Venture capitalists are the startup experts, the ones who have their finger on the pulse of which fledgling companies will boom and which will bust.

As part of Business Insider Prime's comprehensive coverage of the startups that will strike gold in 2019, we asked VCs to name the startups they think are going to be hot this year. They told us about companies they currently have in their portfolios, as well as ones they haven't put any money into yet but are at the center of positive news.

And from those discussions, one particular group of startups came up repeatedly: those that specialize in artificial intelligence tech.

From AI robots to software that uses machine learning to automate tasks, Silicon Valley is chock full of AI-focused startups.

Take, for example, Transfix, a freight marketplace that companies use to hire trucks from carriers. The startup is trying to transform the $800 billion trucking industry by using AI to match loads with carriers. It's raised $131 million so far.

There are hundreds of noteworthy startups focusing on AI today, so BI Prime has gone to the expert venture capitalists to select the cream of the crop and create a full list of 12 AI Startups to Watch that include:

• A company that automatically audits expense reports
• A startup that builds self-driving, robot tractors
• A software bot that helps create other software bots
• And other startups looking to transform industries through AI

BI Prime is publishing dozens of stories like this each and every day. Want to get started by reading the full list?

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• The rise of deepfakes, or videos created using AI that can make it look like someone said or did something they have never done, has raised concerns over how such technology could be used to spread misinformation and damage reputations.
• High-profile figures such as Facebook CEO Mark Zuckerberg, former President Barack Obama, and "Wonder Woman" actress Gal Godot have all appeared in deepfake videos in recent years.
• Congress has been discussing legal measures that could be taken to mitigate the potential damage inflicted by deepfake video content, but doing so without impacting free speech could prove challenging. 
• Using algorithms to identify deepfakes could also be difficult considering those who are creating such videos will likely find ways to circumvent such detection methods, experts say.
• Visit Business Insider's homepage for more stories. 

In a video that surfaced about a month ago, Mark Zuckerberg blankly stared into the camera from what appeared to be an office. He made a simple request of his viewers. "Imagine this for a second," he said. "One man, with total control of billions of people's stolen data. All their secrets, their lives, their futures." 

Except it wasn't really the Facebook CEO. It was a digital replica of him known as a deepfake: a phony video created using AI that can make it look like a person said or did something they have never actually done.  

Recognizable faces ranging from actor Kit Harrington as Jon Snow from "Game of Thrones" to former President Barack Obama have been the subject of such videos over the past year. And while these videos can be harmless, such as the clip of Jon Snow apologizing for the way the beloved HBO series ended, the technology has raised serious concerns about how manipulating videos and photos through artificial intelligence could potentially be used to spread misinformation or damage one's reputation. 

And according to experts, the deepfake movement isn't likely to slow down anytime soon.

"Technologically speaking, there is nothing we can do," said Ali Farhadi, senior research manager for the computer vision group at the Allen Institute for Artificial Intelligence. "The technology is out there, [and] people can start using it in whatever way they can."

'We're entering an era in which our enemies can make anyone say anything at any point in time'

It's unclear precisely when deepfakes were invented, but the trend began to gain widespread attention in late 2017 when a fake porn video purporting to feature "Wonder Woman" actress Gal Gadot was published on Reddit by a user who went by the pseudonym "deepfakes," as Vice reported at the time. 

Since then, a range of doctored videos featuring high-profile celebrities and politicians have appeared online — some of which are meant to be satirical, others which have portrayed public figures in a negative light, and others which were created to prove a point. Videos of famous movie scenes that had been digitally altered to feature actor Nicholas Cage's face went viral in early 2018, representing the lighter side of the spectrum showing how such tools could be used to foster entertainment.

Then last April, BuzzFeed posted an eerily realistic fake video showing former president Barack Obama saying words he had never spoken as part of an effort to spread awareness about the potential risks that come with using such technology in devious ways. "We're entering an era in which our enemies can make anyone say anything at any point in time," the Obama deepfake says in the video. 

Read more: Facebook, Google, and Apple are going on the defense as the battle cry to break up 'big tech' gets louder than ever

Facebook also recently found itself in hot water after it refused to take down a slowed down video of  House Speaker Nancy Pelosi that made it look as if she had been intoxicated. While that video wasn't technically a deepfake, it still raised questions about how easily video can be doctored and distributed. At the end of June, a controversial web app called DeepNude allowed users to create realistic naked images of women just by uploading photos to the app, demonstrating how such AI technologies could be used nefariously. The app has since been shut down.  

The manipulation of digital video and images is not new. But advancements in artificial intelligence, easier access to tools for altering video, and the scale at which doctored videos can be distributed are. Those latter two points are largely the reason why deepfakes may be prompting more concern than the rise of other photo and video editing tools in the past, says John Villasenor, a nonresident senior fellow at the Brookings Institution and a professor of electrical engineering, public policy, law, and management at the University of California, Los Angeles.

"Everyone's a global broadcaster now," said Villasenor. "So I think it's those two things together that create a fundamentally different landscape than we had when Photoshop came out."

Deepfakes are created by using training data — i.e. images or videos of a subject — to construct a three-dimensional model of a person, according to Villasenor. The amount of data required could vary depending on the system being used and the quality of the deepfake you're trying to create. Developing a convincing deepfake could require thousands of samples, says Farhadi, while Samsung developed an AI system that was able to generate a fabricated video clip with just one photo. 

Even though the technology has become more accessible and sophisticated that it once was, it still requires some level of expertise such as an understanding of deep learning algorithms, says Farhadi. 

"It's not like with a click of a button you start generating deepfakes," he says. "It's a lot of work." 

'An arms race'

It's likely impossible to prevent deepfakes from being created or to prohibit them from spreading on social media and elsewhere. But even if it was possible to do so, outright banning deepfakes likely isn't the solution. That's because it's not the technology itself, but how it's being used, that can be problematic, says Maneesh Agrawala, the Forest Baskett Professor of Computer Science and director of the Brown Institute for Media Innovation at Stanford University. So eliminating deepfakes may not address the root of the issue. 

"Misinformation can still be presented even if the video is 100% real," said Agrawala. "So the concern that we have is with misinformation, not so much with the technologies that are creating these videos."

That begs the question as to what can be done to prevent deepfakes from being used in dangerous ways that potentially could cause harm. Experts seem to agree that there are two potential approaches: technological solutions that can detect when a video has been doctored and legal frameworks that penalize those who use the technology to smear others. Neither avenue is fool-proof, and it's still unclear how such fixes would work.

Although a clear solution doesn't exist yet, the question over how to address deepfakes has been a topic of discussion within Congress in recent months. Last December, Republican Senator Ben Sasse of Nebraska proposed a bill known as the "Malicious Deep Fake Prohibition Act of 2018," which seeks to prohibit "fraudulent audiovisual records."  The "DEEP FAKES Accountability Act" proposed by New York Democratic Representative Yvette Clarke in June requires that altered media is clearly labeled as such with a watermark. The proposed bill would also impose civil and criminal penalties for violations.

But imposing legislation to crack down on deepfakes in a way that doesn't infringe on free speech or impact public discourse could be challenging, even if such rules do provide exceptions for entertainment content, as the Electronic Frontier Foundation notes.

The Zuckerberg deepfake, for example, was created as part of an exhibit for a documentary festival. "I think it's important to be careful and nuanced in how we talk about the potential for damage," says Agrawala, who along with other researchers from Stanford, Max Planck Institute for Informatics, Princeton University, and Adobe Research created an algorithm that can edit talking-head videos through text. "I think there are a number of really important use cases for that kind of technology."

Plus, taking legal action is often time-consuming, which could make it difficult to use legal measures to mitigate potential harm stemming from deepfakes.

"Election cycles are influenced over the course of sometimes days or even hours with social media, so if someone wants to take legal action that could take weeks or even months," says Villasenor. "And in many cases, the damage may have already been done."

Read more: Apple CEO Tim Cook called out companies like Facebook, Theranos, and YouTube in a speech pushing for responsibility in Silicon Valley

According to Farhadi, one of the most efficient ways to address the issue is to build systems that can distinguish a deepfake from a genuine video. This can be done by using algorithms that are similar to those that have been developed to create deepfakes in the first place, since that data can be used to train the detectors. 

But that may not be very helpful for detecting more sophisticated deepfakes as they continue to evolve, says Sean Gourley, the founder and CEO of Primer AI, a machine intelligence firm that builds products for analyzing large data sets. 

"You can kind of think of this like zero-day attacks in the cybersecurity space," says Gourley. "The zero-day attack is one that no one's seen before, and thus has no defenses against."     

As is often the case with cybersecurity, it can be difficult for those trying to solve issues and patch bugs to remain one step ahead of malicious actors. The same goes for deepfakes, says Villasenor.

"It's sort of an arms race," he says. "You're always going to be a few steps behind on the detection."

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NOW WATCH: Why Apple's Mac Pro 'trash can' was a colossal failure

• Amazon is retraining a third of its US workforce, the Wall Street Journal reports.  
• This is part of a larger trend in retraining. According to new ZipRecruiter research, half of Americans will need to change careers by 2030.
• Click here for more BI Prime stories.

One of America's largest tech titans is taking a new shape, and it's a harbinger of retraining to come. 

Amazon is slated to spend $700 million to retrain some 100,000 workers by 2025 in light of technology shifts, reports Chip Cutter for the Wall Street Journal. 

The $7,000-per-employee initiative is voluntary for participants, Cutter adds, and the goal is to help employees move into new roles within or outside of the company, like fulfilment center workers retraining for IT support, or nontechnical corporate workers gaining technical skills. 

This places Amazon as something of a first mover among large firms taking on large-scale retraining. Trendline suggests it's a theme that will be increasingly urgent. A recent report from the hiring platform ZipRecruiter estimates that nearly 50% of all workers in the US could be displaced or forced to change jobs by 2030, largely driven by artificial intelligence and automation.

ZipRecruiter aggregated the tens of millions of online job postings and candidate applications within its online employment marketplace to identify trends like how many new jobs were created with the addition of AI in 2018.

It looks like Jeff Bezos and company are trying to get Amazon ahead of that curve.

The bigger picture: For the moment, AI is creating more jobs than it takes away, but this may not last for long. 

ZipRecruiter data scientists point out that, so far, the most successful applications of AI resulted from partnering with human work more than replacing it. According to their report, AI actually created three times more jobs than it removed in 2018.

However, the report also highlights the three stages of AI development and shows that the technology is still at a preliminary stage. This "assisted" stage involves automating repetitive office tasks. The next stage, which we're currently entering, is augmented intelligence, in which AI systems use decision-making abilities to bolster human productivity. From 2030 and beyond, machines are projected to make decisions on their own with some degree of emotional intelligence. 

The human partnership work that AI has performed so far reflects the assisted and augmented intelligence sides of the technology.

What to look out for: Some industries are impacted by AI more than others, but workers across industries are wary of the changes it could bring. 

For now, the ZipRecruiter report states that over half of the work we do could be automated, and by 2030 44% of all US workers could be impacted by automation, with 30% displaced, and 14% faced with a career change. 

