Category Archives: Artificial Intelligence

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Carnegie Mellon University announced Tuesday an initiative to bring together its research and education related to artificial intelligence.

CMU AI will coordinate faculty, students and staff working on AI in robotics, engineering, language, human-computer interaction, machine learning and more.

Having grown large, we've also grown a little further apart, and in the context of AI, we are bringing it back to together, said Jaime Carbonell, director of CMU's Language Technologies Institute. And we expect to grow more

CMU AI will create one of the largest and most experienced artificial intelligence research groups in the world, the university said. Carbonell said researchers classified their work based on the sub-field of artificial intelligence in which they worked, such as robotics, machine translation or machine learning, not under the umbrella of AI. Now those disciplines will be under CMU AI, underscoring the university's commitment to the research and potentially upping the university's public profile and funding.

It certainly helps the messaging, Carbonell said. But more than messaging, it helps the substance.

Carbonell said projects like analyzing social networks for nefarious activity and designing robots to care for the elderly will benefit from harnessing the university's entire AI hive mind.

Artificial Intelligence was essentially created at CMU. In 1956, professors Allen Newell and Herbert A. Simon helped write Logic Theorist, considered the first artificial intelligence computer program.

Since, the university's advancements in artificial intelligence have worked their way into everything from self-driving cars to poker bots that can defeat the best players in the world. CMU AI projects help computers recognize faces and images and power robot soccer players and IBM's Jeopardy-playing Watson. They've improved instant replay in sports and algorithms that match kidney donors with recipients. Two teams of CMU students are working with Amazon to improve Alexa , its home assistant. Another team is using machine learning to develop a reading and math tutor for kids in countries facing teacher shortages. The RoboTutor project is a semifinalist in the $15 million Global Learning XPRIZE.

AI technologies developed at CMU have been acquired by Facebook, Amazon, Google and more.

AI is not something that a lone genius invents in the garage, Andrew Moore, the dean of CMU's School of Computer Science, said in a statement. It requires a team of people, each of whom brings a special expertise or perspective. CMU researchers have always excelled at collaboration across disciplines, but CMU AI will require all of us to work together in unprecedented ways.

RELATED: K&L Gates gives $10M to CMU to study ethics of AI

The initiative will bring together more than 100 professors and researchers working on artificial intelligence in CMU's School of Computer Science's seven departments. Moore will direct the initiative. Carbonell will lead the initiative along with Martial Hebert, the head of the Robotics Institute; Tuomas Sandholm, the computer science professor behind Libratus, the poker bot, and Manuela Veloso, the head of the Machine Learning Department.

The initiative will focus on two things. It will work to educate a new breed of AI scientists. About 1,000 students, more than half of the School of Computer Science, are working on AI-related projects. Moore said these are the people who will improve life through technology and shape the rest of the century.

Exposing these hugely talented human beings to the best AI resources and researchers is imperative for creating the technologies that will make our lives healthier and safer in the future, Moore said.

The initiative will also focus on creating new capabilities for AI. It will bring together work in machine learning, the study of how software can make decisions and learn through experience; machine translation, using computers to understand and translate languages; human-computer interaction, how people and machines can work together, and robotics, which is renowned for its computer vision group studying how computers understand images.

Students who study AI at CMU have an opportunity to work on projects that unite multiple disciplines, Veloso said. CMU students at all levels have a big impact on what AI can do for society.

Aaron Aupperlee is a Tribune-Review staff writer. Reach him at aaupperlee@tribweb.com, 412-336-8448 or via Twitter @tinynotebook.

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CMU to harness power of collaboration to advance artificial ... - Tribune-Review

Before the internet arrived, I used to be an equity analyst. I'd spend my days finding and arranging metaphorical jigsaw pieces to come up with a complete picture and an investment recommendation, which the sales guys would disseminate by telephoning their favorite clients first, and the ones they didn't like so much a bit later.

