IT’S TIME TO GET REAL ABOUT A.I.’S FUTURE, a subject in desperate need of discipline. The technology’s mind-blowing possibilities have apparently inebriated various seers, who take two routes to fantasyland: propagating boldly precise forecasts of jobs to be spawned and destroyed years hence, or spinning tales of A.I. transforming our world into a heaven (or hell). Instead, we wanted to confront the realities of how A.I. is changing business—minus the melodrama.

On the chief source of A.I.-induced anxiety—employment effects—the reality is that no one knows or can know what’s ahead, not even approximately. The reason is that we can never foresee human ingenuity, all the ways in which millions of motivated entrepreneurs and managers worldwide will apply rapidly improving technology. Postmaster General Arthur Summerfield predicted confidently in 1959 that mail would soon be delivered by packing letters into guided missiles, the wonder tech of the day. A growing economy meant more letters, and the future for postal workers seemed bright. It was, for a while. The possibility that mail would cease to be written on paper never occurred to Summerfield, though the necessary technologies for email, texting, and the cell network existed in rudimentary form or were being developed. We risk missing the boat in the same way with A.I.

The second reality to remember is that A.I.’s eventual uses will be determined largely by market forces. Earnest discussions of how A.I. can be directed to make the world a utopia miss that point. They recall RCA chief David Sarnoff’s long-ago prediction that the coming of color TV would enable people to see fine art in their homes. That sounded wonderful, but nobody wanted it for such high-minded uses. A.I. will be used by companies and consumers for countless practical purposes, most of them modest, and the cumulative effect can’t be foreseen. As we try to guess A.I.’s future, the key will be to think like self-interested people (including both good and bad guys) in the real world.

No bad guys here, though. These 25 examples of A.I. at work are beneficial, even inspiring—and they’re real.

How AI Is Changing the Way You Work

Getting Us All To Speak The Same Language

EVER SINCE THE GOLDEN age of the original Doctor Who and Star Trek, science fiction has highlighted devices that can automatically translate languages so that humans can talk to aliens without needing to study far-out dialects. It turns out that companies here on Earth, like Google, are using artificial intelligence technologies to create devices that can translate conversations from one language to another. While Google’s recently released Pixel Buds is a promising start, consider the ways businesses could use the technology when it works seamlessly. American executives could call up their Portuguese-speaking counterparts and brainstorm global partnerships on the fly. Businesses with international offices could more effectively communicate with employees, who could work in tandem with colleagues in other countries who don’t speak the same language. Salespeople could scout for potential leads in new regions and make cold calls that could bring about their next game-changing deal. Although many companies have instituted an English only policy as a way to keep employees speaking the same lingo, on-the-fly translation technology lets non-U.S. employees speak their mother tongues and retain aspects of their cultures—a benefit in this era of globalization.

Reading Your Mind

MOVE OVER, ALEXA. Voice control is cool, but consulting Alexa, Siri, or Cortana can be awkward and disruptive in public. Enter AlterEgo—a noninvasive, wearable device created by MIT researchers that knows what you’re going to say before you even open your mouth. The device can answer many queries within seconds, send private messages, and internally record streams of information to access at a later time—all without any observable external actions. AlterEgo doesn’t really read minds, although it may sound that way. Instead, the device effortlessly facilitates private human machine communication by interpreting electrical impulses in the jaw that are triggered when words or phrases are internally vocalized. Although university-based researchers are still in the process of collecting data and training the system, AlterEgo might also eventually serve as a platform for communication between users in high-noise environments, such as the flight deck of an aircraft or a factory floor, as well as a mode of communication for those with speech impediments. And while AlterEgo could radically speed up the process of writing, planning, and communicating, for now, humans would still be the ones stuck actually reading all those emails.

34%

Percentage of people who believed they had interacted with A.I., according to a study by Pegasystems. (Percentage who actually had: 84%.)

