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AI 24 ways to change your business

 Postmaster Arthur Summerfield confidently predicted in 1959 that economic growth meant more letters, and the future of postal workers seemed bright, despite the primary form of email, SMS and cellular technology. Already existed, but the possibility that humans would no longer write on paper, Summerfield never thought of.




The second reality to remember is that the end use of AI will be largely determined by market forces.


Below, these 25 examples of artificial intelligence are working are beneficial and even inspiring - and they are real.


How artificial intelligence changes the way you work


Let us all speak the same language


Since the golden age of Dr. Mystery and Star Trek, science fiction has always focused on devices that automatically translate languages so that humans can talk to aliens without having to learn distant dialects.


It turns out that companies on the earth, like Google, are using artificial intelligence to create a device that can translate conversations from one language to another. Although Google's recently released Pixel Buds is a promising start, it works well, but it also has to consider how companies use the technology.


US executives can call their Portuguese-speaking peers to brainstorm with their global partners in real time. Companies with international offices can communicate more effectively with employees, and employees can work with colleagues in different languages who speak different languages. Salespeople can look for potential customers in new areas and make "cold calls" (to make a call to a stranger)



Mind reading


Voice control is cool, but consulting Alexa, Siri, or Cortana in public can be embarrassing and disruptive.


A non-invasive wearable device, the AlterEgo (headset), invented by researchers at the Massachusetts Institute of Technology (MIT), knows what you want to say before you speak. It can answer many queries in a matter of seconds, send private messages, and stream information internally for later access—all without any visible external operations.


Although AlterEgo may have read your thoughts, it doesn't. Instead, it can effortlessly promote secret human-computer communication—when a word or phrase utters internally,It triggers electrical pulses in the lower jaw.


Although university researchers are still collecting data and training systems, AlterEgo may eventually become a platform for users to communicate in high-noise environments (such as aircraft cockpits or factory floors), or as a language barrier. way of communication.


Recruit smarter people

The recruitment process is full of challenges. People may be affected by the font size on their surnames, universities, or even their resumes without their knowledge. Some companies are watching whether artificial intelligence can help.


For example, Vodafone, Nielsen, and Unilever's job seekers are all playing a smartphone game designed by AI startup Pymetrics, which measures cognitive and emotional characteristics through an algorithm that avoids race, gender, or other biases. . Then, Unilever asked the best job seekers selected by the software to record videos on HireVue and answer how they deal with the various problems encountered at work.


Another algorithm not only reviews what they say, but also examines their speed of response, as well as the emotional cues they show in their facial expressions to screen the best candidates. Job seekers who pass the early test will get a qualified, on-site interview.


Create the ultimate manager


The opportunity to make judgments about human behavior was once reserved for humans. However, algorithms are increasingly evaluating our behavior and even our intentions and drawing conclusions from them.


This is especially true in the workplace, where HR seeks help from the AI in order to have a scalable view of the risk of possible turnover, the characteristics of high-performing employees, and the motivation of the team's operations (hopefully reliable).


Boston-based Humanyze is trying to use a smart ID card to track employees' interactions throughout the day so that employers can find patterns to determine how the work is actually done. Textio, a start-up in Seattle, uses artificial intelligence to help companies create the right job advertisements.


Big companies have also joined the H-less HR queue: Intel is considering using artificial intelligence to drive a new internal tool that matches employees to other opportunities within the company, all in the name of retaining talent of.



How artificial intelligence is impacting the banking industry and Wall Street


Since the outbreak of the subprime mortgage crisis, there has been a theory in the past decade: machines may be able to issue housing loans more wisely than people. ——Fannie Mae


A recent survey of mortgage lenders found that 40% of mortgage banks have deployed artificial intelligence systems that use the system to automatically handle application processes involving heavy document processing, fraud detection, and predicting borrower defaults. possibility.


For example, the San Francisco-based "Blend" company provides online mortgage application software for 114 lending banks, including loan giant Wells Fargo, which has shortened the approval process by at least a week. So the question is, can such artificial intelligence systems stop the collapse of the mortgage industry?


Maybe it doesn't do that completely, but because the machine can signal a warning more quickly, it can already reduce some of the severity. Nima Ghamsari, co-founder and CEO of "Blend," said: "Data-related erroneous decisions can be immediately detected and corrected." Although banks have not relied on artificial intelligence to make approval decisions, loan executives have perceived another benefit of such an artificial intelligence process: getting more Americans to get home loans. The lowest-income consumer group defined by "Blend" (a group that has historically been reluctant to apply for individual users) is three times more likely to fill out the company's mobile app.


Mary Mack, head of consumer banking at Wells Fargo, said: "This eliminates people's fears." - Jen Wieczner


New challenges for professional investors


In the financial sector, the amount of data collected over the past 10 years has soared that analysts in their twenties and working around the clock have not been able to handle all the data. But the machine can do it. Bloomberg, the FactSet research system, and Thomson Reuters have developed a range of data science tools and technologies—including machine learning, deep learning, and natural language processing (NLP)—to quickly mine valuable knowledge for thousands of financial professionals. .