Construction workers were the least likely to fear unemployment caused by AI, but employees in the warehouse (32%), finance and insurance (27%), and accommodation and food services (26%) industries expressed the most concern. Amazon, with its massive warehouse infrastructure, would in turn be particularly vulnerable. Per Cutter, it has around 275,000 full-timers in the US, with over 600,000 total employees worldwide.  

The ZipRecruiter report states most employers (81%) would prefer hiring a human over putting an autonomous system in place, but that might change as AI grows more sophisticated. Employers have to consider improving training programs for existing employees, and future employees have to carefully consider which skills they choose to acquire. 

Even though AI hasn't wholly disrupted the job market, this report and this one and others, indicate that it's only a matter of time. 

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• Facebook built a poker bot capable of beating human pros.
• It's a major milestone for artificial intelligence (AI) research, which has been working toward this for decades.
• The breakthrough will have implications for AI in fields with limited access to knowledge, from self-driving cars to negotiations.

Facebook has built a poker-playing artificial intelligence (AI) bot capable of beating some of the world's best players of the card game, in a significant step forward for AI research.

Working with Carnegie Mellon, researchers at the California tech giant developed a piece of software called Pluribus that was able to handily defeat an array of A-list poker stars in games of six-player no-limit Texas Hold'em poker, the company announced on Thursday.

Achieving this milestone has long been a target for AI researchers, and it has now been achieved — with major implications for both the field of AI and the game of poker itself. It now joins the ranks of games like Go and Chess before it where the world's best human players have been superseded by computer agents.

Poker offers some unique challenges not seem in more "simple" games: There are multiple players, and each player only has limited information, allowing for bluffing and other advanced strategies (unlike chess where a bot could, with enough computing power, theoretically calculate every possible outcome of every move their opponent makes with absolute certainty). AI agents have previously been able to defeat human opponents in two-player poker, but not six-player, which adds an additional layer of complexity. 

"Poker has served as a challenge problem for the fields of artificial intelligence (AI) and game theory for decades," wrote researchers Noam Brown and Tuomas Sandholm in a paper to be published in Science. "No other popular recreational game captures the challenges of hidden information as effectively and as elegantly as poker."

Pluribus played in two formats: Against five other humans, and against one human and four other versions of itself (the bots could not communicate and didn't know who they played against, preventing any collusion). All the human opponents had won at least $1 million in professional poker over their careers, including major tournament winners Chris Ferguson, Greg Merson, and Darren Elias. 

Of course, this isn't just about poker. It's a major achievement — but it is significant because it has implications far beyond the card-playing space. It's a demonstration that AI can operate at "superhuman" levels in situations with multiple actors and limited access to information, and could be applied anywhere from self-driving car technology to negotiations, Brown said in an interview.

Got a tip? Contact this reporter via encrypted messaging app Signal at +1 (650) 636-6268 using a non-work phone, email at rprice@businessinsider.com, Telegram or WeChat at robaeprice, or Twitter DM at @robaeprice. (PR pitches by email only, please.) You can also contact Business Insider securely via SecureDrop.

Read more:

• Mark Zuckerberg's personal security chief accused of sexual harassment and making racist remarks about Priscilla Chan by 2 former staffers
• Facebook says it 'unintentionally uploaded' 1.5 million people's email contacts without their consent
• Years of Mark Zuckerberg's old Facebook posts have vanished. The company says it 'mistakenly deleted' them.
• Car-bomb fears and stolen prototypes: Inside Facebook's efforts to protect its 80,000 workers around the globe

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• On Thursday, Microsoft revealed that it has generated $9.5 billion in annual contracted partner revenue in the past two years.
• In two years, partners' earnings from cloud-based services are expected to more than double, according to an MDC Research survey of nearly 1,000 Microsoft cloud partners.
• The biggest way partners are helping customers is through data and server migration.
• Read more BI Prime stories here.

Microsoft's partner program — a network of partners who help resell Microsoft's software to large customers— is a major part of its business. 

In fact, 95% of Microsoft's commercial revenue flows through partners. On Thursday, Microsoft revealed that since the partnership program was introduced two years ago, it has generated $9.5 billion in annual contracted partner revenue. Microsoft also boasts that for every $1 it gets in cloud revenue, a partner makes about $10 on average.

An MDC Research survey of nearly 1,000 Microsoft cloud partners also gave insight into what partners are investing in. According to the survey, partners are choosing to invest more in their cloud businesses. In two years, their earnings from cloud-based services are expected to increase from 26% to 59% in revenue.

"Microsoft is a very partner driven organization,"Gavriella Schuster, corporate vice president and One Commercial Partner channel chief, told Business Insider."We know that in order to deliver value to our customers, we need partners across the industry to finish those solutions and bring them to market. Cloud services has actually accelerated that strategy." 

Read more: Here's what Amazon's, Microsoft's, and Google's clouds are each best at, according to a survey of 452 IT professionals

This survey also revealed that partners mostly help customers with dealing with business data, which includes storing it and migrating it to the cloud. It said that 71% of partners deliver data migration solutions and 68% deliver server migration. 

Besides that, 27% of partners deliver analytics and AI solutions. This includes Internet of Things or connected devices (22%), intelligent edge (9%), and mixed reality, which includes virtual and augmented reality (5%). 

For example, surgeons may use mixed reality devices, like Microsoft's own HoloLens, to see how they can operate on a patient. They may also use robotics, powered by Microsoft's software for connected devices, to do the surgery. 

Or, farmers may use connected devices in the soil to inform them of the best time to plant and water crops.

"Those are probably the most profitable practices a partner can build,"Schuster said."Data intelligence is a lot like bunnies in an organization. Once you let someone learn something about their business, they want to learn more. It creates a lot of projects and a lot of opportunities. That's one thing that makes it profitable."

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• Facebook has developed an artificial-intelligence poker bot capable of beating world-class human professionals at the game.
• It's a major milestone for AI research and could revolutionize how the game is played.
• Facebook says its researchers are not releasing the code publicly because of its potential "impact."
• It could erode trust in online poker and spark fears that players are playing against superhuman-level AI bots, Facebook believes. 
• Click here for more BI Prime stories.

Poker may never be the same again.

On Thursday, Facebook and Carnegie Mellon University announced that a joint team of researchers had managed to build artificial-intelligence-powered software capable of beating some of the world's best poker professionals in games of six-player no-limit Texas Hold 'em poker.

Poker's complexity, its multiple participants, and the limited information available to players have long made it a major milestone that AI researchers have been working toward. It's a fiendishly difficult problem to solve, and one that has other real-world applications, whether self-driving cars or negotiations.

But now, as with the games Go and chess, two previous goals for AI development, researchers have managed to develop bots that are capable of "superhuman" performance at the game — and like the games before it, the achievement is likely to radically transform the game.

In an interview with Business Insider, the Facebook AI researcher Noam Brown said the company wasn't releasing the code of the bot, named Pluribus, publicly because of concerns about the impact doing so might have on the poker community, but he discussed how it could still affect the game in the years to come.

"It's going to change the way that professional poker is played," he said, adding that some of the bot's approach might be adopted by human players.

One example, given in the researchers' paper published in the journal Science: The "donk betting" technique, traditionally derided by poker players, was regularly incorporated by Pluribus in its winning strategies.

"Pluribus disagrees with the folk wisdom that 'donk betting' (starting a round by betting when one ended the previous betting round with a call) is a mistake," the researchers wrote. "Pluribus does this far more often than professional humans do."

It also worked to balance its bluffs, making it harder to predict when it was bluffing, and varied bet sizes wildly in a way risk-averse humans are less likely to — also making it harder to read.

An example of where conventional wisdom has been proved right: The idea that "limping" is bad. "Limping (calling the 'big blind' rather than folding or raising) is suboptimal for any player except the "small blind player who already has half the big blind in the pot by the rules, and thus has to invest only half as much as other players to call," the researchers said.

Pluribus played extensively against a suite of human pros over the space of 12 days and 10,000 hands of poker. But this is still a tiny number of games relative to the total number being played around the world every day. As the technology becomes more widely available, it will offer fascinating new insights into unconventional and successful strategies for human-versus-human play.

It may also drastically upend online poker games, eroding trust in the format as players grow wary of playing against unseen opponents lest they're playing against superhuman AI.

The impact of superhuman-level software has had a massive impact on other gaming fields. In chess, it helped birth a new generation of prodigies like Magnus Carlsen who grew up playing AI opponents and used computer games as an essential part of their training routines. Go grandmasters have already begun drawing lessons from Google's formidable AlphaGo software.

The effects on poker will undoubtedly be similar — even if Facebook's decision to keep the code private holds back the tide for a little while yet.

"We've chosen not to release the code in part because of the potential impact on the poker community, and how this might impact" the game, Brown said. 

Running Pluribus was remarkably cheap — costing only about $150 worth of cloud-computing resources to train the model — and the researcher said you could most likely run similar software on an iPhone with only mild dips in performance.

Got a tip? Contact this reporter via encrypted messaging app Signal at +1 (650) 636-6268 using a non-work phone, email at rprice@businessinsider.com, Telegram or WeChat at robaeprice, or Twitter DM at @robaeprice. (PR pitches by email only, please.) You can also contact Business Insider securely via SecureDrop.

• Read more
• Mark Zuckerberg's personal security chief accused of sexual harassment and making racist remarks about Priscilla Chan by 2 former staffers
• Facebook says it 'unintentionally uploaded' 1.5 million people's email contacts without their consent
• Years of Mark Zuckerberg's old Facebook posts have vanished. The company says it 'mistakenly deleted' them.
• Car-bomb fears and stolen prototypes: Inside Facebook's efforts to protect its 80,000 workers around the globe

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NOW WATCH: The incredible story behind Slack, the app that's taken over offices everywhere

• For an endurance sport like cricket, moments of action are often sudden. Less devoted fans may find themselves putting it on as background noise. But AI is here to change that.
• Monty, an AI prediction system, was built to predict certain plays and alert fans when they may be about to happen. In the future, fans may be able to customize which plays they get alerted about.
• Notably, Monty uses Google technology — from the cloud, to its advertising systems — to help Mindshare, its creators, generate ad campaigns that drive people to watch a match when it detects that something exciting is about to happen.
• It could be a sign of automatically-generated, personalized ads yet to come — but the challenge will be to keep it from getting creepy. 
• Visit Business Insider's homepage for more stories.

Would cricket be more interesting if you could tune in just before the most exciting moments?  Asking an AI to predict when a wicket will fall finds the highlights of the match – even when the wicket doesn't fall. AI ads could also find you the perfect socks or persuade you to try something you might hate – if that doesn't get too creepy.

For the most passionate cricket fans, the rhythm of a game – which can go on for eight hours a day for five days in a test match – is part of the attraction; long periods of solid play that test the endurance and temperament of the players as well as their technique, interspersed with sudden moments of dramatic action, when it might take an action replay to see the details of what happened. But when you've paid $600 million for the broadcast rights, you want more than those passionate fans who are prepared to invest time and money to get the most out of the matches, even in as cricket-mad a country as Australia where the match will be playing in every bar.

