Category Archives: Ai

Vendors at this week's RSA cybersecurity show in San Francisco are pushing artificial intelligence and machine learningas the new wayto detect the latest threats, but RSA CTO Zulfikar Ramzan is giving visitors a reality check.

"I think it (the technology) moves the needle," he said on Wednesday. "The real open question to me is how much has that needle actually moved in practice?"

It's not as much as vendors claim, Ramzan warned, but for customers it won't be easy cutting through the hype and marketing. The reality is that a lot of the technology now being pushedisnt necessarily new.

In particular, he was talking about machine learning, a subfield in A.I. thats become a popular marketing term in cybersecurity. In practice, it essentially involves building algorithms to spot bad computer behavior from good.

RSA CTO Zulfikar Ramzan speaking at RSA 2017 in February.

However, Ramzan pointed out that machine learning in cybersecurity has been around for well over a decade. For instance, email spam filters, antivirus software and online fraud detection are all based on this technique of detecting the bad from good.

Certainly, machine learning has advanced over the years and it can be particularly useful at spotting certain attacks, like those that dont use malware, he said. But the spotlight on A.I. technologies also has to deal with marketing and building up hype.

Now all of a sudden, were seeing this resurgence of people using the how as a marketing push, he said, after his speech.

The result has created a lemons market, where clients might have trouble distinguishing between useful security products. Not all are equal in effectiveness, Ramzan claimed. For example, some products may generate too many false positives or fail to detect the newest attacks from hackers.

Theres no doubt you can catch some things that you couldnt catch with these techniques, he said. But theres a disparity between what a vendor will say and what it actually does.

Nevertheless, A.I. technologies will still benefit the cybersecurity industry, especially in the area of data analysis, other vendors say.

Right now, its an issue of volume. Theres just not enough people to do the work, said Mike Buratowski, a senior vice president at Fidelis Cybersecurity. Thats where an A.I. can come in. It can crunch so much data, and present it to somebody.

One example of that is IBM's latest offering. On Wednesday, the companyannouncedthat its Watson supercomputer can now help clients respond to security threats.

Within 15 minutes, Watson can come up with a security analysis to a reported cyber threat, when for a human it might have taken a week, IBM claimed.

Recorded Future is another security firm thats been using machine learning to offer intelligence to analysts and companies about the latest cybercriminal activities. The companys technology works by essentially scanning the internet, including black market forums, to pinpoint potential threats.

That might include a hacker trying to sell software exploits or stolen data, said AndreiBarysevich, director of advanced collection at the company.

When you cover almost a million sources and you only have 8 hours a day, to find that needle in the hay stack, you have to have some help from artificial intelligence, he said.

The RSA 2017 show floor.

Customers attending this weeks RSA show may be overwhelmed with the marketing around machine-learning, but itll only be a matter time, before the shoddier products are weeded out, Barysevich said.

We have hundreds of vendors here, from all over the country. But among them, there are five or ten that have a superior product, he said. "Eventually, the market will identify the best of the best.

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AI faces hype, skepticism at RSA cybersecurity show - PCWorld

Twenty-two years ago, researchers first reported that adolescents with autism spectrum disorder had increased brain volume. During the intervening years, studies of younger and younger children showed that this brain overgrowth occurs inchildhood.

Now, a team at the University of North Carolina, Chapel Hill, has detected brain growth changes linked to autism in children as young as 6 months old. And it piqued our interest because a deep-learning algorithm was able to use that data to predict whether a child at high-risk of autism would be diagnosed with the disorder at 24 months.

The algorithm correctly predicted the eventualdiagnosis in high-risk children with 81 percentaccuracy and 88 percentsensitivity. Thats pretty damn good compared withbehavioral questionnaires, which yield informationthat leads to early autism diagnoses(at around 12 months old) that are just 50 percent accurate.

This is outperforming those kinds of measures, and doing it at a younger age, says senior author Heather Hazlett, a psychologist and brain development researcher at UNC.

As part of the Infant Brain Imaging Study, a U.S. National Institues of Healthfunded study of early brain development in autism, the research team enrolled 106 infants withan older sibling who had been givenan autism diagnosis, and 42 infants with no family history of autism. They scanned each childs brainno easy feat with an infantat 6-, 12-, and 24 months.