Today, at asset management companies and other financial institutions, there are still large teams of analysts and portfolio managers, sifting through data, developing investment theses and making asset allocation decisions. The difference is that we are deluged in data. Not only is the amount of data available to us accelerating, the nature of it is changing, with new sources of information being seen as potentially relevant for analysis. For example, the location-based data that comes from your mobile phone shows whether you are in a mall. Scaled over the whole of the country, this could allow us to see what is happening to footfall, which would help with understanding retail sales in real time. The footage from closed-circuit television that shows what's happening in transportation is another example. Or social media analysis of events happening in real time from people on the ground. Or weather data. The list goes on.

The reality is that there is just too much data for humans to be able to use. Opportunities go wasted because a team of humans just cannot create a sophisticated response to all of this. Of course, funds have been using complex algorithmic-driven trading strategies for years, but this is largely confined to market data. Imagine if we could take all the data that's coming from the real economy and use that to discern price, predict performance, understand risk and make better investment decisions. The only feasible way to do this is to use computing power to ingest the data, understand correlation and causation, and deduce rapidly enough how to respond. Artificial intelligence has now advanced to the stage where this is possible. While this gives rise to some interesting opportunities to radically transform investment management and capital markets, it will be very disruptive for people who work in the industry.

Let's assume that you use very sophisticated AI-driven models to scan data from not just the market but a whole plethora of other sources to define, implement, monitor, refine and adjust your trading strategies. What kind of people do you now need to employ? Performance will come down to how well your combination of engineers, scientists and market people can define and improve those models. The kinds of people employed in the industry will change; we will need people who can model data, and others who can validate the models and the results. And we will not need so many people. Much of the dialogue around the types of jobs that will disappear because of artificial intelligence has centered on relatively unskilled jobs, but in financial services it will be expensive, highly educated Wall Street-types finding themselves out of work. An asset manager friend of mine agreed. He also said there's no way he'd go public about using this strategy; he'd prefer investors think how great his team was at stockpicking!

Change is already underway. One of the most high-profile companies in this space is Kensho, which is backed by Google, Goldman Sachs and S&P Global. Kensho uses AI to scan vast data sets much more quickly and accurately than analysts, and sells the information to banks and other financial institutions.

One competitor is Sentifi, which takes in data from thousands of sources, then filters it for accuracy. The founder got the idea after the nuclear disaster at Fukushima, when an investment manager friend griped that it would take three weeks of sifting through seemingly unconnected data to work out the implications of this event on his portfolio.

One hedge fund taking artificial intelligence to the next level is Numerai - which doesn't even employ the AI talent! This San Francisco fund encrypts its trading data and then crowdsources AI-based algorithms from anyone who wants to have a go. Contributors who develop algorithms that successfully improve performance get paid in bitcoin.

It's a long way from when I used to analyze company reports, scan articles in actual newspapers and use old-fashioned methods like the telephone and company visits to develop my investment ideas.

Hazel Moore is the chairman and co-founder of the London investment bank FirstCapital.

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BankThink Is artificial intelligence the future of capital markets? - American Banker (subscription)

Acquistions of AI startups are rapidly increasing while the health AI market is set to register an explosive CAGR of 40 percent through 2021.

Artificial Intelligence can help drive the Triple Aim in healthcare, reducing cost, improving qualityand expanding access, according toArtificial Intelligence: Healthcares New Nervous System from Accenture.

Acquisitions of AI developers in health will be fast-paced, growing at a compound annual growth rate of 40 percent explosive in the word of Accenture moving from $600 million in 2014 to $6.6 billion in 2021.

What these AI startups will do is to enable machines to sense, comprehend, act and learn, Accenture foresees, to augment administrative and clinical tasks which could free up healthcare labor (doctors, other clinicians, and accountants) to work at their highest-and-best-use.

The most impactful AI-driven application would be robot-assisted surgery, generating $40 billion of value, annually, by 2026. Next in AI-health value-creation include virtual nursing assistants (valued at $20 billion annually), administrative workflow support ($18 billion), fraud detection ($17 billion), and medication error reduction ($16 billion).

