Current laws largely fail to address discrimination when it comes to big data. Photograph: artpartner-images/Getty Images

In May last year, a stunning report claimed that a computer program used by a US court for risk assessment was biased against black prisoners. The program, Correctional Offender Management Profiling for Alternative Sanctions (Compas), was much more prone to mistakenly label black defendants as likely to reoffend wrongly flagging them at almost twice the rate as white people (45% to 24%), according to the investigative journalism organisation ProPublica.

Compas and programs similar to it were in use in hundreds of courts across the US, potentially informing the decisions of judges and other officials. The message seemed clear: the US justice system, reviled for its racial bias, had turned to technology for help, only to find that the algorithms had a racial bias too.

How could this have happened? The private company that supplies the software, Northpointe, disputed the conclusions of the report, but declined to reveal the inner workings of the program, which it considers commercially sensitive. The accusation gave frightening substance to a worry that has been brewing among activists and computer scientists for years and which the tech giants Google and Microsoft have recently taken steps to investigate: that as our computational tools have become more advanced, they have become more opaque. The data they rely on arrest records, postcodes, social affiliations, income can reflect, and further ingrain, human prejudice.

The promise of machine learning and other programs that work with big data (often under the umbrella term artificial intelligence or AI) was that the more information we feed these sophisticated computer algorithms, the better they perform. Last year, according to global management consultant McKinsey, tech companies spent somewhere between $20bn and $30bn on AI, mostly in research and development. Investors are making a big bet that AI will sift through the vast amounts of information produced by our society and find patterns that will help us be more efficient, wealthier and happier.

It has led to a decade-long AI arms race in which the UK government is offering six-figure salaries to computer scientists. They hope to use machine learning to, among other things, help unemployed people find jobs, predict the performance of pension funds and sort through revenue and customs casework. It has become a kind of received wisdom that these programs will touch every aspect of our lives. (Its impossible to know how widely adopted AI is now, but I do know we cant go back, one computer scientist says.)

Its impossible to know how widely adopted AI is now, but I do know we cant go back

But, while some of the most prominent voices in the industry are concerned with the far-off future apocalyptic potential of AI, there is less attention paid to the more immediate problem of how we prevent these programs from amplifying the inequalities of our past and affecting the most vulnerable members of our society. When the data we feed the machines reflects the history of our own unequal society, we are, in effect, asking the program to learn our own biases.

If youre not careful, you risk automating the exact same biases these programs are supposed to eliminate, says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was learning from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is especially nefarious because police can say: Were not being biased, were just doing what the math tells us. And the public perception might be that the algorithms are impartial.

We have already seen glimpses of what might be on the horizon. Programs developed by companies at the forefront of AI research have resulted in a string of errors that look uncannily like the darker biases of humanity: a Google image recognition program labelled the faces of several black people as gorillas; a LinkedIn advertising program showed a preference for male names in searches, and a Microsoft chatbot called Tay spent a day learning from Twitter and began spouting antisemitic messages.

These small-scale incidents were all quickly fixed by the companies involved and have generally been written off as gaffes. But the Compas revelation and Lums study hint at a much bigger problem, demonstrating how programs could replicate the sort of large-scale systemic biases that people have spent decades campaigning to educate or legislate away.

Computers dont become biased on their own. They need to learn that from us. For years, the vanguard of computer science has been working on machine learning, often having programs learn in a similar way to humans observing the world (or at least the world we show them) and identifying patterns. In 2012, Google researchers fed their computer brain millions of images from YouTube videos to see what it could recognise. It responded with blurry black-and-white outlines of human and cat faces. The program was never given a definition of a human face or a cat; it had observed and learned two of our favourite subjects.

This sort of approach has allowed computers to perform tasks such as language translation, recognising faces or recommending films in your Netflix queue that just a decade ago would have been considered too complex to automate. But as the algorithms learn and adapt from their original coding, they become more opaque and less predictable. It can soon become difficult to understand exactly how the complex interaction of algorithms generated a problematic result. And, even if we could, private companies are disinclined to reveal the commercially sensitive inner workings of their algorithms (as was the case with Northpointe).

Less difficult is predicting where problems can arise. Take Googles face recognition program: cats are uncontroversial, but what if it was to learn what British and American people think a CEO looks like? The results would likely resemble the near-identical portraits of older white men that line any bank or corporate lobby. And the program wouldnt be inaccurate: only 7% of FTSE CEOs are women. Even fewer, just 3%, have a BME background. When computers learn from us, they can learn our less appealing attributes.

