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

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

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

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

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

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 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 answer for productivity woes – The Sydney Morning Herald

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

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

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)

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

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

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

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

Artificial intelligence is entering the justice system – Wired.co.uk

Peter Wallqvist: “It’s a good trend that governments are brave enough to pull the trigger on things like this”

Phil Fisk. Set Design: Vicky Lees

The Serious Fraud Office (SFO) had a problem. Its investigation into corruption at Rolls-Royce was inching towards a conclusion, but four years of digging had produced 30 million documents. These needed to be sorted into “privileged” and “non-privileged”, a legal requirement that involves paying junior barristers to do months of repetitive paperwork. “We needed a way that was faster,” says Ben Denison, chief technology officer at the SFO. So, in January 2016, he started working with RAVN.

Pronounced “Raven”, the London startup builds robots that sift and sort data, not only neatly presented material, but also unstructured documents. “Where someone has scanned 300 pages, it’s not uncommon to put one page in upside down,” says co-founder Peter Wallqvist. “We need to deal with that real world of messy datasets.”

The two teams started to feed material from the Rolls-Royce case into the AI. By July they had a viable system, and with the agreement of lawyers on both sides, they set the robot to work. The barristers were wading through 3,000 documents a day. RAVN processed 600,000 daily, at a cost of 50,000 – with fewer errors than the lawyers. “It cut out 80 per cent of the work,” says Denison. “It also saved us a lot of money.” For Rolls-Royce, it had the opposite effect. In January 2017, the engineering company admitted to “vast, endemic” bribery and paid a 671 million fine. “It’s hard to imagine a better outcome,” says Wallqvist.

RAVN’s co-founders – Jan Van Hoecke, Simon Pecovnik, Sjoerd Smeets and Wallqvist – met at Autonomy, the UK’s first unicorn, where they worked on early versions of AI-powered database management. In 2010, the four left to launch RAVN. The self-funded firm now has 51 employees, revenues of 3 million and around 70 clients, mainly city law firms. BT, which signed a “very significant” deal, credits RAVN with annual savings of 100 million, due to automated checks that ensure contracts’ accuracy.

Plus, of course, there’s the SFO, which is using RAVN in increasingly clever ways. That means allowing it to make subjective judgements, including pointing investigators to data it thinks is relevant to a case. “This is potentially very valuable,” says Denison.

Wallqvist believes the system can go even further and make not just assessments, but predictions. For example, by suggesting likely outcomes of mergers and acquisitions. “We’ve gone to the level of figuring out and structuring data,” says Wallqvist. “Now we have the ability to surface that record of the past to predict the future.” Today, Watson. Tomorrow, Holmes.

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Artificial intelligence is entering the justice system – Wired.co.uk

Artificial intelligence genius Andrew Ng has another AI project in the … – Digital Trends

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Artificial intelligence genius Andrew Ng has another AI project in the … – Digital Trends

Artificial Intelligence: Here are the different avatars of AI at the centre of innovation – Financial Express

Future store is one such example of how with AI, running a retail store is childs play.

At the annual Futiju Forum, held in Tokyo recently, technologylargely artificial intelligence (AI) including machine learningwas at the centre of all innovation, with the aim of helping solve everyday problems. Future store is one such example of how with AI, running a retail store is childs play. In a future store, a robot roams inside a shop. Through the use of AI, it collects a combination of various types of data for analysis, including product shelf video data, point-of-sale (POS), and shelf arrangement data. Based on the collected data, the robot suggests which products are to be displayed on shelves and at what time.

This is enabled by sending real-time product shelf condition data to the shop staff and therefore, improving operational efficiency in the store. Another example is its work for the Instruments and Electronics (Shanghai) Associates Group (INESA), a state-owned firm in China that provides smart city solutions.

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Fujitsu created a smart factory for its subsidiary INESA Display Materials. It built an intelligent system to handle data collection on production, quality, efficiency, cost control and reduction in energy consumption, besides storage, processing and visualisation to enable fast access and analysis of information in mass production operations as well as energy monitoring. In our production environment, there are numerous data sources that provide information on processes, equipment and environmental factors, which can directly affect the quality of our product, said Wei Fengrong, director, information, INESA Display Materials Co.

Another technology which has started making waves is an interface device called Ontenna. The device, worn atop the users hair, helps deaf people to perceive rhythms, patterns, and volumes of sounds through their hair as it conveys the characteristics of sounds using vibration and light.

The reporter was in Japan at theinvitation of Fujitsu.