Hiring Smarter

THE HIRING PROCESS IS fraught with challenges. Humans may be subtly or unconsciously swayed by a last name, a college, even the font size of a résumé. Now some companies are seeing if A.I. can help.

Applicants at Vodafone, Nielsen, and Unilever, for example, play a smartphone game designed by A.I. startup Pymetrics that measures cognitive and emotional traits with an algorithm designed to avoid racial, gender, or other bias. Unilever then asks top candidates selected by the software to record a video on HireVue, answering questions about how they would handle various situations encountered on the job. Another algorithm sifts the best candidates by reviewing not just what the individuals said but also how quickly they responded and what emotional cues they revealed in their facial expressions. Those candidates who pass the early tests are rewarded with regular job interviews with a live person.

Unilever says that since it instituted the system it’s getting a higher rate of acceptances when it offers a job, and has increased applicant numbers across several diversity measures, including race, ethnicity, and socioeconomic status—and that it’s drawing from a more diverse pool at three times as many colleges and universities.

20%

Percentage of people who want their voice assistants to help them be “funnier or more attractive,” according to a study by Computerlove in the U.K.

Building The Ultimate Manager

JUDGMENT OF HUMAN behavior was once reserved for, well, humans. But increasingly, algorithms are the ones evaluating and drawing conclusions on our actions and even intentions. That’s especially true in the workplace, where HR departments are turning to A.I. for more scalable (and hopefully, more reliable) insights into possible attrition risks, attributes of high performers, and what makes teams tick. Boston-based Humanyze is experimenting with smart ID badges that track how employees interact with each other throughout the day, enabling employers to look for patterns to figure out how work actually gets done.

Textio, a Seattle startup, uses A.I. to help companies craft the right recruiting ads (the “augmented writing platform” is particularly effective at surfacing language that will attract more diverse candidates). Big companies are getting in on H-less HR too: Intel is looking at using artificial intelligence to power a new internal tool that would match employees to other opportunities within the company, all in the name of retention.

These new capabilities could help companies attract and retain the talent they need (and cut down on on-boarding and recruiting costs by automating these processes). One possible downside? They also risk alienating the very people they claim to serve—employees might not like the increasingly intrusive workplace of tomorrow.

How AI Is Shaking Up Banking and Wall Street

Meet Your New Robot Mortgage Lender

ONE THEORY HAS ARISEN in the decade since the subprime mortgage crisis: Machines may be better than humans at giving out home loans. A new Fannie Mae survey of mortgage lenders found that 40% of mortgage banks have deployed A.I.—using it to automate the document-heavy application process, detect fraud, and predict a borrower’s likelihood of default. San Francisco–based Blend, for one, provides its online mortgage-application software to 114 lenders, including lending giant Wells Fargo, shaving at least a week off the approval process. Could it have prevented the mortgage meltdown? Maybe not entirely, but it might have lessened the severity as machines flagged warning signs sooner. “Bad decisions around data can be found instantaneously and can be fixed,” says Blend CEO and cofounder Nima Ghamsari. While banks are not yet relying on A.I. for approval decisions, lending executives are already observing a secondary benefit of the robotic process: making home loans accessible to a broader swath of America. Consumers in what Blend defines as its lowest income bracket—a demographic that historically has shied away from applying in person—are three times as likely as other groups to fill out the company’s mobile application. Says Mary Mack, Wells Fargo’s consumer banking head: “It takes the fear out.”

A New Edge For Pro Investors…

IN THE WORLD OF FINANCE, there’s been such an explosion of data collected over the past decade that even those twenty-something analysts working around the clock don’t stand a chance of being able to process it all. But machines might. Bloomberg, FactSet Research Systems, and Thomson Reuters have all developed an array of data science tools and techniques—including machine learning, deep learning, and natural language processing (NLP)—to quickly unearth valuable insights for thousands of financial professionals.

