Found on Google from forbes.com
Category: robotics/AI – Page 371
With generative AI models, researchers combined robotics data from different sources to help robots learn better. MIT researchers developed a technique to combine robotics training data across domains, modalities, and tasks using generative AI models. They create a combined strategy from several different datasets that enables a robot to learn to perform new tasks in unseen environments.
Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.
Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.
The next update to Tesla’s Full Self-Driving (Supervised) could arrive this weekend, as the long-awaited v12.4.2 is scheduled to enter an internal testing phase tomorrow.
Tesla first released version 12 of FSD in March, and it was a significant release because it was the first version that relied on end-to-end neural nets, instead of over 300,000 lines of hand-written code. With the switch, CEO Elon Musk said that each revision should result in significant improvements, saying that v12.4 should see a 5 to 10 times improvement in miles per intervention.
However, v12.4 was only released to a limited number of testers earlier this month, more than four weeks after Musk initially said it would be available, and it received a luke-warm response, with a number of bugs and erratic driving behaviours reported. As a result, it has yet to go to a wide release.
…a new neural network based on GPT-4 finds errors in its work and fixes them.
CriticGPT, a model based on GPT-4, writes critiques of ChatGPT responses to help human trainers spot mistakes during RLHF
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Geoffrey Hinton recently ignited a heated debate with an interview in which he says he is very worried that we will soon lose control over superintelligent AI. Meta’s AI chief Yann LeCun disagrees. I think they’re both wrong. Let’s have a look.
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What You Should Know:
– A glimmer of hope emerged today for rectal cancer patients as a collaborative effort between Case Western Reserve University (CWRU), Cleveland Clinic, and University Hospitals (UH) received a $2.78 million grant over five years from the National Institutes of Health and National Cancer Institute. This grant will fuel research leveraging artificial intelligence (AI) to personalize treatment for rectal cancer patients.
– The new research effort signifies a significant step forward in the fight against rectal cancer. By harnessing the power of AI, researchers are on the path to developing more precise treatment strategies, ultimately improving patient outcomes and quality of life.