The researchers saw no change in any of the babies overall brain growth between 6- and12-month mark.But there was a significant increase in the brain surface area of the high-risk children who were later diagnosed with autism. That increase in surface area was linked to brain volume growth that occurred between ages 12 and 24 months. In other words, in autism, the developing brain first appears to expand in surface area by 12 months, then in overall volume by 24 months.

The team also performed behavioral evaluations on the children at 24 months, when they were old enough to begin to exhibit the hallmark behaviors of autism, such as lack of social interest, delayed language, and repetitive body movements. The researchers note that the greater thebrain overgrowth, the more severe a childs autistic symptoms tended to be.

Though thenew findings confirmed that brain changes associated with autism occur very early in life,the researchers did not stop there. In collaboration with computer scientists at UNC and the College of Charleston, the team built an algorithm, trained it with the brain scans, and tested whether it could use these early brain changes to predict which children would later be diagnosed with autism.

It worked well. Using just three variablesbrain surface area, brain volume, and gender (boys are more likely to have autism than girls)the algorithm identified up eight out of 10 kids with autism. Thats pretty good, and a lot better than some behavioral tools, says Hazlett.

To train the algorithm, the team initially used halfthe data for training and the other half for testingthe cleanest possible analysis, according to team memberMartin Styner, co-director of the Neuro Image Analysis and Research Lab at UNC. But at the request of reviewers, they subsequently performed a more standard 10-fold analysis, in which data is subdivided into 10 equal parts. Machine learning is then done 10 times, each time with 9 folds used for training and the 10th saved for testing. In the end, the final program gathers together the testing only results from all 10 rounds to use in its predictions.

Happily, the two types of analysesthe initial 50/50 and the final 10-foldshowed virtually the same results, says Styner. And the team was pleased with the prediction accuracy. We do expect roughly the same prediction accuracy when more subjects are added, said co-author Brent Munsell, an assistant professor at College of Charleston, in an email to IEEE. In general, over the last several years, deep learning approached that have been applied to image data have proved to be very accurate, says Munsell.

But, like our other recent stories on AI out-performing medical professionals, the results need to be replicated before well see a computer-detected biomarker for autism. That will take some time, because it is difficult and expensive to get brain scans of young children for replication tests, emphasizes Hazlett.

And such an expensive diagnostic test will not necessarily be appropriate for all kids, she adds. Its not something I can imagine being clinically useful for every baby being born. But if a child were found to have some risk for autism through a genetic test or other marker, imaging could help identify brain changes that put them at greater risk, she notes.

IEEE Spectrums biomedical blog, featuring the wearable sensors, big data analytics, and implanted devices that enable new ventures in personalized medicine.

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Doctors outperform online apps at diagnosing symptoms 10Oct2016

Artificial intelligence performs just as well as eye docs in diagnosing congenital cataracts 1Feb

Deep learning algorithm identifies skin cancers as accurately as dermatologists 25Jan

Language barriers and human interfaces slow adoption of diagnostic-aid tech 11Nov2016

Medtronic's 252-electrode vest helps doctors pinpoint electrical malfunctions of the heart 9Feb

System captures Mach cone from laser pulse 20Jan

Class-action lawsuits target the biometric privacy policies of several Internet giants 29Dec2016

3D visualization of flu particles helps a vaccine manufacturer predict how to protect against the virus 15Dec2016

With wireless optogenetic tools, neuroscientists steer mice around their cages 28Nov2016

The new device can wrap around objects to image items with 3D curves 14Nov2016

Each fellow gets $825,000 to further their inventions 2Nov2016

Now used to brighten displays, quantum dots could one day guide a surgeons hand 21Sep2016

Researchers use fMRI feedback to "brain train" volunteers so that they had more positive or negative feelings about a person's face 16Sep2016

Raising $2.7 million was the easy part. Making devices for 13,000 backers has proven harder 14Sep2016

Low production costs make technique available to developing countries for detecting a wide variety of diseases 21Jul2016

Iris scanners provide excellent biometric identification, but they can be spoofed 20Jul2016

The U.S. governments Human Connectome Project moves into its second phaseand its brain scans are being used to predict individuals behavior and intelligence 21Jun2016