Bloomberg is a pioneer in the field of sentiment analysis, which the company began developing about 10 years ago. In sentiment analysis techniques, machine learning techniques are used to annotate stock-related news stories or tweets and give an emotional score. Artificial intelligence is also expanding into the field of wealth management. In the past five years, the number of backup data analysts in the investment group has increased more than threefold.


The same is true for amateurs


The “Robot Consultant” service provided by traditional discount brokers like Betterment and Chaeles Schwab has begun to use artificial intelligence technology to serve the broad investment community. Their low-cost investment vehicles rely on artificial intelligence to determine how your assets should be distributed among stocks, bonds, and other asset forms based on your needs and your ability to withstand risk. Their artificial intelligence technology automatically adjusts your portfolio, and when the algorithm predicts that you need to seek some tax avoidance strategy or property planning help, he can also push non-robot consultants to call you.


The next technology frontier is: When artificial intelligence is smart enough, it can help depositors make the right decisions in a long-term “buy-hold” investment process. Bank of America Merrill Lynch and Morgan Stanley are among the number one (awkward) number one players in the emerging field of quantitative investment analysis.


How AI is changing the made way ?


Design 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, recently commercialized from an R&D project 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. —Aaron Pressman


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. —Aaron Pressman




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 SiemensGamesa Renewable Energy, an independent company formed last year by combining Siemens’s wind operations with the wind power business of Spain’s Gamesa. —Geoff Colvin


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. —Erika Fry


AI is making you safer in three ways.


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. —Jen Wieczner



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. —Michal Lev-Ram



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.—Carson Kessler



AI is changing the way people eat, wear,travel and live in 7 ways.


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, 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,’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. —Daniel Bentley



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?, 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. —Claire Zillman




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.” —McKenna Moore



Moneyball 2.0

WHEN NHL SCOUTS looked at Sean Durzi (above) in 2017, they decided to pass on the 19-year-old defenseman. Just one year later, Durzi went second round to the Toronto Maple Leafs. The difference was powerful new A.I. software by Montreal-based startup Sportlogiq that parsed terabytes of data to uncover his powerful playmaking ability—call it Moneyball 2.0. Sportlogiq is just one of several companies using A.I. to help teams spot the next star.



—Carson Kessler




Change How You Shop

BRICK-AND-MORTAR stores have a new calling: They’re perfect A.I. data collection labs. Home Depot is using data from millions of transactions to figure out what else you—the DIY-er grappling with, say, a big kitchen renovation—might need and provide detailed home project guides as well as hyper-targeted cross-selling. Sephora has used A.I.-powered facial recognition by ModiFace (recently bought by L’Oréal) to help shoppers select that exact right makeup shade: The software analyzes millions of other past users to better predict what will look good on you. And MIT-spinoff ­Celect uses machine learning to forecast how shoppers behave, determine what kinds of promotions work better in what part of the store, and figure out where products should be placed for optimal results.


As for that price check in aisle 10? Walmart, for one, has tested robots at 50 stores that scan shelves for out-of-stock items, products placed back in the wrong spot by customers, and incorrect prices —all time-consuming and cumbersome jobs for humans. Though retailers are tight-lipped about in-store tech, companies like Navii and Simbe that make A.I.-powered robots are attracting attention and investors, according to CB Insights. —Phil Wahba


Will This Ad Make You Smile?

MARKETERS DON’T ALWAYS hit the mark—we’re looking at you and that Kendall Jenner spot, Pepsi—but increasingly, they’re leaning on artificial intelligence to make those misfires less likely. Emotion A.I. firm Affectiva says a quarter of Fortune 500 companies use its tech in their creative development processes, testing the reaction to potential ads in A.I.-enhanced survey research. Affectiva’s system, which has been trained on images of 7 million faces (and 3.8 billion facial frames) from 87 countries, decodes the facial expressions of individuals—the tech identifies 20 specific ones as well as eight emotions, including “disgust”—moment by moment as they watch ads.


Media research giant Kantar Millward Brown, which has deployed Affectiva’s product since 2011 (30,000 ads’ worth) found Nike’s lauded Colin Kaepernick ad scored smiles at key points. “We were really able to pinpoint the fact that it was Kaepernick’s message about sacrifice and dreams that triggered the positive response,” says Graham Page, the firm’s managing director. They also found that viewers responded positively, and with surprise, to women players featured in World Cup ads.