"If you want to watch every ball, you have to pay $60; that's like asking an Australian to pay to go to the beach," Jack Smyth, head of innovation at WPP-owned media consultancy Mindshare, told Business Insider. "Most people have the cricket on as background TV and they drop in and out. Fair-weather fans aren't going to spend five days in the lounge watching the match, but if we can give them a good reason to duck into the pub or jump on the live stream, there's an opportunity to get them to buy in." 

To pull them in at just the right moment, Mindshare built an AI prediction system for Fox Sports called Monty, using Google's AutoML Tables machine learning service, to predict when a wicket is going to be taken up to five minutes in advance, and warn fans to switch on the cricket with a near-real-time "Wicket Warning."

Smyth's team trained the system on the data from every game played by the Australian men's cricket team for a year. For each ball bowled, data provider Opta Sports tracks 83 variables and makes those available within seconds; five of those are just about what kind of ball it is – fast or slow, spin or offside. Then there's other data like how long the bowler has been in, how long the batsman has been at the crease, are they the first or last bat of the day, what position the fielders are in, even what the weather is like. Where the fielders are tells you a lot about the tactics the captain of the cricket team is using, but not all the data turned out to be useful.

"Lots of fans think the type of pitch is significant, or the weather and the humidity; but Monty didn't pay attention to the environment, just the mechanics," Smyth explains.

Predicting individual balls didn't leave enough time to tell the fans what was about to happen, so instead the team build a classification model giving a confidence value for whether the wicket would be taken and how – would the batsman be LBW, run out or have the ball caught? An early version of the model kept predicting that batsmen would be run out, over and over again.

"That was a bit of a ghost in the machine moment," Smyth jokes; "the likelihood is remarkably low, but Monty had an unnerving certainty it would happen."

Smyth says AutoML Tables were 'remarkably easy to use' by just tagging the data and the team was able to narrow down the variables that Monty could use to make increasingly accurate predictions. The five minute window was a compromise between predictions being accurate enough to rely on, and arriving soon enough for the team to use them to send push notifications, flash up alerts on digital billboards around the country and decide how much money to bid for online ad impressions through Google Ads.

That's part of the reason Mindshare chose the Google platform, Smyth says — having the cloud in the same place as the system for buying online ad campaigns made life much easier. 

"We chose AutoML because we could built it fairly quickly and we picked [Google] App Engine because we could scale, and because of the native interactions we could have with the broader Google suite – it gives you an unparalleled ability to manage your campaign through to the ad buys," he said. 

When Monty was "supremely confident," at the 80% threshold or above, the team sent a push notification directly to fans who had the Fox cricket app. Below that threshold, the prediction would be used to create five second ads with a wicket warning, or for display media like the digital billboards.

"We were making ads on the fly based on what Monty thought would happen next," says Smyth. But it wasn't just about getting people to tune in. "It's an extension of the broadcast, allowing you to plan the rest of your day." That might go as far as telling you if it's safe to take a bathroom break.

Hitting a six

Fox is understandably cautious about saying quite how accurate Monty is, because it expects other broadcasters to be creating their own AI predictions. The channel is happier talking about how Monty delivered a 150% increase in subscriptions for the same amount of money spent, and how twice as many people remembered Fox for cricket over the competition. In tests, Monty was up to 91% accurate, but in practice, for test matches, Monty predicted which wickets would fall with 87.2% accuracy.

"For our very first notification, the wicket fell nine minutes after the prediction, not five – but that created an immediate rush of excitement in the product team and among cricket fans worldwide. The experience was so magical that the reaction was less about waiting five minutes for a wicket to fall but whether Monty would be right at all."

Anyone using Google Assistant, the voice assistant, could ask for a prediction from Monty at any time, and ask how the model had made that prediction.

"We'd see people who were curious about what was going to happen next. It was like having your own personal commentator," Smyth suggests; "you're in control of what you're watching, and you have a new understanding of the game."

"In the next iteration, fans will be able to customize alerts; not 'will the wicket fall' but 'if it falls will it be worth watching'."

A future of version of Monty could go even further and give fans their own custom highlights. "You could say 'I'm more interested in sixes being scored than in wickets' and we could alert you when certain plays happen. For purists, you could say you don't want alerts but you want to see if you can beat Monty, based on your understanding of the game and tactics."

Machine learning models need to be retrained as situations change and that includes how cricketers develop their game. "As players wax and wane, it's interesting for fans to talk about 'if he was his normal self, he wouldn't have taken that errant swing'; you want to know why he was off his game, does this bowler have his number?"

There could also be a version of Monty for fantasy cricket teams, which is an increasingly lucrative area. "You could ask about mythical matches where modern players could come together with the cricketing greats. Or Monty could be the ultimate manager for your fantasy team. You humanly can't watch every single player so Monty could tap you on the shoulder and say 'these are three players that caught my eye'."

AI is customizing advertising at scale

Behind the scenes, AI is already powering advertising at scale, under the name 'dynamic creative optimization' explains Karsten Weide, Digital Media and Entertainment Program Vice President at IDC. This technology automatically generates and optimizes the design of display ads to maximize effectiveness — and there's research on applying it to video.

"Imagine you're Brand X and you want to address 500 million customers with a campaign, across 45 countries, across PCs, laptops, smart phones, tablets, smart TVs, set-top boxes, across all parts of the funnel with the need to engage in storytelling, accompanying the 'customer journey,' Weide says. "And every single customer needs to receive the exact right message at the exact right moment on the appropriate device."

The volume of data those dynamic ads are based on is increasing – tripling by 2025 with up to a quarter being live data streams, says Weide.

"There's no way to do this without automation, and no way to do this optimally without AI and ML," Weide maintains.

So far, fully AI-powered advertising directed at the public has been relatively rare, and it's more likely to be interactive than video, suggests Steve Guggenheimer, corporate vice president of Microsoft's AI Business.

"I think there's a real opportunity to use AI to both be creative and add individuality to advertising with virtual, real-time engagement," he says. "The more creative or personal you can make things, the more engaging they are and there's an opportunity to be both more creative and more individual at scale – which is a hard thing to do."

AR apps let you see IKEA furniture in your own room, Dulux paint colors on your own walls or L'Oreal and Sephora makeup on your own face using AR. Digital creative agency AnalogFolk used image recognition to create an "Eat Your Feed" ad for Knorr suggesting personalised recipes based on photos in your Instagram feed: skiing photos get you a hearty lamb hotpot to warm you up, snowboarding action shots match a 'cardio boosting One Pot Mushroom Ragout with Fusilli and Spinach'.

More sophisticated is the TasteFace app the AnalogFolk team built for Marmite; the sticky, savory spread that glories in the reputation that you either love it or hate it. TasteFace used the Microsoft Emotion API to analyse the reactions of people tasting free samples of Marmite, converting expressions of anger, contempt, disgust, fear, happiness, sadness, surprise or no reaction into a score for love or hate, and a personalized reaction animation it could share.

That kind of AI-powered ad could work with lots of kinds of interactions, Guggenheimer says.

"The hook is to interactively understand what people say or the gestures they're making or the expression on their face." But it needs to be done cautiously and without so much tracking that it becomes intrusive.

He compares a digital agent that tells you which aisle the printer cartridges are in or compliments your shoes and suggests a cool pair of socks to go with them, with coupons targeting you based on past purchases. Shoppers don't want an experience like Target telling a girl's father she's pregnant or Tom Cruise in Minority Report, trying to hide in a smart shop that announces his name and the shirts he last bought. "Doing something that captures your attention as an individual is great; stalking me is not."

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NOW WATCH: Stewart Butterfield, co-founder of Slack and Flickr, says 2 beliefs have brought him the greatest success in life

• Montreal-based Heyday has developed a chat service for retailers and other companies that interact directly with consumers.
• The service uses artificial intelligence to answer routine questions and to direct customers to knowledgeable human sales representatives when needed.
• The chat service is designed to work with the major messaging apps, including Facebook Messenger and Apple iMessage.
• The company recently raised $2 million in seed funding to promote its service using the pitch deck below.
• Click here for more BI Prime stories.

Two decades ago, people who were trying to figure out the right computer or refrigerator to buy likely would have gone to a store and talked with one of the sales representatives in the appropriate department.

Today, consumers typically do their research and much of their shopping on their phones and computers. But Steve Desjarlais thinks many would love to have the same kind of individualized attention and expert recommendations online that they used to get in brick-and-mortar stores.

Desjarlais's company, Heyday, has created a chat service that's designed to help online retailers do just that. The service uses an artificial-intelligence powered chatbot to answer routine questions, figure out what consumers are shopping for, and to make general product suggestions. When customers need more help, the AI service directs them to human representatives, then helps those representatives answer their questions.

"With Heyday, we can bring back the conversation we had 20 years ago" between customers and sales representatives, Desjarlais said.

Heyday merges AI with human intelligence

Heyday's retail and business customers — which include Ford and French food conglomerate Danone — use its service to power the live-chat features in their apps and on their web sites. When a consumer clicks on a live-chat link, it activates the startup's chatbot. That AI-based service is able to tap into retailers' product, customer, and other databases to answer shoppers' questions.

When customers have questions the bot can't answer, it's designed to connect them with a sales representative that's knowledgeable about the particular products they're shopping for. The representative, who may be in a central call center or on the floor of a store, interacts with customers using a smartphone chat app. The app allows them to make recommendations with a few taps. The system is intended to work seamlessly; the human sales representative can see customers' previous interactions with the bot, so they don't have to repeat questions.

"We give the sales person the tools so they can engage [customers on their phones] in the same way they do with people in front of them," said Desjarlais, who previously worked as the head of product at video-game publisher Ubisoft.

Heyday, which is based in Montreal, has designed its service to be available through many of the major messaging apps, including Apple's iMessage and Facebook Messenger.

After founding the company in 2017, Desjarlais and his partners essentially bootstrapped Heyday for its first two years. They were able to develop the product, build out a 23-person team, and attract big-name customers with basically no marketing budget speak of, he said.

The company now will have the opportunity to promote its service much more widely. In April it closed a $2 million seed funding round led by investment fund Innovobot, and Desjarlais is planning to use the funds primarily on sales and marketing.

"We're really proud of what we achieved, but now that we have raised $2 million, we will just open the flood gates and [promote] our solution everywhere," he said.

Here's the pitch deck Desjarlais and Heyday used to raise its seed-funding round:

SEE ALSO: Here's the pitch deck a German software startup used to raise $10 million to move to San Francisco and take on Oracle

Venture capitalists are the startup experts, the ones who have their finger on the pulse of which fledgling companies will boom and which will bust.

As part of Business Insider Prime's comprehensive coverage of the startups that will strike gold in 2019, we asked VCs to name the startups they think are going to be hot this year. They told us about companies they currently have in their portfolios, as well as ones they haven't put any money into yet but are at the center of positive news.

And from those discussions, one particular group of startups came up repeatedly: those that specialize in artificial intelligence tech.