Brain scans provide a glimpse of a patients prognosis 15Jun2016

The virtual biopsy could lead to more accurate diagnoses of brain injury and PTSD, and better treatments 10Jun2016

New method exploits the luminescence of carbon nanotubes to detect tumors deep inside tissue 24May2016

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AI Predicts Autism From Infant Brain Scans - IEEE Spectrum

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how its being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

The Difference between AI, Machine Learning and Deep Learning

With all the quickly evolving nomenclature in the industry today, its important to be able to differentiate between AI, machine learning and deep learning. The simplest way to think of their relationship is to visualize them as a concentric model as depicted in the figure below. Here, AI the idea that came firsthas the largest area, followed by machine learningwhich blossomed later and is shown as a subset of AI. Finally deep learningwhich is driving todays AI explosion fits inside both.

AI has been part of our thoughts and slowly evolving in academic research labs since a group of computer scientists first defined the term at the Dartmouth Conferences in 1956 and provided the genesis of the field of AI. In the long decades since, AI has alternately been heralded as an all-encompassing holy grail, and thrown on technologys bit bucket as a mad conception of overactive academic imaginations. Candidly, until around 2012, it was a bit of both.

Over the past few years, especially since 2015, AI has exploded on the scene. Much of that enthusiasm has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe including images, video, text, transactions, geospatial data, etc.

On the same trajectory, deep learning has enabled many practical applications of machine learning and by extension the overall field of AI. Deep learning breaks down tasks in ways that make all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the foundation for the present and the future.

DownloadtheinsideBIGDATA Guide to Deep Learning & Artificial Intelligence, courtesy of NVIDIA.

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Defining AI, Machine Learning, and Deep Learning - insideHPC

Facebook (FB) is making the right choice in targeting video as a top priority, allowing it more time to catch up in the field of artificial intelligence, where it lags, says Richard Windsor, an analyst at Edison Investment Research.

Windsor, in an email note to clients, said that of three key areas of digital consumption gaming, search and video placing video first is the right move by Facebook

"This is because Facebook already has a lot of traction in this space and also because it is the least demanding in terms of requiring intelligent automation," he wrote.

Microsoft (MSFT), Alphabet (GOOGL) and Amazon (AMZN) have been investing heavily in artificial intelligence. Among the areas of AI applications are digital assistants for the home. Amazon has a big lead with its Echo device, while Alphabet has Google Home.

Microsoft has deep roots in artificial intelligence with its Cortana platform, available on mobile devices and PCs. This is also true of Apple (AAPL) with its Siri platform. While Siri is available on the iPhone and iPad, Apple has not yet signaled when it might introduce an in-home digital assistant.

IBD'S TAKE:Facebook and Amazon are two of the four closely watched FANG stocks, along with Netflix and Google's parent company, Alphabet. Internet stocks are expected tooutperform the broader marketin 2017.

Facebook on Tuesday made several announcements related to video, which could pose a threat to the YouTube business of Alphabet. The announcements included a Facebook app designed for watching Facebook videos on a television. The app will "roll out soon" to app stores for Apple TV, Amazon Fire TV and Samsung Smart TV, with more platforms to come," Facebook said in a blog post.

Facebook, in October, initially rolled out the ability for users to stream videos from Facebook to your TV. The announcement Tuesday expands this capability.

"With the launch of a TV app being just the latest move Facebook has made in video, it is increasingly clear that media consumption is Facebook's No. 1 priority for 2017," Windsor wrote.

While the Facebook TV app will initially be available on Amazon TV and Apple TV, Windsor says it will quickly spread to Microsoft Xbox, Sony (SNE) PlayStation and other streaming TV devices. He does not expect to find the app on Chromecast by Alphabet, "as Facebook's video aspirations are clearly a challenge to YouTube," he wrote.

The focus on video gives Facebook more time to develop its artificial intelligence technology.

"Wecontinue to see Facebook as the laggard in AI," Windsor wrote. "Targeting video is sensible as it gives it more time to improve its AI before having to apply it to more difficult tasks."

The fact that video is a fast-growing, but maturing, medium for digital advertising also means that the time to really address it is now, Windsor wrote. "The app on the TV is just the beginning and we would not be surprised to see this being followed up with premium content taking it into the realm of Netflix (NFLX), Hulu, YouTube and Amazon Prime."