Artificial Intelligence, AI, can help drive the Triple Aim in healthcare, reducing cost, improving quality, and expanding access, according toArtificial Intelligence: Healthcares New Nervous System from Accenture.

Acquisitions of AI developers in health will be fast-paced, growing at a compound annual growth rate of 40 percent explosive in the word of Accenture moving from $600 million in 2014 to $6.6 billion in 2021.

What these AI startups will do is enable machines to sense, comprehend, act and learn, Accenture foresees, to augment administrative and clinical tasks, which could free up healthcare labor (, doctors, other clinicians, and accountants) to work at their highest-and-best-use.

The most impactful AI-driven application would be robot-assisted surgery, generating $40 billion of value, annually, by 2026. Next in AI-health value-creation include virtual nursing assistants (valued at $20 billion annually), administrative workflow support ($18 billion), fraud detection ($17 billion), and medication error reduction ($16 billion).

Acquistions of AI startups are rapidly increasing while the health AI market is set to registr an explosive CAGR of 40 percent through 2021.

Artificial Intelligence can help drive the Triple Aim in healthcare, reducing cost, improving quality, and expanding access, according toArtificial Intelligence: Healthcares New Nervous System from Accenture.

Acquisitions of AI developers in health will be fast-paced, growing at a compound annual growth rate of 40 percent explosive in the word of Accenture moving from $600 million in 2014 to $6.6 billion in 2021.

What these AI startups will do is to enable machines to sense, comprehend, act and learn, Accenture foresees, to augment administrative and clinical tasks which could free up healthcare labor (doctors, other clinicians, and accountants) to work at their highest-and-best-use.

The most impactful AI-driven application would be robot-assisted surgery, generating $40 billion of value, annually, by 2026. Next in AI-health value-creation include virtual nursing assistants (valued at $20 billion annually), administrative workflow support ($18 billion), fraud detection ($17 billion), and medication error reduction ($16 billion).

Artificial Intelligence, AI, can help drive the Triple Aim in healthcare, reducing cost, improving quality, and expanding access, according toArtificial Intelligence: Healthcares New Nervous System from Accenture.

Acquisitions of AI developers in health will be fast-paced, growing at a compound annual growth rate of 40 percent explosive in the word of Accenture moving from $600 million in 2014 to $6.6 billion in 2021.

What these AI startups will do is enable machines to sense, comprehend, act and learn, Accenture foresees, to augment administrative and clinical tasks, which could free up healthcare labor (, doctors, other clinicians, and accountants) to work at their highest-and-best-use.

The most impactful AI-driven application would be robot-assisted surgery, generating $40 billion of value, annually, by 2026. Next in AI-health value-creation include virtual nursing assistants (valued at $20 billion annually), administrative workflow support ($18 billion), fraud detection ($17 billion), and medication error reduction ($16 billion).

This blog was first published on Health Populi.

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Artificial Intelligence is becoming the new operating system in health - Healthcare IT News (blog)

If you struggle with stress or anxiety, you are far from alone. In fact, most US workers say they suffer from stress on the job. Thankfully, technology and science are teaming up to fix this growing issue with a whole slew of meditation and relaxation based tools. One example that's currently sweeping the industry is Aura, an app that helps you reach inner calmness through short, guided meditation sessions. Right now, you can get lifetime Premium access for just $59.99 via the Popular Science Shop.

While few of us have time for yoga classes and prolonged mindfulness, we can all spare 10 minutes. Aura helps you hit maximum relaxation in the minimum time by employing artificial intelligence that tailors your meditations to your state of mind.

When you open Aura, the app first asks about your mood and how long you have to meditate. Sessions last between 3 and 10 minutes with accompanying audio that has been crafted by meditation teachers and therapists. Aura even helps you track your mood over time, so you can see the improvement.

As a Premium member, you get unlimited sessions for life. Worth $399, Aura Premium lifetime subscriptions are now just $59.99 for a limited time.

