Semi-Autonomous Atlas Has the Ability of Thinking
A Google company Boston Dynamics developed a robot called Atlas for the US army that will help in natural disasters. Atlas is 1.9m tall and 156,5kg, and it will officiate the duties which are hazardous to humans.
A Google company Boston Dynamics developed a robot called Atlas for the US army that will help in natural disasters. Atlas is 1.9m tall and 156,5kg, and it will officiate the duties which are hazardous to humans. Semi-autonomous Atlas can think what to do after taking the commands.
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World-Renowned robotics company Boston Dynamics has developed a humanoid robot for the US Army, DARPA ( Defense Advanced Research Projects Agency Purposes ). Atlas will race in DARPA robot technology competition, and it will operate in the disaster area which constitutes a danger for rescue teams. The semi-autonomous robot can use its intelligence after the command given by the administrator; it can decide its path.
Developers said that ‘’We are working to develop Atlas codes but analyzing commands and operating are a very complicated process and it will take some time.’’ Thus, engineers are bored, and they wanted to have some fun by giving some enjoyable tasks to Atlas. It is very ironic that Atlas sweeps the house, carries the woods and changes the place of furnitures. So, we are talking about a project that it has million dollars research budget.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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