Facebook, when it reported fourth-quarter earnings on Feb. 2 that blew past expectations, continued to benefit from a surge in advertiser demand, driven by newer ad formats such as video.

On the earnings conference call CEO Mark Zuckerberg said, "I see video as a megatrend on the same order as mobile. That's why we're going to keep putting video first across our family of apps and making it easier for people to capture and share video in new ways."

Facebook stock was down 0.3% to 133.44 onthe stock market today, and still in a buy zone from a breakout last month at 129.37, out of a cup-with-handle base.

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Facebook Push Into Video Allows Time To Catch Up On AI Applications - Investor's Business Daily

A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

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The giant human-like robot bears a striking resemblance to the military robots starring in the movie 'Avatar' and is claimed as a world first by its creators from a South Korean robotic company

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty Images

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi

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Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session

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A test line of a new energy suspension railway resembling the giant panda is seen in Chengdu, Sichuan Province, China

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A test line of a new energy suspension railway, resembling a giant panda, is seen in Chengdu, Sichuan Province, China

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A concept car by Trumpchi from GAC Group is shown at the International Automobile Exhibition in Guangzhou, China

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A Mirai fuel cell vehicle by Toyota is displayed at the International Automobile Exhibition in Guangzhou, China

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A visitor tries a Nissan VR experience at the International Automobile Exhibition in Guangzhou, China

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A man looks at an exhibit entitled 'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London

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A new Israeli Da-Vinci unmanned aerial vehicle manufactured by Elbit Systems is displayed during the 4th International conference on Home Land Security and Cyber in the Israeli coastal city of Tel Aviv

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Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S

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The Jaguar I-PACE Concept car is the start of a new era for Jaguar. This is a production preview of the Jaguar I-PACE, which will be revealed next year and on the road in 2018

AP

Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03' and other robots during a demonstration in Tokyo, Japan

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Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03'

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Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03' performs during its unveiling in Tokyo, Japan

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Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China

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Singulato Motors' concept car Tigercar P0

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The interior of Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China

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A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China

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Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo. The Connected company is a part of seven Toyota in-house companies that was created in April 2016

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A Toyota Motors employee demonstrates a smartphone app with the company's pocket plug-in hybrid (PHV) service on the cockpit of the latest Prius hybrid vehicle during Toyota's "connected strategy" press briefing in Tokyo

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An exhibitor charges the battery cells of AnyWalker, an ultra-mobile chasis robot which is able to move in any kind of environment during Singapore International Robo Expo

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A robot with a touch-screen information apps stroll down the pavillon at the Singapore International Robo Expo

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An exhibitor demonstrates the AnyWalker, an ultra-mobile chasis robot which is able to move in any kind of environment during Singapore International Robo Expo

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Robotic fishes swim in a water glass tank displayed at the Korea pavillon during Singapore International Robo Expo

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An employee shows a Samsung Electronics' Gear S3 Classic during Korea Electronics Show 2016 in Seoul, South Korea

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Visitors experience Samsung Electronics' Gear VR during the Korea Electronics Grand Fair at an exhibition hall in Seoul, South Korea

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Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Ford EEBL Emergency Electronic Brake Lights is demonstrated during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Full-scale model of 'Kibo' on display at the Space Dome exhibition hall of the Japan Aerospace Exploration Agency (JAXA) Tsukuba Space Center, in Tsukuba, north-east of Tokyo, Japan

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Miniatures on display at the Space Dome exhibition hall of the Japan Aerospace Exploration Agency (JAXA) Tsukuba Space Center, in Tsukuba, north-east of Tokyo, Japan. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module 'Kibo' of the International Space Station (ISS) and develop launch vehicles

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The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. At this biennial event, the participating companies exhibit their latest service robotic technologies and components

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The robot developed by Seed Solutions sings and dances to music during the Japan Robot Week 2016 at Tokyo Big Sight

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Government and industry are working together on a robot-like autopilot system that could eliminate the need for a second human pilot in the cockpit

AP

Aurora Flight Sciences' technicians work on an Aircrew Labor In-Cockpit Automantion System (ALIAS) device in the firm's Centaur aircraft at Manassas Airport in Manassas, Va.