From AI robots to software that uses machine learning to automate tasks, Silicon Valley is chock full of AI-focused startups.

Take, for example, Transfix, a freight marketplace that companies use to hire trucks from carriers. The startup is trying to transform the $800 billion trucking industry by using AI to match loads with carriers. It's raised $131 million so far.

There are hundreds of noteworthy startups focusing on AI today, so BI Prime has gone to the expert venture capitalists to select the cream of the crop and create a full list of 12 AI Startups to Watch that include:

• A company that automatically audits expense reports
• A startup that builds self-driving, robot tractors
• A software bot that helps create other software bots
• And other startups looking to transform industries through AI

BI Prime is publishing dozens of stories like this each and every day. Want to get started by reading the full list?

>> Download it here FREE

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• FaceApp, the photo-editing app that uses artificial intelligence to apply filters, has seen a resurgence of interest in recent days.
• People have been using the app's "Old" filter to share photos of what they might look like after aging on social media.
• Celebrities such as the rapper Drake and the pop group the Jonas Brothers have been using the app, too.
• Visit Business Insider's homepage for more stories.

If you've been seeing your Facebook, Instagram, and Twitter feeds filled with photos of friends and family members that seemingly aged overnight, you're not alone.

In recent days, there's been a resurgence of interest in FaceApp, the popular photo app that uses artificial intelligence to apply filters that can make you look older or younger, or swap your gender, in addition to other effects. The app launched in 2017, but it has been flooding social-media feeds everywhere over the past several days. Celebrities from the rapper Drake to the famous chef Gordon Ramsay and the pop group the Jonas Brothers have been using the app to see how they might age. 

But there are a few things you should keep in mind before you start uploading your photos. The app's terms give FaceApp, which is based in Russia, the freedom to use user content however the company wishes. There has also been some concern over how the app accesses a user's photo library even if that person doesn't give the app permission to do so. But TechCrunch reported that Apple offers a tool developers can build into their apps that lets users choose one photo from their library to upload without giving that app access to the entire library. 

If you do want to try using FaceApp to see how you'll age, you can do so by following the steps below. 

SEE ALSO: Check out all the new emoji coming to your iPhone this fall

Download the FaceApp app from the App Store or Google Play and decline the offer to subscribe to the app when opening it.

Take a photo of yourself or choose a photo from your image library. Tapping the "Photos" button at the bottom will let you choose a photo from your camera roll without giving the app access to your entire photo library.

Scroll over to the "Age" filter at the bottom of the screen.

Choose "Old" and wait for the photo to process.

You can also use demo photos provided by the app or choose images of celebrities by tapping the button near the bottom of the app.

Celebrities have been taking part in the #AgeChallenge too, including the actress Busy Philipps.

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And the comedian Sarah Silverman.

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And Drake.

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• Read more:
• Viral app that makes you look old with shocking precision may be quietly keeping all your data
• Everyone from Drake to the Jonas Brothers is posting photos where they look old — these are the best ones
• The DNC reportedly sent out an alert to 2020 campaigns telling them to delete the viral aging app FaceApp over Russia concerns
• Top Democrat urges FBI and FTC to investigate FaceApp over its Russia ties

• Intel Capital, the venture capital arm of the giant chipmaker, looks for more in its potential investments than the possibility of big returns.
• It scrutinizes startups for ones to which it can add value, either through its technical expertise or relationships with customers, Nick Washburn, a senior managing director at the organization, told Business Insider.
• Intel Capital often seeks to match up portfolio companies that are working on solving particular problems with customers that are contending with those specific issues, he said.
• Intel also invests in startups that could benefit from its chip, memory, and other technologies, he said.
• Click here for more BI Prime stories.

Intel's venture capital arm approaches startup investing a bit differently than other Silicon Valley investment shops.

Sure, like other venture capital organizations, Intel Capital is looking for tech companies with the potential for significant growth and outsized returns, Nick Washburn, a senior managing director at the division, told Business Insider in a recent interview. And like other corporate venture shops, Intel Capital favors companies that make sense strategically, that could help boost its future business, he said.

But one of its biggest criteria for potential investments is that the companies need to be ones that will benefit from having a relationship with Intel.

"It's really important that we need to be able to add value," Washburn said. "If we don't have value beyond our dollars," he continued, "we're really not doing what we need to be doing for our portfolio companies."

Read this: VC investor explains how he finds surprising startups by focusing on the founders' opinions instead of their initial product idea

Intel Capital, which invests about $300 million to $500 million in some 30 to 40 startups each year, can help its portfolio companies in at least two big ways, he said. The chipmaker has relationships with lots of big corporate customers, who use Intel-powered computers in their data centers, among other things. Its interactions with those customers give Intel insight into some of the problems many of them are facing. The company can serve as a kind of matchmaker, pairing up portfolio companies whose technology solves particular issues with clients that are looking for ways to solve those problems.

"We can open a lot of doors to Intel's...customers and bring a lot of credibility to our startups," Washburn said.

The other kind of help Intel offers is through its technical expertise. The chipmaker doesn't force portfolio companies to use its technology, Washburn said. But it often invests in companies that can benefit from it. For example, many of the artificial intelligence companies it's backed run their software on computers that have Intel chips, he said. Intel has helped them optimize their AI applications for those processors, he said.

What's more, Intel has a program where it essentially loans out some of its senior engineers to its portfolio companies. To reach the upper ranks among Intel's engineers, workers need to do projects outside the company, Washburn said. Intel tries to pair up engineers who have expertise in particular areas with startups in need of assistance in those exact areas, he said. The chipmaker pays the salaries of the engineers, and any intellectual property they develop while working for the startup belongs to the startup.

The program is "a really good win-win" for portfolio companies, Washburn said.

Intel's been focusing its investments in areas including not just AI, but data center hardware and software and cloud computing; advanced chip manufacturing; the internet of things; and autonomous computing systems, he said. Last week, Intel Capital announced it led a $15 million series A investment in Mesmer, a California startup that's using artificial intelligence technology to do quality assurance testing of apps.

• Read more about venture capital and startups:
• Here's the pitch deck a Montreal startup used to raise $2 million to get the word out about its AI-powered chatbot after funding it themselves for two years
• Here's the pitch deck a German software startup used to raise $10 million to move to San Francisco and take on Oracle
• Slack's $17 billion direct listing could be the IPO game-changer Silicon Valley has been waiting for. But others say it's a techie delusion.
• This VC and his firm don't focus on particular technologies or sectors. Instead, they look for startups with a kind of network potential. Here's why.

SEE ALSO: This pitch deck helped a 65-year-old company raise $50 million and show investors why its personality testing service was suddenly growing like a hot startup

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NOW WATCH: Most hurricanes that hit the US come from the same exact spot in the world

• FaceApp, a controversial AI-powered photo editing app, can show you what you look like as an elderly person.
• The app might have psychological effects — both good and bad — for the user, including perpetuating the downsides of being old.
• On the other hand, the app might help adults come to terms with their eventual old age.
• Business Insider spoke to a social-personality psychologist about the psychological effects of becoming an elderly person on FaceApp.
• Visit Business Insider's homepage for more stories.

If you've ever wanted to find out how you'll look in, say, 40 years, you can do it on a controversial app that's going viral, FaceApp.

FaceApp is an AI-powered photo editing app that can make you look like an elderly person, a kid, or it can even swap your gender (but beware, the Russian app apparently collects your data, keeping your photos for ads and other purposes).

Despite being released in 2017, the app is gaining some major social media attention, with celebrities like Gordon Ramsay, Drake, and the Jonas Brothers posting pictures of themselves as senior citizens. It may seem like a fun pastime, but the app might have potential psychological effects, both good and bad.

To learn about these effects, Business Insider spoke with William Chopik, social-personality psychologist and professor at Michigan State University, who said that the filter may be good for coming to terms with old age, but, like other social media, it might appeal to users' vanity.

"People are naturally drawn to know more about themselves," Chopik told Business Insider. "Life is really uncertain, so any type of feedback that helps us predict what the future is like is useful."

Despite the fact that the filter makes users look old, it could have the opposite effect of making them poke fun at the idea of aging. "So, in a way, psychologically, it might be a tongue-in-cheek type of demonstration," said Chopik, who imagined what a user might say: "'Old people exist and everyone becomes old someday, but look at me now and how different I look.' It stresses our youth in the here and now."

The old age filter may perpetuate the idea that being old is undesirable, Chopik said, but it may have positive effects as well. "In another way, it helps normalize the fact that everyone ages, which might ultimately reduce the stigma of older adults. It's a little tricky and the jury isn't out yet."

Some research suggests that people feel younger than they really are, according to Alan Castel, psychology professor at UCLA and author of "Better With Age.""In terms of one's "subjective" age (how old people feel), after the age of 40 people tend to feel younger (sometimes by 20%) than their actual age," Castel told Business Insider. "We feel younger than we actually are, and often don't think about how we age when we are younger."

But regardless of how users feel about aging, Chopik is hopeful that their actual golden years will be better than they imagine. "FaceApp specifically shows people what they'll look like when they're older," he said. "It doesn't show them how they'll feel about themselves and other people — research suggests that all those things get better."

Some of that research includes a 2013 study that might keep us from discounting the future. In the study, college students were shown middle-aged versions of themselves and then asked to play a trivia game. They were also given the opportunity to cheat at that game. The students who were shown what they'd look like 20 years later were 74% less likely to cheat, which indicates that we're less likely to make bad decisions when we have a clearer understanding that we're aging.

I wanted a clearer understanding myself, so I tried FaceApp (actually a friend uploaded my picture against my will) and the result was frightening:

My 23-year-old face was given wrinkles no 23-year-old should have. The level of detail made the image almost convincing, even to me. I suddenly had crow's feet, slightly whiter hair, a redder face, thinner lips and eyebrows, sagging earlobes, and wrinkles galore. My friend even told me I looked like a witch. Judging by the face I happened to be making, I wouldn't disagree.

I, for one, didn't enjoy seeing my face in old age. It looked like a morbid parody of myself, and was frankly unnerving. I'd rather wait to find out how I look.

SEE ALSO: Staring death in the face taught Aetna's former CEO how to be a more human leader

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NOW WATCH: How to use FaceApp — the popular new app that can make you smile and change your gender

• Microsoft announced Monday that it will partner with artificial intelligence research company OpenAI, which was founded by Tesla CEO Elon Musk and Y Combinator chairman Sam Altman, to build AI technology for Microsoft Azure.
• OpenAI's services will run exclusively on Microsoft's cloud.
• This technology can be used to address issues in climate change, healthcare, education, and other big problems.
• The ultimate goal: Build Artificial General Intelligence, the kind of human-like AI that you find in sci-fi movies and TV shows. 
• Visit Business Insider's homepage for more stories.

On Monday, Microsoft announced it is expanding its partnership with artificial intelligence research company OpenAI to build supercomputing AI technology for its cloud. That partnership is both technological and financial, with Microsoft investing $1 billion in OpenAI.

