Google designed its AutoML project to be an artificial intelligence that could help humans create other AI systems. Now AutoML can do that, and it's creating more powerful, efficient systems than human engineers can.
Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that’s more efficient and powerful than the best human-designed systems.
An AutoML system recently broke a record for categorizing images by their content, scoring 82 percent. While that’s a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system’s 39 percent.
These results are meaningful because even at Google, few people have the requisite expertise to build next generation AI systems. It takes a rarified skill set to automate this area, but once it is achieved, it will change the industry. “Today these are handcrafted by machine learning scientists and literally only a few thousands of scientists around the world can do this,” WIRED reports Google CEO Sundar Pichai said. “We want to enable hundreds of thousands of developers to be able to do it.”
Google's machine learning software has learned to replicate itself
Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that’s more efficient and powerful than the best human-designed systems.
An AutoML system recently broke a record for categorizing images by their content, scoring 82 percent. While that’s a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system’s 39 percent.
These results are meaningful because even at Google, few people have the requisite expertise to build next generation AI systems. It takes a rarified skill set to automate this area, but once it is achieved, it will change the industry. “Today these are handcrafted by machine learning scientists and literally only a few thousands of scientists around the world can do this,” WIRED reports Google CEO Sundar Pichai said. “We want to enable hundreds of thousands of developers to be able to do it.”
Google's machine learning software has learned to replicate itself
