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Nvidia Unveils Robot Hands Capable of Matching Human Abilities With New AI

Nvidia has, in recent quarters, portrayed devotion to artificial intelligence. Such commitment is evident in Eureka, which enables robots to learn complex skills quicker than someone striving to be a surgeon. Additionally, the new SteerLM intends to replace human annotation in artificial intelligence training.

Eureka has played a significant role in enabling Nvidia researchers to attain substantial progress in robotic dexterity. Allegedly, this artificial intelligence (AI) agent can teach intricate skills to bots, for instance, pen-spinning habits as skillfully as human beings.

Robot to Outpace Human Capabilities

This new tactic was mentioned in a Thursday paper and entails building on present developments in large language models (LLMs), for instance, GPT-4 by OpenAI. Eureka takes advantage of generative artificial intelligence to independently write complex reward algorithms that are later utilized by robots to learn through trial-and-error reinforcement learning.

 The paper shows that so far, the strategy has demonstrated more than 50% more efficacy compared to programs written by humans. In an official blog post, Nvidia claims that Eureka has also taught cobot arms, deft hands, creatures, and more robots to utilize scissors, open drawers, and almost 30 functions.


Eureka is the most recent indicator of Nvidia’s initial work guiding artificial intelligence with language models. Lately, the firm acquired SteerLM, a tactic that enables artificial intelligence assistants to be more effective by providing training concerning human feedback. Like Eureka, SteerLM uses improvements in language models.

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Significance of AI Assistive Tools

However, it directs them to another task: enhancing the alignment of artificial intelligence assistants. The assistants are trained by making them practice conversations, for instance, a robot learning through doing. Different attributes, such as hilarity, helpfulness, and quality, offer feedback concerning the assistant’s responses.

A perfect example concerns a robot using videos branded as good or terrible to learn how to dance instead of a person reviewing numerous random dances and picking those that are perfect or not.

Eureka Versus Isaac Gym AI-Language Model

Consistent practice and provision of feedback ensure the assistants can give responses personalized to a user’s needs. In turn, AI becomes more helpful for real-world uses.

Utilizing improved neural networks in new creative means, whether teaching chatbots or robots, is a common thread. At present, Nvidia is pushing the limits on software and hardware facades.

Concerning Eureka, the idea entailed merging simulation technologies similar to those from Isaac Gym with the language models’ pattern-identification ability. Eureka successfully ‘acquires the capability to learn,’ enhancing its reward algorithms over several training runs. Additionally, it takes human input to improve its rewards.

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So far, the self-enhancing strategy has shown high generalizability and trained all kinds of robots, including wheeled, legged, dexterous, and flying hands.

Besides breaking barriers, Eureka and SteerLM are teaching the skill of finesse and insightful interaction to robots and artificial intelligence. For each witty chat and spin of a pen, they are portraying a future that involves artificial intelligence imitating and innovating.

Editorial credit: sdx15 /

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Stephen Causby

Stephen Causby is an experienced crypto journalist who writes for Tokenhell. He is passionate for coverage in crypto news, blockchain, DeFi, and NFT.

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