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Stefan Schwart and Udo Klingenberg preparing a self-built flight simulator to land at Hong Kong airport, from Rostock, Germany

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An elated customer at the launch of PlayStation VR at the GAME Digital Westfield White City midnight launch.

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Elon Musk: Humans must become cyborgs to avoid AI domination - The Independent

Forbes

IoT And AI: Improving Customer Satisfaction Forbes Truethe Internet of Things (IoT) and artificial intelligence (AI) hold huge promise in helping us better engage and satisfy our customers. But that promise still depends heavily on our ability to process and act on the data we're gathering in a way ...

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IoT And AI: Improving Customer Satisfaction - Forbes

Slide: 1 / of 1. Caption: Getty Images

Artificial intelligence is not one thing, but many, spanning several schools of thought. In his book The Master Algorithm, Pedro Domingos calls them the tribes of AI.

As the University of Washington computer scientist explains, each tribe fashions what would seem to be very different technology. Evolutionists, for example, believe they can build AI by recreating natural selection in the digital realm. Symbolists spend their time coding specific knowledge into machines, one rule at a time.

Right now, the connectionists get all the press. They nurtured the rise of deep neural networks, the pattern recognition systems reinventing the likes of Google, Facebook, and Microsoft. But whatever the press says, the other tribes will play their own role in the rise of AI.

Take Ben Vigoda, the CEO and founder of Gamalon. Hesa Bayesian, part of the tribe that believes in creating AI through the scientific method. Rather than building neural networks that analyze data and reach conclusions on their own, he and his team useprobabilistic programming, a technique in which they start with their own hypotheses and then use data to refine them. His startup, backed by Darpa, emerged from stealth mode this morning.

Gamalons tech can translate from one language to another, and the company isdevelopingtools that businesses can use to extract meaning from raw streams of text. Vigoda claims his particular breed of probabilistic programming can produce AI that learns more quickly than neural networks, using much smaller amounts of data. You can be very careful about what you teach it, he says, and can edit what youve taught it.

As others point out, an approach along these lines is essential to the rise of machines capable of truly thinking like humans. Neural networks require enormous amounts of carefully labelled data, and this isnt always available. Vigoda even goes so far as to say that his techniques will replace neural networks completely, in all applications. That is very, very clear, he says.

But just as deep learning isnt the only way to artificial intelligence, neither is probabilistic programming. Or Gaussian processes. Or evolutionary computation. Or reinforcement learning.

Sometimes, the AI tribesbadmouth each other. Sometimes, they play up their technology at the expense of the others. But the reality is that AI will risefrom many technologies working together. Despite the competition, everyone is working toward the same goal.

Probabilistic programming lets researchers build machine learning algorithms more like coders build computer programs. But the real power of the technique lies inits ability to deal with uncertainty. This can allow AI to learn from less data, but it can also helpresearchers understand why an AI reaches particular decisionsand more easily tweak the AI if they dont agree with those decisions. True AI will need all that, whether it powers a chatbot trying to carry on a human-like conversation or an autonomous car trying to avoid an accident.

But neural networks have proven their worth with, among other things, image and speech recognition, and theyre not necessarily in competition with techniques like probabilistic programming. In fact, Google researchers are building systems that combine the two. Their strengths complement one another. Deep neural networks and probabilistic models are closely related, says David Blei, a Columbia University computer scientist and an advisor to Gamalon who has worked with Google research on these types of mixed models. Theres a lot of probabilistic modeling happening inside neural networks.

Inevitably, the best AI will combine several technologies. Take AlphaGo, the breakthrough system built by Googles DeepMind lab. It combined neural networks with reinforcement learning and other techniques. Blei, for one, doesnt see a world oftribes. It doesnt exist for me, he says. He sees a world in which everyone is reaching for the same master algorithm.

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AI's Factions Get Feisty. But Really, They're All on the Same Team - WIRED

Many experts in the field firmly believe 2017 will be a breakout year for both artificial intelligence and robotics, since the two often go together. Spoiler alert: it's all good.

AI Makes Robots Smarter

Robots use an increasing number of sensing modalities including taste, smell, sonar, IR, haptic feedback, tactile sensors, and range of motion sensors. They are also becoming better at picking up on facial expressions and gestures, so their interactions with humans become more natural, said Kevin Curran, IEEE senior member and professor of cyber security at Ulster University.