Joanna Bryson, a researcher at the University of Bath, studied a program designed to learn relationships between words. It trained on millions of pages of text from the internet and began clustering female names and pronouns with jobs such as receptionist and nurse. Bryson says she was astonished by how closely the results mirrored the real-world gender breakdown of those jobs in US government data, a nearly 90% correlation.

People expected AI to be unbiased; thats just wrong. If the underlying data reflects stereotypes, or if you train AI from human culture, you will find these things, Bryson says.

People expected AI to be unbiased; thats just wrong

So who stands to lose out the most? Cathy ONeil, the author of the book Weapons of Math Destruction about the dangerous consequences of outsourcing decisions to computers, says its generally the most vulnerable in society who are exposed to evaluation by automated systems. A rich person is unlikely to have their job application screened by a computer, or their loan request evaluated by anyone other than a bank executive. In the justice system, the thousands of defendants with no money for a lawyer or other counsel would be the most likely candidates for automated evaluation.

In London, Hackney council has recently been working with a private company to apply AI to data, including government health and debt records, to help predict which families have children at risk of ending up in statutory care. Other councils have reportedly looked into similar programs.

In her 2016 paper, HRDAGs Kristian Lum demonstrated who would be affected if a program designed to increase the efficiency of policing was let loose on biased data. Lum and her co-author took PredPol the program that suggests the likely location of future crimes based on recent crime and arrest statistics and fed it historical drug-crime data from the city of Oaklands police department. PredPol showed a daily map of likely crime hotspots that police could deploy to, based on information about where police had previously made arrests. The program was suggesting majority black neighbourhoods at about twice the rate of white ones, despite the fact that when the statisticians modelled the citys likely overall drug use, based on national statistics, it was much more evenly distributed.

As if that wasnt bad enough, the researchers also simulated what would happen if police had acted directly on PredPols hotspots every day and increased their arrests accordingly: the program entered a feedback loop, predicting more and more crime in the neighbourhoods that police visited most. That caused still more police to be sent in. It was a virtual mirror of the real-world criticisms of initiatives such as New York Citys controversial stop-and-frisk policy. By over-targeting residents with a particular characteristic, police arrested them at an inflated rate, which then justified further policing.

PredPols co-developer, Prof Jeff Brantingham, acknowledged the concerns when asked by the Washington Post. He claimed that to combat bias drug arrests and other offences that rely on the discretion of officers were not used with the software because they are often more heavily enforced in poor and minority communities.

And while most of us dont understand the complex code within programs such as PredPol, Hamid Khan, an organiser with Stop LAPD Spying Coalition, a community group addressing police surveillance in Los Angeles, says that people do recognise predictive policing as another top-down approach where policing remains the same: pathologising whole communities.

There is a saying in computer science, something close to an informal law: garbage in, garbage out. It means that programs are not magic. If you give them flawed information, they wont fix the flaws, they just process the information. Khan has his own truism: Its racism in, racism out.

Its unclear how existing laws to protect against discrimination and to regulate algorithmic decision-making apply in this new landscape. Often the technology moves faster than governments can address its effects. In 2016, the Cornell University professor and former Microsoft researcher Solon Barocas claimed that current laws largely fail to address discrimination when it comes to big data and machine learning. Barocas says that many traditional players in civil rights, including the American Civil Liberties Union (ACLU), are taking the issue on in areas such as housing or hiring practices. Sinyangwe recently worked with the ACLU to try to pass city-level policies requiring police to disclose any technology they adopt, including AI.

But the process is complicated by the fact that public institutions adopt technology sold by private companies, whose inner workings may not be transparent. We dont want to deputise these companies to regulate themselves, says Barocas.

In the UK, there are some existing protections. Government services and companies must disclose if a decision has been entirely outsourced to a computer, and, if so, that decision can be challenged. But Sandra Wachter, a law scholar at the Alan Turing Institute at Oxford University, says that the existing laws dont map perfectly to the way technology has advanced. There are a variety of loopholes that could allow the undisclosed use of algorithms. She has called for a right to explanation, which would require a full disclosure as well as a higher degree of transparency for any use of these programs.