Bloomberg was a pioneer of sentiment analysis (an example of NLP), which it began developing around a decade ago, in which machine-learning techniques are used to flag a news story or tweet as being relevant to a stock and assign a sentiment score. A.I. is also spreading to wealth management. Investment groups have more than quadrupled their number of “alternative data” analysts over the past five years, as asset managers scramble to unlock the potential of trading signals contained in website scrapes, language analysis, credit card purchases, and satellite data. Firms reported to be using A.I. for investment research include BlackRock, Fidelity, Invesco, Schroders, and T. Rowe Price. BlackRock, the world’s largest asset manager, has been a forerunner in adopting A.I. and is setting up a BlackRock Lab for Artificial Intelligence.

72%

Percentage of people who are afraid of robots taking over their tasks, according to Pew Research.

…And For The Amateurs Too

“ROBO-ADVISER” SERVICES, offered by startups like Betterment and traditional discount brokerages like Charles Schwab, are already using A.I. to serve the investing masses. Their low-fee investment tools rely on algorithms to determine how your assets should be split among stocks, bonds, and other assets, based on your needs and your stomach for risk. Their A.I. can automatically rebalance your portfolio; it can also nudge a (nonrobotic) adviser to call you when the algorithms predict you need help with tax strategy or estate planning.

The next frontier: A.I. smart enough to help savers make good decisions about long-term, buy-and-hold investments. Bank of America Merrill Lynch and Morgan Stanley are among the bigger players in an emerging discipline known (awkwardly) as quantamental analysis. They aim to combine the quantitative processing for which basic A.I. is best suited (basically, the capacity to spot patterns in gargantuan loads of data) with additional algorithms trained in the sophisticated analysis associated with super smart humans—assessing, say, the growth potential of an industry or the strategic acumen of a company’s management. Machine learning could eventually enable a quantamental system to learn from its mistakes. The ultimate goal: Warren Buffett–like stock-picking wisdom at low prices—and perhaps a name catchier than “quantamental.”

How AI Is Changing How We Build Things

Designing More Efficiently

SURE, COMPUTER ALGORITHMS ARE TAKING over tech and science and medicine … but the creatives are still safe, right? Not exactly. A new program from software developer Autodesk called Dreamcatcher (rendering above) can use A.I. techniques to assist human designers as they go about their creative tasks. Already in use by companies including Airbus, Under Armour, and Stanley Black & Decker, the software is an example of the burgeoning field of generative design. A designer inputs requirements, limitations, and other qualities into the program—even the total cost of materials. The software then produces hundreds or even thousands of options. As the human designer winnows the choices, the software susses out preferences and helps iterate even better options. Airplane manufacturer Airbus used the software to redesign an interior partition in the A320 and came up with a design that was 66 pounds, or 45%, lighter than the previous setup.

Melding Humans And Robots

ROBOTS HAVE BEEN ON THE ASSEMBLY LINE doing all kinds of manufacturing for decades. Lately, a new feature is being added to the automated work machines: humans. Dubbed “cobots,” short for collaborative robots, the new setups range from robotic helpers that can hand the correct part to a human worker to an almost Ironman like robotic exoskeleton suit that a person wears to gain added strength and A.I. software guidance. BMW has a cobot nicknamed Miss Charlotte that is helping assemble doors at its Spartanburg, S.C., plant. Mercedes-Benz is turning to cobot technology to help personalize each car that the luxury-automaker assembles in some of its most expensive categories. Replacing larger automated systems, humans with more nimble cobot helpers can be quicker at choosing from among the huge variety of parts needed to customize S-Class sedans, for example. MIT professor Julie Shaw is working on software algorithms developed with machine learning that will teach cobots how and when to communicate by reading signals from the humans around them. Some researchers have even looked at connecting cobots to human brainwave readouts. Mind-reading assistive robots? Now that’s collaboration.

48%

Percentage of people who found chatbots pretending to be human “creepy,” according to Mindshare.