Page noted that beyond helping clients sharpen their campaigns, Kantar has gained broad insights that benefit all clients. Ads that viewers describe as “progressive,” with protagonists featured in modern roles (rather than traditional ones) are 25% more effective, according to the firm.—Erika Fry




Growing Your Next Meal

ON THE SURFACE, FARMING SEEMS LIKE a simple endeavor: Pop seeds in the ground, water, harvest, repeat. But in reality, how food is grown is built on a series of intricate equations. “A lot of the data we deal with in agriculture is very complex,” says Nate Storey, the cofounder and chief science officer of Plenty, an indoor vertical-farming enterprise. Environmental factors (airflow, carbon dioxide, light, and humidity, to name a few), the genetics of the plant, and the things we do to it, like fertilizing and watering, are all interacting variables. Now Plenty and a number of other startups are using A.I. to help manage the complex decisions that go into farming. For example, Plenty and its indoor-ag rivals Bowery and Gotham Greens are all building systems that collect and analyze data sets of images that can help identify whether a plant has an issue, like nitrogen or iron deficiency or a pest problem, through machine learning and then preemptively treat it. “The software can learn what the problems are and do it in an automated fashion at a large scale that we couldn’t individually do,” Storey says. —Beth Kowitt



3 Ways AI Is Changing Healthcare


Making Health Care Human Again

THE CURRENT U.S. HEALTH CARE PICTURE is pretty bleak: more than 12 million serious diagnostic errors each year, a third of the $3.6 trillion spent attributed to waste, reduction in life expectancy for what will be three years in a row (which is unpre­cedented), and peak levels of physician burnout, depression, and suicide. That’s all happening at a time when there is more medical data per individual than ever, imagined with wearable sensor physiology, scan anatomy (above), DNA sequencing, gut microbiome biology, just to name a few layers. Enter deep-learning A.I., with neural networks that will impact every type of clinician, from helping to accurately read scans, slides, skin lesions, eyegrounds, and more, to health systems, promoting the use of remote monitoring that ultimately obviates the need for regular hospital rooms, and at the consumer level, by providing a virtual medical coach to better manage or even prevent diseases. It’s still early in the integration of A.I. into medical practice, with far more hype than validation. But it’s our best shot to deal with all of the formidable challenges: to use the wealth of data to reduce errors and waste, and the gift of time to markedly improve the clinician-patient relationship. — Eric Topol, MD, is the founder and director of the Scripps Research Translational Institute and author of the forthcoming book Deep Medicine.



Outsmarting Your Doctor

IN JUST THE PAST few years, there have emerged credible if still-in-the-works A.I.-powered technologies that can read radiology scans (like Imagen), identify tumors and track the spread of cancer (Arterys), detect eye conditions using retinal imaging (Google’s DeepMind), flag dangerously abnormal potassium levels via a “bloodless blood test” (Mayo Clinic Ventures and AliveCor), and otherwise assist with the tricky business of diagnosing, or even predicting, disease. Historically, diagnostic error rates have been put at 5% to 20%, though the rate is higher for some conditions, while the health care system is strained by doctor shortage and burnout—some things A.I. may be able to treat. —Erika Fry


Reinventing Drug R&D

THE MEDICINE BUSINESS IS FILLED WITH TWISTS OF FATE. A drug may appear safe for humans in early studies with small groups of patients only to crash and burn in spectacularly expensive fashion in a large-scale clinical trial. In fact, return on investment for the largest biopharmaceutical companies in the U.S. fell to a dismal 3.2% in 2017, according to Deloitte. Which is why American companies like BERG and Roivant Sciences and U.K.-based Exscientia want to harness the power of A.I. to better deploy resources. BERG has partnered with major drugmakers like AstraZeneca and Sanofi Pasteur to use clinical data fed through an algorithm to identify promising biological targets for drugs and molecules that may be able to treat diseases like Parkinson’s. Sanofi is also analyzing huge amounts of data to gain a deeper understanding of why certain flu vaccines are effective for some people but not for others (a critical public health question considering last year’s devastating flu season). A.I. as a central medicine-making tool is still in its early stages. But the promise is clear: Being able to funnel pharma R&D efforts to the most promising targets can avoid a huge waste of time and money and, hopefully one day, lead to a more streamlined drug development process that benefits companies and patients alike. —Sy Mukherjee



Reversing Disease

AMERICA’S HEALTH care system has been criticized for favoring triage over cheaper, proactive approaches—and businesses pay the price in lost productivity and skyrocketing health care costs. Virta Health CEO Sami Inkinen is taking a different tack, using A.I. to prevent patients at risk for diabetes from developing the full-blown disease and, in early trials, even reversing Type 2 diabetes through its purely digital platform. Virta aims to shift customers’ lifestyles by connecting them with coaches who give them personalized recommendations on diet and other factors. It also provides digitally connected tools to measure blood sugar, ketones, blood pressure, and weight. Using a patient’s anticipated blood sugar and weight improvement, clinicians can prioritize patients in their hourly workflow. Virta is not alone: IBM’s Watson Health unit and medtech giant Medtronic are collaborating on an app called Sugar.IQ that offers similar tools. —Sy Mukherjee


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