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Artificial Intelligence: Here are the different avatars of AI at the centre of innovation – Financial Express

Companies use AI to Find Human Employees to Work with Artificial Intelligence – TrendinTech

Lately, it seems that more and more jobs that used to be performed by humans are being taken over by one form or another of artificial intelligence, or AI. But, despite all jokes made to the contrary, there is still a need for real actual human beings, say experts. You just need to have what is required to work with a machine that thinks. Machines augmented with AI have long been replacing human workers on assembly lines, factory floors, and in manual labor. Most recently they are being added to jobs that usually were thought to require the judgment and intuition of a person, in fields like finance, law, and medicine.

But humans have not been made obsolete yet. There remain plenty of jobs for those who develop, program, manage, and market AI to work alongside it or improve its operation. As an exercise in irony, recruiters are now using AI to find employees with the correct qualifications and intelligence for these slots.

However, as job descriptions have gone from traditional roles like chief clerk to modern titles like chief digital officer the attributes for job seekers have changed too, though employers and employees alike are having difficulties effectively specifying what they are.

Most people will tell you that to work in AI you need traits like a growth mindset, you need to be adaptable and have an owner mentality those are the buzzwords. The reality is that the job itself has to be properly defined, says Caitlin MacGregor, CEO, and co-founder of Plum, an Ontario-based online recruiter.

Plum matches prospective employees with possible employers by using a specialized algorithm based on their surveys. Like a dating app but for job opportunities rather than your love-life. But, Macgregor goes on to say, no ones quite sure what to make of these opportunities in the new economy.

They default to questions like where people went to school, what degrees they have, how many years theyve worked, what titles theyve had. Those markers never really were able to predict success; in this digital age, we need to be really clear what does, she says.

We know based on 30 years of research that intelligence is the number one predictor of performance, across all roles and all industries. We need to be measuring for intelligence before we even pick up a rsum, instead of waiting until someone has been three months on the job, Ms. MacGregor adds.

At the same time, the qualities that usually accompany intelligence, like adaptability and flexibility, are desired as well, according to Marlina Kinnersly, CEO, and co-founder of Fortay.co, an AI-based hiring site similar to Plum.

You want them to be able to think fast and learn on their feet, she says.

The Toronto-based firm looks for workers who will be a good culture fit or team alignment for the corporation but also thinks independently enough to add new perspectives to the company, says Kinnersly.

However, the search isnt on until the company can truly describe what their corporate culture is. Thats their baseline to find the right candidates, she says.

In a paper for Brookings Institute, Christian Bodewig, a World Bank executive, reveals a few qualifications for employees working along AI should have. Amongst these skills, he lists cognitive skills in numeracy and literacy, creative critical thinking, and advanced problem solving, as well as contentiousness and whatever technical skills required for the specific job as all being important.

In contrast, Thomas L. Friedman, a columnist for The New York Times, says employers want someone who will get up, dress up, show up, shut up, and never give up.

Potential employees should acquire these skills as early as possible, according to Bodewig because the window for building cognitive skills closes with late adolescence.

Overall, in this new AI dominated employment market, an intelligent, independent thinker, and team player needs more than he has in the past to procure job security. Math skills help, according to Henry Kim, an associate professor of operations management and information system from Schulich School of Business at Torontos York University. A point he is always reminding his 11-year-old daughter too:

She loves Anne of Green Gables, so when she grows up, she wants to run a caf in Prince Edward Island where Anne is set. I told her that that is actually a job that AI robots cant do, so she should go for it, Dr. Kim explains.

But you have to make money to open that caf. Which means youll need a good-paying job in a workforce of the future, where a lot of the well-paying white-collar jobs we have today will not exist. However, there will definitely be well-paying jobs in the future for programming and working with AI robots.

So, I tell her do your math homework.

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Companies use AI to Find Human Employees to Work with Artificial Intelligence – TrendinTech

Artificial Intelligence-proof your career – Livemint

Intelligent machines are taking over thousands of jobs, and being qualified is no longer enough to keep your job. Earlier this year, consulting firm McKinsey and Co. released a study that said 51% of all jobs could be automated in the next 20 years. Even specialized professions like medicine, law and banking are feeling the heat of Artificial Intelligence (AI). A few months ago, investment bank JP Morgan made the news by introducing intelligent machines to review financial deals that once kept employees busy for thousands of hours. Diagnostics and other decision-making skills previously thought of as the exclusive preserve of human beings, will soon be better handled by machines.

