• Employers are increasingly turning to artificial intelligence-driven tools to carry out recruitment and hiring. Companies like Amazon, FedEx, Target, and Capital One have tested or used AI hiring software.
• These tools sift through heaps of resumes quickly, evaluate candidates' answers to written questions and interactive games, and conduct video interviews with candidates.
• Vendors tout AI hiring tools as a way to root out discrimination, but experts and activists warn that AI-driven hiring tools are just as biased as the humans who train them.
• Visit Business Insider's homepage for more stories.

The next time you apply for a job, the fate of your application may not be in human hands — instead, an algorithm could determine whether or not you make the cut.

Employers are increasingly turning to artificial intelligence-driven tools to carry out recruitment and hiring. Target, Hilton, Pepsi, Amazon, and Ikea are among the companies who have tested or used algorithms to determine who to hire, and the list is growing across the employment spectrum from low-wage jobs to white-collar positions.  

Advocates say AI is the key to rooting out human bias and ending discrimination in the hiring process. But its critics warn that AI-driven hiring tools are just as biased as the humans who train them.

Predictive hiring tools are mostly used to weed out candidates deemed unfit for hire, rather than affirmatively choose who gets a job. These tools sift through huge numbers of resumes in short time periods, evaluate candidates' answers to written questions and interactive games, and conduct video interviews with candidates.

Read more:How AI is changing everything

Companies who sell the AI tools say they expect that the job of human recruiters will soon be entirely replaced by robots in some sectors, and investor interest in these vendors is growing. Researchers at Cornell found that there are at least 19 companies providing AI-driven hiring tools worldwide, with funding ranging from $1 million to $93 million per vendor. 

But AI experts have raised concerns about the technology's rapid growth.

"Technology is rapidly changing how people find jobs, learn about jobs, apply to those jobs, and are evaluated for those jobs," Aaron Rieke, managing director at tech equity nonprofit Upturn, told Business Insider. "The fear is if that is not done very carefully and thoughtfully, there could be problems down the road."

Meanwhile, AI-driven hiring tools haven't drawn much scrutiny from regulators and lawmakers. However, the House Education and Labor Committee is holding a series of hearings on the impact of AI on the workforce this fall, which could be a precursor to legislation.

AI hiring tools could automatically discriminate against certain job applicants

Just like all algorithms, AI hiring tools are trained by humans, typically using data sets from the real world meant to help AI recognize the features of a "good" or "bad" candidate. 

Accordingly, tech experts and activists have warned that AI hiring tools might pick up on preexisting human biases, especially in fields like tech, where progress towards diversity has been slow. 

In a report published in December, Upturn researchers highlighted how these assumptions could lead to unintentional discrimination by AI hiring tools.

"What can naturally happen is you build a model that identifies common characteristics of your current workforce, which isn't diverse," Rieke said. "Or might reflect the fact that hiring managers have traditionally given preference to male candidates over women."

AI hiring tools have the potential for bias, even if that's not a company's intention, according to Joy Buolamwini, a computer scientist based at the MIT Media Lab. Buolamwini founded the Algorithmic Justice League, which conducts research and advocacy on AI bias.

"Even if a company has good intentions, products that are trained on data collected from current employees deemed successful could learn discriminating characteristics that are due to identity traits instead of job competency," Buolamwini wrote in an email to Business Insider. "Data is not necessarily neutral."

Can AI be trained to root out human bias?

Vendors of AI-driven hiring tools argue the opposite, claiming that AI is the most surefire way to ultimately eliminate human bias in the hiring process.

Eric Sydell, the executive vice president of innovation at Modern Hire, told Business Insider he believes algorithms can be programmed with the specific goal of ruling out the unfair preferences that humans act on. Modern Hire sells a software that allows companies to put candidates through interviews and "virtual job tryouts," and is used by companies including Capital One, Allstate, SunTrust, and FedEx.

"AI has tremendous potential to make things more fair and more valid," Sydell said. "There's a million of those [biases] that humans are holding in their heads at all times … with AI we can eradicate human biases."

Rieke said it's too soon to tell whether AI-driven hiring tools are already working against bias, but he believes it's possible that such tools could one day prove less biased than human recruiters.

"The old-fashioned, human way of doing this is a process that has consistently yielded staggering bias against people," Rieke said. "I think there's the possibility for hiring tools to fairly and equitably hire people … but the devil's in the details."

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• Emerging tech is making it more efficient and inexpensive for police across the country to scale up their surveillance operations.
• Drones, facial recognition, and algorithm-driven policing are a few of the many technologies aiding police operations.
• Human rights advocates have raised the alarm about new policing technologies, highlighting their potential threats to privacy, free speech, and due process.
• Visit Business Insider's homepage for more stories.

Emerging technology is giving police departments new ways to track suspects and head off possible crimes. It's also rapidly expanding the scope of police surveillance of civilians.

Police across the country are using tech to widen the scope of people and platforms that they monitor. This surveillance is rarely publicized given the sensitive nature of police investigations, but details of police tactics have regularly surfaced through lawsuits, public records disclosures, and success stories touted by police departments as examples of successful crime prevention.

Human rights advocates have raised the alarm about the acceleration of police surveillance using new technology. A report published by the Brennan Center for Justice this week compiles tactics used by the New York Police Department, highlighting their potential threats to privacy, free speech, and due process.

Read more:AI could be the key to ending discrimination in hiring, but experts warn it can be just as biased as humans

While this technology is new, it's already widely used across the US. Databases of people's faces compiled by local police departments now contain over half of American adults, according to a study by researchers at Georgetown.

Some cities have begun to take action to regulate police use of technology for surveillance. San Francisco became the first city to ban the use of facial recognition software by police in May, and the New York City Council is currently weighing a bill to force the NYPD to publicly disclose their tactics involving surveillance technology.

But in the absence of such regulation, police tactics are largely opaque, and there isn't much court precedent for how transparent police departments are obligated to be regarding their surveillance methods.

Here's what we do know about how police departments across the US are using technology for surveillance.

SEE ALSO: ICE is reportedly using fake Facebook accounts to track undocumented immigrants and lure them into sting operations

Facial recognition

Facial recognition technology is now sophisticated enough for police to use stationary cameras to perform real-time scans of crowds of people walking through the streets or public squares, with AI searching for potential matches to faces of known suspects. The technology is used by the FBI, as well as by police departments scattered across Florida, Ohio, Maryland, and California.

Facial recognition technology is fallible, however, and could potentially make a false match. For that reason, a coalition of activists and AI programmers have called for law enforcement agencies to stop using the technology, NBC News reported.

Video analytics

Like facial recognition, video analytics software scans footage and uses artificial intelligence to identify people and objects that could provide clues to law enforcement, like bags, clothes, or cars that could be tied to a suspect.

It's unclear exactly how many police departments nationwide use video analytics software. IBM licensed its version of the software to multiple police departments before withdrawing it from the market in April, although it continues to support police departments with existing contracts.

Social media tracking

Law enforcement agencies are taking advantage of social media platforms' massive reach and ready-made surveillance tools, such as the ability to search users by name and identify a network of their friends and family.

In a survey of more than 500 local police departments in 2016, three-quarters reported using social media to solicit tips and gather intelligence for investigations. Another 60% reported that they directly contacted social media companies to obtain information related to a suspect.

Algorithm-driven policing

Police are using algorithms to parse crime data in order to predict where future crimes are likely to occur, as well as to generate lists of individuals who the AI thinks may be likely to commit a crime. 

Little is known about the degree to which police departments use this technology, but a three-year legal fight between the Brennan Center and the NYPD has exposed how the department sought out such AI and used it routinely. The technology has also been used by police departments across California.

Cell tower simulators, or “Stingrays”

These devices send out powerful signals that mimic cell towers, tricking people's cell phones within range into connecting to them. Once connected, police can use Stingrays to learn cell phones' location and unique ID numbers.

Stingrays are used by 75 police departments in 27 states, according to the ACLU. Fourteen national agencies, including Immigration and Customs Enforcement, the FBI, and the IRS are also known to use Stingrays.

Drones, surveillance towers, and more cameras

Advances in tech are making it more inexpensive for police departments to maintain complex networks of surveillance towers, cameras, and drones to monitor large swaths of areas simultaneously. 

While maintaining networks of cameras is more common among large urban police forces, more than 165 police departments across the US purchased drones in 2016. Police use drones to map cities, hunt down suspects or victims, investigate crime scenes, and monitor traffic.

• IBM's Thomas J. Watson Research Center in Yorktown Heights, New York, may be nondescript, but it houses some of the brightest minds working on artificial intelligence today. I spent a day there speaking with several top executives on IBM's AI ambitions. 
• The company is serious about the technology and thinking in decades, not years. A major challenge, however, will be the move from narrow to broad AI.
• IBM has produced some of the most high-profile AI machines of the past decade, like one that can go head-to-head with the world's best debaters.
• It's continuing to build upon that legacy, including a new program in development that can automatically provide play-by-play commentary for soccer matches. 
• Click here for more BI Prime content.

Tucked in a luscious forest in Yorktown Heights, New York, a hamlet about an hour outside New York City by train, is IBM's Thomas J. Watson Research Center.  

It's a rather nondescript croissant-shaped building that may surprise those who were expecting a modern-looking facility where legions of robots roam down bright white hallways and regularly interact with employees.

But it houses some of the brightest minds working on artificial intelligence, who are doing the early-stage work on what will become commercial applications that change how we watch sports, debate one another, or even judge whether an algorithm is biased.  

After spending a day at the center and meeting with several executives, I left with four main takeaways of where IBM is at on AI, where it's heading, and the challenges it faces to get there.

IBM is thinking about AI in decades, not years

From machines that go head-to-head with the greatest debaters or pinpoint the most exciting moments of a sporting event to a slew of offerings that ensure algorithms are fair and explainable, IBM is serious about artificial intelligence. 

