Building Ai Is Hard—So Facebook Is Building Ai That Builds Ai
Google is all set take a small step by creating AI that can build AI. If you have gone through the company’s annual developer’s conference, CEO himself announced a project called AutoML that can automate one of the hardest parts of designing deep learning software: choosing the right architecture for a neural network. As a result, the researches came up with a machine learning system that used reinforcement learning technique- the trial and error approach at the heart of many of Google’s most notable AI exploits—to figure out the best architectures to solve language and image recognition tasks.
The term Artificial Intelligenceis making a remarkable impact on our day to day lives. Capable of learning very human tasks by simply analyzing vast amounts of digital data, such kind of systems re-injecting online services with a power that just wasn’t viable in years past. Right from identifying faces in photos and recognizing commands spoken into smartphones and translating conversations from one language to another, this approach makes lives 100X times better. Mobile app development companies use this technology to create their app more secure.
Recently I noticed Twitter has been set ablaze by a series of tweets mentioning an article on NY Times.
And soon the news found its way to the place everyone heads over to talk about something interesting — reddit
Although there are tech giants like Google and Facebook who pay top dollar for some really smart brains. I am sure you will agree with me when I say that only a few of us have the talent and the training required to push the state-of-the-art forward, and paying for these top minds is a lot like paying for an NFL quarterback. As a result, it turns out to be a bottleneck in the continued process of AI. Besides, even the top researchers are unable to come up with such services without trial and error.
On that note, Demis Hassabis, co-founder of DeepMind, the Google outfit behind the history-making AI that beat the world’s best Go player says “It’s almost like being the coach rather than the player,” This, in turn, will accelerate the progress of AI inside the Internet apps and services that you and I use every day.
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In simple words, it means in order to make computers smarter, they themselves must handle more of the grunt work. Thankfully, some IT companies have started developing computing systems that can test countless machine learning algorithms on behalf of their engineers that can cycle through so many possibilities on their own. No kidding! Facebook has come up with automated machine learning engineer, an artificially intelligent system that helps create artificially intelligent systems. It’s a long way from perfection. However, the end goal is to create new AI models using as little human grunt work as possible.
Last but certainly not the least, Google’s AutoML has already been successful in developing an algorithm that can identify objects in images with a better degree of precision than algorithms designed by machine learning experts themselves.
It’s very simple! You add artificial general intelligence on top of any of the products from companies such as Google, Facebook, and Microsoft, and what you come up with is pretty remarkable