But Garry Kasparov has a different take on the issue. On 11 May 1997, Russian chess grandmaster Kasparov became the first world champion to be defeated by a machine. Yet in his new book Deep Thinking: Where Artificial Intelligence Ends And Human Creativity Begins, he is optimistic about the future of people with skills even as he concedes the inevitability of intelligent machines becoming more prominent. The sensation of being challenged, surpassed and possibly replaced by automaton, or an invisible algorithm, is becoming a standard part of our society, he writes. So while smarter computers are one key to success, doing a smarter job of humans and machines working together is far more important.

Is it possible to beat this threat of being displaced? Theres ample research and books on the subject, and here are some of the things they suggest you could do to robot-proof your career.

Build empathy

Employers want people who are empathetic and collaborative, who can guide relationships and work in teams. Because empathy is something that even intelligent machines are incapable of. Recognizing the importance of this skill is Geoff Colvin in his book Humans Are Underrated : What High Achievers Know That Brilliant Machines Never Will. The critical 21st century skill is empathy: we empathize to survive, he says, pointing to the healthcare profession. So while machines may be superior with diagnostics, a patient still needs to have a conversation with an expert. An empathetic doctor can help the patient deal with his condition better and recover faster. This, in turn, leads to lower healthcare costs and fewer lawsuits, says Colvin.

Empathy is a skill that can be developed through learning how to study the thoughts and feelings of others, and then responding appropriately. This involves inviting people to speak about their worries and concerns, hearing them out and then reassuring them, says Colvin.

Be a good communicator

A skill like communication is less easy to automate, says Anu Madgavkar, partner with McKinsey Global Institute, the research arm of McKinsey and Co., Mumbai. Intelligent machines cannot communicate the way human beings do. So people with better communication skills will be harder to replace with AI. The bigger message for professionals is that they should learn to communicate in a more compelling way, learn to work in teams, to excel at social interactions, says Madgavkar.

Become a lifelong learner

Previously in history, even in the 20th century, life was divided into two main parts: in the first part, you mostly learned, acquired knowledge and skills, and built yourself a personal and a professional identity. In the second part, you mostly made use of those skills and those identities. The pace of change in the 21st century will be such that most of what you learn as a teenager will be completely irrelevant by the time youre 40, says Yuval Noah Harari, author of Homo Deus: A Brief History Of Tomorrow, in a February interview with Time magazine, where he emphasized the necessity of life-long learning.

The good news is that anytime, anywhere learning is a reality now. For instance, if you want to do a project on design thinking, you can go immediately to the massive open online courses at online platforms like edX and Coursera and do a course on it, says Vijay Thadani, co-founder, NIIT.

Get those number skills

Digital literacy should be taken as seriously as language literacy, says Infosys chief executive Vishal Sikka, in an Infosys commissioned study on how to amplify human potential. The most important academic subjects that decision-makers see as focus areas for future generations are computer sciences, business and management and mathematics, says the study, which looked at the skills professionals need to acquire to integrate AI in a positive way into organizations and society.

Be constructive

Many perceive AI as a threat. Prominent among them are entrepreneur Elon Musk (our biggest existential threat) and scientist Stephen Hawking (the development of full AI could spell the end of the human race). From elevator operators to bank tellers and airplane pilots, history is full of examples of how technology has made jobs redundant.

But technology has also made life safer, easier and better. Its better to accept AI as a part of development, and look at the avenues it opens up rather than see the situation as man versus machine, says Kasparov.

Start to look at tasks hard to mechanizeanything that involves human creative energy, from photography and theatre, to baking, art, running, cooking classes, teachinganything thats not linear, says Mumbai-based Gurprriet Siingh, senior client partner at consulting firm Korn Ferry Hay Group. He says skills like empathy, creativity, flexibility and the ability to communicate can never be automated, and so education today should emphasize development of those skills.

Many of the most promising jobs today didnt even exist 20 years ago, says Kasparov, pointing to the demand for talent in new professions like app designers, 3D print engineers, drone pilots, social media managers and genetic counsellors. This is a trend that will accelerate as technology continues to create different professions .

Learn to work with machines

The future of increased productivity and business success isnt men or machines. Its both, argue Thomas H. Davenport and Julia Kirby in their book Only Humans Need Apply. Augment your skills, learn to work with machines, they say. The doctor who relies on diagnostic software, the lawyer who relies on research machines, the logistics manager who works with drones or the customer service manager who works with a chatbot, all of these professionals will be able to work better by complementing their human skills of empathy, of communication and creativity with machine intelligence. As the McKinsey report states, Humans will still be needed in the workforce; the total productivity gains we estimate will only come if people work alongside machines.