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Aura uses artificial intelligence to deliver personalized meditations - Popular Science

FRANKFURT: German carmaker Volkswagen said on Tuesday it would cooperate with US chipmaker Nvidia on deep learning software that could be used to manage traffic flows or make it easier for humans to work with robots.

"Artificial intelligence is the key to the digital future of the Volkswagen Group," Volkswagen Chief Information Officer Martin Hofmann said in a statement.

"We want to develop and deploy high-performance AI systems ourselves. This is why we are expanding our expert knowledge required. Cooperation with Nvidia will be a major step in this direction," he said.

Nvidia came to prominence in the gaming industry for designing graphics processing chips, but in recent years has been a key player in the automotive sector for providing the so-called "brain" of the autonomous vehicle.

The U.S.-based group separately announced it was also partnering with Volvo Cars and Swedish auto supplier Autoliv to develop self-driving car technology for vehicles due to hit the market by 2021.

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Volkswagen, Nvidia to cooperate on artificial intelligence - Economic Times

When Anna-Katrina Shedletsky was working as an engineer at Apple, she often found herself traveling overseas and spending weeks at the factory to fix a problem on an electronics assembly line. Finding the root cause of an anomaly can be like finding a needle in a haystack. It is a highly manual failure analysis process that can cause significant delays to the schedule.

And, when it comes to producing product, time is money. Not to mention the small window of opportunity once the product hits the market. For example, the Apple AirPods wireless earbuds shipped a few months after the release of the iPhone 7which had no headphone jack. A costly, and somewhat embarrassing problem.

Luckily, Anna, who was the product design lead for Apple Watch, wasnt part of the AirPods debacle. And, actually, she wasnt part of Apple at all at the time, as she was busy launching her own businesswhich is creating a system that will help electronics companies identify and fix assembly problems much faster. Oh, and drawing on her and her colleagues experiences spending hundreds of days at manufacturers responsible for millions of Apple products, she and her team have a deep understanding of the inefficiencies in the new product development processand the value of not having to travel far from home. To that end, the product Shedletsky designed can remotely analyze anomalies on the line.

Shedletsky is the co-founder and CEO of Instrumental. Established in May of 2015, the California start-up has raised $10.3 million backed by Eclipse Ventures, First Round Capital and Root Ventures. Whats unique here is that the hardware/software product leverages machine learning to identify problems quickly.

The system includes inspection stations and software tools that enable engineers to remotely review images of any unit, while virtually tearing down a device to understand what went wrong, take measurements, communicate with the global team, and make fixes or specification changes to stop delays before they start.

Heres how it works: First, inspection systems take a lot of images of the product while on the assembly line. Then, it makes those images remotely searchable and comparable. And, lastly, it applies learning and reacting to assembly line data so engineers can prevent further issues.

The machine-learning feature, called Detect, launched this month, highlights units that appear defective giving customers a significant edge in resolving product issues.

Detect uses Convolutional Neural Networks, a machine learning technique, to process hundreds of units and identify the most interesting units to review in seconds, Shedletsky said. Detect requires no foresight of what might go wrong, no training, and no golden units. It works on both small and large datasets.

When used in combination with other Instrumental software tools, an engineer can identify an issue and then take the next step by virtually disassembling concerning units and even taking measurements to understand what is wrong. These remote and on-demand first pass failure analysis tools save significant time and communication between companies and the factories that make their products.

And, while Instrumental Detect automatically processes hundreds of units and identifies the most interesting issues in seconds, in the near future, the companywill begin alerting engineers directly when it discovers anomalous units.

With the Instrumental system, teams can:

Triage defective units automatically

Restart downed lines hours or days faster

Identify root cause in minutes

Testhypotheses without building more units

Monitor and set cosmetic specifications remotely

Keep teams aligned around the globe

Right now, the company is putting a lot of effort into electronic manufacturing, but they are expanding quickly to any brand building serialized units. According to the company, Instrumental customers, including Fortune 500 companies, have used the system to virtually disassemble 16,000 units and to take over 40,000 measurements, all remotely. Multiple customers have saved over $350,000 in the first several months by using Instrumental to respond to issues, the company said.