The company is mapping its AI journey in decades, not years, and pursuing revolutionary technology that could redefine how companies operate. Among the other notable milestones, it launched a joint research laboratory with the Massachusetts Institute of Technology in 2017 and had 175 papers published at eight AI conferences in the past year alone. And with $2.58 billion in revenue in 2018, IBM again ranked as a market leader in AI product. 

Aside from the machines themselves, the company is also trying to position itself as a leader in ethical AI to help overcome escalating concerns with the technology. Part of that effort is trying to change the negative connotations that surround the term "artificial intelligence." 

"AI is a loaded term," Dario Gil, the director of IBM Research, told Business Insider. "If only we could just start adding a little bit more precision around language, that would be helpful." 

The journey from narrow to broad AI will be difficult 

Many AI-based applications in use try to solve a specific problem, like figuring out when to restock a shelf or trying to eliminate bias in hiring decisions.

While the platforms are transforming operations, Sriram Raghavan, the vice president of IBM Research AI, argues that ultimately, it's an inefficient system. With so many models, organizations are unlikely to "spend six months and a few hundred million dollars" to implement each one of them, he said.

So instead of a bespoke application that requires a large amount of data, IBM is focused on developing what they refer to as "broad AI," or models that can manage a wide variety of tasks simultaneously with much less information. That effort, however, will take decades, according to Raghavan. 

"We are making progress on it significantly," he told Business Insider. But "it's going to be a journey. We're talking about inventing brand-new techniques." 

Trust in AI remains a key problem

Companies are rushing to adopt artificial intelligence, but trust in the platforms is still a major problem. 

Mass amounts of data are fed into systems that can guide life-changing decisions for people, like whether you get brought in for an interview for your dream job. A rush of negative headlines has also raised concerns over how fair some of the algorithms are, an indicator in many cases of the lack of diverse data being used to power the AI tools.

IBM is trying to demystify the questions around the technology in a number of ways. But one problem remains in defining what a fair model is. To solve that issue, IBM introduced "AI Fairness 360," a library of algorithms that can be used to check whether a data set is biased. 

"You actually grow this culture of understanding AI biases. And as we all evolve, then eventually, maybe one day, it's not going to be a problem," Saska Mojsilovic, who heads the Foundations of Trusted AI group at IBM, told Business Insider. 

Read more: Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AI

Explaining the AI is also a challenge. Say a financial institution uses an algorithm to determine whether someone qualifies for a loan. If the application is denied, that company needs to be able to outline to the customer the reasoning behind the decision.

IBM recently introduced a tool kit known as "AI Explainability 360" that consists of algorithms, demos, and other resources, and provides insight into how models come to a final conclusion, including one that outlines which information was used to come to the decision. It also shows which features that, if they were present, would have reversed the choice. So if a loan application is denied, the algorithm could provide a route for a customer to improve their chances the next time.  

If you want to see IBM's AI capabilities, watch a major sporting event 

One technology in use is an AI-based program that automatically analyzes the sound of the crowd, the reaction of a player or players, and other factors to determine the most exciting moments of events like the Masters Tournament and the US Open. 

Even in sports, however, IBM thinks about how to make the model more fair.

One concern, for example, was how to adequately measure audience reaction on holes or courts where the crowd may not be as large as others. The team employed Watson OpenScale, a product that takes real-time feedback and adjusts AI models to make them more trustworthy. In golf, for example, the platform monitors the estimated crowd size and automatically reweights that category when considering the overall output. 

"It's a nice illustration of what it really means to have to monitor your models once they are in deployment," said John Smith, who heads the development of vision, speech, and language AI tools at IBM Research.  

IBM is experimenting with automated sports play-by-play commentary. The company is testing the product on past soccer matches because it "wanted a challenge," according to Smith. 

Once the model is successfully trained, the hope is it will able to ingest the raw footage and transform the raw pixels into language. It's a huge evolution from AI-based applications that can scan still images to determine the object and come up with a caption.

SEE ALSO: Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AI

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• Since retiring in September, Alibaba founder Jack Ma has revealed he's turning his focus on education and that he hopes to travel to different schools across the world next year.
• At a conference on Tuesday, the former English teacher-turned-billionaire said that the education system needs to change to teach children "how to be a human" in today's digital age.
• Ma expressed concern that the younger generation would be ill-equipped to survive the digital era.
• "Machines don't have hearts, machines only have chips. So, this is what I think: human beings should always learn to be wise," he said.
• Visit Business Insider's homepage for more stories.

Ask Jack Ma what he's been working on since retiring, and he will tell you about his plans to visit schools and teachers around the world next year.

The retired founder of Chinese giant Alibaba revealed during the Forbes CEO Conference in Singapore on Tuesday that he had already built up "a bit of a kindergarten,""a bit of a primary school," and a middle school as part of his retirement plans to return to teaching.

The former English teacher added that he had been working with teachers in rural areas for the past five years and that he had ideas he wanted to develop.

Read more:Chinese e-commerce giant Alibaba is coming to the US in a major way

To laughter from the audience, Ma — who failed his primary school, middle school, and university entrance exams — joked that he would never be hired by Alibaba today amid competition from Stanford and Harvard graduates.

More seriously, he expressed concern that the younger generation would be ill-equipped to survive the digital era if education systems did not change within the next 20 to 30 years.

Describing smart people as those who use their brains, Ma said he was "so proud" of Alibaba's intelligent employees, but said he thought it is infinitely preferable to be a wise person who uses their heart.

Read more: Alibaba, the $435 billion Chinese shopping giant, is gunning for Amazon in Europe

"Most smart people want to win. We want to make smart people learn how to live like a human, how to care about others. When smart people learn how to care about others, how to care about the future, how to be human, then a company becomes warm, [things go] smoothly, and [it has] soft power. Otherwise, you have a group of gangsters," he said.

"This is why I believe we need more wise people and leaders," Ma continued. "A smart machine will be always smarter than you are; a machine can never be wiser."

"Machines don't have hearts, machines only have chips. So, this is what I think: human beings should always learn to be wise."

The billionaire also suggested the education system be changed so that children are taught how to "be a human" in an era of artificial intelligence.

He identified skills like music, dancing, painting, and sports as "very important", as these are the kinds of artistic pursuits that engage people's hearts.

Read more: Chinese tech billionaire Jack Ma says it's a 'blessing' for his staff to work grueling 12-hour shifts, 6 days a week

Other key skills he said he considered essential were independent thinking, innovation, and creativity.

"When young people learn [about success] too much, they think they can easily succeed," Ma said. "Learn from the mistakes, don't avoid them. When you make the mistake, know how to face it, how to solve it, how to challenge it. This is called wisdom. This is what we should teach our kids."

"It is important," he said, to applause from the audience. "There are a lot of problems, but [there are] always solutions. We have to do it."

The Alibaba founder added that his focus was mainly on young people. "Trust the young people. I trust young people more than I trust senior people, successful people — because there is no expert of the future, there are only experts of yesterday," he said.

"Working with most of the successful people — they only talk about yesterday. We are entering into a century, into a world, that is so new. When you're working with young people, you're talking about the future."

SEE ALSO: Alibaba cofounder Jack Ma — the richest person in China — is retiring with a net worth of $38 billion. Here’s his incredible rags-to-riches story.

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• As with many other job functions, AI is changing how HR departments work. The technology can reduce bias in hiring and eliminate more mundane job tasks, but organizations can still be hesitant to adopt it over legal concerns.
• Now CareerBuilder is using AI to improve the quality of job postings and, ultimately, better match qualified candidates with open positions. The technology is also making it easier for candidates to create compelling resumes. 
• In one example of the potential of AI, CareerBuilder found that veterinary technicians could make good prison guards, an in-demand position. The applications also helped one of the US's largest investment banks double the amount of diversity within its staff — covering gender, age, and race. 
• Click here for more BI Prime content.

Job loss is one of the biggest fears that surrounds the adoption of artificial intelligence. But for CareerBuilder, the technology is actually helping to find people work.

As with many other job functions, AI is leading to massive changes in human-resources departments across corporate America. The technology can reduce bias in hiring and automate many of the more mundane job tasks. But there still remains a hesitancy to adopt it, given the strict set of employment laws that companies must adhere to and the opacity of how the AI actually comes to a decision.

At CareerBuilder, the employment platform is harnessing AI to reduce the number of vendors that HR departments typically work with on recruitment, improve the quality of job postings, and, ultimately, better match candidates to open positions.

That can even mean finding jobs that, at face value, would seem unrelated to a certain position but actually rely on many of the same skill sets, like veterinary technicians and prison guards. The company is also using the tech to help candidates craft better resumes and make it easier to apply for jobs on mobile devices.

"The HR industry is going through a change that it has never gone through before," CEO Irina Novoselsky told Business Insider. "We've actually brought the AI to the fingertips of recruiters."

CareerBuilder, which is known for its signature job platform but provides services such as background checks and candidate tracking, is relied on by 70% of the Fortune 500 companies, the company said, giving it immense insight into the top hiring challenges that firms face. Novoselsky outlined how AI would only improve CareerBuilder's ability to address those problems.

Reduction in vendors and applications, improvements in hiring

The historic metric of success in HR departments was getting the largest number of applications possible for an opening. That, theoretically, gave companies ample qualified candidates to choose from, according to Novoselsky. 

But that pool also required the typical recruiting team to sift through hundreds of resumes. And once they advanced applicants, the departments often had to work with as many as 15 different vendors in the hiring process, including for background checks and the onboarding process.

"Having a lot of volume was considered a good thing. Now, with the advent of AI and technology, HR functions are having to fundamentally change what they consider successful [to] the least amount of candidates that are the most qualified," Novoselsky said.

CareerBuilder is trying to replace all of that. It offers several applications that can help organizations reduce their catalog of vendors, including tracking the supply and demand for candidates in specific markets. And on the recruitment side, the company is using AI to aid companies in crafting better job postings. 

