Jensen Huang, co-founder and CEO of Nvidia Corp., gives a talk in Taipei, Taiwan. Nvidia CEO Jensen Huang delivers a keynote address during the Nvidia GTC Artificial Intelligence Conference at SAP Center, March 18, 2024, in San Jose, California. STACKING CHIPS: Nvidia CEO Jensen Huang said Wednesday that the leading artificial intelligence chipmaker will invest hundreds of billions of dollars in the U.S. supply chain over the next four years. SPOT THE AI LIE: It's becoming more common for images to be made with AI tools. As the artificial intelligence generation gets more advanced, it's getting trickier to tell the difference between AI-made and human-made images. However, there are still signs to look out for.

Neurotech company Synchron has unveiled the latest version of its brain-computer interface, which uses Nvidia technology and the Apple Vision Pro to enable individuals with paralysis to control digital and physical environments with their thoughts. In a video demonstration at the Nvidia GTC conference this week in San Jose, California, Synchron showed off how its system allows one of its trial participants, Rodney Gorham, who is paralyzed, to control multiple devices in his home. From his sun-filled living room in Melbourne, Australia, Gorham is able to play music from a smart speaker, adjust the lighting, turn on a fan, activate an automatic pet feeder, and run a robotic vacuum. Gorham has lost the use of his voice and much of his body due to having amyotrophic lateral sclerosis, or ALS. The degenerative disease weakens muscles over time and eventually leads to paralysis.

In January, the emergence of DeepSeek's R1 artificial intelligence program prompted a stock market selloff. Seven weeks later, chip giant Nvidia, the dominant force in AI processing, seeks to place itself squarely in the middle of the dramatic economics of cheaper AI that DeepSeek represents. On Tuesday, at the SAP Center in San Jose, Calif., Nvidia co-founder and CEO Jensen Huang discussed how the company's Blackwell chips can dramatically accelerate DeepSeek R1. Also: Google claims Gemma 3 reaches 98% of DeepSeek's accuracy - using only one GPU Nvidia claims that its GPU chips can process 30 times the throughput that DeepSeek R1 would normally have in a data center, measured by the number of tokens per second, using new open-source software called Nvidia Dynamo. "Dynamo can capture that benefit and deliver 30 times more performance in the same number of GPUs in the same architecture for reasoning models like DeepSeek," said Ian Buck, Nvidia's head of hyperscale and high-performance computing, in a media briefing before Huang's keynote at the company's GTC conference.

NVIDIA's GPU Technology Conference, also known as GTC, is coming up. The event is happening March 17-21 in San Jose, California, but you can also follow along with all the big developments here at Engadget. We'll have a liveblog for the keynote with CEO Jensen Huang on March 18 at 1PM ET (or 10AM PT), which is when most of the big news will drop. His speech will also be livestreamed for free, so you can watch it on NVIDIA's website too. And for those of you who want it saved to your YouTube watch history, here's the company's livestream on YouTube as well.

Large Language Model (LLM) Agents leverage the advanced reasoning capabilities of LLMs in real-world applications. To interface with an environment, these agents often rely on tools, such as web search or database APIs. As the agent provides the LLM with tool documentation along the user query, the completeness and correctness of this documentation is critical. However, tool documentation is often over-, under-, or ill-specified, impeding the agent's accuracy. Standard software testing approaches struggle to identify these errors as they are expressed in natural language. Thus, despite its importance, there currently exists no automated method to test the tool documentation for agents. To address this issue, we present ToolFuzz, the first method for automated testing of tool documentations. ToolFuzz is designed to discover two types of errors: (1) user queries leading to tool runtime errors and (2) user queries that lead to incorrect agent responses. ToolFuzz can generate a large and diverse set of natural inputs, effectively finding tool description errors at a low false positive rate. Further, we present two straightforward prompt-engineering approaches. We evaluate all three tool testing approaches on 32 common LangChain tools and 35 newly created custom tools and 2 novel benchmarks to further strengthen the assessment. We find that many publicly available tools suffer from underspecification. Specifically, we show that ToolFuzz identifies 20x more erroneous inputs compared to the prompt-engineering approaches, making it a key component for building reliable AI agents.