Theres no going back, robotics and automation have already changed manufacturing. Intelligence like the kind we are building at Instrumental will change it again, Shedletsky said. We can radically improve how companies make products today and we hope to soon fundamentally change manufacturing as a whole.

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Artificial Intelligence on the Assembly Line - Automation World

The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off.In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything.

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On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'...

It was, for decades, primitive technology. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it won't be long before machines, like their human creators, begin thinking for themselves, creatively. Independently with judgment -- sometimes better judgment than humans have.

As we first reported last fall, the technology is so promising that IBM has staked its 106-year-old reputation on its version of artificial intelligence called Watson -- one of the most sophisticated computing systems ever built.

John Kelly, is the head of research at IBM and the godfather of Watson. He took us inside Watson's brain.

Charlie Rose: Oh, here we are.

John Kelly: Here we are.

Charlie Rose: You can feel the heat already.

John Kelly: You can feel the heat -- the 85,000 watts you can hear the blowers cooling it, but this is the hardware that the brains of Watson sat in.

Five years ago, IBM built this system made up of 90 servers and 15 terabytes of memory enough capacity to process all the books in the American Library of Congress. That was necessary because Watson is an avid reader -- able to consume the equivalent of a million books per second. Today, Watson's hardware is much smaller, but it is just as smart.

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What happens when Charlie Rose attempts to interview a robot named "Sophia" for his 60 Minutes report on artificial intelligence

Charlie Rose: Tell me about Watson's intelligence.

John Kelly: So it has no inherent intelligence as it starts. It's essentially a child. But as it's given data and given outcomes, it learns, which is dramatically different than all computing systems in the past, which really learned nothing. And as it interacts with humans, it gets even smarter. And it never forgets.

[Announcer: This is Jeopardy!]

That helped Watson land a spot on one of the most challenging editions of the game show "Jeopardy!" in 2011.

[Announcer: An IBM computer system able to understand and analyze natural language Watson]

It took five years to teach Watson human language so it would be ready to compete against two of the show's best champions.

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Five years after beating humans on "Jeopardy!" an IBM technology known as Watson is becoming a tool for doctors treating cancer, the head of IBM ...

Because Watson's A.I. is only as intelligent as the data it ingests, Kelly's team trained it on all of Wikipedia and thousands of newspapers and books. It worked by using machine-learning algorithms to find patterns in that massive amount of data and formed its own observations. When asked a question, Watson considered all the information and came up with an educated guess.

[Alex Trebek: Watson, what are you gonna wager?]

IBM gambled its reputation on Watson that night. It wasn't a sure bet.

[Watson: I will take a guess: What is Baghdad?]

[Alex Trebek: Even though you were only 32 percent sure of your response, you are correct.]

The wager paid off. For the first time, a computer system proved it could actually master human language and win a game show, but that wasn't IBM's endgame.

Charlie Rose: Man, that's a big day, isn't it?

John Kelly: That's a big day

Charlie Rose: The day that you realize that, "If we can do this"

John Kelly: That's right.

Charlie Rose: --"the future is ours."

John Kelly: That's right.

Charlie Rose: This is almost like you're watching something grow up. I mean, you've seen

John Kelly: It is.

Charlie Rose: --the birth, you've seen it pass the test. You're watching adolescence.

John Kelly: That's a great analogy. Actually, on that "Jeopardy!" game five years ago, I-- when we put that computer system on television, we let go of it. And I often feel as though I was putting my child on a school bus and I would no longer have control over it.

Charlie Rose: 'Cause it was reacting to something that it did not know what would it be?

John Kelly: It had no idea what questions it was going to get. It was totally self-contained. I couldn't touch it any longer. And it's learned ever since. So fast-forward from that game show, five years later, we're in cancer now.

Charlie Rose: You're in cancer? You've gone

John Kelly: We're-- yeah. To cancer

Charlie Rose: --from game show to cancer in five years?