Powering Clean Energy

IF WIND ENERGY IS TO BE decisively cheaper than fossil-fuel power, the process of transforming wind into electricity must get more efficient. Machine-learning technology developed at Siemens is helping. Researchers realized that huge wind turbines could use data on weather and component vibration to fine-tune themselves continually, for example, by adjusting the angles of rotor blades. But “you cannot analytically calculate this,” says researcher Volkmar Sterzing.

That’s the right kind of problem for A.I. and machine learning. Sensors were already generating the needed parameters, but “previously, these were used only for remote maintenance and service diagnostics,” says Sterzing. “Now they are also helping wind turbines generate more electricity.” The technology can even adjust turbines to the unpredictable airflows coming through the turbines in front of them.

Deploying this A.I. broadly is now an opportunity for Siemens Gamesa Renewable Energy, an independent company formed last year by combining Siemens’s wind operations with the wind power business of Spain’s Gamesa.

Keeping An Eye On The Mortals

HUMANS ARE NOT GREAT at knowing their own limits—they eat too much, sleep too little, and overestimate what can be achieved in a period of time. That may seem a matter of little consequence when it comes to, say, Thanksgiving dinner, but in certain professions—like long-haul trucking and heavy-equipment operation—such fallibility can be dangerous and catastrophically costly.

That’s why companies are increasingly using A.I., guardian angel–like, to safeguard employees in high-risk jobs. Systems, trained on hundreds of hours of employee sensor data, monitor conditions—like an operator’s heart rate, body temperature, and indicators of fatigue level or nervousness—in real time and signal when that individual needs to rest or take a break, explains Mike Flannagan, an SVP at business software firm SAP. (SAP has a Connected Worker Safety product that does this.)

As for the rest of us? We can expect to see this type of technology soon in our own garages, where automakers are dreaming up ways for our cars to keep an eye on us. While the tech is currently limited to a coffee cup icon that flashes on the dash in a few models, Nils Lenke, head of innovation management for automotive at Nuance Communications, an A.I. firm that works with most of the major carmakers, says fatigue-detecting voice and facial recognition technology will soon be standard in new vehicles.

3 Ways AI Is Making You Safer

Making Weapons That Pick Their Targets

ONCE THE STUFF OF APOCALYPTIC SCI-FI tales, killer robots capable of choosing and taking out our nation’s enemies are now within reach—if companies and the Pentagon decide to go that far. Defense officials have so far stopped short of developing Lethal Autonomous Weapons Systems (the government’s official term), which could theoretically strike without a human order as easily as Facebook can tag friends in your photos without your say-so.

But the A.I.-driven technology that could form the basis for such attacks is well underway. Project Maven, the Pentagon’s most high-profile A.I. initiative, aims to use machine-learning algorithms to identify terrorist targets from drone footage, assisting military efforts to combat ISIS (more than 20 tech and defense contractors are reportedly involved, though they have not all been publicly named). Although supporting war efforts is nothing new for the defense industry, the Pentagon has increasingly looked to Silicon Valley for expertise in A.I. and facial recognition. That growing relationship has recently sparked controversy, with Google announcing this summer that it would withdraw from Project Maven after several employees quit in protest. Going forward, companies’ only barrier to winning lucrative new A.I. defense contracts may be their own unwillingness.

2022

Year in which A.I. will be better than humans at folding laundry, according to researchers at Oxford and Yale.

Averting Threats

THE FAILURE TO prevent attacks in cyberspace and IRL (in real life) is an expensive line item—the average cost of an individual data breach was nearly $4 million in 2017. But the surge in attacks of late has an upside: It means there’s also more data to mine. Machine-learning techniques have been used to detect patterns and filter emails for decades, but newer systems from vendors like Barracuda Networks can use A.I. to actually learn the unique communication patterns of particular companies and their execs in an effort to pinpoint potential phishing scams and other hacking attempts. In the world of physical security, A.I. is even being used in security cameras to “see” and try to stop threats. New cameras from startup Athena Security can identify when a gun is pulled and even automatically alert the police. In short: The more data we have, the more we can use A.I. to fight crime.

Embezzler Beware!