"Basically, AI is crucial for all their learning and adaptive behavior so they can adapt existing capabilities to cope with environmental changes. AI is key to helping them learn new tasks on the fly by sequencing existing behaviors," he said.

Karsten Schmidt, head of technology at the Innovation Center Silicon Valley for SAP Labs echoed this sentiment. "In 2017, we will see AI gain greater acceptance and momentum as humans come to increasingly rely, trust and depend more on AI-driven decisions and question them less. This will happen as a direct result of improved AI learning due to more usage and a broader user base, and as the quality and usefulness of AI software in turn improves," he said.

Meet Your AI Co-Worker

Many people fear losing their jobs to robots, but more than likely you will have a robot for a co-worker. Then again, if you've been in the workforce long enough, you've probably already had a robot for a co-worker, just in human form.

"In 2017, we are seeing a growing emergence of robots designed to operate alongside people in everyday human environments. Autonomous service robots that assist workers in warehouses, deliver supplies in hospitals, and maintain inventory of items in grocery stores are emerging onto the market," said Sonia Chernova, assistant professor at Georgia Tech College of Computing.

These systems need humans because one thing robotics researchers are still struggling with is robotic arms. There's no substitute for the human arm to pick things up and manipulate objects. "[Robot arms] have of course been used successfully for decades in manufacturing, but current techniques work reliably only in controlled factory environments, and are not yet robust enough for the real world," said Chernova.

This could lead to the rise of "AI Supervisors," said Tomer Naveh, CTO of Adgorithms, an AI-based digital marketing platform. Robots already have taken on many labor-intensive, manual (read: boring) tasks we do in our everyday life but robots will get smarter, and need AI to do it, he said.

"AI systems will get better at communicating their decisions and reasoning to their operators, and those operators will respond with new rules, business logic, and feedback that make it more and more useful in practice over time. As a result we will see people shifting from doing tasks by themselves, to supervising AI software on how to do it for them," he said.

That's actually a disturbing thought.

Changing Retail

AI and robotics will slowly move into another area where human error is common: retail. To some degree there is already automation in optical scanners and retail tracking used by stores to manage inventory, but it will be considerably improved.

The retail industry, for example, has been unable to address the problem of non-scanned items at checkout, which accounts for 30% of retailers annual losses. They only discover the loss in inventory well after the fact.

"AI is stepping in to address issues of this caliber across industries, and as a result, its often gathering just as much data as its processing. This resulting data is becoming a secondary benefit to businesses that use AI. AI Apps created to detect these non-scans are now also providing retailers with information about their origins, whether theyre fraudulent or accidental, and how customers and cashiers are gaming the system," said Alan OHerlihy, CEO of Everseen, developer of AI products for point of sale systems.

And as consumers have positive experiences with drone deliveries, public opinion may go a long way towards opening up regulations for further drone use, said Jake Rheude, director of business development for Red Stag Fulfillment, an eCommerce fulfillment provider.

"Consumers are already fully on board with the concept of drone delivery. According to The Walker Sands Future of Retail 2016 Study, 79% of US consumers said they would be 'very likely' or 'somewhat likely' to request drone delivery if their package could be delivered within an hour. And 73% of respondents said that they would pay up to $10 for a drone delivery. This is an unprecedented level of acceptance for new technology with so little real word experience from consumers," he said.

AI in Your Home

Another prediction made by umpteen science fiction movies usually with an alarmist tone is that AI will come into the home in a big way. It already has if you have an iPhone, with Siri, or use Windows 10 and Cortana. Gradually it will move into other devices, the experts predict.

"Alexa, Cortana and Siri are great, but they still lack the sophistication and accuracy to be relied upon as a utility. In 2017, advances in natural language processing and natural language generation will transform what digital assistants understand and how they analyze and respond with legitimately useful information. The era of just opening a related Wikipedia page are over," said Matt Gould, AI expert and co-founder of Arria NLG, which develops technology that translates data into language.

To make these devices work optimally, they need to develop an emotional quotient, or an EQ, predicts Dr. Rana el Kaliouby, CEO and co-founder, Affectiva, which develops facial recognition software. "We expect to see Emotion AI really come to the fore this year, and once AI systems develop social skills and rapport, AI interfaces will be more engaging and sticky, and less frustrating for their users, driving even wider adoption of the technology," she said.

