Artificial intelligence was once a futuristic fantasy. Now it's here. It's changing the world for the better or worse.

The AI revolution is advancing faster than anyone anticipated. This year, the market for AI chips reached $5 billion and expected to be $10 billion by 2022.

Among the most promising projects are driverless cars, face recognition technologies and biotech industry advancements with vaccines against COVID-19.

Among the leading ML applications of 2020-2021 is the project Face++, actively developed by a large ML engineer team with the financial support of one of the world-famous investors, Kai-Fu Lee. He mentioned: 'We have about ten billion-dollar-companies here', speaking about his recent investment into ML startups.

Face recognition system with artificial intelligence heart has been made possible by three innovations: super-fast computer chips, all the world's data now available online, and a revolution in programming called deep learning.

Another example of utilization of ML algorithms is AutoML-Zero, a new machine-learning program, designed by the ML scientist from Google that could develop AI programs with little to no human input, using only basic mathematical concepts a typical high school student would likely master. 'Our final objective is to actually develop novel machine learning concepts that even researchers may not be able to find,' he comments.

The negative impact of AI

Together with AI's positive impact, we have some negative trends of dismissing human with AI solutions. This was predictable, but now can be estimated as a real threat to thousands of marketers, editors, writers.

One of such actual examples is dismissing 17000 in Microsoft. While the PR manager of Microsoft says that the layoffs are not directly related to implementing new AI technologies, no connected to coronavirus pandemic, the danger of losing the job and being dismissed by the robots is coming closure to each of us.

As always, technical progress changing habits and life. Next time we'll discuss the ethical issues of delegating the decision making to robots. Like whom to blame in case of an error with medical disease diagnostics or a car crash?