At wealth management firm ORO Wealth, for instance, the role of human portfolio advisers who work with intelligent machines is important. Even though the investment recommendations are machine-based, we need humans beings to work alongside. Because only a human adviser can empathize, can sense hesitation or lack of enthusiasm for a particular investment on the clients part. In which case they will go back to the machine-based algorithm, which will recommend alternative products, says Mumbai-based Vijay Kuppa, co-founder of ORO Wealth.

The skill and flexibility to work with a machine will help the workforce to become more productive. As Kasparov puts it, Smart machines will free us all…taking over the more menial aspects of cognition and elevating our mental lives towards creativity, curiosity, beauty and joy. These are what truly make us human, not any particular activity or skill like swinging a hammeror even playing chess.

First Published: Sun, Jun 25 2017. 03 47 PM IST

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Is Artificial Intelligence Overhyped in 2017? | HuffPost – HuffPost

Is AI over-hyped in 2017? originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Joanne Chen, Partner at Foundation Capital, on Quora:

To quote Bill Gates We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.

In short, over the next ten years, I dont believe AI will be overhyped. However, in 2017, will all of our jobs be automated away by bots? Unlikely. I believe the technology has incredible potential and will permeate across all aspects of our lives. But today, my sense is that many people dont understand what the state of AI is, and thus contribute to hype.

Artificial intelligence, a concept dating back to the 50s, is simply the notion that a machine can performance tasks that require human intelligence. But AI today is not what the science fiction movies portray it to be. What we can do today falls in the realm of narrow AI (vs general intelligence), which is the idea that machines can perform very specific tasks in a constrained environment. With narrow AI, there are a variety of techniques that you may have heard of. Ill use examples to illustrate differences.

Lets say you want to figure out my age (which is 31).

1) Functional programming: what we commonly know as programming, a way to tell a computer to do something in a deterministic fashion. I tell my computer that to compute my age, it needs to solve AGE = todays date birth date. Then I give it my birth date (Dec 4, 1985). There is 0% chance the computer will get my age wrong.

2) Machine learning: an application of AI where we give machines data and let them learn for themselves to probabilitically predict an outcome. The machine improves its ability to predict with experience and more relevant data. So take age for example. What if I had 1,000 data sets of peoples ages and song preferences? Song preference is highly correlated with generation. For example, Led Zeppelin and The Doors fans are mostly 40+ and Selena Gomez fans are generally younger than 25. Then I could ask the computer given that I love the Spice Girls and Backstreet Boys, how old does it think I am? The computer then looks at these correlations and compares it with a list of my favorite songs to predict my age within x% probability. This is a very simple example of using machine learning..

3) Deep Learning: is a type of machine learning emerged in the last few years, and talked widely about in the media when Google DeepMinds AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go.

Deep learning goes a step further than ML in that it enables the machine to learn purely by providing examples. In contrast, ML requires programmers to tell the computer what it should look for. As a result, deep learning functions much more like the human brain. This especially works well with applications like image recognition.

4) Deep reinforcement learning: DRL goes one step further and combines deep learning with reinforcement learning which is the notion of learning by trial-and-error, solely from rewards or punishments. DRL mimics how children learn they see observe other people doing things, they try things out and depending on the reward, they either repeat them or not!

Machine learning technologies have become more available (and the reason why there has been increasing media hype around this space) has been driven by advancements in three areas:

1) Infrastructure to run ML algorithms massive improvements in storage, processing capabilities (i.e. GPUs that speed up parallel processing), and accessibility for rapid innovation (cloud).

2) New available algorithms developed.

3) Data proliferation to train algorithms.

Between algorithms innovation and data availability, I believe data plays a more crucial role in advancements. If you look at the chart below, breakthroughs in AI have been quickly followed by availability of datasets, while many of the corresponding algorithms have been available for over a decade.

AI will permeate our lives in the next ten years. Think of the possible time, money, and manpower saved by automating simple processes. And as the technology becomes more advanced, the use cases will get even more exciting. I think its a wonderful time as an entrepreneur to be able to leverage this technology, and I couldnt be more excited as an investor.

This question originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter, Facebook, and Google+. More questions:

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Is Artificial Intelligence Overhyped in 2017? | HuffPost – HuffPost

Beware the Hype of Artificial Intelligence – Fortune

Artificial intelligence has made great strides in the past few years, but its also generated much hype over its current capabilities.

Thats one takeaway from a Friday panel in San Francisco involving leading AI experts hosted by the Association for Computing Machinery for its 50th annual Turing Award for advancements in computer science.

Michael Jordan, a machine learning expert and computer science professor at University of California, Berkeley, said there is way too much hype regarding the capabilities of so-called chat bots. Many of these software programs use an AI technique called deep learning in which they are trained on massive amounts of conversation data so that they learn to interact with people.