OpenAI was originally launched by Tesla CEO Elon Musk and Y Combinator chairman Sam Altman in December 2015, with a $1 billion endowment from tech all-stars. Musk co-founded OpenAI partly because he was concerned about the potential dangers of AI, having described it as "summoning a demon"— though he's since left, citing a conflict of interest with Tesla's budding AI business.

Since it was founded in 2015, OpenAI has created technology that helps computers understand language, train robots that can do tasks like housework, and even beat humans in the computer game "Dota 2." Earlier this year, OpenAI brought Altman on full-time as its CEO, around the same time that it moved away from being a non-profit.

Its expanded partnership with Microsoft has a few parts to it, according to the press release. OpenAI will move to use the Microsoft Azure cloud exclusively for developing and running its software, and the two will work together on building new AI systems. Microsoft will also be OpenAI's "preferred partner for commercializing new AI technologies," likely meaning that OpenAI software will carry integrations with the Azure cloud. 

More broadly, though, the press release indicates that the two companies carry a shared vision around Artificial General Intelligence (AGI), the kind of human-like AI you see in movies and TV shows, capable of actual learning and understanding.

"OpenAI and Microsoft's vision is for Artificial General Intelligence to work with people to help solve currently intractable multi-disciplinary problems, including global challenges such as climate change, more personalized healthcare and education," says the press release. 

Microsoft first partnered with OpenAI in 2016. OpenAI used Microsoft's cloud as its preferred cloud, while Microsoft got increased access to OpenAI's technology and experts. 

Within the company, Microsoft has been investing in more AI technology for its cloud. Already, companies like Walmart are using Microsoft's AI technology to improve its business. And in November, Microsoft announced it would acquire XOXCO, a software product design and development studio that is known for creating AI bots.

Read more:Here's why Walmart is betting on Microsoft's AI to challenge Amazon in online and physical retail

"AI is one of the most transformative technologies of our time and has the potential to help solve many of our world's most pressing challenges," Microsoft CEO Satya Nadella said in a statement. "By bringing together OpenAI's breakthrough technology with new Azure AI supercomputing technologies, our ambition is to democratize AI – while always keeping AI safety front and center – so everyone can benefit."

SEE ALSO: This startup is giving away all its database software for free as open source, and it says it's not afraid of Oracle or Amazon

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NOW WATCH: Stewart Butterfield, co-founder of Slack and Flickr, says 2 beliefs have brought him the greatest success in life

• As head of the Defense Intelligence Agency, Lt. Gen. Robert Ashley is charged with informing policymakers about threats from other countries and non-state actors.
• But some of the biggest concerns he has are about threats that are hard to detect.
• Visit Business Insider's homepage for more stories. 

The emergence of near-peer allies such as China and Russia presents new concerns to the Defense Intelligence Agency, the organization's director, Lt. Gen. Robert Ashley, said July 19 at the Aspen Institute's Security Forum.

The DIA is charged with providing policymakers with intelligence on the military threats posed by foreign governments and non-state actors.

Here are four issues America's highest ranking military-intelligence officer says are keeping him up at night:

SEE ALSO: The US is ready to use more aggressive cyber operations to strike back at enemies

Cyberwarfare

Without mentioning specific nations posing a threat, Ashley emphasized his broad concern over cyberattacks from hostile powers.

With an increasing number of devices and systems connected to the internet, the vulnerability of infrastructure, power grids, banking and more to cyber attacks is a major concern, he said.

"That's kind of the one that keeps me up at night," he said. "Not to make light of it, but there's some great briefs the NSA will give you and you literally throw your phone away on the way out."

Threats to National Security Space

"We're seeing a period of great competition that is moving its way into space, and the risks there, obviously from a warfighting standpoint, is position, navigation and timing, [meteorological data and] missile early warning systems.

Ashley referred to an unclassified document released by the DIA earlier this year that lays out the threats posed by Iran, North Korea, Russia and China in space. Among the capabilities under development by those four nations are directed energy weapons, direct ascent weapons, and co-orbital satellites. Co-orbital satellites are able to essentially nestle up to another satellite in orbit and possibly stealing data or directly damaging the other satellite.

"You have the ability to damage a sensor, you can cut lines, you in fact could disable that with a co-orbital satellites," explained Ashley.

Another major concern to national security space is environmental. Space debris, deactivated satellites and the broken pieces of other satellites and launch vehicles, pose a major threat to active spacecraft. Objects as small as 10 centimeters can destroy military satellites in a collision, and there are about 21,000 of those objects, said Ashley. Finding a solution to managing that space debris is a problem the DIA is focused on, he noted.

Hypersonics

Hypersonic weapons, missiles able to reach Mach 5 and beyond, pose a unique threat in that they operate in a fundamentally different way than the traditional ballistics the United States' missile defense systems were built to defend against.

While traditional ballistic missiles have a set course that make intercepts possible, the speed of hypersonics and their ability to maneuver en route makes interception significantly more complex.

"Part of what we have to develop, and this gets into artificial intelligence, algorithms, advanced analytics, is can you be predictive in nature in how that vehicle is going to operate. And so we have to gather a lot of data, structuring algorithms, and see how we can do that," said Ashley.

Defeating hypersonic weapon systems requires rethinking missile defense, and Ashley noted that the military is already taking apart hypersonic missiles and learning how they operate in order to neutralize the threat.

Artificial intelligence

Developing artificial intelligence and machine learning programs to process the vast amounts of data that the intelligence community collects is essential, Ashley noted.

"By giving rote tasks to machines, like processing images and identifying hospitals, human analysts are freed up to interpret that data and make hard decisions," said Ashley.

"It is allowing analysts to spend time doing analysis and not having to do a lot of just rigorous kinds of work. It also gives you insights that you may never see because of its ability to aggregate information together," he said.

Ashley referred to the still-under-development Machine-Assisted Analytic Rapid-Repository System (MARS), a system that will use machine learning to process vast amounts of information from intelligence community databases and present it in useful ways for human analysts. MARS will replace the Modern Integrated Database, which was built more than two decades ago.

"By applying artificial intelligence, computer vision, we can have a much richer information environment with all that data in there," said Ashley. "Quantum computing, quantum sensing and quantum communications are all integral to the way ahead."

• Over the next five years, we're likely to see significant changes in fields such as artificial intelligence, space exploration, combinations of augmented and mixed reality, and quantum computing, says futurist James Canton.
• Advancements in artificial intelligence can be particularly impactful when it comes to healthcare.
• Many large tech companies like Google and Facebook, among others, have been establishing strong presences in these fields, although Canton believes we'll see promising newcomers that can challenge these incumbents.
• Visit Business Insider's homepage for more stories. 

In the not too distant future, your next checkup could be conducted by a virtual doctor. And your smartphone will not only have access to digital assistants like Alexa and Siri, but also vastly intelligent artificial intelligence systems that are capable of doing much more than reciting the weather or fetching answers to questions.

That's according to Dr. James Canton, CEO and chairman of the Institute for Global Futures, a San Francisco-based think tank that advises clients on upcoming business and technology trends.

For years, large tech firms like Facebook and Google have been emphasizing the impact that emerging fields like AI and augmented reality could have on our everyday lives. But because these technologies are still in their early stages, it can be difficult to appreciate how significant they will really be. 

That's where Canton comes in. 

In an interview with Business Insider, he described how our lives will change over the next five years as a result of advances in several important technologies. 

"There's so much more innovation that's available than people are capable of embracing," Canton said. "So there's always a lag between the innovation breakthroughs and the actual application in the marketplace."

SEE ALSO: If Amazon is really working on a robot for the home, it's going to take on a challenge that caused at least 3 startups to fail

Reality will be a "blend" of the physical environment and data streams

Artificial intelligence technology is advancing very rapidly and is going to fundamentally change how we get our healthcare, with virtual doctors and AI-powered diagnostics on the horizon, Canton said. 

Read more: 3 things we learned from Facebook's AI chief about the future of artificial intelligence

"We came out of the AI winter," he said, referring to a period when AI tech fell out of favor within the industry. Canton attributes today's AI renaissance to advances in how computers can learn — such as reinforced machine learning  — and the abundance of data now available for machines to learn from. Innovations like 3D-printed organs will also have a big impact on healthcare by boosting lifespans, he said.

The AI sector is set to grow financially in the coming years too. Market research firm Gartner predicts that the global business value derived from artificial intelligence could hit $3.9 trillion in 2022. 

Another area that's likely to boom in the coming years is what Canton calls "blended reality," which is a convergence of augmented reality, virtual reality, and telepresence. This type of technology would make it possible to view digital information in the real world without having to wear glasses or rely on your smartphone, Canton says. "This will be instantaneous," he said. "Those cumbersome things will disappear, and you'll be able to moderate how much [digital information] you want." 

Hundreds of startups going to space

The new space race,  which involves nations around the globe as well as billionaire-backed private companies like Elon Musk's SpaceX and Jeff Bezos' Blue Origin, will also be a catalyst of change over the next five years — potentially resulting in a new startup scene.

"You're going to have hundreds of companies around the world that are going to compete for everything from lunar landers to terraforming Mars," Canton said. 

The emergence of supercomputers

Quantum computing is another game-changer, with the ability to process data at blazing fast speeds. That's because while today's computer's use bits in the form of binary 0s and 1s, quantum computers operate through quantum bits.

These so-called "qbits" are particles that are capable of representing numerous combinations of 0s and 1s, as MIT Technology Review explains. Quantum computing can be especially critical when it comes to enhancing cybersecurity, since it will enable us to "encrypt smarter," says Canton.

New players could challenge the dominance of today's tech giants

Many of today's largest tech firms have already been dipping their toes into parts of these fields. Facebook sells virtual reality products under its Oculus brand, while Apple offers tools for developers to create augmented reality iPhone apps and Microsoft sells its Hololens mixed reality headset. Both IBM and Google have also been conducting research in quantum computing.

But it's too soon to know who will emerge as the industry leader in these platforms, or if it will even end up being one of these high-profile tech firms at all.

"So it doesn't necessarily mean that today's tech giants are necessarily going to be the leaders of tomorrow," Canton said. "I think there's going to be a lot of new players that come into the game that understand blended reality, understand telepresence, virtual reality, all of that, and can come in and displace folks."

• The online travel market is cut-throat, and brands struggle to keep people's attention.
• Best Western is trying to beat the odds, using artificial intelligence to personalize ads using IBM Watson Advertising.
• It's already found people are spending twice as long with these ads as other Watson ads.
• Read how AI is transforming health, transportation, investing, and more in other articles from our special report, How AI is Changing Everything. 

In the competitive online travel market, brands struggle to keep people's attention.

A 2018 report by Expedia Group Media Solutions and Millward Brown Digital found people who are booking travel packages visit sites 38 times in the 45 days before they actually make a reservation.

Best Western thinks it has found a way to beat the odds with the help of AI. Using IBM Watson's advertising products, it's running a campaign that spits out personalized travel recommendations and tries to get people to click through to its site and book rooms by asking them about their travel plans.

Read more:Big brands like Microsoft and Gap are using AI to create personalized ads for people — and the results blow away ads written by humans

"Travel is on the bleeding edge of customers' digital experience," Best Western CMO Dorothy Dowling said. "So travel has been on the forefront of digital innovation. This is a dynamic ad unit that provides much more personalization."