HOW DO YOU catch a financial criminal? Instead of bulking up compliance staff to sift through thousands of transactions in search of suspicious activity, banks across the globe like HSBC and Danske Bank are increasingly turning to A.I. to flag financial scams, money laundering, and fraud. (This push has gained even more momentum recently as several banks were hit with huge fines for failing to detect illegal funds flowing through their accounts.) HSBC partnered with A.I. startup Ayasdi to automate some of its compliance. In its 12-week pilot with HSBC, Ayasdi’s A.I. technology achieved a 20% reduction in false positives (transactions that looked suspicious but were legit), while retaining the same number of suspicious-activity reports as human review.

7 Ways AI Is Changing How You Shop, Eat, and Live

Driving So You Don’t Have To

“THE MACHINE KNOWS WHERE IT’S GOING!” CRIED Michael Scott, protagonist of NBC’s The Office, before launching a Ford Taurus rental car into a lake near Scranton, Pa. Getting an autonomous vehicle to drive safely under idealized road conditions has technically been possible for a while now, but for the real world, the cars are going to have to learn to drive a little bit more like us. That’s where Comma.ai, a startup founded by notorious iPhone hacker George Hotz, comes in. Rather than teaching its computer systems what a tree or a stop sign looks like, Comma.ai’s Openpilot technology analyzes the patterns of everyday drivers to train its self-driving models. The company is pulling in millions of miles of driving data from a dashcam app called Chffr and a plug-in module called Panda, then aggregating that data to create an autonomous system that mimics human drivers. The company—whose technology currently works with select Honda, Toyota, and Hyundai vehicles—is styling itself as the Android to Tesla’s Autopilot iPhone, an open-source system that is pegging its success to the notion that users will make it better. Let’s just hope Michael Scott isn’t one of them.

16%

Percentage of women who said they would feel comfortable riding in a driverless car, vs. 38% for men, according to a Reuters/IPSOS poll.

Your New Travel Companion

EYJAFJALLAJÖKULL, it turns out, has stayed with us long after the ash faded away. The Icelandic volcano that erupted in 2010 affected millions of fliers and, in doing so, ushered in a new era in travel communications. With information flow capabilities strained, airlines discovered social media as an effective, real-time way to reach passengers. “Once that happened,” says Rob Harles, Accenture Interactive’s head of social media and emerging channels, that mode of communication “was an unstoppable force.” Since then, however, the number of travelers has ballooned, with 1.25 billion arrivals in 2016, an increase of 30%. Human-powered social media interaction on that scale is “impossible,” Harles says.

Enter the customer service chatbot that’s able to answer travelers’ basic questions: Is my flight delayed? When is my hotel checkout? Booking.com, for instance, has a bot that the company claims can solve 60% of customer queries automatically. The next stage of this technology is for bots to understand the nature of your trip—business or pleasure?—and to make recommendations based on your preferences throughout your journey, ranging from suggesting a flight upgrade to reserving a table at the best vegan café in, say, Pittsburgh. So what’s currently known as a chatbot may soon resemble a full-blown automated concierge.

Upgrading The Call Center

“CAN I HELP YOU?” By 2020, IBM estimates that 85% of customer service interactions will be handled without a human agent. Machine learning and natural language processing make it possible for chatbots, enhanced phone support, and self-service interfaces to perform most of the functions of human representatives.

As for the 2.7 million Americans who are employed as customer service representatives? Some may be redeployed to tasks that bots can’t do (like dealing with truly irate customers). Companies relying on this technology say it can help eliminate human error, drastically increase speed in data retrieval, and remove bias from customer service interactions.

And don’t think this ends with bots. Swiss investment bank UBS recently teamed up with New Zealand A.I. expert FaceMe to digitally clone chief economist Daniel Kalt to interact with clients just as he would in the flesh. The bank said the avatar, built using IBM Watson A.I. technology and trained by the real Kalt, is part of its exploration to provide a “mix of human and digital touch.”