Monthly Archives: June 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.

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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

Media Contact:

Research and Markets Laura Wood, Senior Manager press@researchandmarkets.com

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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...

Artificial intelligence could be the most revolutionary force affecting productivity in the United States economy, says the president of the Federal Reserve Bank of San Francisco.

"Everyone in Silicon Valley thinks statisticians are mis-measuring the productivity provided by the internet, but it's not that," says John C. Williams, on a trip to Sydney this week.

"Instead, the technologies that we now use and love mostly affect our consumption of leisure rather than affect our output in factories or offices."

Positive data showing the US economy is nearing full employment and that inflation is edging higher prompted the US central bank to recently raise interest rates for the second time in three months.

The US Fed also announced it will push ahead with plans to gradually shrink its $US4.5 trillion ($6 trillion) bond portfolio.

But wages and productivity growth remain stubbornly low, prompting the question: are economists mis-measuring the advent of the digital economy and the role of the internet in sophisticated labour markets?

The productivity gains from the inventions of electricity and the combustion engine had much more influence on humans' output capacity, says Mr Williams, and the only innovation in recent times that might rival those is artificial intelligence.

"AI is interesting because that says we could replace sophisticated human functions with computers," he told an audience at the University of Technology Sydney. "Potentially, that could be revolutionary in terms of our productivity."

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Productivity growth in the US has averaged 0.6 per cent over the last five years, down from 2.2 per cent during 1947-2007, according to JP Morgan data.

It's a problem affecting Australia as well, with the Reserve Bank of Australia also flagging the role of the internet in domestic productivity output.

Mr Williams also reiterated the US Federal Reserve's plan to "normalise" interest rate movements and said the US had reached a "turning point" in its transition from economic recovery to expansion.

"The more public understanding, the less chance that [our] actions will fuel unnecessarily volatility in the markets," said Mr Williams.

"Therefore, our process has been widely telegraphed and it will continue to be gradual, predictable and transparent, or in a word, boring,"

The pick-up in inflation and solid unemployment rate have solidified the US Federal Reserve's case for keeping the US economy expanding for as long as possible.

"Gradually raising interest rates to bring monetary policy back to normal helps The Fed keep the economy growing at a rate that can be sustained for a longer time," said Mr Williams.

"If we delay too long, the economy will eventually overheat, causing inflation or some other problem. At some point, that would put us in the position of having to quickly reverse course to slow the economy. That risks stalling the expansion and setting us back into recession."

While Mr Williams is not a member of the Federal Open Market Committee this year and does not vote on monetary policy directly, economists broadly agree he is a relatively good signal of future policy. He was the director of research at the San Francisco Fed when now-Fed chair Janet Yellen was president of the bank.

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Artificial intelligence could be the answer for productivity woes - The Sydney Morning Herald

Financial Services compliance departments are constantly turning to technology to find efficiencies and satisfy increasingly tough regulatory examinations. It started with simple robotics, which can provide great operational efficiencies and help standardize processes. Never ones to rest on their laurels, compliance departments have begun looking to Artificial Intelligence (AI) as the next technological step to enhance and improve their programs. PayPal has cut its fraud false alerts in half by using an AI monitoring system that can identify benign reasons for seemingly bad behavior. HSBC recently announced a partnership to use AI in its Anti-Money Laundering (AML) program. Despite the adoption by some large players, there is still a lot of hesitancy and concern about the use of AI in financial crimes compliance.

WHAT IS AI AND HOW DOES IT WORK?

AI is computer software that can make decisions normally made by a human. What does this mean? In essence this means that it is computer software that can analyze large amounts of data and use patterns and connections within that data to reach certain results about that data.

Just like people, AI needs to learn in order to make decisions. It can do this in two ways: supervised or unsupervised learning. Supervised is the most common method, whereby data, the goal, and the expected output of that data are provided to the software allowing it to identify algorithms to get to the expected result. Supervised learning allows AI to use a feedback loop to further refine its intended task. If it identifies potential fraud, that turns out not to be, it can incorporate that feedback and uses it for future evaluation.

Unsupervised learning provides the software with only the data and the goal, but with no expected output. This is more complex and allows the AI to identify previously unknown results. As the software gets more data, it continues to refine its algorithm, becoming increasingly more efficient at its task.

HOW CAN IT HELP IN FINANCIAL CRIMES COMPLIANCE?