Best Western also used AI last summer with an ad that supplied discounts and tips in response to questions like, "Can I stay at the beach?" and "Can I bring my dog?" People spent one to two minutes per session with those ads — twice as long as the average time spent with other IBM Watson ads, according to Best Western. Best Western is using results from that campaign to inform its recommendations this year, Dowling said. 

"In travel, people are shopping longer and even after they purchase," Dowling said. "So we're going for engagement and getting them to purchase, but also to make sure they don't cancel and then repurchase somewhere else. We're really on a journey to know our customers better."

AI is still considered experimental

Marketers are in the early stages of using AI. A report by Sojern Travel Platform, a programmatic advertising company for travel marketers, listed personalized ads and real-time offers as the top challenge for marketers surveyed, cited by 46%, followed closely by "achieving ROI and profitability targets for advertising investments,""targeting travelers during a specific point along their path to purchase," and "keeping up with the fast-paced advertising and technology landscape" (all at 45%). 

Best Western still considers AI an experimental part of its ad mix, to reach people early in the travel planning process. For its loyalty program members, it also created an AI-driven chatbot last year to answer questions. Using any new technology also requires the marketer to work with multiple partners.

"AI is nascent in the travel vertical," Dowling said. "AI is another dimension we use to understand some of the signals the travel is giving us across the journey. It is a learning environment for us to see what the customer is bringing back to us — and it's about refining the answers we give."

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NOW WATCH: Burger King's CMO explains why the biggest risk in marketing is not taking one

• When The New York Times published an opinion piece by Peter Thiel this week, it failed to disclose his potential conflicts of interest.
• The piece, which was sharply critical of Google, describes him only as "an entrepreneur and investor."
• It doesn't mention he's on the board of Facebook and chairman of Palantir, two companies that compete with Google.
• The Times has faced criticism in the past for failing to disclose pertinent information about some of its op-ed writers.
• Visit Business Insider's homepage for more stories.

Peter Thiel's sharp attack on Google this week in The New York Times' editorial page was missing some key information that could have informed readers about his potential motivations for writing the piece.

The Times' described Thiel as "an entrepreneur and investor." Neither it nor Thiel in his piece disclosed that he has at multiple potential conflicts of interest when it comes to writing about Google and particularly about the search giant's work with the US military and with overseas entities — the focus of his piece.

Thiel sits on the board of Facebook, which competes with Google in the digital advertising market. He is also the chairman and founder of Palantir, which competes with Google in offering companies, governments, and organizations tools to analyze data. And he's an investor in Anduril, a company founded by Oculus VR founder Palmer Luckey, that's providing artificial-intelligence tools to the US military.

Neither The Times nor Thiel mentioned any of this.

Thiel's piece criticizes Google for ceasing working with the US military on a project that involved artificial intelligencee and for setting up a lab in China that's developing artificial intelligence. As he noted, the Chinese government requires any technology developed in the country to be shared with its military.

Read this: Peter Thiel just cranked up his attack on Google's 'naive' relationship with China in a blistering New York Times op-ed

But as the chairman of Palantir, Thiel wouldn't seem to be a disinterested observer. Palantir has done its own work for the US military and has been under fire itself for providing tools to US Immigration and Customs Enforcement that are reportedly being used to round up and deport undocumented immigrants.

Like Google, Palantir also has some questionable overseas ties. The company worked with Cambridge Analytica, the UK-based data analytics firm that illegitimately accessed the personal data of Facebook members and used it to assist Donald Trump's 2016 presidential campaign.

Readers called out the omission of Thiel's conflicts

Times readers took note of its omission of all of these potential conflicts.

"Can anyone explain why the fact that Peter Thiel is on the board if directors of Facebook is not mentioned?" asked "Johnray" in the reader comments area of Thiel's piece. "Isn't that a potential conflict of interest worth noting to readers? Mr Thiel has far less credibility when criticizing Google once his personal, business, and political interests are factored in."

Added "Cookin" in another reader comments: "Given the relationship of Peter Thiel's Palantir Technologies with Cambridge Analytica — widely reported in the press in the spring of 2018 — Mr. Thiel is the last person any American should trust to define what is good or bad for our country."

Members of the media also called out The Times for its lack of disclosure about Thiel. Dan Frommer, the founder and editor in chief at The New Consumer — and a former reporter and editor at Business Insider — said it "seems wild that [The Times] lets Peter Thiel dump on Google without a single note that he is a sitting Facebook board member."

Thiel may be trying to deflect attention from Palantir

The piece's failure to mention Palantir's work with ICE is important, said Jacinta Gonzalez, senior campaign director at Mijente, a Latinx advocacy group. Thiel is trying to distract from the criticism Palantir has received for that work, calling his piece "a marketing pitch" to the US Defense Department, she said in a statement

"Thiel cloaks his argument in concern for everyday Americans, when his true aim is to deflect from his own collaboration with violators of human rights in our own government," Gonzalez said in the statement. "Stopping their work for immigration enforcement," she continued, "would endanger billions more dollars in military contracting, so Thiel and [Palantir CEO Alex] Karp are unwilling to even talk about the issue."

Representatives of The Times and its editorial page did not immediately respond to emails seeking comment. Likewise, representatives of Palantir did not respond to an email seeking comment.

The Times' editorial page has come under fire in the past for failing to disclose potential conflicts of interest. Three years ago, after The Times ran an op-ed by The Information CEO Jessica Lessin that argued that Facebook shouldn't be responsible for fact-checking articles posted to its site, its own public editor criticized the paper's editorial page editors for failing to disclose Lessin and her husband's deep personal ties to Facebook founder Mark Zuckerberg.

After this post was published, Thiel's bio in the op-ed was quietly updated to note his affiliation with Facebook and Palantir. But there was no note in the op-ed letting readers know that the bio had been changed, and that the first version of the story, which was up for hours, did not include this information.

Before:

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SEE ALSO: Peter Thiel turns both barrels on Silicon Valley's 'extreme strain of parochialism'

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NOW WATCH: The incredible story behind Slack, the app that's taken over offices everywhere

• The artificial intelligence framework TensorFlow started within Google as an internal tool.
• In November 2015, TensorFlow was made available to the public as open source.  Since then, TensorFlow has quickly spread in popularity among developers, getting put into use at companies like Uber, Twitter, and more.
• AI jobs are also in high demand, contributing to the popularity of the tool.
• TensorFlow has even been used to discover new planets, detect illegal deforestation, and translate Vatican texts.
• Click here for more BI Prime stories.

In 2016, more than 100 million people worldwide watched closely as the legendary world champion Go player Lee Sedol played against a computer program — and lost. Then, in 2017, NASA scientists discovered two new planets with the Kepler space telescope.

What these seemingly-disparate achievements have in common is that they were possible with the technology behind TensorFlow, an open source AI project. It was originally started by Google engineers, but has proven especially popular for any project requiring the processing of big data.

TensorFlow was created by the Google Brain team, led by Google senior fellow and AI researcher Jeff Dean. It was originally built as an internal tool called DistBelief, but by November 2015, Google launched it to the public as TensorFlow, making it available as open source— allowing anyone to freely use, download or modify it. The name is a reference to tensors, the operations carried out by the kinds of neural networks created by the software.  

Now, with over 9,000 contributors, it's the third most popular open source project in the world, according to Microsoft's code hosting website GitHub. Part of the reason why it's popular is the increasing need for companies to use AI technology. Over the last year, postings for AI jobs rose 29.1%, according to a report by job site Indeed.

Today, companies like Twitter, eBay, PayPal, Airbnb, and Uber are using TensorFlow to power some or all of their AI technology. It's also used in industries like retail, healthcare, manufacturing, finance, food, and banking — for example, Coca-Cola uses TensorFlow in its mobile app.

"TensorFlow is truly comprehensive. It always had a really large community," Sandeep Gupta, product manager for TensorFlow at Google, told Business Insider. "All of these reasons have contributed to TensorFlow's really rapid growth."

What is machine learning?

TensorFlow was built as a tool for machine learning, the field of AI that helps computers learn from data, identify patterns, and make decisions without humans telling it what to do. A person could feed the algorithm data, such as a large set of images, and the algorithm would automatically be able to categorize them. 

Within Google, Gupta says the company uses machine learning very heavily in most of its products. For example, Gmail relies on machine learning to compose Smart Replies, a feature that can suggest automatic replies based on the email that was just received. Google Photos is able to categorize and organize photos. 

It still uses TensorFlow for tasks like analyzing images and text, getting put to use in Google Search, Maps, Photos, Gmail, and Translate, as well as internally for its own operations.  

"It's the exact same version of TensorFlow that's out there in open source," Gupta said. "It's integrated in Google's infrastructure."

Read more: Here's how AI is automating cybersecurity, helping programmers to be more productive, and automatically making business decisions

TensorFlow just released new updates in June, as part of the official second version of the tool. For this new version, Gupta says the team focused on making it easier to use. It partnered with companies and the open source community to make sure it's user-friendly, so that more developers can take advantage of the potential of AI. 

"It's trying to make sure machine learning is as easy to use and get started," Gupta said. "It makes it possible for people trying to use machine learning for challenging problems...We continue to look for improvements on how it can make the whole end to end solution better."

From winning Go to discovering planets

Gupta says he's constantly surprised about what he sees TensorFlow being used for. He says he's seen it used to identify diseases and air qualit,  and improve agriculture and dairy production. He's even seen a university in Rome use TensorFlow to transcribe ancient texts from the Vatican archives.

In March 2016, Google's Go-playing AI, called AlphaGo, beat champion Lee Sedol, achieving a 4-1 victory. This program was built using the first version of the Tensor Processing Unit, a custom AI chip designed for TensorFlow that has the power to run complicated AI problems. Computer scientists would go on to use TensorFlow to build even more powerful versions of this program, like AlphaGo Master, AlphaGo Zero, and AlphaZero.

In 2017, NASA scientists even used TensorFlow to discover new planets with the Kepler space telescope. With a dataset of over 15,000 telescope signals, scientists trained TensorFlow to sort out planets from non-planets by looking for patterns. 

Then, they brought it to real life. Using this model, they looked at 670 stars, and they discovered two new planets: Kepler 80g and Kepler 90i.

In 2018, a group of engineers used TensorFlow to detect illegal deforestation in the central Amazon, training it to detect the sounds of logging and chainsaws.

At the Google I/O developer conference, the team showcased a TensorFlow application that can identify the dance moves of famous dancers and give tips on how to improve dancing. 

"There's lots of applications in art and education and music where people are using TensorFlow to make it easier for people to understand and interact with and control devices," Gupta said.

It's commonly used at other tech companies as well. At Twitter, developers use machine learning to rank tweets on users' Twitter feed, as well as for advertising. 

Yi Zhuang, a senior staff engineer at Twitter, says this is to ensure users see the most relevant information, determining clickthrough rate, and deciding which ads to show. It's also used to make sure users feel safe on Twitter, Zhuang says.