The scientific literature on the topic now reflects a debate on the nature of fairness itself, and researchers are working on everything from ways to strip unfair classifiers from decades of historical data, to modifying algorithms to skirt round any groups protected by existing anti-discrimination laws. One researcher at the Turing Institute told me the problem was so difficult because changing the variables can introduce new bias, and sometimes were not even sure how bias affects the data, or even where it is.

The institute has developed a program that tests a series of counterfactual propositions to track what affects algorithmic decisions: would the result be the same if the person was white, or older, or lived elsewhere? But there are some who consider it an impossible task to integrate the various definitions of fairness adopted by society and computer scientists, and still retain a functional program.

In many ways, were seeing a response to the naive optimism of the earlier days, Barocas says. Just two or three years ago you had articles credulously claiming: Isnt this great? These things are going to eliminate bias from hiring decisions and everything else.

Meanwhile, computer scientists face an unfamiliar challenge: their work necessarily looks to the future, but in embracing machines that learn, they find themselves tied to our age-old problems of the past.

Follow the Guardians Inequality Project on Twitter here, or email us at inequality.project@theguardian.com

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Rise of the racist robots how AI is learning all our worst impulses ... - The Guardian

Kavya Kopparapu might be considered something of a whiz kid. After all, she had yet to enter her senior year of high school when she started Eyeagnosis, a smartphone app and 3D-printed lens that allows patients to be screened for diabetic retinopathy with a quick photo, avoiding the time and expense of a typical diagnostic procedure. In June 2016, Kopparapus grandfather had recently been diagnosed with diabetic retinopathy, a complication of diabetes that damages retinal blood vessels and can eventually cause blindness. He caught the symptoms in time to receive treatment, but it was close. A little too close for Kopparapus comfort. According to the IEEE Spectrum, Kopparapu, her 15-year-old brother Neeyanth and her classmate Justin Zhang trained an artificial intelligence system to scan photos of eyes and detect, and diagnose, signs of diabetic retinopathy. She unveiled the technology at the OReilly Artificial Intelligence conference in New York City in July. After diving into internet-based research and emailing opthamologists, biochemists, epidemiologists, neuroscientists and the like, she and her team worked on the diagnostic AI using a machine-learning architecture called a convolutional neural network. CNNs, as theyre called, parse through vast data sets -- like photos -- to look for patterns of similarity, and to date have shown an aptitude for classifying images. The network itself was the ResNet-50, developed by Microsoft. But to train it to make retinal diagnoses, Kopparapu had to feed it images from the National Institute of Healths EyeGene database, which essentially taught the architecture how to spot signs of retinal degeneration. One hospital has already tested the technology, fitting a 3D-printed lens onto a smartphone and training the phones flash to illuminate the retinas of five different patients. Tested against opthalmologists, the system went five for five on diagnoses. Kopparapus invention still needs lots of tests and additional data to prove its efficacy before it sees widespread clinical adoption, but so far, its off to a pretty good start. Eyeagnosis is operating in a space that's recently become interesting to some very large companies. Last fall, a team of Google researchers published a paper in the Journal of the American Medical Association showing that Google's deep learning algorithm, trained on a large data set of fundus images, can detect diabetic retinopathy with better than 90 percent accuracy. That algorithm was then tested on 9,963 deidentified images retrospectively obtained from EyePACS in the United States, as well as three eye hospitals in India. A second, publicly available research data set of 1,748 was also used. The accuracy was determined by comparing its diagnoses to those done by a panel of at least seven U.S. board-certified ophthalmologists. The two data sets had 97.5 percent and 96.1 percent sensitivity, and 93.4 percent and 93.9 percent specificity respectively.

And Google isnt the only player in that space. IBM has a technology utilizing a mix of deep learning, convolutional neural networks and visual analytics technology based on 35,000 images accessed via EyePACs; in research conducted earlier this year, the technology learned to identify lesions and other markers of damage to the retinas blood vessels, collectively assessing the presence and severity of disease. In just 20 seconds, the method was successful in classifying diabetic retinopathy severity with 86 percent accuracy, suggesting doctors and clinicians could use the technology to have a better idea of how the disease progresses as well as identify effective treatment methods.