John Kelly: --in five years. In five years.

Five years ago, Watson had just learned how to read and answer questions.

Now, it's gone through medical school. IBM has enlisted 20 top-cancer institutes to tutor Watson in genomics and oncology. One of the places Watson is currently doing its residency is at the university of North Carolina at Chapel Hill. Dr. Ned Sharpless runs the cancer center here.

Charlie Rose: What did you know about artificial intelligence and Watson before IBM suggested it might make a contribution in medical care?

Ned Sharpless: I-- not much, actually. I had watched it play "Jeopardy!"

Charlie Rose: Yes.

Ned Sharpless: So I knew about that. And I was very skeptical. I was, like, oh, this what we need, the Jeopardy-playing computer. That's gonna solve everything.

Charlie Rose: So what fed your skepticism?

Ned Sharpless: Cancer's tough business. There's a lot of false prophets and false promises. So I'm skeptical of, sort of, almost any new idea in cancer. I just didn't really understand what it would do.

What Watson's A.I. technology could do is essentially what Dr. Sharpless and his team of experts do every week at this molecular tumor board meeting.

They come up with possible treatment options for cancer patients who already failed standard therapies. They try to do that by sorting through all of the latest medical journals and trial data, but it is nearly impossible to keep up.

Charlie Rose: To be on top of everything that's out there, all the trials that have taken place around the world, it seems like an incredible task

Ned Sharpless: Well, yeah, it's r

Charlie Rose: --for any one university, only one facility to do.

Ned Sharpless: Yeah, it's essentially undoable. And understand we have, sort of, 8,000 new research papers published every day. You know, no one has time to read 8,000 papers a day. So we found that we were deciding on therapy based on information that was always, in some cases, 12, 24 months out-of-date.

However, it's a task that's elementary for Watson.

Ned Sharpless: They taught Watson to read medical literature essentially in about a week.

Charlie Rose: Yeah.

Ned Sharpless: It was not very hard and then Watson read 25 million papers in about another week. And then, it also scanned the web for clinical trials open at other centers. And all of the sudden, we had this complete list that was, sort of, everything one needed to know.

Charlie Rose: Did this blow your mind?

Ned Sharpless: Oh, totally blew my mind.

Watson was proving itself to be a quick study. But, Dr. Sharpless needed further validation. He wanted to see if Watson could find the same genetic mutations that his team identified when they make treatment recommendations for cancer patients.

Ned Sharpless: We did an analysis of 1,000 patients, where the humans meeting in the Molecular Tumor Board-- doing the best that they could do, had made recommendations. So not at all a hypothetical exercise. These are real-world patients where we really conveyed information that could guide care. In 99 percent of those cases, Watson found the same the humans recommended. That was encouraging.

Charlie Rose: Did it encourage your confidence in Watson?

Ned Sharpless: Yeah, it was-- it was nice to see that-- well, it was also-- it encouraged my confidence in the humans, you know. Yeah. You know--

Charlie Rose: Yeah.

Ned Sharpless: But, the probably more exciting part about it is in 30 percent of patients Watson found something new. And so that's 300-plus people where Watson identified a treatment that a well-meaning, hard-working group of physicians hadn't found.

Charlie Rose: Because?

Ned Sharpless: The trial had opened two weeks earlier, a paper had come out in some journal no one had seen -- you know, a new therapy had become approved

Charlie Rose: 30 percent though?

Ned Sharpless: We were very-- that part was disconcerting. Because I thought it was gonna be 5 perc

Charlie Rose: Disconcerting that the Watson found

Ned Sharpless: Yeah.

Charlie Rose: --30 percent?

Ned Sharpless: Yeah. These were real, you know, things that, by our own definition, we would've considered actionable had we known about it at the time of the diagnosis.

Some cases -- like the case of Pam Sharpe -- got a second look to see if something had been missed.

Charlie Rose: When did they tell you about the Watson trial?

Pam Sharpe: He called me in January. He said that they had sent off my sequencing to be studied by-- at IBM by Watson. I said, like the

Charlie Rose: Your genomic sequencing?