"It's very difficult for humans to identify which tweets are most relevant and most interesting to our users," Zhuang told Business Insider. "That's why we applied machine learning to solve these problems."

And at Uber, developers use TensorFlow in its app, for purposes such as customer support.

"When users are trying to get help and file tickets, we try to optimize the process and try to enable them to answer their own questions by predicting which problem that they may have," Alex Sergeev, a staff software engineer at Uber, told Business Insider.

Often, developers use TensorFlow with a related tool called Keras — a library for the Python programming language, created by French Googler François Chollet. Keras is used to train AI models for prototyping, research, and production. While it's not required to use them together, developers often find that Keras helps make TensorFlow easier to manage. 

"Most people who use TensorFlow are using it through Keras,"Ali Ghodsi, CEO and co-founder of Databricks, told Business Insider."With Keras, it's actually really simple to write machine learning programs. You can think of it kind of like that. If you want simplicity, go with Keras."

Why is it so popular?

TensorFlow isn't the only AI framework out there. Other popular ones include scikit-learn, used for data analysis, and PyTorch, created by Facebook engineers and used by researchers.

But KellyAnn Fitzpatrick, industry analyst at RedMonk, says that TensorFlow has become possibly the most widespread of all of them because of the strong community around it and support from Google, which also provides resources for people to learn how to get started in AI and machine learning. 

"Having community support and support from an entity like Google never hurts," Fitzpatrick told Business Insider. "You have software developers learning about machine learning, but you also have data scientists and data engineers having to acquire good coding skills. There's a cross-pollination of knowledge there."

Since TensorFlow is open source, it's allowed a larger community of people to contribute to the code. With more hands on the wheel, the project advances faster. That popularity has also created somewhat of a network effect, meaning that the influx of developers has caused even more developers to join in. 

Zhuang says that before Twitter used TensorFlow, it used a wide variety of machine learning toolkits, but many of them didn't have deep learning capabilities — that is, the ability for computers to learn to discern some differences that are intuitive to humans, such as recognizing the difference between a cat and a dog. Deep learning is especially important in self-driving cars, as it's important to recognize street signs and not run into pedestrians. 

Before TensorFlow, Zhuang says, deep learning was mostly done as academic work at universities and research labs, but there weren't a lot of options for anybody trying to use it in their real-life software. Google saw this gap, and built the AI library that companies needed, says Zhuang. 

"TensorFlow is practical and simplifies deep learning," Zhuang said. "If you look at deep learning before these frameworks became available, deep learning was a mysterious field, meaning only researchers who have deep knowledge in deep learning was able to apply it in solving product problems.

'The field is moving very fast'

Google's Gupta says TensorFlow's popularity reflects how fast the field of machine learning is growing. It's brought together developers, data scientists, and all kinds of other tech specialities. 

Even though the project is now out in the open, Gupta says it still benefits Google because it can benefit from the project's advances. Other projects started within Google, like the cloud project Kubernetes, have also found success when they became available as open source.

"We see those benefits in lots of direct and indirect ways," Gupta said. "The biggest is accelerating our own research and the research of our community."

And since TensorFlow is one of the largest and most comprehensive machine learning platforms, it's quickly become popular among users. Online, TensorFlow community members are sharing what they've been up to with the technology, whether it's detecting dance moves or beating humans at games. 

As word of mouth spreads, it has attracted interest among developers, who may want to reuse and experiment with these projects. Today, there are scores of educational resources out there that make it easier for developers to learn and get started with it. With AI skills and jobs in high demand, it doesn't hurt for them to try.

"The field is moving very fast," Gupta said. "If you have a framework that's open source, it really accelerates the signs and the field of machine learning."

SEE ALSO: Microsoft is investing $1 billion in OpenAI, the Elon Musk-founded company that's trying to build human-like artificial intelligence

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NOW WATCH: Jeff Bezos is worth over $160 billion — here's how the world's richest man makes and spends his money

• Gabriela de Queiroz, senior engineering and data science manager at IBM, leads a team of data scientists and software engineers to contribute to open source artificial intelligence projects.
• Outside of her day job, she is the co-founder of R-Ladies, a worldwide group that teaches women how to program in R — a programming language usually used in data science and statistics.
• de Queiroz says that even though she learned to code much later in life and faced language barriers, she says it's never too late to get started.
• Visit Business Insider's homepage for more stories.

Gabriela de Queiroz, senior engineering and data science manager at IBM, never even began to learn about AI programming until she moved from Brazil to the United States. 

Nowadays, she's leading a team of data scientists and software engineers at IBM who create and contribute to open source AI projects like TensorFlow, the popular AI framework created by Google.

And that's just her day job. In her free time, she's the founder and organizer of a global meetup called R-Ladies — a group for women to learn the programming language R, commonly used in statistics. While she's recently come into the AI field, she's been a data scientist and statistician at heart for much longer than that. 

"I'm an R person," de Queiroz told Business Insider. "I've been using R for 12 years now. R is a big part of who I am. I'm very involved in the R community. R is the thing I'm most passionate about."

de Queiroz now makes it her mission to make AI in general, and R programming in particular, more accessible to larger groups. At IBM, her team is focused on making AI better and more useful for any developer, even if they don't have a background specifically in the field. With R-Ladies, she works to make women feel welcome in the tech industry and get them comfortable learning new skills.

"It's important to have a community and a safe place where you can be yourself and ask questions without judgment," de Queiroz said.

A non-traditional path

de Queiroz says she had a non-traditional path to tech. Back in Brazil, she studied epidemiology and conducted research on how air pollution affects people's health. She then spent some time working as a music producer. 

In 2012, though, de Queiroz moved to San Francisco, where she obtained a masters degree in statistics. After that, she worked at startups doing data science, until she joined IBM last year, where her love for data lent itself well to her newfound interest in AI. 

Read more:These are the programming languages that are used by America's most valuable startups, from Airbnb to WeWork

With Portugeuse as her first language, de Queiroz recalls that it took her a while to get up to speed in programming due to language barriers. When she was studying programming in school, she had to record her classes, listening to them over and over again to understand what her teacher was saying.

Most of the world's programming languages are in English, which can make it more difficult for people who are not native English speakers.

"When you want to learn how to program, you need to learn how to program, and you need to learn English," de Queiroz said. "I would write something, and I would not get what was going on."

'Like going to Disneyland'

de Queiroz first learned R when she was still doing air pollution analysis in Brazil, and continued to study the field when she moved to San Francisco. Soon after she moved, she discovered the meetup scene and started going to meetups everyday. 

"When I moved here in 2012, it was kind of like going to Disneyland," de Queiroz said. "When you're a kid, you go to Disneyland and think, wow, there's so much I can learn here and go and do. I felt wow, there's this meetup thing and you can go to a meetup and you can learn for free and get food for free. That's perfect. It's free knowledge and free food."

Soon, she decided to start a meetup of her own to teach R to others. However, she noticed pretty quickly that these spaces were dominated by men. She says she would usually stay in a corner and not feel safe about asking questions. It was a similar situation in Brazil, de Queiroz recalls, but no less frustrating.

"It's a very male-dominant culture so we don't have much voice. You can say something, but they will not listen to whatever you have to say or you have to be careful about the things you talk about or the way you say things," de Queiroz said.

So, she decided to create a meetup focused on women, and she launched the first R-Ladies meetup in October 2012. 

It wasn't a complete hit at first. At the first meetup, there were only eight people. de Queiroz says she felt disappointed, until someone told her that there likely weren't that many people at the meetup that day because it was Halloween.

"Regardless, I got very good feedback, and I decided to keep learning and going," de Queiroz said.

'Hey, there's no competition here'

Since then, R-Ladies has grown to become a worldwide organization with more than 100 chapters in 40 countries. As for de Queiroz, she became the first Latino invited to join the R Foundation, where she serves today. And nowadays, IBM, her employer, sponsors R-Ladies, making it more than just her side project. 

She recalls that at an R-Ladies event in Indonesia, the women were sitting at the front learning, while their husbands were in the back taking care of their children. 

"I thought that was so fantastic because in some cultures, the women have to stay with the child and they cannot learn, but they have their husbands there being so supportive and taking care of the children in the corner while their wives were learning," de Queiroz said. "That picture amazed me. I was like, wow, that's so amazing to see this kind of thing in this community."

For people who have never learned to program, de Queiroz says it's never too late. 

"When you talk to people in tech, they say, 'I've been programming since I was 9. I made my first robot when I was 7,'" de Queiroz  said. "I learned not to compare myself with others. I've been programming for maybe 10 years, and they've been programming for 20 to 30 years…I've allowed myself to say hey, there's no competition here. I bring a lot of things to the table."

SEE ALSO: Software intelligence company Dynatrace soared 49% on its first day of trading. Its top execs explain how a 'technology refresh' helped the company grow.

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NOW WATCH: Jeff Bezos is worth over $160 billion — here's how the world's richest man makes and spends his money

• CB Insights compiled a list of artificial intelligence companies that have reached a valuation of at least $1 billion.
• These AI unicorns are using the technology across industries like cybersecurity, transportation, healthcare, agriculture, retail and finance.   
• Healthcare is the top industry for AI deals, with companies raising $7.3 billion across 922 deals over the past six years, according to CB Insights 
• Read the full list of all 41 companies which includes ZipRecruiter, Dataminr, SenseTime, and ByteDance. 
• Click here for more BI Prime stories.

CB Insights has compiled a list of every artificial intelligence company that has reached the $1 billion valuation mark, or what is often called unicorn status. 

A total of nine startups reached a valuation of at least $1 billion in 2019 so far. Since 2013, 45 AI startups have joined the unicorn club. 

CB Insights says it defines AI companies as, "those that use machine learning as a core differentiator, sell AI software, or built AI chips." The list excludes hardware-focused robotics startups and VR companies.

In its report"AI In Numbers" on financing through the middle of 2019, CB Insights found that healthcare was the leading industry for AI deals, coming in at $7.3 billion across 922 deals over the past six years. AI startups in finance and insurance raised $5.2 billion across 621 deals, while retail-focused firms attracted $2.7 billion across 561 deals. 

Read more: One sector has emerged as the hottest area for AI investment. A top investor at Andreessen Horowitz told us why it's the 'natural next step' for the industry.

Take a look at all 41 of the current AI unicorns across various industries like transportation, healthcare, cybersecurity, agriculture, finance, and retail. The list, based on information compiled by CB Insights, excludes companies that have gone public or been acquired. 

iCarbonX - $1 billion

What it does: An AI platform that facilitates research related to the treatment of diseases, preventive care and nutrition, and is used to develop personalized medicine. 

Where it's based: China 

Year founded: 2015

Key investors: China Bridge Capital, Tencent Holdings, Vcanbio 

Intellifusion - $1 billion

What it does: The company offers facial recognition technology and is accelerating visual recognition in IP cameras, robots and cloud. 

Where it's based: China 

Year founded: 2014

Key investors: BOC International, TopoScend Capital, Hongxiu VC

Globality - $1 billion

What it does: The company is a marketplace for business services, and can help link large companies with small and midsize service providers. 