Read more: The head of IBM's Watson walks us through the exact model tech leaders can use to build excitement around any AI project

Businesses, for example, often seek tech employees who can code in Hadoop, an open-source-software platform, and Java, a common programming language. Some candidates, however, may list only one or the other on their resume, which could lead HR departments to disregard those applications. Using analytics, however, CareerBuilder found that 95% of people that list one or the other actually have the ability to code in both.

The technology can also analyze the job descriptions of existing employees to determine whether an internal candidate is better suited for an opening. There's also the added benefit of alerting companies to people they may have never considered for some roles.

There's a large demand, for example, for prison guards, according to Novoselsky. Using AI to analyze the skill sets that are required for the role, CareerBuilder found an unlikely match: veterinary technicians. Both need to exhibit skills like compassion and be able to remain calm in high-stress environments. 

"When you say a title, you paint already the image of what the person filling that role looks like," she said. "Removing that image and really doing it based on the underlying skill set" uncovers new possibilities.

Better job descriptions, better matches

The heart of the recruitment process is still the job posting.

It's the first thing many candidates see, and it can be the make-or-break moment between a qualified person applying for a role or moving along to the next opening. CareerBuilder is hoping to improve those postings, which can vary wildly, from highly detailed to overly generic.

Using AI, the company grades a job description on how a candidate would perceive it and provides recommendations on how to improve it, whether that be the need for more specific language, a change to gender-neutral pronouns, or alerts to wording that may be viewed as socioeconomically biased.

The technology can also notify organizations if they are seeking talent in regions where there aren't enough qualified candidates to meet the demands they are seeking.

"We know every single company across North America, and we know every single job they've ever posted," Novoselsky said. "We took all that data, and we created something that works on both sides."

The applications have added benefits aside from finding the most talented people for roles. CareerBuilder, for example, helped one of the US's largest investment banks double its amount of diversity company-wide, covering gender, age, and race. 

While AI is becoming more common, the technology is still just in its early stages. So while CareerBuilder is pursuing many initiatives now, it's likely that even more advanced applications are on the way. 

SEE ALSO: Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AI

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• John Meyer's upstart venture company is trying to upturn what he sees as a hidebound industry.
• With Starship Capital, Meyer aims to invest very early on in technologies and companies that have "exponential" potential.
• His strategy is to serve as an advisor before investing, helping companies with hiring and, more importantly, figuring out if there's a market for their products.
• Meyer, a serial entrepreneur who was early to the app revolution, has developed a way of using Facebook ads to figure out demand and customer-acquisition costs before startups even launch their products.
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The venture capital industry prides itself on finding and funding startups that can disrupt big markets. But if you ask John Meyer, it's the venture industry itself and its traditional way of launching new companies that's ripe for disruption.

Meyer, a serial entrepreneur who cashed in early on the smartphone app revolution by making some of the first simple apps in the iPhone App Store, set up his own early-stage venture firm called Starship Capital last year to test his theory.

The tech industry and broader society are on the cusp of seeing massive changes thanks to new technologies such as artificial intelligence, Meyer told Business Insider in a recent interview. The startups that help spearhead those changes are often going to need more help — financially and otherwise — earlier in their life cycles than young tech companies have typically required in the past, he said.

"The goal of Starship is to track down what these sort of exponential leap-forward technologies are, and, at sort of the earliest moment possible, be able to assist" the companies developing them, he said.

In this new era, it's going to be important for venture firms — especially those that are looking at really nascent technologies and companies — to be flexible, Meyer said. Instead of specializing in enterprise software, say, they're going to need to be able to invest in a range of different of different sectors.

Perhaps more importantly, they will need to have the flexibility to supersize very early investments to help new companies get off the ground, he said. Instead of needing just $500,000 or $1 million in seed funding, the next-generation startups may need as much as $5 million up front, he said.

Starship is designed to be nimble

Meyer structured Starship so that it can nimbly respond to such opportunities and needs. It's not restricted to investing in a particular sector. And the firm's design allows it to scale up investments as needed.

Instead of raising a discrete venture fund that makes a certain number of investments of around the same size size, Meyer secured some $25 million in what he calls "soft commitments" from investors. Whenever he finds a startup he wants to invest in, he can go to his backers, present the opportunity, and discuss with them the appropriate amount to invest, which may well be much more than what he could do with a standard fund. 

That model "allows us to be quite nimble with the deals we have access to," Meyer said.

Thus far, Starship has backed four startups. Of those, he only named one — BuildX, which aims to automate much of the commercial construction process. Another company Starship has backed, which Meyer declined to name, is developing an automated legal service that uses machine learning to help provide legal advice to those who can't afford a lawyer.

With each company Starship has funded, it started in an advisory capacity before making an investment, a strategy that Meyer plans to continue.

Indeed, that's another area where he thinks Starship will be different from more traditional venture firms. His aim is to work with founders and entrepreneurs who are less than six months into developing their business idea or startup.

"These companies are quite early stage and often times run by first-time founders that could use some additional expertise," he said.

Meyer's goal is to de-risk startups before they even launch products

When his firm takes an advisory role, he or his partner steps in and acts almost like an executive at the startup, helping out with hiring and fundraising. More importantly, they also try to help the founding team prove where there's a real market for their concept or, if not, help them quickly pivot to an idea for which there is a market.

"I've seen too many cases of startup founders [who] raise money for a company, get it off the ground, and then they realize they don't even really have much demand for that product to begin with," Meyer said. Starship's goal, he continued, is to "dramatically de-risk extremely early stage companies and assist them in getting to that phase where they can go and raise money from one of the tier-one or tier-two seed-stage funds.

A big part of how Starship helps companies — particularly those developing consumer-targeted products — prove that there's a market for their ideas is by taking advantage of Facebook's advertising platform. Even before its startup partners have launched product, Starship helps them create a brand and build a website. Then, it helps the startup advertise its potential product on the social media giant or on its Instagram subsidiary. Starship uses Facebook's tools to target ads at what are likely to be the most receptive consumers and then uses a cookie on the startups website to track the response to the ads.

With a budget of less than $1,000, Starship and the startup can get a very good idea of how much demand there is for the company's prospective product and its likely customer-acquisition costs. Because the process isn't very time consuming or costly, Starship and the startup can tweak the wording of the ads and the potential cost of the product to see how such changes affect demand.

"Literally within a week, [we can] get to a point where we can then decide whether or not to invest in this consumer startup in a way that is a much more confident decision and I think data-backed decision than most other early stage funds," Meyer said. 

Meyer's entrepreneurial experience informed Starship's strategy

Much of Meyer's ideas for Starship came from his own experiences as a entrepreneur. He started writing smartphone apps as a freshman in high school, right after Apple opened up its App Store, and soon had a thriving business creating relatively simple flashlight and photography apps. He then used his proceeds to found Fresco News, dropping out of college to build a company around the news-sharing app.

Read this: This 19-Year-Old Developer Is So Successful, He Turned Down Apple

Since then, he has been a founder-in-resident at Atomic, a startup incubator, and a Thiel Fellow, the name given to those who secure a fellowship from venture capitalist Peter Thiel's personal foundation — which carries the requirement that the recipient drop out of college or otherwise not attend. Last year, he cofounded Homebound, a company that uses digital technology to help speed up and reduce the cost of the homebuilding process.

Through his experience at Atomic, he got some ideas about how to help out really early-stage companies. And through his Thiel fellowship, he's made connections to other young entrepreneurs who are exploring cutting-edge ideas.

But the time he spent building apps was probably the most crucial, he's said. From that experience, he learned the importance of rapidly prototyping and iterating on products. He also during that period developed his ideas of using Facebook to glean demand. As the app market became more competitive, he needed to find a way to figure out if there was demand before investing a bunch of money in a new project; he discovered he could do that through Facebook.

"That was sort of the genesis of the strategy within Starship now," he said.

Got a tip about venture capital or startups? Contact this reporter via email at twolverton@businessinsider.com, message him on Twitter @troywolv, or send him a secure message through Signal at 415.515.5594. You can also contact Business Insider securely via SecureDrop.

• Read more about startups and venture capital:
• Here's why the founder of a beloved productivity app is refocusing his startup incubator on health from artificial intelligence
• An Austin startup used this pitch deck to raise $14 million to help real estate agents, plumbers, and other small businesses operators better market themselves online
• Here's the pitch deck that Silicon Valley startup Arrcus used to raise $30 million to take on Cisco and Arista Networks
• This VC's firm backed Fitbit and Ring. He says one of the keys to its success is focusing on founders' emotional intelligence, not just their IQ.

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• Robotic process automation, or RPA, tools allow businesses use to speed up processes by automating the patchwork of applications and systems in their networks.
• RPA software can perform basic data entry tasks, eliminate keying errors and even generate automatic email responses to customers.
• Here are 8 of the top robotic process automation companies, according to reviews from IT professionals on IT Central Station. 
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Businesses are trying to do things easier and faster with the help of software that can connect and automate the patchwork of applications and systems in their networks. 

These tools, called robotic process automation, or RPA, can speed up processes and eliminate keying and other errors — saving a business a lot of money. For example, RPA systems can take on basic data entry tasks or generate automated email responses to customers.

It is still a small market, with total revenue of roughly $850 million in 2018, according to analyst firm Gartner. But it is a fast growing market. Gartner estimated the RPA segment posted a year-over-growth of 63% in 2018.

IT Central Station, the review site where IT professionals are able to weigh in on the software they use, recently conducted a survey on RPA tools.

Here are the top robotic process automation companies, according to IT professionals on IT Central Station. 

UiPath

Rating: 4.5 out of 5

Ranking Score: 79

Most Compared To: Automation Anywhere (41%), Blue Prism (12%), Work Fusion (8%)

UiPath started in Bucharest, Romania in 2005 creating automation scripts. This was before robotic process automation was considered a market. In fact, the company says they realized the market potential of RPA only in 2012 after a customer told them, "We've been using your software to automate processes. Come take a look." UiPath is now based in New York. The company has raised $1 billion from investors, including Sequoia and Accel.