While there are varied uses in this space, one of the most relevant is to monitor transactions for potential criminal activity. Instead of using rule-based monitoring that looks for very specific red flag activity, AI software can use a large amount of data to filter out false alerts and identify complex criminal conduct. It can rule out false positives by identifying innocuous reasons for certain activity (investigation that normally needs to be done by an analyst) or see connections and patterns that are too complex to be picked up by straight forward rule-based monitoring. The reason it is able to do this is that AI software acts fluidly and can identify connections between data points that a human cannot. Its ability to analyze transactions for financial crime is only limited by the data available to it. Some specific uses are:

Fraud Identification: Identifying complex fraud patterns and cutting down on the number of false alerts by adding other data (geolocation tagging, IP addresses, phone numbers, usage patterns, etc.). See Paypals success in the first paragraph.

AML Transaction Monitoring and Sanctions Screening: Similar to fraud identification, it can greatly reduce the amount of false alerts by taking into account more data. It can also identify complex criminal activity occurring across products, lines of business, and customers.

Know Your Customer: Linkage detection between accounts, customers, and related parties to fully understand the risk of a party to the bank. Also, through analysis of unstructured data it can identify difficult to identify relevant negative news.

Anti-Bribery, Insider Trading, and Corruption: It can be used to identify insider trading or bribery by analyzing multiple source of information including emails, phone calls, messaging, expense reports, etc.

ANY CONCERNS?

Seems amazing, right? You might be wondering why everyone isnt immediately implementing these solutions throughout their financial crime compliance programs. While there have been some early adopters, there is still a lot of hesitation to use AI in the Financial Crime compliance space due to the highly regulated nature of the field. There is no doubt that AI will bring a huge lift in the future, but here are some of the concerns that need to be ironed out before we see large scale adoption:

Black box image of AI decisioning

By using more data than a human could synthesize, it may select patterns and results that wouldnt necessarily make sense to a person. As a result, AI providers need to ensure that AI derived decisions are supported by an auditable rationale that is clear to person. Clear documentation around how the AI gets to its results will be necessary.

Algorithmic Bias

Because AI software functions are based on the data it is provided, the impact of misinformation or biased information could be very large. This can occur when unintentional bias within the source data and training is uploaded into the algorithms the AI uses to perform its task. No one wants to end up with an AI transaction monitoring system that is flagging transactions based on racial or nationality bias.

Lack of regulatory acceptance

Currently, there appears to be a lack of regulatory acceptance mostly due to the first two concerns described above. That being said, in the United States, the Securities and Exchange Commission and the Financial Industry Regulatory Authority are both working on limited use of AI in their organizations. This is a strong step in having them able to understand and test it.

WHAT TO DO?

Now you know how AI can help your program and some of the concerns you need to be mindful of, but what now? Here are a couple of next steps you can take to successfully implement AI into your Financial Crime Compliance Program:

Lastly, knowledge is power. Keep researching and make sure you understand the reality of what AI can bring to the table for you and your program.

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Artificial Intelligence: The Next Step in Financial Crime Compliance Evolution - Finextra (blog)

Hedge funds seeking artificial intelligence expertise need to cast the net wide these days, due to a shortage of people and a massive uptick in demand over the past 12 months.

Citadel has just turned to Microsoft for the new role of chief AI officer. Li Deng, who joined the tech firm straight out of academia 17 years ago, has just joined Citadels hedge fundoperation inSeattle, but will work also across Chicago and New York.

Deng announced his move to Citadel on LinkedIn yesterday, saying that he was very excited about the opportunities for artificial intelligence innovation here and the firms passion for growing its leadership in this space. Citadel didnt immediately respond to requests for comment.

Deng was chief scientist of AI and partner research manager at Microsoft. He joined in December 1999 from Waterloo University in Washington where he was a professor. He clearly has a passion for expanding AI knowledge he headed up Microsofts AI school, as well as founding its deep learning technology centre.

Citadel is the latest big buy-side firm to create a new role heading up AI and machine learning as hedge funds rely on ever-more complex datasets to gain an edge over the competition.

Man Group brought in William Ferreira as head of machine learning for its discretionary hedge fund business GLG in April. It was a newly-created role and he previously worked at Florin Court Capital. David Ferrucci, who previously headed up IBMs development of super-computer Watson, joined Bridgewater Associates in 2012 and now heads up its AI function, the Systematized Intelligence Lab, which has been growing this year

Hes kept his hand in academia, and was affiliate professor at the University of Washington for over 17 years until he joined Citadel in May. Hes written numerous books on using deep learning for automatic speech recognition as well as deep learning applications and methods.

Citadel already has a head of machine learning. Pradeep Natarajan joined from Amazon, where he was a senior research scientist, in October 2014.

Its also its second stab at poaching from Microsoft it brought in Kevin Turner, the tech firms ex-COO as CEO of Citadel Securities in August last year, but he left just seven months later. Hes now founder and CEO of his own start-up Forward Progress Ventures.

Contact:pclarke@efinancialcareers.com

Image: Getty Images

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Citadel has just hired a new head of artificial intelligence from Microsoft - eFinancialCareers