Where it's based: US

Year founded: 2015

Key investors: SoftBank Group

Momenta - $1 billion

What it does: The company develops driving technology for self-driving cars. 

Where it's based: China 

Year founded: 2016

Key investors: Blue Lake Capital, CCB International, Cathay Innovation, China Merchants Capital, Daimler 

Meero - $1 billion

What it does: A platform for photography that uses AI to enhance images.

Where it's based: France 

Year founded: 2016

Key investors: Eurazeo, Avenir Growth Capital, Prime Ventures

OrCam Technologies - $1 billion

What it does: The company created an eyeglasses device that can read words to people who can't see.

Where it's based: Israel 

Year founded: 2010

Key investors: Aviv Venture Capital, BRM Group, Clal Insurance Enterprises Holdings, Intel Capital, Meitav Dash 

SoundHound - $1 billion

What it does: The company specializes in using AI technology to understand sounds. It offers an app that can identify music when a user hums or sings a melody into their phone. 

Where it's based: US

Year founded: 2005

Key investors: Cota Capital, Daimler, Felicis Ventures, Global Catalyst Partners, Global Catalyst Partners Japan 

TuSimple - $1 billion

What it does: The company uses AI technology to make trucking safer and more efficient, by introducing self-driving trucks.  

Where it's based: US

Year founded: 2015

Key investors: CDH Investments, Sina, Composite Capital Partners 

Unisound - $1 billion

What it does: The company offers speech recognition technology for devices like wearables, navigation tools, and smart appliances. 

Where it's based: China 

Year founded: 2012

Key investors: CICC Jiacheng Investment, CLP Health Fund, China International Capital Corporation, China Internet Investment Fund, Grains Valley Venture Capital 

ZipRecruiter - $1 billion

What it does: The company runs an online jobs marketplace which uses some AI technology.

Where it's based: US

Year founded: 2010

Key investors: Basepoint Ventures, Industry Ventures, Institutional Venture Partners, Undisclosed Investors, Wellington Management 

Outreach - $1.1 billion

What it does: The company offers a sales engagement platform that aims to improve customer interactions. 

Where it's based: US

Year founded: 2014

Key investors: Lone Pine Capital, Four Rivers Group, DFJ Growth, Spark Capital, Sapphire VenturesMeritech Capital Partners, Lemonade Capital, Mayfield Fund, Trinity Ventures 

4Paradigm - $1.2 billion

What it does: The company provides AI technology and services, helping financial firms detect fraud, reduce risk, and improve efficacy. 

Where it's based: China 

Year founded: 2014

Key investors: Agricultural Bank of China, Bank of China, Bank of Communications, China CITIC Bank 

Butterfly Network - $1.25 billion

What it does: The company created a handheld ultrasound scanner called the Butterfly iQ.

Where it's based: US

Year founded: 2011

Key investors: Aeris Capital, Bill & Melinda Gates Foundation, Fidelity Investments, Fosun Pharmaceutical, Jamie Dinan 

C3.ai - $1.4 billion

What it does: The company provides AI software for functions like fraud detection, supply network optimization, and energy management. 

Where it's based: US

Year founded: 2009

Key investors: Breyer Capital, InterWest Partners, Makena Capital Management, Pat House, Sutter Hill Ventures 

Dataminr - $1.6 billion

What it does: The company provides alerts about important or breaking news and events in real time.

Where it's based: US

Year founded: 2009 

Key investors: Andrea Wuerfel, BoxGroup, Brainchild Holdings, Credit Suisse NEXT Investors, Declaration Partners  

Darktrace - $1.65 billion

What it does: Darktrace helps defend companies against cyberattacks.

Where it's based: UK

Year founded: 2013

Key investors: Hoxton Ventures, Insight Partners, Invoke Capital, KKR, Samsung Electronics 

Pony.ai - $1.7 billion

What it does: The company is building technology to create self-driving vehicles. 

Where it's based: US

Year founded: 2016

Key investors: Adrian Cheng, China Merchants Capital, ClearVue Partners, Comcast Ventures, DCM Ventures 

Afiniti - $1.8 billion

What it does: The company has developed AI for customer call centers that can match customers with agents based on behavioral patterns.

Where it's based: US

Year founded: 2006

Key investors: Elisabeth Murdoch, Fred Ryan, GAM Holding, Ivan Seidenberg, John Browne

Graphcore - $1.7 billion

What it does: The company created a chip and software to help power AI technology.

Where it's based: UK

Year founded: 2016

Key investors: Ahren Innovation Capital, Amadeus Capital Partners, Atomico, BMW i Ventures, C4 Ventures 

InsideSales.com - $1.7 billion

What it does: The company sells an AI-powered sales platform.

Where it's based: US

Year founded: 2004

Key investors: Acadia Woods Partners, Epic Ventures, Fraser Bullock, Hummer Winblad Venture Partners, Ireland Strategic Investment Fund 

Avant - $2 billion

What it does: Using advanced algorithms and machine-learning, the company offers a customized approach to help online banking customers borrow money.

Where it's based: US

Year founded: 2012 

Key investors: August Capital, Balyasny Asset Management, Credit Suisse, CreditEase, DFJ Growth Fund 

Preferred Networks - $2 billion

What it does: The software development company applies real-time machine learning technologies to transportation, manufacturing, and biotech and healthcare. 

Where it's based: Japan 

Year founded: 2014

Key investors: Chugai Pharmaceutical, FANUC, Hakuhodo DY Holdings, Hitachi, JXTG Holdings 

Lemonade - $2 billion

What it does: The company sells insurance for renters and homeowners. 

Where it's based: US

Year founded: 2015

Key investors: SoftBank Group, Allianz X, OurCrowd, Thrive Capital, General Catalyst

Cambricon - $2 billion

What it does: The company has developed a processor chip that simulates human nerve cells and brain processing to conduct deep learning. 

Where it's based: China 

Year founded: 2016

Key investors: Alibaba Entrepreneurs Fund, Alibaba Innovation Investment, CAS Investment Management Co., CITIC Securities, CMB International Capital 

BenevolentAI - $2.1 billion

What it does: The company is an advanced AI technology platform that synthesizes and extracts important data from complex scientific data sets to help find potential new medical treatments.

Where it's based: UK

Year founded: 2013

Key investors:H. Lundbeck, Lansdowne Partners, Undisclosed Investors, Upsher-Smith Laboratories, Woodford Investment Management 

Uptake Technologies - $2.3 billion

What it does: The company provides predictive analytic software that collects and interprets sensor data for clients from industries like mining, rail, energy, aviation, retail, and construction.

Where it's based: US

Year founded: 2014

Key investors: Baillie Gifford $ Co., Caterpillar, GreatPoint Ventures, Lightbank, New Enterprise Associates 

YITU Technology - $2.4 billion

What it does: The company works on areas including computer vision, voice and face recognition, reasoning, and robotics. 

Where it's based: China 

Year founded: 2012

Key investors: China Industrial Asset Management, China Merchants Bank, Etisalat, Gaocheng Capital, Gaorong Capital 

Aurora - $2.5

What it does: The company develops self-driving car technology. 

Where it's based: US

Year founded: 2017

Key investors: Sequoia Capital, Amazon.com, Geodesic Capital, Shell Ventures, Reinvent Capital, Lightspeed Venture Partners, Greylock Partners  

Automation Anywhere - $2.6 billion

What it does: The company helps businesses automate key tasks like data collection and entry.

Where it's based: US

Year founded: 2003 

Key investors: General Atlantic, Goldman Sachs, New Enterprise Associates, SoftBank Group, Workday Ventures

Nuro - $2.7 billion

What it does: The company is working on self-driving vehicles.

Where it's based: US

Year founded: 2016

Key investors: SoftBank Investment Advisers

Horizon Robotics - $3 billion

What it does: The company offers AI technology that can analyze traffic patterns and road conditions and is also working on technology for cars.

Where it's based: China 

Year founded: 2015

Key investors: SK Group, SK Hynix, CITIC Securities International Co, China Minsheng Investment Group, Oceanwide Capital 

CrowdStrike - $3 billion

What it does: The cybersecurity company helps protect against hacking and cyberattacks.

Where it's based: US

Year founded: 2011

Key investors: Accel, General Atlantic, Institutional Venture Partners, March Capital Partners, Rackspace 

Tempus - $3.1 billion

What it does: Started by Groupon founder Eric Lefkofsky, Tempus aims to help doctors use data to find better cancer treatments for patients.

Where it's based: US

Year founded: 2015

Key investors: Baillie Gifford & Co., Franklin Templeton Investments, Kinship Trust Company, Lightbank, New Enterprise Associates 

Zoox - $3.2 billion

What it does: The company is working on self-driving vehicles.

Where it's based: US

Year founded: 2014

Key investors: AID Partners, Blackbird Ventures, Breyer Capital, Composite Capital Partners, IDG Capital 

CloudWalk - $3.3 billion

What it does: The company provides a payment network that processes transactions from credit and debit cards and other payment products. 

Where it's based: US

Year founded: 2013

Key investors: Plug and Play Accelerator, Plug and Play Ventures, Undisclosed Investors, Visa Acceleration Program 

Indigo Agriculture - $3.5 billion

What it does: The agricultural company uses microbiology and other technology to improve the growth of crops like cotton, wheat, corn, soybeans, and rice. 

Where it's based: US

Year founded: 2016

Key investors: Activant Capital Group, Alaska Permanent Fund, Altitude Life Science Ventures, Baillie Gifford & Co., Flagship Pioneering

Face++ - $4 billion

What it does: The company offers facial recognition technology to clients in government, retail and other sectors.

Where it's based: China 

Year founded: 2011

Key investors: Abu Dhabi Investment Authority, Alibaba Group, Ant Financial Services Group, Bank of China Group Investment, Boyu Capital 

SenseTime - $4.5 billion

What it does: The company offers AI technologies in areas ranging from image recognition to autonomous driving. It says its tech can be used in smart cities, mobile phones, online entertainment, and retail.

Where it's based: China

Year founded: 2014

Key investors: Advantech Capital, Alibaba Group, All-Stars Investment, Bank of China Group Investment Zheshang Capital, CDH Investments

Tanium - $6.7 billion

What it does: The company providers cybersecurity and aids IT teams to handle cyberthreats. 

Where it's based: US

Year founded: 2007

Key investors: Adage Capital Management, Andreessen Horowitz, Baillie Gifford & Co., Executive Press, Franklin Templeton Investments 

UiPath - $7.1 billion

What it does: The company provides a software platform to help organizations automate business processes. It is used in industries like healthcare, finance, and human resources.

Where it's based: United States 

Year founded: 2005

Key investors: Accel, Coatue Management, Credo Ventures, Dragoneer Investment Group, Earlybird Venture Capital 

ByteDance - $75 billion

What it does: The company provides online creative content platforms. The company's most well known platform is TikTok, an app allowing users to share short videos. 

Where it's based: China

Year founded: 2012

Key investors: Bank of America, Bank of China, Barclays Bank, CCB International, CMB Wing Lung Bank