When the average person on the street hears the words Artificial Intelligence, they usually think sentient robots coming to take their job, and potentially their life. Which is understandable, partly because films like Terminator have conditioned us to think like that, and partly because it could well be true. Stephen Hawking and Elon Musk say it might happen, and they're very rarely wrong about anything. When people hear Machine Learning on the other hand, the tendency is not so much to grab a weapon and hide under the bed until the robot apocalypse comes. It's to express a healthy reverence for how such algorithms will benefit technology and make our lives easier.

To do that, he and his team collected thousands of geotagged posts tweeted between July 2013 and July 2014 in New York state, and then winnowed them down to tweets containing booze-related keywords (ranging from "beer keg" to "shitfaced"). Each tweet passed through three human "Turkers," who were asked three questions: Q1: Does the tweet make any reference to drinking alcoholic beverages? Q3: if so, is it likely that the tweet was sent at the time and place the tweeter was drinking alcoholic beverages? The success rate--that is, the rate at which the machines' answers matched the Turkers' consensus--ranged from 92 percent for the algorithm answering Q1, to 82 percent for the drunk-spotting algorithm answering Q3.

Maybe the one single thing more regrettable than drunk texting is drunk tweeting. Publicly broadcasting intoxication is definitely not the best way to bolster one's social media clout, and yet a lot of people can't resist boasting about their alcoholic escapades. Researchers have now trained an algorithm to spot alcohol-related tweets, and even to guess if the tweeter was drinking at the time of posting. Nabil Hossain at the University of Rochester, upstate New York, decided to combine Twitter and machine learning to keep track of alcohol use across a given community. To do that, he and his team collected thousands of geotagged posts tweeted between July 2013 and July 2014 in New York state, and then winnowed them down to tweets containing booze-related keywords (ranging from "beer keg" to "shitfaced").

Originally published at innoarchitech.com here on March 18, 2016. Welcome to the fifth and final chapter in a five-part series about machine learning. In this final chapter, we will revisit unsupervised learning in greater depth, briefly discuss other fields related to machine learning, and finish the series with some examples of real-world machine learning applications. Recall that unsupervised learning involves learning from data, but without the goal of prediction. This is because the data is either not given with a target response variable (label), or one chooses not to designate a response.

Some things in life cannot be offset by a mere net gain in intelligence. The last few years have seen the widespread recognition that sophisticated AI is under development. Bill Gates, Stephen Hawking, and others warn of the rise of "superintelligent" machines: AIs that outthink the smartest humans in every domain, including common sense reasoning and social skills. Superintelligence could destroy us, they caution. In contrast, Ray Kurzweil, a Google director of engineering, depicts a technological utopia bringing about the end of disease, poverty and resource scarcity.

In 2012, IBM Watson went to medical school. So said The New York Times, announcing that the tech giant's artificially intelligent question-and-answer machine had begun a "stint as a medical student" at the Cleveland Clinic Lerner College of Medicine. This was just a metaphor. Clinicians were helping IBM train Watson for use in medical research. But as metaphors go, it wasn't a very good one.

Patients realize that their electronic devices help them with their day-to-day lives, including their health care consumer products, such as fitness bands. As a consumer, I am concerned with the "pain points" of health care, including my interactions with health care professionals, convenience, utility, and price. A health coach that is neither disruptive nor burdensome to my world, and highly personalized to me, is the ultimate expression of a consumer experience. An AI avatar can provide this. We are at the dawn of yet another AI era, equivalent to the integration of multiple devices into a single smartphone.

The predictions that robots and artificial intelligence will soon take over the world may be a little premature. To be sure, the field of artificial intelligence is booming, and the technology is rapidly evolving. As its name implies, AI uses computer learning to handle all manner of basic human tasks, such as decision-making and data analysis. Its potential applications include everything from manufacturing to, yes, even being a CEO, by some estimates. And if you're a startup in artificial intelligence, the time to get funding is now, according to new research from CB Insights, the venture capital research firm. AI companies received 310 million in 54 funding deals in 2015, a seven-fold increase in the last four years.

Artificial intelligence (AI), one of 20 core technologies I identified back in 1983 as the drivers of exponential economic value creation, is rapidly working its way into our lives from Amazon's Alexa and Facebook's M, to Google's Now and Apple's Siri. An example of how far AI has come is the recent news that a Google supercomputer, using its advanced AI software, was able to win a stunning 3-0 victory in a man vs. machine face-off against Go grandmaster Lee Sedol, one of the game's all-time champions. For those who are not familiar with Go, it is a 3,000-year-old game that is widely considered to be the most complex game ever invented because it is reported to have more possible board configurations than there are atoms in the universe. Until just a few months ago, it was thought that a computer could not defeat a human grandmaster for at least another decade due to the game's complexity. How did Google's AlphaGo program advance so much faster than many expected?

Courtesy of Oscar SalazarOscar Salazar was a co-founder of Uber. Imagine a world where any child with a smartphone has access to a personal tutor who's familiar with his or her learning style. Where bots recognize the symptoms of a sickness you describe, so a healthcare provider can quickly and efficiently help you get better. And where transportation companies know where and when you need to get somewhere before you even tell them. That's the world Oscar Salazar, a co-founder of Uber and now an independent entrepreneur, is excited about.