She predicts that in the future, all of our devices will be equipped with a chip that can adapt our experiences to our emotions in real time, by reading facial expressions, analyzing tone of voice and possessing built-in emotion awareness. "The ability of technology to adapt to our mood and preferences could enhance experiences ranging from driving a car to ordering a pizza," she said.

And this should mean less typing, said Scott Webb, president of Avionos. "Physical interaction with hand-to-keyboard commands will give way to more organic input methods like voice and physical response as we move forward," he said.

Better Security

It's been said before but is worth repeating that AI will improve security because, like in so many other cases, security AI won't be prone to human failings of boredom, fatigue, illness and disinterest that often causes a security lapse. It will also have much faster reaction times and much better recognition of unusual patterns.

"Machine learning and the models generated through processes around machine learning are helping enterprises analyze massive amounts of data and identify trends, anomalies, and things not detectable through standard modeling. Machine learning algorithms are helping security researchers dynamically identify threats, airlines improve maintenance and reliability of their aircraft, and provide the back bone for self-driving cars to analyze data in real-time to make decisions," said David Dufour, senior director of engineering at antimalware vendor WebRoot.

That immediacy is needed with catching data breaches, as well. The average time to discover a network attacker is about five months, giving attackers plenty of time to achieve their goals, said Peter Nguyen, director of technical services at LightCyber, which does behavior based security software.

"Finding signs of an attacker is difficult and demands the use of AI. Instead of trying to encounter, identify and block threats by their known characteristics, the way to find an active attacker is through their operational activities. Using machine learning, its possible to learn the good behavior of all users and devices and then find anomalies. Then, AI can be focused to find those anomalies that are truly indicative of an active attack," he said.

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SHA-1 End Times Have Arrived

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Cryptographers Dismiss AI, Quantum Computing Threats - Threatpost

A consumers credit score used to be a commonly understood number the time-honored FICO score that banks all used in their underwriting. But banks increasingly are relying on dozens of scores that reflect a variety of data sources, analytics and use of artificial intelligence technology.

The use of AI offers lenders the ability to get a precise look into someones creditworthiness and score those previously deemed unscorable.

But such scoring techniques also bring uncertainty: What it will take to convince regulators that AI-based credit scores are not a black box? How do you get a system trained to look at the interactions of many variables, to produce one clear reason for declining credit? Data scientists at credit bureaus and banks are working to find answers to questions like these.

The benefits of AI-powered credit scores

There are two main reasons to use artificial intelligence to derive a credit score. One is to assess creditworthiness more precisely. The other is to be able to consider people who might not have been able to get a credit score in the past, or who may have been too hastily rejected by a traditional logistic regression-based score. In other words, a method that looks at certain data points from consumers credit history to calculate the odds that they will repay.

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Machine learning can take a more nuanced look at consumer behavior.

A neural network more closely mimics the way humans think and reason, whereas linear models are more dogmatic youre imposing structure on data as opposed to letting the data talk to you, said Eric VonDohlen, chief analytics officer at the online lender Elevate. The more complex reasoning of artificial intelligence can find things in the data that wouldnt be apparent otherwise.

And instead of considering one variable at a time, an artificial intelligence engine can look at interactions between multiple variables.

Its harder for the workhorse, logistic regression, to do that, said Dr. Stephen Coggeshall, chief analytics and science officer at ID Analytics. You have to do a lot of data preprocessing using expert knowledge to even attempt to find those nonlinear interactions.

Consumers with several chargeoffs in their histories would most likely be considered high-risk borrowers by most traditional models. But an AI engine might perceive mitigating variables; though the consumers might have skipped payments on three debts in the past 24 months, they have paid on time consistently for the past year and have successfully obtained new lines of credit.

It looks like that bad performance or bad history is in your past, VonDohlen said. That would be a simple example of how an AI world might help cast data in a more positive and more accurate light.

AI-based credit scoring models let Elevate make sharper predictions of credit risk, approve the right people and offer better pricing to people who deserve it, VonDohlen said.

Elevate is deploying its new, AI-based models gradually, starting with 1% of potential borrowers, testing the results, and gradually applying them to more people.

