This article is an excerpt from the Pearson Addison-Wesley book "Pragmatic AI" by Noah Gift. Reprinted here with permission from Pearson and 2019. What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage.

Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology).

Experiments at the Large Hadron Collider (LHC), the world's largest particle accelerator at the European particle physics lab CERN, produce about a million gigabytes of data every second. Even after reduction and compression, the data amassed in just one hour is similar to the data volume Facebook collects in an entire year – too much to store and analyze. Luckily, particle physicists don't have to deal with all of that data all by themselves. They partner with a form of artificial intelligence called machine learning that learns how to do complex analyses on its own. A group of researchers, including scientists at the Department of Energy's SLAC National Accelerator Laboratory and Fermi National Accelerator Laboratory, summarize current applications and future prospects of machine learning in particle physics in a paper published today in Nature.

Takahiro Hayashi won his first nationwide title in shogi -- Japanese chess -- while still in high school, and by the age of 22, he was the world amateur champion. His coaches were urging him to turn pro. But Hayashi wanted to be an entrepreneur, not a chess player. And so, in 2009, he found himself in a room with some local venture capitalists, presenting a 120-page pitch about his social game firm. The financiers kind of tuned out.

Big data remains a game for the 1 percent. Or the 15 percent, as new O'Reilly survey data suggests. According to the survey, most enterprises (85 percent) still haven't cracked the code on AI and machine learning. A mere 15 percent "sophisticated" enterprises have been running models in production for more than five years. Importantly, these same companies tend to give more time and attention to critical areas like model bias and data privacy, whereas comparative newbies are still trying to find the On button.

I meet with a lot of business and tech leaders, and nearly all of them ask at some point about artificial intelligence. They're worried that their company is missing out on this coming AI revolution, and falling behind rivals, because they don't have the deep tech skills to put it to use. I tell them that getting real value from AI, and from its related discipline of machine learning, doesn't have to be that hard. The reason being they can tap into AI embedded within cloud services, which they can quickly launch and put to use. Here are four AI use cases I give as examples of how they can quickly tap the benefits of AI without much work--and without an army of data scientists.

The Defence Ministry will soon come up with a detailed roadmap on working closely with the industry on leveraging Artificial Intelligence (AI) -- latest technology that emphasises on creation of intelligent machines that work and react like humans -- in Armed Forces, said Ajay Kumar, Secretary, Department of Defence Production, Ministry of Defence. A task force, headed by Tata Sons Chairman N Chandrasekaran to study use and application of AI in military, recently gave its recommendations, which has been converted into a Draft Executive Order. Consultation is underway with different wings of the Defence Ministry on this, he said at a CII conference on Aerospace & Defence Manufacturing Technologies on the Theme: Fostering a Competitive, Innovative and Robust Industry. "AI is a big driver in all our platforms. We have already asked Defence PSUs to come up with suggestions as to where they can start leveraging in AI.

As government regulation for commercial drone usage seems to be trending in a very positive direction for the companies involved, there is an ever-growing opportunity for drone startups to utilize artificial intelligence to deliver insights without requiring much human effort. Sterblue, a French drone software startup that is launching out of Y Combinator's latest class of companies, is aiming to get off-the-shelf drones inspecting large outdoor structures up close with automated insights that identify anomalies that need a second look.

As government regulation for commercial drone usage seems to be trending in a very positive direction for the companies involved, there is an ever-growing opportunity for drone startups to utilize artificial intelligence to deliver insights without requiring much human effort. Sterblue, a French drone software startup that is launching out of Y Combinator's latest class of companies, is aiming to get off-the-shelf drones inspecting large outdoor structures up close with automated insights that identify anomalies that need a second look. The startup's software is specifically focused on enabling drones to easily inspect large power lines or wind turbines with simple automated trajectories that can get a job done much quicker and with less room for human error. The software also allows the drones to get much closer to the large structures they are scanning so the scanned images are as high-quality as possible. Compared to navigating a tight urban environment, Sterblue has the benefit of there being very few airborne anomalies around these structures, so autonomously flying along certain flight paths is as easy as having a CAD structure available and enough wiggle room to correct for things like wind condition.

Enter some text, and let an online algorithm generate an image for you via artificial intelligence! Cris Valenzuela's online text-to-image generator makes it really simple for non-geeks. The right image was "flower garden."