Lower-tech options are also taking a stab at improving access to screenings. Using a mix of in-office visits, telemedicine and web-based screening software, the Los Angeles Department of Health Services has been able to greatly expand the number of patients in its safety net hospital who got screenings and referrals. In an article published in the journal JAMA Internal Medicine, researchers describe how the two-year collaboration using Safety Net Connects eConsult platform resulted in more screenings, shorter wait times and fewer in-person specialty care visits. By deploying Safety Net Connects eConsult system to a group of 21,222 patients, the wait times for screens decreased by almost 90 percent, and overall screening rates for diabetic retinopathy increased 16 percent. The digital program also eliminated the need for 14,000 visits to specialty care professionals

Originally posted here:

Teenage team develops AI system to screen for diabetic retinopathy - MobiHealthNews

Artificial intelligence as a technology is rapidly growing, but much is still being learned about how AI and autonomous systems make decisions based on the information they collect and process.

To better explain those relationships so humans and autonomous systems can better understand each other and collaborate more deeply, researchers at PARC, the Palo Alto Research Center, have been awarded a multi-million dollar federal government contract to create an "interactive sense-making system" that could answer many related questions.

The research for the proposed system, called COGLE (CommonGroundLearning andExplanation), is being funded by the Defense Advanced Research Projects Agency (DARPA),using an autonomous Unmanned Aircraft System (UAS) test bed but would later be applicable to a variety of autonomous systems.

The idea is that since autonomous systems are becoming more widely used, it would behoove humans who are using them to understand how the systems behave based on the information they are provided, Mark Stefik, a PARC research fellow who runs the lab's human machine collaboration research group, told ITPro.

"Machine learning is becoming increasing important," said Stefik. "As a consequence, if we are building systems that are autonomous, we'd like to know what decisions they will make. There is no established technique to do that today with systems that learn for themselves."

In the field of human psychology, there is an established history about how people form assumptions about things based on their experiences, but since machines aren't human, their behaviors can vary, sometimes with results that can be harmful to humans, said Stefik.

In one moment, an autonomous machine can do something smart or helpful, but then the next moment it can do something that is "completely wonky, which makes things unpredictable," he said. For example, a GPS system seeking the shortest distance between two points could erroneously and catastrophically send a user driving over a cliff or the wrong way onto a one-way street. Being able to delve into those autonomous "thinking" processes to understand them is the key to this research, said Stefik.

The COGLE research will help researchers pursue answers to these issues, he said. "We're insisting that the program be explainable," for the autonomous systems to say why they are doing what they are doing. "Machine learning so far has not really been designed to explain what it is doing."

The researchers involved with the project will essentially have roles educators and teachers for the machine learning processes to improve their operations and make it more useable and even more human like, said Stefik. "It's a sort of partnership where humans and machines can learn from each other."

That can be accomplished in three ways, he added, including reinforcement at the bottom level, using reasoning patterns like the ones humans use at the cognitive or middle level, and through explanation at the top sense-making level. The research aims to enable people to test, understand, and gain trust in AI systems as they continue to be integrated into our lives in more ways.

The research project is being conducted under DARPA's Explainable Artificial Intelligence (XAI) program, which seeks to create a suite of machine learning techniques that produce explainable models and enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

PARC, which is a Xerox company, is conducting the COGLE work with researchers at Carnegie Mellon University, West Point, the University of Michigan, the University of Edinburgh and the Florida Institute for Human & Machine Cognition. The key idea behind COGLE is to establish common ground between concepts and abstractions used by humans and the capabilities learned by a machine. These learned representations would then be exposed to humans using COGLE's rich sense-making interface, enabling people to understand and predict the behavior of an autonomous system.

Read the rest here:

Advancing AI by Understanding How AI Systems and Humans Interact - Windows IT Pro

Marc Vontobel, CTO & Pascal Kaufmann, CEO, Starmind

Artificial intelligence is stuck today because companies are likening the human brain to a computer, according to Swiss neuroscientist and co-founder of Starmind Pascal Kaufmann. However, the brain does not process information, retrieve knowledge, or store memories like a computer does.

When companies claim to be using AI to power "the next generation" of their products, what they are unknowingly referring to is the intersection of big data, analytics, and automation, Kaufmann told ZDNet.

"Today, so called AI is often just the human intelligence of programmers condensed into source code," said Kaufmann, who worked on cyborgs previously at DARPA.

"We shouldn't need 300 million pictures of cats to be able to say whether something is a cat, cow, or dog. Intelligence is not related to big data; it's related to small data. If you can look at a cat, extract the principles of a cat like children do, then forever understand what a cat is, that's intelligence."