Credit bureaus are starting to adopt AI in their credit scores, too.

Equifax calls the machine-learning software that it uses in credit scores NeuroDecision Technology.

Technologies like Hadoop, which allow massive amounts of data to be stored and analyzed quickly, are making AI-based credit scores possible, said Peter Maynard, senior vice president of global analytics at Equifax.

Before, if you gave me a million observations, it would take a week to sort through it, Maynard said.

ID Analytics uses what it calls convolutional neural nets, a flavor of deep learning, in its fraud and credit scores, Coggeshall said. For its Credit Optics Full Spectrum credit score, AI engines look at consumer payment data from wireless, utility and marketplace loan providers, to score consumers who have thin or no credit bureau files, including young people and new credit seekers.

FICO also offers a score called XD thats based on telephone and utility bill payments and property records. It gives high marks to people who have faithfully paid their phone, oil and gas bills and who have not moved around too much.

Experian is taking a more cautious approach. It uses traditional logistic regression methods for its credit scores, but in its labs it experiments with machine learning.

When the technology seems to make a significant difference in performance, the company will provide credit scores based on machine learning, said Eric Haller, executive vice president of Experians Global DataLabs. For now, he sees machine learning giving only a nominal lift in results.

The opportunity is not building the next VantageScore, because believe it or not, those scores work really well, he said.

To let clients experiment with machine learning, Experian offers an analytical sandbox with its credit data loaded into it.

They can load their own data in and well sync it up with historical credit archive data, and weve overlaid it with a set of machine-learning tools, Haller said. Most large financial institutions are using the sandbox today, he said.

TransUnion, the other major credit rating agency, did not respond to requests for an interview.

Now for the confusing part

The cons of AI-enhanced credit scores include the risk that the full underwriting process will be hidden from consumers and that the practice would raise transparency questions among regulators.

Last month, the Consumer Financial Protection Bureau imposed $23 million in fines to TransUnion and Equifax, noting they claim banks use their scores to determine creditworthiness, when that isnt always the case.

In their advertising, TransUnion and Equifax falsely represented that the credit scores they marketed and provided to consumers were the same scores lenders typically use to make credit decisions, the CFPB said in a press release announcing the fines. In fact, the scores sold by TransUnion and Equifax were not typically used by lenders to make those decisions.

Some say the regulator was misguided.

Both scores being sold by TransUnion and Equifax, VantageScore and the Equifax RiskScore, are real credit scores that are Equal Credit Opportunity Act compliant, are commercially available to lenders and are, in fact, used by lenders, said credit expert John Ulzheimer.

Equifax says it ran all of its AI-based scoring technology past the OCC, Fed and CFPB and got a positive response. ID Analytics said it worked closely with lawyers, compliance officers and regulators to assure the technology complied with various lending rules.

Another challenge to using artificial intelligence, specifically neural networks, in credit scores and models, is that its harder to provide the needed reason code to borrowers the explanation of why they were denied credit.

Concerns about the reason code are the main reason many businesses dont use nonlinear machine-learning models for credit scores yet.

A lot of the confusion and heartburn is around, How do you boil an extremely data-rich learning process into a marginal rationale for declining a loan? VonDohlen said.

However, neural networks, which are essentially designed to think like a brain, can also be used to help find the one variable that represents the greatest risk.

Its almost never the case that you would decline someone for a rats nest of variable relationships, VonDohlen said. The reasons for credit denial, he said, "are almost always very clear.

Equifax has developed a proprietary algorithm that can generate reason codes for consumers, Maynard said.

Experian is also working on techniques that would make AI credit-based score decisions more explainable and auditor friendly.

Were not operating under any assumption that a black box credit scoring model would even work or be accepted in the market, Haller said. We are 100% focused on how do we bridge the gap such that we can bring better performance to models, but still maintain the same integrity, where they can be explained to the OCC and our clients are comfortable with understanding how the models are working and the results theyre getting.

But in the end, consumers wont be confused, according to Ulzheimer.

Regardless of how many scoring systems are being used, they are all based on three credit reports, he said. If you've got three great credit reports, then every single scoring system being used is going to yield a high score.

Penny Crosman is Editor at Large at American Banker.

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Is AI making credit scores better, or more confusing? - American Banker