Here they come… 🦾🤖
Category: robotics/AI – Page 321
And this shows one of the many ways in which the Economic Singularity is rushing at us. The 🦾🤖 Bots are coming soon to a job near you.
NVIDIA unveiled a suite of services, models, and computing platforms designed to accelerate the development of humanoid robots globally. Key highlights include:
- NVIDIA NIM™ Microservices: These containers, powered by NVIDIA inference software, streamline simulation workflows and reduce deployment times. New AI microservices, MimicGen and Robocasa, enhance generative physical AI in Isaac Sim™, built on @NVIDIAOmniverse
- NVIDIA OSMO Orchestration Service: A cloud-native service that simplifies and scales robotics development workflows, cutting cycle times from months to under a week.
- AI-Enabled Teleoperation Workflow: Demonstrated at #SIGGRAPH2024, this workflow generates synthetic motion and perception data from minimal human demonstrations, saving time and costs in training humanoid robots.
NVIDIA’s comprehensive approach includes building three computers to empower the world’s leading robot manufacturers: NVIDIA AI and DGX to train foundation models, Omniverse to simulate and enhance AIs in a physically-based virtual environment, and Jetson Thor, a robot supercomputer. The introduction of NVIDIA NIM microservices for robot simulation generative AI further accelerates humanoid robot development.
Nvidia’s upcoming artificial intelligence chips will be delayed by three months or more due to design flaws, a snafu that could affect customers such as Meta Platforms, Google and Microsoft that have collectively ordered tens of billions of dollars worth of the chips, according to two people who help produce the chip and server hardware for it.
Nvidia this week told Microsoft, one of its biggest customers, and another large cloud provider about a delay involving the most advanced AI chip in its new Blackwell series of chips, according to a Microsoft employee and another person with direct knowledge.
Would be more impressive if it was attached to a humanoid robot body.
A robot can hold a squash, pumpkin or melon in one hand, while it is peeled by the other.
By Alex Wilkins
Electrically powered artificial muscle fibers (EAMFs) are emerging as a revolutionary power source for advanced robotics and wearable devices. Renowned for their exceptional mechanical properties, integration flexibility, and functional versatility, EAMFs are at the forefront of cutting-edge innovation.
A recent review article on this topic was published online in the National Science Review (“Emerging Innovations in Electrically Powered Artificial Muscle Fibers”).
Schematic of electrically powered artificial muscle fibers categorized from the mechanism, material components, and configurations, as well as their application fields. (Image: Science China Press)
Weather and climate experts are divided on whether AI or more traditional methods are most effective. In this new model, Google’s researchers bet on both.
Baidu’s novel self-reasoning AI framework aims to enhance language model reliability, potentially eliminating ‘hallucinations’ and setting new standards for AI accuracy and transparency.
It seems that Silicon Valley giants, AAA game developers, and other companies desperately clinging to the AI trend and trying to integrate the technology into any product they own will soon have to rethink their marketing strategies, as a new study conducted by researchers from Washington State University indicates that using terms like “AI” or “artificial intelligence” in product descriptions can negatively impact sales.
To explore the impact of including “AI” in goods and service descriptions on consumers’ purchase intentions, the team conducted six experiments and surveyed over a thousand people, discovering that the use of these terms decreases purchase intention and lowers emotional trust, leading to what any company fears the most – diminishing sales numbers.
Furthermore, the researchers found that putting artificial intelligence in the spotlight can be even more detrimental when it comes to high-risk products – those consumers typically think twice about buying, such as expensive gadgets and medical services – compared to low-risk items, primarily because of the greater likelihood of incurring monetary losses or facing health risks.
In Light: Science & Applications journal UCLA researchers introduce an innovative design for diffractive deep neural networks (D2NNs). This new architecture, termed Pyramid-D2NN (P-D2NN), achieves unidirectional image magnification and demagnification, significantly reducing the number of diffractive features required.