The company showed off the new Galaxy S25 series at its Galaxy Unpacked event today in San Jose, California. Samsung has once again loaded its flagship phones with artificial intelligence capabilities, and while many of those features are tricks we've seen before--even almost a decade ago--they have now been infused with large language models that make them more effective. One of the Galaxy S25 Ultra's stylus features is being rebranded from "Smart Select" to "AI Select," and a Samsung executive joked, "Smart Select really wasn't that smart," highlighting the efficacy of the new multimodal LLMs powering the feature (and the lengths these companies go to make every little feature sound "smart"). The Galaxy S25 range is comprised of the Galaxy S25 ( 800), Galaxy S25 ( 1,000), and Galaxy S25 Ultra ( 1,300). The phones are available for preorder today and will officially go on sale February 7. Here's what's new, including a breakdown of the AI features that were given the spotlight.

Nvidia is developing real-world robots that are equipped with artificial intelligence capabilities. PUSH BACK: The new rule, which industry leaders say could come as early as the end of this week, effectively seeks to shore up the U.S. economy and national security efforts by adding new restrictions on how many U.S.-made artifical intelligence products can be deployed across the globe. Jensen Huang, co-founder and chief executive officer of Nvidia Corp., speaks during the Nvidia GPU Technology Conference (GTC) in San Jose, Calif., on Monday, March 18, 2024. 'UTTERLY UNTRUE': Open AI CEO Sam Altman on Tuesday responded to a lawsuit in which his sister accused him of sexually abusing her for nearly a decade. Altman, along with his mother and two brothers, issued a joint statement denying the claims of his sister, Ann Altman.

In the realm of lithography, Optical Proximity Correction (OPC) is a crucial resolution enhancement technique that optimizes the transmission function of photomasks on a pixel-based to effectively counter Optical Proximity Effects (OPE). However, conventional pixel-based OPC methods often generate patterns that pose manufacturing challenges, thereby leading to the increased cost in practical scenarios. This paper presents a novel inverse lithographic approach to OPC, employing a model-driven, block stacking deep learning framework that expedites the generation of masks conducive to manufacturing. This method is founded on vector lithography modelling and streamlines the training process by eliminating the requirement for extensive labeled datasets. Furthermore, diversity of mask patterns is enhanced by employing a wave function collapse algorithm, which facilitates the random generation of a multitude of target patterns, therefore significantly expanding the range of mask paradigm. Numerical experiments have substantiated the efficacy of the proposed end-to-end approach, highlighting its superior capability to manage mask complexity within the context of advanced OPC lithography. This advancement is anticipated to enhance the feasibility and economic viability of OPC technology within actual manufacturing environments.

Seven in 10 secondary school students have used large language models (LLMs) for their studies, according to a survey of more than 300 US pupils. "I realised that a lot of the people around me were using large language models, and more specifically ChatGPT, for a lot of school assignments," says Tiffany Zhu, an 11th-grade student (equivalent to year 12 in the UK) at The Harker School in San Jose, California.

With rapid advances in machine learning, many people in the field have been discussing the rise of digital minds and the possibility of artificial sentience. Future developments in AI capabilities and safety will depend on public opinion and human-AI interaction. To begin to fill this research gap, we present the first nationally representative survey data on the topic of sentient AI: initial results from the Artificial Intelligence, Morality, and Sentience (AIMS) survey, a preregistered and longitudinal study of U.S. public opinion that began in 2021. Across one wave of data collection in 2021 and two in 2023 (total N = 3,500), we found mind perception and moral concern for AI well-being in 2021 were higher than predicted and significantly increased in 2023: for example, 71% agree sentient AI deserve to be treated with respect, and 38% support legal rights. People have become more threatened by AI, and there is widespread opposition to new technologies: 63% support a ban on smarter-than-human AI, and 69% support a ban on sentient AI. Expected timelines are surprisingly short and shortening with a median forecast of sentient AI in only five years and artificial general intelligence in only two years. We argue that, whether or not AIs become sentient, the discussion itself may overhaul human-computer interaction and shape the future trajectory of AI technologies, including existential risks and opportunities.