Daily Archives: June 26, 2017

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