Automation Anywhere

Rating: 4.0 out of 5

Ranking Score: 67

Most Compared To: UiPath (61%), Blue Prism (8%), Pega Robotic Process Automation (3%)

Automation Anywhere, which is based in San Jose, was founded in 2003 and currently has more than 2,300 employees. It offers RPA tools mainly to companies, including enterprises, in financial services, insurance, health care, tech, manufacturing, telecom and logistics. The company has raised $550 million from investors, including SoftBank and Goldman Sachs, according to Crunchbase.

Blue Prism

Rating: 4.0 out of 5

Ranking Score: 37

Most Compared To: UiPath (35%), Automation Anywhere (16%), Pega Robotic Process Automation (11%)

Blue Prism, which is based in Warrington, UK, was founded in 2011 and has about 1,000 employees. Gartner said it was one of the first vendors to describe RPA as a market "having realized the broad potential of task automation." Blue Prism says it has more than 1,500 customers worldwide.

Kryon

Rating: 4.2 out of 5

Ranking Score: 35

Most Compared To: UiPath (52%), Blue Prism (15%), Automation Anywhere (9%)

Kryon was founded in 2008 and is based in Tel Aviv and New York City.  Gartner's report on robotic processing automation cited Kryon's "strong capabilities around process/task discovery … based on captured keystrokes, mouse clicks, data inputs and outputs of business users." Kryon has raised $53 million from investors including Vertex Ventures.

IBM

Rating: 4.2 out of 5

Ranking Score: 17

Most Compared To: UiPath (55%), Blue Prism (11%), Automation Anywhere (10%)

IBM, a major player in the enterprise software market, is significant vendor in what is still a small and emerging market like robotic process automation. Gene Chao, vice president of IBM's automation division, underscored the importance of RPA in a blog post, saying: "As machines are quickly learning to complete the repetitive and time-consuming tasks that take up much of our workdays, workers are being freed up to think more creatively and ambitiously about their jobs."

Kofax

Rating: 4.5 out of 5

Ranking Score: 15

Most Compared To: UiPath (40%), Blue Prism (19%), Automation Anywhere (10%)

Kofax was founded in 1985 as an enterprise software company focused on capturing and managing digital information. The Irvine, California-based company eventually expanded to robotic process automation. It has about 2,400 employees and more than 25,000 customers in different markets, including financial services, insurance, government, health care, and supply chain. Kofax is currently owned by the private equity firm Thomas Bravo.

WorkFusion

Rating: 4.0 out of 5

Ranking Score: 13

Most Compared To: UiPath (55%), Automation Anywhere (14%), Blue Prism (11%)

WorkFusion launched in 2012 as part of a research project of co-founders Max Yankelevich and Andrew Volkov at MIT's Computer Science and Artificial Intelligence Lab. Gartner says the company has "a strong product," but it is hampered by limited sales and marketing resources "compared with its bigger rivals." WorkFusion has raised $121 million from investors that include RTP Ventures and NGP Capital.

NICE Robotic Automation

Rating: 4.3 out of 5

Ranking Score: 13

Most Compared To: UiPath (48%), Blue Prism (22%), Automation Anywhere (14%)

NICE Robotic Automation, which is based in New Jersey, is known for software that helps businesses manage the way it engages with its employees. It covers such tasks as onboarding and recruitment, time management and ways to make sure employee issues are tracked and addressed. Gartner says the company has solid customer support and a "simple, flexible, all-inclusive pricing model." 

Here's IT Central Station's ranking criteria, for the curious:

• Companies are accelerating efforts to create "innovation cultures" internally to, among other things, find out ways to use advanced technology, including artificial intelligence, to overhaul operations like recruitment. 
• Comcast is no different. The telecommunications giant relies on three different programs to figure out ways to improve customer service, Chief Information Officer Rick Rioboli recently told the "Technovation" podcast.
• Among the AI-based applications already in use at Comcast is a system that relies on voice recognition and chatbots to match customer calls or texts with stored data to personalize answers to questions or concerns. 
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Large companies are in a rush to create "innovation cultures" to stave off new competition and find ways to use advanced technology, like artificial intelligence, to improve operations.

The people leading those efforts are increasingly the chief technology, information, and data officers, who are emerging from the shadows of the IT department and gaining new power internally as a result of the digital overhauls.

At the telecommunications giant Comcast — a company with over $94 billion in annual revenue— Chief Information Officer Rick Rioboli oversees a tech team that has gone from "order takers" to one that builds applications that are used across the organization. While he says most people link e-commerce and digital tightly together, Comcast is homing in on how to improve the customer-service function.

"There's a lot of those micro-interactions that we've now turned into digital that make the customer experience much more enjoyable than having to pick up the phone," he recently told the "Technovation" podcast.

Rioboli, for example, led the team that created the "voice-remote" feature, which functions like Apple's "Siri" and allows customers to control their TV by speaking into the remote control. And the company is continually seeking ways to improve those operations.

Ultimately, the three-part model employed by Comcast is just one system that other corporate behemoths, and smaller companies alike, can use to drive innovation.

Using innovation arms to inform the AI future

Comcast built its innovation efforts around three arms: a venture fund, a Silicon Valley-based research and development team, and an internal accelerator known as Lift Labs. Such a setup is increasingly common. Firms like Stanley Black & Decker, for example, also rely on similar teams to drive new product development.

As the head of IT, Rioboli partners with all three to, among other things, figure out how AI can be used to enhance the customer-service function. He advises Comcast Ventures, for example, on which startups or early-stage companies are worth investing in. But the benefit flows both ways. "By hearing from them what trends they see in technology, [it] really helps me understand where things are going," Rioboli said.

Comcast Labs, the R&D wing, is under the cable division. The goal is to look out as far as 10 years to figure out the most promising technology and begin to prototype it internally. That helps the IT team align its strategy to the future needs of the business.

Read more: The head of IBM's Watson walks us through the exact model tech leaders can use to build excitement around any AI project

For Lift Labs, which launched in 2017, the company partners with Techstars, a well-known startup accelerator, to find 12 early-stage firms and work with them over 13 weeks to help grow their business. At the end of the program, some startups will exit the accelerator and others will become Comcast customers. Among those in the 2019 class that are working with Comcast now is GameOn, a company that uses athletic stats and gambling odds to create prediction games for sporting events. 

Through those relationships, Comcast is figuring out how best to turn the underlying AI technology into customer-facing applications.

In one instance in use, Comcast is using AI and machine learning to use voice recognition and chatbots to match customer calls or texts with stored data to personalize answers to questions or concerns, a program known as the "Xfinity System."

Regardless of the medium — whether a customer calls and talks to agent, visits a retail store, or accesses their account online — all the interactions are fed to a "common brain," according to Rioboli. It's application like this that, as technology like AI advances, will allow Comcast to get even better at predicting and solving consumer issues.

SEE ALSO: 77% of internal-innovation efforts fail. Here's a 4-step checklist for ensuring that doesn't happen to your company.

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• Companies can have a difficult time separating out the hype around AI from the actual benefits the technology can have for their operations.
• The journey to adopt AI internally, however, is one that can take as long as 10 years. That's why Simon Moss, the global head of AI and automation at the consulting firm Infosys, says companies need to start now.
• The first step should be a small, low-risk move. But as the efforts scale, it's imperative that organizations form a central entity to manage all the AI projects, Moss said. 
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Interest in artificial intelligence is at a fever pitch, but it can be difficult for corporations to determine whether it's all hype or if the advanced tech can actually improve operations.

While a healthy amount of cynicism remains around the technology, it's imperative that organizations begin to think about incorporating it now — especially because the journey can take as long as 10 years, according to Simon Moss, the global head of AI and automation at the consulting firm Infosys.

"The decisions around a cluster of separate but deeply related technology innovations are existential to whether a firm will be strategically successful or not over the next decade," he told Business Insider. "It's a huge challenge. Not a wave, but a tide as impactful as the internet, transforming the very DNA of the enterprise."

Companies are already using AI in a number of different ways, including to help figure out whether store shelves need to be restocked or to cut back the number of applications that human-resources departments must review for open positions.

Still, the vast amount of AI efforts fail. That means, to avoid the pitfalls, executives need to focus more heavily on answering a key question: How do I get started?

Infosys is an organization with over 228,000 employees that has helped companies like Pfizer and the e-commerce firm Radial Inc. on their digital journeys. At the firm, Moss has been spending ample time helping companies overcome that challenge.

He shared the three steps companies should take to bypass the hype and start their AI journey off on solid footing. 

Determine the end goal before starting

What companies are hoping to get out of AI is likely to vary not only by organization but also within various business units. That can make figuring out the overarching goals more difficult.

For some, it may be achieving operational efficiency in supply chains. For others, like retail companies, it can be better understanding the end consumer to enhance marketing efforts and tailor promotional offers.

It's a critical part of the foundation of AI-based projects because it helps inform other questions, including the state of the digital infrastructure and whether the initiative can succeed. Without the right stored data, for example, it can be impossible to power the applications.

Begin with 'very small projects' that can be easily measured

Gone are the days of sweeping enterprisewide projects that can cost upward of hundreds of millions of dollars. Instead, like other consultants, Moss advises companies to start small.

Organizations, for example, could relatively easily develop and use technology like natural language processing to understand, organize, and consume vast amounts of documents. Or they could employ robotic process automation (RPA) in sectors like customer service that can, among other things, help agents quickly compile information on those calling in.

It's a common area for companies to start their AI journey on. In fact, Gartner has estimated that spending on RPA could top $2.4 billion in the next three years. The key, according to Moss, is choosing a low-risk area that can produce a quick return on investment.

"That gives a client a sense of confidence they can control their destiny, and frankly control their careers, that they're not signing up for a $100 million system-integration project," he said.

As employees become more comfortable with the technology, Moss says executives can begin scaling those initially rudimentary projects to incorporate deeper machine learning and drive a higher value.