Pam Sharpe: Right. I said, "Like the computer on 'Jeopardy!'?" And he said, "Yeah--"

Charlie Rose: Yes. And what'd you think of that?

Pam Sharpe: Oh I thought, "Wow, that's pretty cool."

Pam has metastatic bladder cancer and for eight years has tried and failed several therapies. At 66 years old, she was running out of options.

Charlie Rose: And at this time for you, Watson was the best thing out there 'cause you'd tried everything else?

Pam Sharpe: I've been on standard chemo. I've been on a clinical trial. And the prescription chemo I'm on isn't working either.

One of the ways doctors can tell whether a drug is working is to analyze scans of cancer tumors. Watson had to learn to do that too so IBM's John Kelly and his team taught the system how to see.

It can help diagnose diseases and catch things the doctors might miss.

John Kelly: And what Watson has done here, it has looked over tens of thousands of images, and it knows what normal looks like. And it knows what normal isn't. And it has identified where in this image are there anomalies that could be significant problems.

[Billy Kim: You know, you had CT scan yesterday. There does appear to be progression of the cancer.]

Pam Sharpe's doctor, Billy Kim, arms himself with Watson's input to figure out her next steps.

[Billy Kim: I can show you the interface for Watson.]

Watson flagged a genetic mutation in Pam's tumor that her doctors initially overlooked. It enabled them to put a new treatment option on the table.

Charlie Rose: What would you say Watson has done for you?

Pam Sharpe: It may have extended my life. And I don't know how much time I've got. So by using this Watson, it's maybe saved me some time that I won't-- wouldn't have had otherwise.

But, Pam sadly ran out of time. She died a few months after we met her from an infection never getting the opportunity to see what a Watson adjusted treatment could have done for her. Dr. Sharpless has now used Watson on more than 2,000 patients and is convinced doctors couldn't do the job alone. He has started using Watson as part of UNC's standard of care so it can help patients earlier than it reached Pam.

Charlie Rose: So what do you call Watson? A physician's assistant, a physician's tool, a physician's diagnostic mastermind?

Ned Sharpless: Yeah, it feels like to me like a very comprehensive tool. But, you know, imagine doing clinical oncology up in the mountains of western North Carolina by yourself, you know, in a single or one-physician-- two-physician practice and 8,000 papers get written a day. And, you know-- and you want to try and provide the best, most cutting-edge, modern care for your patients possible. And I think Watson will seem to that person like a lifesaver.

Charlie Rose: If you look at the potential of Watson today, is it at 10 percent of its potential? Twenty-five percent of its potential? Fifty percent of its potential?

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Artificial intelligence positioned to be a game-changer - CBS News

In Brief CERN and the Large Hadron Collider depend on a massive computer grid, as does the global network of scientists who use LHC data. CERN scientists are now teaching an AI system to protect the grid from cyber threats using machine learning. Guarding A Global Grid

It takes a truly massive network of hundreds of thousands of computers to help scientists around the world unravel the mysteries of the Universe, which is the purpose of the CERN grid (CERN stands for Conseil Europen pour la Recherche Nuclaire, in English, the European Laboratory for Particle Physics). Naturally, however, particle physicists arent the only ones who want to access that kind of computing power. Hackers are also interested in CERNs grid, and CERN scientists are skipping past standard cybersecurity measures and deploying artificial intelligence (AI) tostay protected.

It is the job of any cybersecurity effort to detect unusual activity and identify possible threats. Of course, systems can look for known code worms and viruses, but malware changes too fast for humans to keep up with it. This is where AI and machine learning comes in. CERN scientists are teaching their AI system to distinguish between safe and threatening behavior on the network and take action when it detects a problem.

CERN is home to the Large Hadron Collider (LHC) as well as its massive computer grid. Scientists use the LHC to study high-speed collisions between subatomic particles in 2017 alone, they collected an estimated 50 petabytes of data about these particles. CERN provides this critically important data to universities and laboratories around the world for research.

