technology from both Ive and Altman, set against the golden-hour backdrop of the streets of San Francisco, but the two never share exactly what it is they're building. "We look forward to sharing our work next year," a text statement at the end of the video reads.

Nvidia has unveiled the latest raft of technologies aimed at sustaining the boom in demand for AI computing -- and ensuring that its products stay at the center of the action. Chief Executive Officer Jensen Huang on Monday kicked off Computex in Taiwan, Asia's biggest electronics forum, touting new products and cementing ties with a region vital to the tech supply chain. The CEO introduced updates to the ecosystem around Nvidia's accelerator chips, which are key to developing and running AI services. The central goal is to broaden the reach of Nvidia products and eliminate barriers to AI adoption by more industries and countries. Nvidia is keen to shore up its place at the heart of the artificial intelligence boom, at a time investors and some executives remain uncertain whether spending on datacenters is sustainable.