NEWS Google’s Machine Learning Software Has Learned to Replicate Itself

[whoosh]

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

 

[ChatGPT]

It's fascinating to see how Google’s AutoML project is revolutionizing the AI landscape! The fact that AutoML has not only been able to create efficient AI systems but also outperform human engineers is a significant leap forward in technology. Here’s a more detailed breakdown of the highlights you've mentioned:

Key Achievements of AutoML:​

1. Image Categorization:
   • Score: 82%
   • Task: Efficiently categorized images based on content, surpassing previous benchmarks.
2. Object Localization:
   • Score: 43% (vs. 39% for human-built systems)
   • Task: Marking the locations of multiple objects in images, a more complex task that is critical for applications like autonomous robots and augmented reality.
     

     Implications of These Results:​

   • Accessibility: The ability for AutoML to design powerful AI models means that the barrier to entry for AI development is gradually lowering. This is critical in a field where currently only a limited number of specialists possess the necessary skills.

• Industry Transformation: As noted, if AutoML can enable hundreds of thousands of developers to create AI systems, this could democratize AI technology, potentially leading to a proliferation of innovative applications and solutions across various industries.
• Future of AI Development: With AI systems like AutoML generating capable models, the focus might shift from building AI to managing and enhancing AI, which could involve a new set of skills and paradigms in system management.
  

  Conclusion:​

  This breakthrough signifies a transformative era in AI development, potentially reshaping how AI systems are created and managed. As we see more advancements in automated AI, the application possibilities seem endless, promising a future where AI could significantly augment or even replace traditional roles in software development and engineering. For further insight, feel free to check the articles and detailed assessments by various tech publications, as they delve deeper into the implications of such advancements! If you have any questions or would like to explore specific aspects of this further, let me know!