He even said that it's not "true AI" that led to AlphaGo -- a creation of Google subsidiary DeepMind -- mastering what is revered as the world's most demanding strategy game, Go.

The technology behind AlphaGo was able to look at 10 to 20 potential future moves and lay out the highest statistics for success, Kaufmann said, and so the test was one of rule-based strategy rather than artificial intelligence.

The ability for a machine to strategise outside the context of a rule-based game would reflect true AI, according to Kaufmann, who believes that AI will cheat without being programmed not to do so.

Additionally, the ability to automate human behaviour or labour is not necessarily a reflection of machines getting smarter, Kaufmann insisted.

"Take a pump, for example. Instead of collecting water from the river, you can just use a pump. But that is not artificial intelligence; it is the automation of manual work ... Human-level AI would be able to apply insights to new situations," Kaufmann added.

While Facebook's plans to build a brain-computer interface and Elon Musk's plans to merge the human brain with AI have left people wondering how close we are to developing true AI, Kaufmann believes the "brain code" needs to be cracked before we can really advance the field. He said this can only be achieved through neuroscientific research.

Earlier this year, founder of DeepMind Demis Hassabis communicated a similar sentiment in a paper, saying the fields of AI and neuroscience need to be reconnected, and that it's only by understanding natural intelligence that we can develop the artificial kind.

"Many companies are investing their resources in building faster computers ... we need to focus more on [figuring out] the principles of the brain, understand how it works ... rather than just copy/paste information," Kaufmann said.

Kaufmann admitted he doesn't have all the answers, but finds it "interesting" that high-profile entrepreneurs such as Musk and Mark Zuckerberg, none of whom have AI or neuroscience backgrounds, have such strong and opposing views on AI.

Musk and Zuckerberg slung mud at each other in July, with the former warning of "evil AI" destroying humankind if not properly monitored and regulated, while the latter spoke optimistically about AI contributing to the greater good, such as diagnosing diseases before they become fatal.

"One is an AI alarmist and the other makes AI look charming ... AI, like any other technology, can be used for good or used for bad," said Kaufmann, who believes AI needs to be assessed objectively.

In the interim, Kaufmann believes systems need to be designed so that humans and machines can work together, not against each other. For example, Kaufmann envisions a future where humans wear smart lenses -- comparable to the Google Glass -- that act as "the third half of the brain" and pull up relevant information based on conversations they are having.

"Humans don't need to learn stuff like which Roman killed the other Roman ... humans just need to be able to ask the right questions," he said.

"The key difference between human and machine is the ability to ask questions. Machines are more for solutions."

Kaufmann admitted, however, that humans don't know how to ask the right questions a lot of the time, because we are taught to remember facts in school, and those who remember the most facts are the ones who receive the best grades.

He believes humans need to be educated to ask the right questions, adding that the question is 50 percent of the solution. The right questions will not only allow humans to understand the principles of the brain and develop true AI, but will also keep us relevant even when AI systems proliferate, according to Kaufmann.

If we want to slow down job loss, AI systems need to be designed so that humans are at the centre of it, Kaufmann said.

"While many companies want to fully automate human work, we at Starmind want to build a symbiosis between humans and machines. We want to enhance human intelligence. If humans don't embrace the latest technology, they will become irrelevant," he added.

The company claims its self-learning system autonomously connects and maps the internal know-how of large groups of people, allowing employees to tap into their organisation's knowledge base or "corporate brain" when they have queries.

Starmind platform

Starmind is integrated into existing communication channels -- such as Skype for Business or a corporate browser -- eliminating the need to change employee behaviour, Kaufmann said.

Questions typed in the question window are answered instantly if an expert's answer is already stored in Starmind, and new questions are automatically routed to the right expert within the organisation, based on skills, availability patterns, and willingness to share know-how. All answers enhance the corporate knowledge base.

"Our vision is if you connect thousands of human brains in a smart way, you can outsmart any machine," Kaufmann said.

On how this is different to asking a search engine a question, Kaufmann said Google is basically "a big data machine" and mines answers to questions that have been already asked, but is not able to answer brand new questions.

"The future of Starmind is we actually anticipate questions before they're even asked because we know so much about the employee. For example, we can say if you are a new hire and you consume a certain piece of content, there will be a 90 percent probability that you will ask the following three questions within the next three minutes and so here are the solutions."