Create an AI 'center of excellence'

Often, the various business units within a company will be testing out different applications.

Human resources, for example, could be using AI-based tools to cut down on the number of applications they review per open position. Meanwhile, store operations might be tapping into the technology to try to reduce shoplifting incidents.

That's why having a central entity to manage all those projects is critical.

Organizations need a "marketplace that allows a common philosophy, that allows common security — particularly around personal identity information — that puts in a framework of scale and resiliency around design, and begins to impose a common philosophy around what is a huge amount of intrepreneurial endeavors," Moss said.

One major benefit is to cut down on the number of bespoke efforts. Instead of different departments hiring their own data scientists to pursue AI-based endeavors, a "center of excellence" can flag any duplication and find ways for platforms to be used across business units.

Read more: The head of IBM's Watson walks us through the exact model tech leaders can use to build excitement around any AI project

The involvement of such an entity, however, will vary by organization. Those just starting out on their journey, for example, might just use it as a project database to keep other leaders informed. More established companies might employ a center to ensure all initiatives align with a core philosophy that defines and guides all AI-based efforts.

One thing to avoid is placing too many protocols around how the entity operates, Moss said.

"You don't want to suffocate that with too much bureaucracy and administration," he said. "That's a balance between discipline, rigor, and innovation that is something that cannot be designed as a cookie cutter. It has to be specific to the culture of the customer."

Bypassing the hype and starting on an AI journey can be difficult. But these three steps can serve as a key launchpad to help organizations at least start to formulate a strategy.

SEE ALSO: Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AI

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• A new study in the journal Science just found a widely-used algorithm gave more complex treatment to white patients than sicker black patients.
• The study's findings point to one of the many risks to implementing more AI in healthcare. Business Insider Intelligence predicts that spending on healthcare AI is projected to grow at an annualized 48% between 2017 and 2023. 
• Algorithms also strip doctors and nurses from the autonomy to diagnose and treat patients individually.
• Visit Business Insider's homepage for more stories.

Technology could likely be making healthcare more racially biased, a new study finds.

One algorithm that identifies which patients would benefit from complex health procedures favored treating white patients than sicker black ones between 2013 and 2015. 

The algorithm predicted black patients would cost less, which signalled to medical providers that their illnesses must not be that bad. But, in reality, black patients cost less because they don't purchase healthcare services as much as white people on average. 

The study, published in the prestigious journal Science, stated black patients don't seek out healthcare due to a lack of access and a general mistrust in the system. Facing more barriers to accessing healthcare, in turn, indirectly drives down the projected "cost" of illness in black patients.

Health systems use this algorithm on 200 million people each year, the report states. If the algorithm were to eliminate the racial bias, black patients who receive additional help would increase from 17.7% to 46.5%, the report states.

The trouble with algorithmic healthcare

AI and algorithms are on the rise in the health industry. Business Insider Intelligence predicts that spending on healthcare AI is projected to grow at an annualized 48% between 2017 and 2023. 

Yet experts and researchers have long called out the bias algorithms can perpetuate. Amazon built a hiring tool that discriminated against women. Tweets from black people were more likely to be dubbed "toxic" in a Google-funded AI tool. Facial recognition tools used by the US government have been shown to misidentify black people much more often than white people.

Another reason is that algorithms use data that historically perpetuated racial inequality, like zip codes. In the 1930s, the US explicitly segregated African-American neighborhoods from white ones through policies known as "redlining." Today, these policies have lasting impact on racial makeup on zip codes — which means using these zip codes in algorithms perpetuates racial inequality. 

Algorithms, plus other technology like difficult-to-use electronic medical recording systems, are a factor contributing to doctor and nurse burnout.

Gerard Brogan, a registered nurse and the director of nursing practice National Nurses United and their California branch, says algorithm takes autonomy from clinicians. While algorithms take the average of patient outcomes to find treatment, most nurses and doctors prefer to provide treatment tailored to each individual.

"Traditionally, both nurses and doctors are independent professionals, but because it's now an industry, we're looking at care where algorithms are dictating care rather than professional judgment," Brogan said. "Bill Gates a few years ago said in 15 years time there will be nurses, there will be no doctors, because no one can out-think a computer," Brogan said. "Algorithms may beat people at chess, but they don't hold peoples' hands."

SEE ALSO: Half of all US nurses and doctors are burned out — and they say the healthcare system is to blame

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• Google believes quantum computing could eventually be used to solve large-scale problems that "would otherwise be impossible," an engineer in the company's quantum research lab recently said to The Financial Times.
• The comments come after Google announced a breakthrough in quantum computing on Wednesday — a field that enables computers to perform exponentially faster than traditional machines.
• Google designed an experimental processor that could process computations in 200 seconds that would take today's fastest supercomputers 10,000 years to achieve, the company says.
• Companies like IBM and Microsoft are working to advance the field as well.
• Visit Business Insider's homepage for more stories. 

Google researchers working on the company's quantum computing project have provided more insight about how such an achievement could be applied in real-world scenarios, saying to The Financial Times that it could help address issues on a global scale.

"We're looking forward to giving humanity a new tool for solving what would otherwise be impossible problems," Erik Lucero, a hardware engineer at Google's quantum research lab, said to The Financial Times.

Scientists at Google said earlier this week that the company had made a massive leap forward in quantum computing— a cutting-edge field that will enable computers to perform exponentially faster than today's machines. Examples of issues that quantum computing could eventually help tackle include assisting in the development of new drugs and enabling more efficient distribution of limited resources.

One such use case for quantum computing could entail using nitrogen from the atmosphere to create fertilizer, The Financial Times reports. The company eventually plans to build a large-scale quantum computing machine that could significantly push artificial intelligence forward, the report also said. 

Google on Wednesday revealed its leap forward in quantum computing in a paper published in the journal Nature. As part of an experiment, the company developed a processor that was able to perform a computation in just 200 seconds that would take the world's fastest supercomputers 10,000 years to achieve, Google said.

The experiment was part of an effort on Google's part to determine whether quantum computing will be useful and worth investing in, the firm wrote on its artificial intelligence blog announcing the accomplishment.

Quantum computers can process data at an exponentially faster rate because the way in which they perform computations is different than that of traditional computers.

While today's computers process data in zeros and ones, quantum computers use "Qubits," which makes it possible for zeroes and ones to exist simultaneously. This enables quantum computers to carry out more computations at once. 

Google is also currently researching how quantum computing can be used to further artificial intelligence, a burgeoning field that the search giant has been investing in for years.

"It stands to reason this could be a very valuable resource for machine learning," Google's Hartmut Neven told The Financial Times. "We are playing around with this."

Competition in quantum computing

Quantum computing is largely still in the research stage, but companies such as IBM, as well as Google, are moving quickly to advance the technology and eventually bring it to the masses.

IBM announced its IBM Q System One in January, which is positioned as being the first quantum gate model computer that businesses could actually use. IBM also opened the IBM Quantum Computation Center in New York in September, where clients can explore practical applications for the technology.

Microsoft, too, is delving into quantum computing, as the company debuted its Quantum Network earlier this year — a collection of partners that will work with the company to advance the field. 

James Canton, CEO and chairman of the Institute for Global Futures, a San Francisco-based think tank that advises clients on upcoming technology trends, says that quantum computing could vastly improve cybersecurity and the way we process data.

"You're talking about a multidimensional and entirely new paradigm of super-fast, super-small super-intelligent machines," Canton said when discussing quantum computing in a previous interview with Business Insider. 

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• Alongside nontraditional tech firms like Walmart, industry behemoths like IBM and Microsoft have spent the past decade pivoting from their hardware and software product offerings to more cutting-edge technology like artificial intelligence.
• The shift, however, can be more difficult for those legacy tech firms, argues Inderpal Bhandari, IBM's global chief data officer. 
• While the in-house tech talent makes it easier to form teams around AI-based efforts, many employees can be stuck in the ways of the past, making the necessary cultural shift more difficult, he told Business Insider. 
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Legacy tech giants like Microsoft and IBM have spent the last decade pivoting from the bread-and-butter software and hardware that once defined their product offerings to more cutting-edge technology like cloud computing and artificial intelligence.

Doing so, however, often requires a major cultural shift internally — and not just at newly software-heavy organizations like Walmart. While it may seem counterintuitive, that shift can actually be harder in the historically technology-forward companies like IBM, according to Inderpal Bhandari, the company's global chief data officer. 

"In a tech company, people are also used to doing tech things in a set way. And if you try out the change, that's going to be tough," he told Business Insider. "In a nontechnical company, you come in, it's like you pretty much have a clean slate to start out."

Bhandari knows from experience. Appointed to chief data officer of pharmacy services provider Medco in 2006, he was one of the first individuals to hold the now increasingly common role.

Since then, he's worked in the position at pharmacy benefit manager Express Scripts, which acquired Medco in 2012, and Cambia Health Solutions — giving him a wide amount of experience leading organizations through their digital overhauls. 

Operating as the 'change agent in chief'

IBM is pivoting aggressively to artificial intelligence. A big part of that is changing the culture internally, a job that falls to Bhandari and other tech leaders.

"If you actually had to ask me what is the shortest description of my job, it would be 'change agent in chief,'" he said. "Many CDOs will walk in totally proficient with the technical aspects of the job, but they won't understand that their role is really to be a change agent. And that will then lead them to great difficulties." 

A key way to overcome those obstacles is building the right team, one aspect that Bhandari says is easier to do in a legacy technology company.

"In the case of a non-tech company, you would probably have to build much more of the team using external talent and bringing people in," he said. "In the case of a company like IBM, you don't need that much by way of external talent, maybe three or four leaders, and then the rest you're able to attract internally."

At IBM, Bhandari uses an "adoption and value creation" team to encourage cultural change from the bottom up. The group's mission is to push AI adoption across business unit by explaining the advantages, including faster scale at less cost.

"They're empowered, if they find a like-minded crew in another business and they both want to do something, they can go off and do something," he said.

