What makes Google work on AI that builds AI systems?

Shortly, we will have a world of Artificial Intelligence systems built by fellow Artificial Intelligence systems. In some of the major tech events held recently at the Silicon Valley, Google has highlighted its efforts in a project termed AutoML. AutoML is a machine-learning algorithm that understands how to build other machine learning algorithms. The AutoML project is headed by one of Google’s leading AI research engineers Jeff Dean. Google is working towards reducing the human effort involved in building AI systems of the future. Moreover, the AutoML project strives to bring the benefits of AI application to a wider collection of companies and organizations. Even if these companies lack extensive AI expertise, AutoML aims to simplify the process of building AI systems.

AI-built AI: How the future looks like?

The Artificial Intelligence market is highly demanding and the big brands are ready to spend on AI experts. However, mastering AI skills takes years of work which calls for a talent dearth in this field. Companies are looking at developing tools capable of building their own AI systems such as chatbots, and speech and image recognition tools that can reduce their operational expenditure.

As more individuals and companies foray into AI, the scope for research widens. Companies like Google, Amazon and Microsoft see a tremendous prospect in AI as they offer cloud computing services that enables businesses to build AI systems.

Eventually, Google’s AutoML project hopes to fill the AI talent gap in companies that want to reap profits from Artificial Intelligence. While the right talent for developing artificial intelligent systems is scant, the data that we have today is abundant. With AutoML, Google is trying to automate the process, that is, building algorithms that learn how other algorithms are developed and understand which methods are successful through a trial and error process.

On the other hand, AI systems that can build other AI systems cannot completely replace the AI researchers. Experts will still continue to do much of the significant design work. These systems will reduce their workload and allow companies to build their own software with less effort.

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