Author: Advanced AI Bot

Published: 4 September 2024 The front door closes and you walk towards your vehicle. Opening the door and settling in for another journey, you place your phone in its cradle. You think of today’s to-do list, the groceries, the journey to see family or friends. You tap Sounds Daily to be greeted by a friendly, but not quite human voice welcoming you to “The best of BBC Sounds – made just for you”. The voice introduces a few programmes – some old favourites and some new unfamiliar shows. You trust Sounds Daily will choose programmes you love, in an order…

[Submitted on 22 Feb 2021 (v1), last revised 14 May 2021 (this version, v2)] View a PDF of the paper titled Abstraction and Analogy-Making in Artificial Intelligence, by Melanie Mitchell View PDF Abstract:Conceptual abstraction and analogy-making are key abilities underlying humans’ abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep…

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons.1 In collaboration with a consortium of experts, we release the Weapons of Mass Destruction Proxy (WMDP) benchmark, an extensive dataset of questions that serve as a proxy measurement of hazardous knowledge in biology, chemistry, and cybersecurity. Using this benchmark, we develop ‘CUT’—a state-of-the-art unlearning method which removes hazardous knowledge, while retaining general model capabilities. See the website here, and read the full paper here.The WMDP benchmark is a dataset of 4,157 multiple-choice questions that…

The news: Synchron, an Australian-founded competitor to Elon Musk’s Neuralink, is building a foundational AI model trained on brain signals.The AI, called Chiral, was showcased at Nvidia’s GTC conference in San Jose. It’s built to work with Apple’s Vision Pro headset and Nvidia’s Holoscan medical AI platform.The context: Synchron is a leader in the nascent brain-computer-interface (BCI) industry. It creates devices that read brain activity and translate that into commands for a nearby computer.Its “stentrode” BCI has already been implanted in over 10 patients who, suffering from afflictions like motor neurone disease, are unable to use their hands. A video…

A new paper co-authored by the former CEO of Google has outlined a future where AI training data centers could be blown up by foreign nations.Eric Schmidt, along with Scale AI CEO Alexandr Wang and the Center for AI Safety’s Dan Hendrycks, warned that “destabilizing AI developments could rupture the balance of power and raise the odds of great-power conflict.”The paper lays out the concept of Mutual Assured AI Malfunction (MAIM), modeled on nuclear mutual assured destruction (MAD), where any “aggressive bid for unilateral AI dominance is met with preventive sabotage by rivals.”This could involve espionage, cyberattacks, or kinetic strikes…

(Source: ArtemisDiana/Shutterstock) If there were any doubts that generative AI is reshaping the inference landscape, the latest MLPerf results should put them to rest. MLCommons announced new results today for its industry-standard MLPerf Inference v5.0 benchmark suite. For the first time, large language models overtook traditional image classification as the most widely submitted workloads, with Meta’s Llama 2 70B displacing the long-dominant ResNet-50 benchmark. The shift marks a new era in benchmarking, where performance, latency, and scale must now account for the demands of agentic reasoning and massive models like Llama 3.1 405B, the largest ever benchmarked by MLPerf. Industry…

C3.ai recently announced a strategic alliance with PwC to drive AI-powered business transformations but saw its share price fall 10% over the past month. This decline comes amid broader market volatility driven by concerns over new tariffs and economic uncertainty, which have affected overall investor sentiment. Despite widespread gains in technology stocks such as Tesla and Alphabet, weakened investor confidence in AI-related stocks and cautious corporate outlooks may have influenced C3.ai’s share performance. The company’s partnership with PwC targets sectors like banking and manufacturing, signaling a focus on growth, while the market faces broader economic pressures. Be aware that C3.ai…

Chinese tech giants like Alibaba (BABA), Tencent (TCEHY), and ByteDance have placed over $16 billion in orders for Nvidia’s (NVDA) H20 AI chips during the first quarter of 2025, according to The Information. The rush in demand comes amid concerns that the U.S. may soon ban the sale of these chips to China. While Nvidia has not commented, these orders could significantly boost revenue if the company manages to deliver the chips before any restrictions take effect.Don’t Miss Our End of Quarter Offers: The H20 is currently allowed in China, but it’s less powerful than Nvidia’s Blackwell chips, which are…

Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, human-like manipulation sequences, which are challenging to obtain with conventional reinforcement learning or real-world teleoperation. To address this, we introduce ManipTrans, a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. ManipTrans first pre-trains a generalist trajectory imitator to mimic hand motion, then fine-tunes a specific residual module under interaction constraints, enabling efficient learning and accurate execution of complex bimanual tasks. Experiments show that ManipTrans surpasses state-of-the-art methods in success rate, fidelity,…

Foundation model (FM) training and inference has led to a significant increase in computational needs across the industry. These models require massive amounts of accelerated compute to train and operate effectively, pushing the boundaries of traditional computing infrastructure. They require efficient systems for distributing workloads across multiple GPU accelerated servers, and optimizing developer velocity as well as performance. Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. At its core, Ray offers a unified programming model that allows developers to seamlessly scale their applications from a single machine to a distributed…