Starmind is being currently used across more than 40 countries by organisations such as Accenture, Bayer, Nestl, and Telefonica Deutschland.

While Kaufmann thinks it is important at this point in time to enhance human intelligence rather than replicate it artificially, he does believe AI will eventually substitute humans in the workplace. But unlike the grim picture painted by critics, he doesn't think it's a bad thing.

"Why do humans need to work at all? I look forward to all my leisure time. I do not need to work in order to feel like a human," Kaufmann said.

When asked how people would make money and sustain themselves, Kaufmann said society does not need to be ruled by money.

"In many science fiction scenarios, they do not have money. When you look at the ant colonies or other animals, they do not have cash," Kaufmann said.

Additionally, if humans had continuous access to intelligent machines, Kaufmann said "the acceleration of human development will pick up" and "it will give rise to new species".

"AI is the ultimate tool for human advancement," he firmly stated.

Link:

True AI cannot be developed until the 'brain code' has been cracked: Starmind - ZDNet

GETTY

In parts of the US, when a suspect is taken in for questioning they are given a computerised risk assessment which works out the likelihood of the person reoffending.

The judges can then use this data when giving his or her verdict.

However, an investigation has revealed that the artificial intelligence behind the software exhibits racist tendencies.

Reporters from ProPublica obtained more than 7,000 test results from Florida in 2013 and 2014 and analysed the reoffending rate among the individuals.

GETTY

The suspects are asked a total of 137 questions by the AI system Correctional Offender Management Profiling for Alternative Sanctions (Compas) including questions such as Was one of your parents ever sent to jail or prison? or How many of your friends/acquaintances are taking drugs illegally?, with the computer generating its results at the end.

Overall, the AI system claimed black people (45 per cent) were almost twice as likely as white people (24 per cent) to reoffend.

In one example outlined by ProPublica, risk scores were provided for a black and white suspect, both of which on drug possession charges.

GETTY

The white suspect had prior offences of attempted burglary and the black suspect had resisting arrest.

Seemingly giving no indication as to why, the black suspect was given a higher chance of reoffending and the white suspect was considered low risk.

But, over the next two years, the black suspect stayed clear of illegal activity and the white suspect was arrested three more times for drug possession.

However, researchers warn the problem does not lie with robots, but with the human race as AI uses machine learning algorithms to pick up on human traits.

Joanna Bryson, a researcher at the University of Bath, told the Guardian: People expected AI to be unbiased; thats just wrong. If the underlying data reflects stereotypes, or if you train AI from human culture, you will find these things.

Asus

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Asus Zenbo: This adorable little bot can move around and assist you at home, express emotions, and learn and adapt to your preferences with proactive artificial intelligence.

This is not an isolated incident either.

Microsofts TayTweets (AI) chatbot was unleashed on Twitter last year which was designed to learn from users.

However, it almost instantly turned to anti-semitism and racism, tweeting: "Hitler did nothing wrong" and "Hitler was right I hate the Jews.

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REVEALED: AI is turning RACIST as it learns from humans - Express.co.uk

Microsoft last week filed its annual report with the SEC, and with it a new vision that emphasizes AI. The documents state the company will no longer be focused on mobile, but instead on implementing AI solutions.

In the documents, under the heading Our Vision the company put:

Our strategy is to build best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with artificial intelligence (AI)

Which should come as a surprise to no-one, Microsoft has gobbled up AI companies like Pac-Man eating dots, and is using them to do things like teach computers to be amazing at Ms. Pac-Man.

Microsoft started off 2017 by purchasing an AI company, which was added to its already robust machine-learningresearch team.

The companys Microsoft Research AI (MSR AI) group has been doing some impressive work, including helping the blind better understand their surroundings. And Microsoft makes hardware now, in the form of an AI co-processor chip for Hololens 2.0.

Microsoft isnt the only major legacy tech company fully prepared to shift to an AI-driven vision. IBM is famously flaunting their ride on the AI hype train with their cloud-based Watson, and Apple of course has Siri.

Theres no need to ask if AI is the future or not, because it is. Its only a matter of time now before Cortana gets appointed CEO (Sorry Satya!).

Microsoft's New Artificial Intelligence Mission Is Nothing To Dismiss on Seeking Alpha

Read next: Instagram now lets two people go Live in the same stream

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Microsoft shares its vision to become AI industry-leader - TNW