The hurdles that IBM must overcome on its own AI path shows just how difficult a shift it is, regardless of what industry the organization is in. Luckily, Business Insider has insight from experts that can help guide companies on their journey.

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• McDonald's is one of a slew of consumer-facing companies that are trying to harness artificial intelligence and machine learning to better understand customers.
• The fast-food chain is, among other things, testing out how AI can be used to scan license plates so McDonald's knows a customer's profile before they order in the drive-thru. 
• The company is spending big on the efforts, including purchasing two tech-forward startups, launching a new Silicon Valley-based tech team, and hiring for new positions that focus on AI.

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McDonald's is turning to artificial intelligence and machine learning to anticipate what you plan to eat before you even place an order.

It's part of a move underway across corporate America to better understand consumer behavior to tailor offerings to improve operational efficiencies, cut down on costs, and spur more sales.

At McDonald's, technology like scanning a license plate in the drive-thru to know the customer profile is part of an attempt by the fast-food chain to increase sales of items like Big Macs and Chicken McNuggets. Like other companies, the goal is to mirror the ability of Amazon to deeply know its consumers and adjust interactions with them accordingly. 

"You just grow to expect that in other parts of your life. Why should it be different when you're ordering at McDonald's?" chief information officer Daniel Henry told the New York Times. "We don't think food should be any different than what you buy on Amazon."

Currently vacant positions help illuminate McDonald's ambitions in this area. The company is hiring for, among other things, a senior director to help lead the development of new tech offerings for the drive-thru, a communication change manager to help push the adoption of digital internally, and a senior manager of technology innovation to support the development of new applications.

McDonald's declined an interview request for this article. The company also declined to outline how many tech-focused positions it plans to hire for or the amount of total investment it expects to spend on digital. 

To date, McDonald's appears not to have seen a material impact from its tech investments. Revenue and profits fell below Wall Street expectations during the most recent quarter.

But such initiatives often take as long as a decade to manifest, and the projects the chain is implementing now will define the future of how customers order their favorite items.

Pushing technology at 'a speed that it's never gone before' 

CEO Steve Easterbrook made modernizing in-store technology a key focus of his strategy when he came onboard in 2015. And it had to happen fast: he'd promised investors a quick turnaround.

"Technology needed to go at a speed that it's never gone before," former-chief information officer Frank Liberio said previously. "I remember him talking to me saying, 'Frank, I've never not lived up to a commitment to Wall Street, so this is something you have to figure out how to get done.'"

One of the first initiatives for Liberio was the development of the McDonald's app and scaling its delivery partnership with Uber Eats and other services. The company, however, is on to much more advanced applications. And to support that push, it's spending big. 

McDonald's, for example, purchased AI company Dynamic Yield for $300 million. Technology from the firm that helps cater recommendations to customers based on the time of day or weather is now in over 9,500 US locations and will be in all stores by the end of 2019, Easterbrook recently told investors.

The company also bought voice-technology startup Apprente. The chain is using that acquisition to help enhance its voice recognition capabilities, a sign of where the ordering process is heading. The senior director of drive-thru that McDonald's is hiring for, for example, would be tasked with, among other things, creating products that cover "voice order taking." 

To house all the innovation efforts, McDonald's launched what it calls "McD Tech Labs," a Silicon Valley-based team of engineers and data scientists. It's part of a trend in corporate America to launch separate "innovation hubs" within the organization that is tasked with developing and scaling new products.

For many consumers, the offerings that companies like McDonald's or Walmart roll out will be their first experience with AI and machine learning. That makes those efforts even more important, as they have the opportunity to differentiate the organizations over the next decade or undermine overall sales growth if the initiatives are rejected by customers.

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• Facial recognition has made headlines this year for the rapid rise in companies and government agencies using it for tracking and surveillance — but it's not the only AI-driven surveillance technology.
• Emerging technologies can recognize humans and track people's location by detecting their heartbeat, walking gait, and even microbial traces left behind by skin cells or sweat.
• More far-flung ideas formulated by researchers include a device to detect emotions using radio waves and a biometric car seat with butt-detection software.
• While some of the technologies have only surfaced in academic research, others are already being implemented by global military powers like the US and China.
• Visit Business Insider's homepage for more stories.

For private companies and government agencies trying to track peoples' movements, technology is making the task increasingly easy.

Facial recognition and analysis are becoming increasingly popular surveillance tools — the technology was rolled out in airports across the world this summer as a tool for verifying flyers' identity, and is widely used by police departments for tracking suspected criminals.

Privacy-minded activists and lawmakers are now hitting back at facial recognition. The technology has been banned for law-enforcement purposes across California, and a similar bill is being weighed in Massachusetts. Meanwhile, artists and researchers have begun to develop clothes designed to thwart algorithms that detect human faces.

But emerging technology presents alternate means of identifying and tracking humans beyond facial recognition. These methods, also driven by artificial intelligence, detect the presence of humans using devices ranging from lasers to WiFi networks.

The vast range of biometric data that technology can register makes regulation difficult. Meanwhile, some of the emerging surveillance technology is already being embraced by military powers like the US and China. 

Here's a rundown of emerging technology that can detect humans and track their location.

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Gait recognition identifies humans in video footage by detecting their stride, and the software is already being used by the Chinese government to monitor people.

Gait recognition analyzes how people move as they walk, including stride length and the angle of their arms. Watrix, a Chinese company that has developed a version of the software being piloted by Chinese police, claims it is 94% accurate.

The Pentagon is piloting gait-recognition tech that identifies people's walk based on the movements of their smartphone.

In a different iteration of gait recognition, the Pentagon is reportedly testing software that would enable a smartphone to identify who's carrying it based on their walking pattern, according to the Washington Post. The technology could theoretically be used to quickly deactivate government agents' smartphones if they are stolen.

Researchers are also developing gait-recognition tech that could identify pedestrians using sensors in the floor.

Researchers at the University of Manchester developed software that can use "floor-only sensor data" to identify specific people based on the rhythm of their walk without the aid of any visuals.

The Pentagon has commissioned a laser that can identify people by their heartbeat from 200 yards away.

The device, known as the Jetson, uses laser vibrometry to detect "surface movement" generated by a person's heartbeat, according to the MIT Technology Review. The laser works through clothes, and can match heartbeats to a database of "cardiac signatures" to identify individuals.

Researchers developed a technique to turn home WiFi devices like Amazon Echo and Google Nest into "adversarial motion sensors."

In a paper entitled "Et Tu Alexa?," researchers from the University of Chicago and University of California at Santa Barbara wrote that hackers can easily use WiFi devices in peoples' homes to detect when humans are moving around based on WiFi interference. The findings mean WiFi can be used as a surveillance tool to detect whether someone is physically located inside a specific structure. However, the detection technology is not capable of distinguishing between different humans, or even between humans and large animals.

An MIT researcher suggests that WiFi signals could be combined with heartbeat detection and AI to remotely track people's emotions.

The technology is still mostly theoretical, but an MIT researcher developed a concept for artificial intelligence software that can track users' motions and heart rate using wireless sensors and predict their emotional state accordingly. 

Algorithms could track people using the 36 million microbial cells per hour that each human emits.

Algorithms could potentially be turned towards tracking microbial cells — bacteria that lives inside our bodies — that people constantly emit, according to Wired. A 2015 study found that the microbial cells can be effectively used to identify specific individuals with up to 80% confidence.

Scientists even developed a prototype for a biometric car seat capable of butt-recognition.

Tokyo-based engineers developed a car seat that recognizes the shape of users' rear and their weight, according to Wired. The technology is theoretically meant to prevent car theft, but has not yet hit the market — however, it illustrates the sheer range of biometric data that can potentially be gauged by artificial intelligence.

• The rise of artificial intelligence is giving companies capabilities they've never had before to automate job tasks, recruit top talent, and better know their customers, among other applications. But adopting the advanced tech is not an easy shift, and many efforts still fail.
• Business Insider is exploring how companies can successfully implement the tech without succumbing to the roadblocks that stymie so many efforts.
• These reports can help prepare executives as they ready themselves to lead the AI-efforts, as well as provide guidance for getting the enterprise onboard. 
• Business Insider regularly interviews executives about their company's AI efforts. You can read them all by subscribing to BI Prime.

Companies across corporate America are harnessing artificial intelligence and machine learning to, among other things, automate the more mundane-aspects of jobs, help to recruit top talent, and know more about customers to tailor promotional offers or product recommendations. 

But the move to adopt the advanced technology comes with significant challenges and often requires a major cultural shift. It's one reason why many AI-based projects still fail.

Business Insider is exploring how companies can successfully implement the tech without succumbing to the roadblocks that stymie so many efforts.

Using AI to predict fast food orders:All the ways McDonald's is using AI to learn what you'll order — sometimes even before you know what you'll want

Why tech companies struggle with AI culture:IBM's global chief data officer explains why changing culture to support AI is harder in a traditional tech company — and one way it's easier

How to kickstart your AI effort successfully:Here are the 3 steps companies that are just pivoting to AI should take to guarantee the process starts on a solid footing

Using AI to find better talent:CareerBuilder used artificial intelligence to figure out veterinary technicians could make good prison guards. It's just one way the hiring platform is employing the technology to revolutionize HR.

Learning from an AI leader:I spent a day at IBM's mysterious research hub north of NYC, where I met some of the top AI leaders in the country. Here are 4 takeaways on where they think the tech is headed.

How Walmart evaluates AI projects:Walmart has 1,500 data scientists and is hiring more amid a push to adopt artificial intelligence. The retailer's chief data officer recently shared the 3 questions that guide all its AI projects.

Regulating AI is still a key challenge:There are no laws regulating the use of AI in the hiring process, and it's setting back how companies recruit. Here are the people trying to change that.

