There have been many "ages" throughout human history, most notably the industrial age and the digital age. Now, we have officially entered the age of artificial intelligence (AI). Within this AI age are many technologies, including machine learning and deep learning. These are fundamentally transforming and altering the business landscape. Its ability to revolutionize the world has been likened to what electricity did in its day.
Istanbul, a city of 14 million people and a crossroads of cultural exchange dating back millennia, may also be where Turkey's next major earthquake strikes. Cities along the North Anatolian Fault, which stretches from eastern Turkey to the Aegean Sea, have experienced an advancing series of strong quakes during the past 80 years, beginning in 1939 when a devastating 7.8-magnitude rupture leveled the city of Erzincan and killed 33,000 people. Most recently, in 1999, 7.4-magnitude quake near the city of İzmit left 17,000 dead and half a million homeless. A few months later, another shock hit Düzce, 60 miles away. Brendan Meade, an applied computational scientist and associate professor of earth and planetary sciences, recently built a computer model of conditions in the North Anatolian Fault.
There's no doubt in my mind that machine learning (ML) as part of a data science strategy can help revolutionize many aspects of everyday life. Below I highlight a few examples of how different industries are able to leverage machine learning for competitive differentiation and customer benefit. There are tens of thousands of daily published journals and papers across the world. It is impractical for every clinician to read and absorb these. ML can help identify patterns and correlations that humans alone would otherwise miss -- possibly resulting in diagnosis and treatment plans that are suboptimal.
Bot backlash is upon us. On Facebook, where companies once raced to implement time-saving bots, failure rates are reported to have hit 70 percent. Only three in 10 interactions go off without a hitch, according to recent reports. Customers complain that AI-powered bots bumble all but the most basic of queries, give nonsensical responses and waste more time than they save. The irony, of course, is that all this botched customer service is playing out on social media, which not long ago promised to be the antidote to tiresome phone trees and subpar support.
One of the most controversial psychological studies in recent memory appeared last month as an advance release of a paper that will be published in the Journal of Personality and Social Psychology. Yilun Wang and Michal Kosinsky, both of the Graduate School of Business at Stanford University, used a deep neural network (a computer program that mimics complex neural interactions in the human brain) to analyze photographs of faces taken from a dating website and detect the sexual orientation of the people whose images were shown. The algorithm correctly distinguished between straight and gay men 81 percent of the time. When it had five photos of the same person to analyze, the accuracy rate rose to 91 percent. For women, the score was lower: 71 percent and 83 percent, respectively.
Imagine AI, current AI, not some AI in the future, being able to identify a perfect stacked, ranked list of every person in the country who works against whatever your agenda is from top to bottom. Imagine AI, current AI, not some AI in the future, being able to identify a perfect stacked, ranked list of every person in the country who works against whatever your agenda is from top to bottom. GLENN: Well, and imagine -- we know that AI -- we know that a year or 18 months ago, we heard AI imitate the voice of Barack Obama, Bill Clinton, and I think Hillary Clinton. You want to start Civil War, show Donald Trump meeting with Vladimir Putin and -- and show him doing all kinds of wicked plans against the United States.
Though traditional personality-assessment techniques, such as the Myers–Briggs test, are designed for objectivity, somewhere along the way "managers still inject personal bias," says Mark Newman, founder and CEO of HireVue, a recruiting-technology company. Koru, another human resources software developer, also gauges personal attributes, using a written test to evaluate "impact skills," such as grit, curiosity, and polish. The year-old company Interviewed, which has worked with clients such as Instacart and IBM, administers "blind auditions" in which applicants for customer-service jobs field chats or calls from bots that represent consumers. An algorithm's ability to understand something like empathy, Bakke says, points to a new hiring technique--one in which machines assess, but humans make the final call.
The automobile is being dismantled, reimagined, and rebuilt in Silicon Valley. Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving. While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It's also developing new technologies that could help solidify this position.
Pinterest lets you find visually-similar images in order to track down that recipe or jacket you're looking for, and Pornhub is using machine learning to automatically identify porn stars in videos. Stock image company Shutterstock, though, has developed one of the more novel implementations of this sort of technology: using machine learning to identify the layout of images. You can search for various elements (in the case of the link above, wine and cheese) and then move icons about to specify where you want them to appear in the image. It's a good example of how this sort of machine learning can deliver improvements for users that aren't very exciting, but are, at least, pretty useful.
The key takeaway from a machine learning and chatbot perspective is that AI/ML is reaching a stage whereby businesses can leverage inexpensive digital channels such as chatbots to further engage with customers, reach market share or simply just improve the customer experience. Historically, the finance industry implemented rules engines and expert systems to identify potentially fraudulent transactions. We're noticing a trend this year with other companies such as Google offering similar products in the form of Google Home. These voice assistants will continue to evolve over the coming months and as consumers become more comfortable with digital assistant's in their home, the underlying machine learning algorithms that power them will become ever more advanced.