The LHC and CERN itself require a massive amount of data storage and computing power, which is what prompted the creation of the Worldwide LHC Computing Grid. The grid connects computers in more than 40 countries from more than 170 research facilities, and works like a power grid to some extent, providing computing resources to facilities based on demand. This presents a unique cybersecurity challenge: keeping the massive globally-distributed grid secure while maintaining the computing power and storage unimpeded.

Machine learning can train a system to detect potential threats while retaining the flexibility that it needs to provide computing power and storage on demand. F-Secure senior security researcher Jarno Niemel told Scientific American that the biggest challenge for the project will be developing algorithms that can accurately distinguish between normal and malicious network activity without causing false alarms. For now, the AI upgrades are still being tested. If they work well protecting just the part of the grid that ALICE (A Large Ion Collider Experiment) uses, the team can deploy AI cybersecurity measures throughout the system.

Excerpt from:

An Advanced AI Has Been Deployed to Fight Against Hackers - Futurism

Artificial intelligence is being used to improve banking, marketing, the legal field and now to find which one of the more than 30,000 strains of medical marijuana is best for you.

Potbot uses AI to "read" through peer-reviewed medical journals to find studies on cannabinoids, the active compounds in marijuana. Using the research, it pairs 37 symptoms like insomnia, asthma and cancer with branded marijuana strains to find which type of weed is best suited to treat each one.

The company has raised $5 million to date, according to Potbotics CEO David Goldstein. Part of the reason for its success is the technology doesn't actually involve marijuana directly, making it completely legal he said. The app is available in Apple's App Store and the Google Play store. In addition, the bigger pharmaceutical companies haven't entered the space, giving the marijuana industry a "start-up mentality."

"We definitely see there's interest in the industry, for sure," Goldstein said. "It's one that has real potential in the United States and internationally. A lot of investors like non-cannabis touching entities, because they feel like they are hedging their bets a little bit."

There are some challenges, including having to look at state-by-state regulations instead of being able to scale quickly like other tech companies, he pointed out. Potbotics is focusing in the New England area for now.

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This medical marijuana start-up uses artificial intelligence to find which strain is best for you - CNBC

The Artificial Intelligence &Cognitive Systems and Artificial in BFSI will witness a CAGR of 45.9% during the forecast period 2016-2022.

The increasing usage of cloud-based solutions in the BFSI industry, rising demand for the data-driven solutions, increasing internet banking penetration, and scope of deriving market risk are fostering the market growth. The market is segmented into technologies, deployment types, verticals and regions.

Globally, BFSI is the second most customer data-centric industry, where players have a bundle of new business opportunities from Cognitive Systems and Artificial Intelligence (AI). It is an evolving data driven technology that works on on-premises and cloud-based software. The system replaces the human thought process with a simulated digital model that includes a self-learning system, which derives patterns by using data mining, speech recognition, and language processing techniques. The cognitive systems require AI platform to derive the complicated business issues.

Globally, the growing demand for digital technology and changing customer demands have led the BFSI players to adopt cognitive systems and AI implementation in their operations to deal with ever-changing regulatory & compliance laws to face the market risk and understand both income tax & corporate tax laws in an efficient way. It is also showing a strong presence in analyzing consumer behavior patterns to bring new offerings and is finding new distribution channels for the financial institutions.

Companies Mentioned

Key Topics Covered:

1 Industry Outlook

2 Report Outline

3 Market Snapshot

4 Market Outlook

5 Market Characteristics

6 Deployment Type: Market Size & Analysis

7 Technologies: Market Size & Analysis

8 Verticals: Market Size & Analysis

9 Regions: Market Size & Analysis

10 Vendor Profiles

11 Companies to Watch for

For more information about this report visit https://www.researchandmarkets.com/research/5nkrdm/cognitive_systems

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Research and Markets - Cognitive Systems & Artificial Intelligence in BFSI Market to Grow at a CAGR of 45.9% by 2022 ... - PR Newswire (press...