What to weigh before pouring resources into AI:Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AI

Using employees as AI testers:Walmart has cracked the code for merging AI rollouts with employee feedback to produce buzzy (and cost-saving) new tech

Using AI to improve retail operations:'It's the art of the possible': How Walmart and Target are harnessing AI to rocket past the competition

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• In his new role as chief technology officer of Moda Operandi, Arpan Nanavati is hoping to use consumer data to solve one of the industry's core problems: understanding fashion taste.
• The role requires Nanavati to figure out how to better personalize recommendations for customers and deliver products more quickly, both of which are underscored by advanced technology like machine learning.
• Nanavati pushes a culture of "extreme ownership" under which employees feel empowered to make decisions without explicit approval from management. 
• Click here for more BI Prime stories.

At face value, it may seem that groceries and luxury fashion have nothing in common. But for Arpan Nanavati, the opportunity to take the digital skills he honed at Walmart and try to disrupt the fashion industry at Moda Operandi was a no brainer.

While they are ultimately peddling different products, the job of executives in both sectors is anticipating and managing customer expectations. More commonly, those two tasks are achieved through technology like machine learning and artificial intelligence. So when Nanavati came on as chief technology officer at Moda, he knew a key step to solving a pressing problem in the fashion industry was harnessing consumer information. 

"Tech is driving that strategy to optimize and monetize the data," he told Business Insider. The core problems, "which have not been solved before, is how we can leverage data and tech to understand fashion taste."

Nanavati previously served as the director of engineering at Walmart Labs, where he managed the company's online grocery platform. Now at Moda, he plans to use the customer-centric mindset he honed at the retail behemoth's technology arm to create personalized shopping experiences. He'll focus on tackling two key challenges in his new role: how to better match customers with fashion recommendations and deliver that product in the quickest way. Underscoring both of these areas is technology, which makes the role of chief technology officer even more critical.

Moda allows customers to purchase clothes shown in runway shows directly from the designers, a shift from the historic paradigm in which major retailers like Barneys would decide which items to put on the store floor.

In his first interview since joining, Nanavati shared what he believes are the top tasks of chief technology officers and why a culture of "extreme ownership" is critical to achieving them.

Hire the right people and localize decision-making

Increasingly, technology is becoming a more central part of the organization as industries like financial services and retail pursue digital overhauls. But making it a priority comes with its own challenges, particularly for industries that have traditionally viewed IT as an outpost to solve issues like computer problems. 

"Tech-driven strategy is very common when it's a tech company," Nanavati said. That's different from consumer-focused companies like retailers, where tech goes from being an "institutional arm to being a strategy arm." 

To help navigate that shift, Nanavati tries to instill a culture of "extreme ownership" in his team. That means thinking about problems and solutions from the lens of the business owner and, in Moda's case, taking an approach that many software engineers and other tech workers may not be used to.

"In our case, it is truly cross-functional because you have to be able to understand the fashion designers, your merchandising team, and not just have an engineering mindset," he said. "You have to think like an owner, and you have to be able to connect the dots from the consumer perspective and the business-owner perspective."

Marrying engineering prowess with customer insights

An increasing barrier to digital-transformation efforts is teaching employees with traditional tech backgrounds to use what is often anecdotal evidence — like a desire among customers for quicker shipping — from other business units to develop solutions.

And the impediment can spell doom for digital efforts. A recent survey of companies that pursued major tech initiatives found that just 14% of the respondents said their attempts resulted in sustained performance improvement. Culture remains a key reason for the struggle.

To try to overcome those issues, Moda localizes decision-making. The company forms what it calls "squads," or small teams that include representatives from different parts of the business, including sales and IT. Those groups physically sit together in the office and are empowered to make decisions and act on them without explicit buy-in from the brass.

Companies will take different approaches to changing the culture to one focused on big data and advanced tech. Ultimately, however, executives need to find ways to marry the expertise of the engineering team with the customer insight that is often siloed in other parts of the business to craft solutions that actually improve the costumer experience. 

SEE ALSO: Rise of the CTO: What social media startup Tsu's latest hire says about a changing power shift in the C-suite

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• Chief technology, data, and innovation officers are often managing enterprise-wide digital upgrade efforts.
• To succeed, the leaders need tech chiefs within respective business units — like logistics or human resources — to implement the overall digital strategy, argues IBM's Rob Thomas, a model he refers to as "hub-and-spoke." 
• But overcoming cultural barriers within organizations remains a challenge in adopting artificial intelligence and other advanced tech. One way executives can spur excitement around the platforms is by running and participating in internal AI contests.
• Click here for more BI Prime stories.

Technology leaders are increasingly spearheading efforts at top companies to push a new digital-first agenda. But often, they can't do it alone and need counterparts within specific business units to help advance the strategy.

Despite efforts to adopt artificial intelligence, machine learning, and other data-heavy initiatives, many projects still fail. One key reason is the difficulty in changing the company culture. Often, sectors like the IT department and sales are not used to coordinating with each other. But breaking down those silos is key to advancing applications that actually serve the customer or the organization and lead to cost-cutting or new revenue generation.

One way to ensure projects advance is to appoint leaders within each respective business unit to help support the chief technology, data, or innovation officers, argues IBM's Rob Thomas, a system he refers to as the "hub-and-spoke" model because the structure resembles one in which a central point is connected to several secondary points.

"You need somebody that has a seat at the table at the top that's saying it's important to the company," he told Business Insider. Organizations also "need somebody in those business units that owns this day-to-day, but is accountable back to the company strategy."

It's the model Thomas uses at IBM, where he is the general manager of data and Watson artificial intelligence — a role that gives him authority over investments, sales and marketing, and product development relating to the company's signature AI product. 

Direction at the top, execution at the bottom

Under Thomas' model, the leader at the top — which could be a chief technology, data, or innovation officer — works with the CEO to figure out what areas of the business are best suited for a tech overhaul, whether that be supply chain, talent acquisition, or another sector.

Once the strategy is established, the tech leads within each unit are responsible for experimenting with applications based upon the data they have available to meet that goal. It's a way to solve the problem of top-down mandates.

Such a system doesn't work, argues Thomas, since often the C-suite is unfamiliar with exactly what information the sectors have stored. Data is needed to power the AI-based applications, and without intimate knowledge of what is available, it is difficult to determine what ideas can actually be executed on. 

Technology chiefs have to empower unit heads to "go figure out which use cases you want to target under the umbrella of the strategy and the standards that were set," he said.

Changing the culture for AI: Without support it's 'just a science project'

While investments in tech upgrades sometimes total in the tens of billions of dollars, many projects still fail as a result of resistance within the workforce. That's why it's so imperative for executives to take steps to change the internal culture.

One way to get staff more engaged is to host an "AI challenge," or a contest where employees can submit their best ideas and judges choose a winner. It's a tactic Thomas employs and one he argues every CEO should pursue.

"The fact that I reviewed submissions, the fact that I was a judge as people were presenting, all those things indicate a level of importance," he said. "It shows you care."

Read more: Large CPG companies are under tremendous pressure to keep up with the pace of innovation. M&M's and Snickers maker Mars is investing in 2 accelerator programs to stay ahead.

Another is to pair chief data officers or other tech leaders within business units with other AI advocates on the operational-side of the organization. In a manufacturing firm, for example, that could be the supply chain lead. At a financial services firm, it could be the retail banking manager. That champion can help push projects with coworkers who may not have the technical chops to build the product, but have a vested interest in the outcome — like reducing costs or driving profits.

"AI projects happen when the business team comes together with the technology team," said Thomas. I've never seen "a pure technical project that's led to a meaningful business outcome because there's no context to it. It's just a science project."

Organizations should also celebrate the small victories to empower employees to try ideas out themselves. Many companies, argues Thomas, don't even realize the successes they have. "Success begets more risk-taking," he said. "It starts to snowball in a positive way."

Given the large investments behind many digital transformation efforts, it's imperative companies take into consideration the internal reporting structures and the roles that will lead the initiative. Doing so can help mitigate potential problems like cultural resistance. 

SEE ALSO: Accenture's head of artificial intelligence shares the 4-step plan every company should consider before investing in AI

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• On Wednesday, the work planning application Trello announced that it has reached 50 million registered users.
• When Trello became part of Atlassian in 2017 in a $425 million deal, it had 19 million registered users — suggesting that joining the Australian software giant has only made it more popular.
• Trello also announced that the automation tool Butler will be part of its core product, and it introduced a new feature called Templates. Both features are designed to mitigate workplace anxiety, the team says.
• We talked to Trello CEO Michael Pryor about where the push for automation comes from.
• Click here for more BI Prime stories.

Not every software acquisition works out — sometimes, a buyer either stifles or outright kills the thing they bought, often leaving a trail of disappointed users in the wake. 

That doesn't appear to be the case at organizational app Trello, which had 19 million users in 2017, when Australian software giant Atlassian purchased it in a deal valued at $425 million. Now, on Wednesday, Trello says that it has 50 million users, meaning that it more than doubled. 

To mark the milestone, Trello also added new features — including an automation tool called Butler, which can suggest and automatically perform certain tasks — to help make it that much easier for those users to stay organized. It also introduced Templates, which can make it easier to set up one of Trello's trademark planning boards. 

"We're trying to take the busy work out of the way they collaborate with each other," Michael Pryor, co-founder and head of Trello at Atlassian, told Business Insider. "If you think about 50 million registered users, we've become this operating system for work that people are relying on everyday to organize, collaborate, keep track of what's going on."

Trello started as a project at Fog Creek Software, the venerable New York City software house that's now known as Glitch. It's built around the concept of Kanban, the organizational philosophy pioneered at Toyota, which posits that tasks are more manageable when broken down into their component parts.

Pryor says that Trello was able to grow to 50 million users mostly through word of mouth. While Atlassian has traditionally made tools for developers, Trello is aimed at a much broader audience. He says it's widely used not only at work, but even for extracurriculars like planning parties, trips, and even weddings. 

"If you let people run solution and share it with people, they can get started and solve those problems quicker," Pryor said. "As we look to the next 100 million, we're focused on reducing that busy work and making work easier for people."

