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DeepMind RoboCat: A Versatile AI Model for Adaptive Robotic Arms

DeepMind, the renowned AI research lab, has made significant strides in the field of robotics with the development of RoboCat, an advanced AI model. What sets RoboCat apart is its remarkable ability to solve and adapt to multiple tasks using various real-world robots—a breakthrough achievement in the realm of robotics.

According to Alex Lee, a research scientist at DeepMind and a key contributor to the RoboCat project, “We demonstrate that a single large model can solve a diverse set of tasks on multiple real robotic embodiments and can quickly adapt to new tasks and embodiments.”

Inspired by Gato, another successful AI model by DeepMind that excels in text, image, and event analysis, RoboCat underwent extensive training on a combination of simulated and real-world robotics data. The dataset comprised images and actions collected from different robot-controlled models within virtual environments, as well as data from humans controlling robots and previous versions of RoboCat itself.

The training process began by collecting 100 to 1,000 demonstrations of specific tasks performed by a robotic arm controlled by a human. Subsequently, RoboCat was fine-tuned on each task, leading to the creation of specialized “spin-off” models that underwent an average of 10,000 practice iterations for further refinement.

The researchers leveraged both the data generated by the spin-off models and the demonstration data to continually expand RoboCat’s training dataset. Multiple iterations of RoboCat were trained using this approach.

In its final form, RoboCat was trained on a remarkable total of 253 tasks and benchmarked on 141 variations of these tasks, both in simulation and the real world. DeepMind claims that after observing 1,000 human-controlled demonstrations, RoboCat successfully acquired the ability to operate different types of robotic arms.

Despite its impressive capabilities, RoboCat’s success rate in various tasks during DeepMind’s testing exhibited a wide range, ranging from 13% to 99%. Notably, the number of demonstrations had a predictable impact on the success rate, with fewer demonstrations resulting in less frequent successes.

Nevertheless, DeepMind reports that in some scenarios, RoboCat achieved proficiency in new tasks with as few as 100 demonstrations.

Looking ahead, Alex Lee envisions a future where RoboCat could significantly reduce the number of demonstrations required to teach it new tasks, aiming for a groundbreaking milestone of fewer than 10 demonstrations.

The research team’s ongoing efforts to refine RoboCat hold promise for revolutionizing the robotics field, potentially lowering the barriers to solving new tasks and empowering robots to adapt swiftly to diverse challenges.

Google’s DeepMind: Optimized Algorithms Not Trained on Human Code

Google’s DeepMind AI group has released a reinforcement learning tool that can develop extremely optimized algorithms. It does this without first being trained on human code examples because it is set up to treat programming as a game.

This is according to a report by Ars Technica published on Thursday.

DeepMind already had the ability to teach itself how to play games, conquering games as varied as chess, Go, and StarCraft. The software was effective at learning to play by itself and discovering options that allowed it to maximize a score through approaches to the games that humans haven’t thought of. 

Removing the need for human models

Today, large language models write effective code because they have trained on human models. However, this training means they are not likely to develop something that humans haven’t done previously. 

That’s why to optimize well-understood algorithms, it’s best not to base them on human code. The question that surfaces is how do you get an AI to identify a truly new and unique approach?

Programmers at DeepMind decided to replicate the approach they used with Chess and Go and transformed code optimization into a game. They engineered algorithms that treated the latency of the code as a score and tried to minimize that score resulting in software that had the ability to write tight, highly efficient code.

They did this through a complex AI system called AlphaDev that consists of several distinct components. Its representation function tracks the overall performance of the code as it’s developed, including the general structure of the algorithm and the use of x86 registers and memory.

Benefits of New System of Google’s DeepMind

The main advantage of this new system is that its training doesn’t have to involve any code examples, as it generates its own code examples and then proceeds to evaluate them. Through this system, it collects information about combinations of instructions that are effective in sorting, reported Ars Technica.

In January 2023, Google Research and DeepMind launched MedPaLM, a large language model aligned to the medical domain.

The software was meant to generate safe and helpful answers in the medical field. It combines HealthSearchQA, a new free-response dataset of medical questions sought online, with six existing open-question answering datasets covering professional medical exams, research, and consumer queries. 

Meanwhile, last month, Demis Hassabis, the CEO of DeepMind, said artificial general intelligence (AGI), a machine intelligence that can comprehend the world as humans do, might be developed “within a decade.” 

Google Forms AI Dream Team By Merging ‘Brain’ and ‘DeepMind’ Projects

Alphabet and Google’s CEO Sundar Pichai has just released a public announcement that Google’s “Brain” and “DeepMind” artificial intelligence (AI) projects are to be combined into one. The former is a Google-owned artificial intelligence initiative, while the latter was a 2014 acquisition.

According to Pichai, Demis Hassabis, CEO of “DeepMind,” will head the creation of Google DeepMind and “lead the development of our most capable and responsible general AI systems.” The chief scientist for Google Research and Google “DeepMind” will be Jeff Dean, a co-founder of the Brain team and a former senior vice president of Google Research and Health.

“Together, in close collaboration with our fantastic colleagues across the Google Product Areas, we have a real opportunity to deliver AI research and products that dramatically improve the lives of billions of people, transform industries, advance science, and serve diverse communities,” Hassabis writes in a memo to employees. “By creating Google DeepMind, I believe we can get to that future faster. Building ever more capable and general AI, safely and responsibly, demands that we solve some of the hardest scientific and engineering challenges of our time,” he added.

This is interesting, as Google and “DeepMind” have occasionally clashed. “DeepMind” apparently failed in its long-running effort to break away from Google in 2021 as the tech giant started pressuring “DeepMind” to turn its research into a product. Google, though, is likely looking to consolidate its research teams as it forges ahead with its foray into the AI sector.

It also comes off the back of “Bard’s” seemingly botched release. In March, Google released early access to “Bard,” a competitor to “Bing Chat” and “ChatGPT.” However, as many technology outlets have reported, “Bard” is far less capable than its competitors thus far. This, despite Pichai stating that updates are on the way, it has been reported that Google employees criticized the product before its introduction and encouraged management not to make it available.

According to reports, Google is also pouring significant resources into “Magi,” a group of new search features with AI capabilities, in response to Microsoft’s close partnership with OpenAI on Bing Chat, an AI-powered chatbot linked with the latter’s Bing search engine. Microsoft sees this as a danger. Over 160 people comprise “Magi’s” task team, which was established this year.

“We’ve been an AI-first company since 2016, because we see AI as the most significant way to deliver on our mission,” Pichai wrote in the announcement. “The pace of progress is now faster than ever before. To ensure general AI’s bold and responsible development, we’re creating Google DeepMind to help us build more capable systems more safely and responsibly,” he added.