Trello becomes 'smarter'

Trello's new automation feature comes by way of an acquisition of its own, after the company snapped up Butler in December. Butler already began as a tool for Trello power users, but Justin Gallagher, Trello's head of product management, says the team spent "a good portion" of the past year integrating the two even more tightly.

Butler had a "huge reception," Pryor says, but since it was an add-on and not built by Trello itself, it limited how users could use it. Now that it's part of the core product, more users can use it. For example, a team that has weekly meetings can use the tool to automatically create an agenda, set due dates, and send emails. 

"For automation, it's like, we can look at what you're doing so you don't have to repeat the same four tasks every week when you have your one on one in the morning," Pryor said.

In addition, a big part of integrating Butler is also about reducing workplace anxiety, Gallagher says. Nowadays, employees often use a variety of different tools and may receive an overload of notifications, which could lead to anxiety. By having Butler perform repetitive tasks, it takes a load off of employees' shoulders. 

"The things that are repetitive, the more mundane tasks, Trello can do that for you," Gallagher said. "That leaves more time for the deeper work. We're thinking about how Trello becomes smarter."

A 'blank page' problem

As for Templates, Pryor says the idea partly came from seeing people post blogs about how they configure Trello to solve different types of problems. By streamlining the process, it makes it easier for, say, a manager to quickly get set up with a Trello board for handling one-on-one meetings. 

"We kind of collected them all in a community directory you can go to and anyone can browse through and see the different ways people are solving problems with Trello," Pryor said.

Gallagher says that users may get some anxiety from not knowing how to get started with their project, so Templates can alleviate this "blank page" problem. 

"People feel a lot more secure moving forward when it feels like a proven way to do this and can say, 'I'm confident this is going to be a thing that has a good chance of working,'" Gallagher said. "What we're doing is pulling that into the product itself. We're creating a gallery where people can share those stories with each other."

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• Steve Herrod thinks he has an advantage over other venture capitalists — he's got a deep background in technology.
• Herrod has a PhD in computer science and was the chief technology officer of VMware before joining General Catalyst as a managing director.
• His experience building the technology department at VMware has helped him offer advice to founders about how to build their tech teams.
• His knowledge of technology has helped him to evaluate startups, particularly those touting their use of artificial intelligence.
• Click here for more BI Prime stories.

Steve Herrod, by his own admission, focuses on the "nerdy" stuff.

But even in Silicon Valley, where it's long been said that nerds rule, Herrod's found that being able to understand and offer advice on the technical challenges startups face has given him an edge over other venture capitalists. His technical acumen and experience has helped him establish a rapport with the companies' founders and get into deals, he told Business Insider in a recent interview.

"I think that that's been something that's been attractive to a lot of these founders as I'm going in and looking to invest with them," said Herrod, a managing director at General Catalyst.

Herrod came by his technical expertise honestly. He has bachelor's, master's, and PhD degrees in computer science. He worked in software engineering for more than 15 years, spending the last five of them as VMware's chief technical officer.

But beyond his degrees and titles, he has also plenty of practical experience. At VMware, he learned how to build a large technical team as he helped the company grow its engineering staff from 20 people to 2,000. As its CTO, he learned how to guide VMware's developers to anticipate problems years in advance and to make sure they assigned particular issues to the engineers and groups that were best suited to tackle them. 

During his time there, the company acquired more than 20 startups — including Nicira, which would go on to form a cornerstone of VMware's strategy — so he developed a good sense of how to work with nascent companies and integrate them into a larger corporate parent.

"I certainly made plenty of mistakes and learned lots of lessons," he said. 

Herrod's tech background has helped him ignore the hype

When he decided to make the switch to being a venture capitalist six years ago, helping establish General Catalyst's West Coast presence in the process, he figured those lessons would come in handy. Many of the investors behind the startups that he met with as VMware's CTO didn't have his level of technical expertise. He thought that he could be a resource to the types of companies he planned to focus on — very early-stage startups that were developing software products that would be sold to corporate technology departments.

At that stage, the companies are "almost always [just] a technical team and a general idea," he said.

From his experience, he said, he can help them with hiring and building teams and focusing on the right problems.

Herrod's engineering background has proved helpful in another way in his second career — sussing out the puffery from the reality when it comes to the technologies startups are working on. A few years ago, lots of firms were touting themselves as big data companies, he said. Then a bunch of them claimed to be cloud companies. Now the big thing startups are saying their working on is artificial intelligence.

Much of what people claim to be AI is really just calculating basic statistics or doing elementary data processing, Herrod said. When founders say their company is working in or on AI, it's often just marketing, he said.

"AI gets a ton of undue hype," he said. "But also there's a lot of real stuff going on there. And so being able to separate those two and hope the investment's a good one has been a big focus."

He's having fun

Real AI essentially involves computing systems learning from patterns, experiences, or examples and applying that understanding to future problems, Herrod said. Where AI gets exciting — and what Herrod looks for — is when it has the potential to make a process 10 times better, faster, or less costly.

One company whose technology has that potential is Espressive, he said. The Silicon Valley firm as developed an AI-powered virtual assistant for corporate help desks. The assistant, dubbed Barista, is designed to automatically field help desk calls and inquiries from employees. The system learns from past interactions to improve how it responds to future ones.

Companies are interested in the system not because it has AI, but because it promises to greatly improve their ability to handle help desk cases.

"At the end of the day...AI is a means to an end," Herrod said. "It's not why anyone buys any product."

AI isn't the only technology he concentrates on. He also looks at companies involved in cybersecurity, networking, and the handling of data. This summer, he led General Catalyst's investment in Securiti.ai, which uses AI to help companies comply with the growing number of privacy regulations around the world, including Europe's General Data Protection Regulation and the California Consumer Privacy Act. 

Herrod may no longer be leading an engineering team, but he still enjoys geeking out with the companies and founders he meets with.

"It's been fun," he said, continuing, "You just get to see so many cool ideas and interesting people."

Got a tip about venture capital or startups? Contact this reporter via email at twolverton@businessinsider.com, message him on Twitter @troywolv, or send him a secure message through Signal at 415.515.5594. You can also contact Business Insider securely via SecureDrop.

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• Microsoft on Monday introduced new tools to make artificial intelligence easier for companies to use.
• The tools are improvements to the Microsoft Azure cloud's AI platform intended to make machine learning more accessible for developers at all skill levels.
• Microsoft Azure AI chief Eric Boyd said the company sets itself apart by thinking about "how to make this stuff really simple." 
• Simplifying artificial intelligence and machine learning for cloud customers could make Microsoft more competitive in the fierce cloud computing battle with market-leading Amazon Web Services.
• Artificial intelligence is one of the technologies Microsoft CEO Satya Nadella recently said will play a key role in the company's future.
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Microsoft Azure AI chief Eric Boyd said the guiding principle that sets the Redmond-based company apart when it comes to artificial intelligence is "how to make this stuff really simple."

With that in mind, Microsoft on Monday introduced new tools to make artificial intelligence easier for companies to use, including making the machine learning features of its Azure cloud platform more accessible to developers and users of all skill levels. 

Artificial intelligence is one of the technologies Microsoft CEO Satya Nadella recently said will play a key role in the company's future. Microsoft's AI platform, Azure AI, is at the center of it. The platform now has 20,000 customers, and more than 85% of Fortune 100 companies have used Azure AI in the past 12 months, the company says. 

A big opportunity in keeping it simple

Simplifying artificial intelligence and machine learning for cloud customers could make Microsoft more competitive in the fierce cloud computing battle with market-leading Amazon Web Services.

Azure AI started by taking Microsoft tools it has built for use internally, and makes them available to customers. 

The idea behind Azure AI to create tools that help companies accelerate the work they do, and make it easier to do that work with a limited number of data scientists or specialists. Rather than start from scratch, developers can take what Microsoft has already accomplished, and do the relatively minor amount of work to customize it for their needs.

"If you want to do speech-to-text, you could hire a bunch of data scientists and train your own speech-to-text model, but it will be much better to just use the service we already have," Boyd said.

The business is a "major investment" for Microsoft, said Boyd. He declined to reveal the number of employees working in Azure AI, but said it's "thousands of people across the company."

A new user experience

The set of tools announced on Monday includes what Boyd characterizes as a new user experience that makes it easy for people at all skill levels to build machine learning models — the complex math equations that underlie the algorithms that power what we know as artificial intelligence.

Boyd was referring to Microsoft's redesigned ML Designer, a tool that lets developers use a simple graphical interface to put together models using existing data sources, with little to no code required.

Beyond the ML enhancements, Microsoft also added other tools to make it easier for customers to use AI in their own wares. For instance, it's launched a Bot Framework Composer, which lets customers use a graphical interface to code their own chatbots and automated customer service agents.

Other new announcements include Personalizer, an AI-powered tool to help its customers build systems that recommend content or webpages to individual users — kind of like YouTube's vaunted algorithm, or Amazon's product recommendation features.

Microsoft also introduced a new update to its Text Analytics service that can detect personally identifiable information (PII) from documents, which could help hospitals or other regulated industries build software that automatically blanks out sensitive information. 

The bigger picture

Microsoft has an advantage when it comes to AI, Boyd said, because it knows exactly what enterprises need from working with so many thousands of them over the years. That's translated into helping customers take advantage of cutting-edge AI in their own lines of business, he said. 

Energy management company Schneider Electric, for example, uses Azure machine learning to train models that have shorten the time it takes company scientists to analyze data they collect from pumps all over the world from one month to one day, Boyd said.

Lexmark, a printer manufacturer, uses Azure machine learning to build models using data it collects from printers to figure out when the printers should signal an ink shortage to minimize downtime.

Amazon Web Services, Microsoft's chief rival, started out mostly providing services to startups, though has grown into a fierce competitor for larger customers. Microsoft, meanwhile, sees a real differentiator in this deep experience in working with even the most established companies.

"We have learned what are they things that they need and what really works for them," Boyd said.

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