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But despite several big tech companies and new startups promising powerful chat bots that speak like humans when prodded, Jordan believes the complexity of human language it too difficult for bots to master with modern techniques like deep learning. These bots essentially perform parlor tricks in which they respond with comments that are loosely related to a particular conversation, but they cant say anything true about the real world.

We are in era of enormous hype of deep learning, said Jordan. Deep learning has the potential to change the economy, he added, but we are not there yet.”

Also in the panel, Fei-Fei Li, Googles ( goog ) machine learning cloud chief and Stanford University Professor, said We are living in one of the most exciting and hyped eras of AI. Li helped build the ImageNet computer-vision contest, which spurred a renaissance in AI in which researchers applied deep learning to identify objects like cats in photos.

But while everyone talks about ImageNets success, we hardly talk about the failures, she said, underscoring the hard work researchers have building powerful computers that can see like humans.

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Still, Li is excited that current AI milestones will eventually lead to more breakthroughs that will touch every single industry, like healthcare. We are entering a new phase in AI, she said.

What will help usher more breakthroughs in deep learning will be the continuing advancements in powerful computing hardware, like Nvidia’s GPUs that make it possible to crunch tremendous amounts of data faster than ever, explained Ilya Sutskever, the research director of Elon Musk-backed AI research group OpenAI . Deep learning will keep booming in tandem with advancements in computing hardware that shows no signs of slowing down .

“Compute has been the oxygen of deep learning,” Sutskever said.

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Beware the Hype of Artificial Intelligence – Fortune

The Real Threat of Artificial Intelligence – The New York Times – New York Times

This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-scale decimation of jobs mostly lower-paying jobs, but some higher-paying ones, too.

This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)

We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?

Part of the answer will involve educating or retraining people in tasks A.I. tools arent good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and cross-domain thinking for example, the work of a trial lawyer. But these skills are typically required by high-paying jobs that may be hard to retrain displaced workers to do. More promising are lower-paying jobs involving the people skills that A.I. lacks: social workers, bartenders, concierges professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The solution to the problem of mass unemployment, I suspect, will involve service jobs of love. These are jobs that A.I. cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous or, potentially soon, Virtual Reality Anonymous (for those addicted to their parallel lives in computer-generated simulations). The volunteer service jobs of today, in other words, may turn into the real jobs of the future.

Other volunteer jobs may be higher-paying and professional, such as compassionate medical service providers who serve as the human interface for A.I. programs that diagnose cancer. In all cases, people will be able to choose to work fewer hours than they do now.

Who will pay for these jobs? Here is where the enormous wealth concentrated in relatively few hands comes in. It strikes me as unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.

As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training that would make them employable or commit to a certain number of hours of service of love voluntarism.

To fund this, tax rates will have to be high. The government will not only have to subsidize most peoples lives and work; it will also have to compensate for the loss of individual tax revenue previously collected from employed individuals.

This leads to the final and perhaps most consequential challenge of A.I. The Keynesian approach I have sketched out may be feasible in the United States and China, which will have enough successful A.I. businesses to fund welfare initiatives via taxes. But what about other countries?

They face two insurmountable problems. First, most of the money being made from artificial intelligence will go to the United States and China. A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product. Its a virtuous circle, and the United States and China have already amassed the talent, market share and data to set it in motion.

For example, the Chinese speech-recognition company iFlytek and several Chinese face-recognition companies such as Megvii and SenseTime have become industry leaders, as measured by market capitalization. The United States is spearheading the development of autonomous vehicles, led by companies like Google, Tesla and Uber. As for the consumer internet market, seven American or Chinese companies Google, Facebook, Microsoft, Amazon, Baidu, Alibaba and Tencent are making extensive use of A.I. and expanding operations to other countries, essentially owning those A.I. markets. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.

The other challenge for many countries that are not China or the United States is that their populations are increasing, especially in the developing world. While a large, growing population can be an economic asset (as in China and India in recent decades), in the age of A.I. it will be an economic liability because it will comprise mostly displaced workers, not productive ones.

So if most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software China or the United States to essentially become that countrys economic dependent, taking in welfare subsidies in exchange for letting the parent nations A.I. companies continue to profit from the dependent countrys users. Such economic arrangements would reshape todays geopolitical alliances.

One way or another, we are going to have to start thinking about how to minimize the looming A.I.-fueled gap between the haves and the have-nots, both within and between nations. Or to put the matter more optimistically: A.I. is presenting us with an opportunity to rethink economic inequality on a global scale. These challenges are too far-ranging in their effects for any nation to isolate itself from the rest of the world.

Kai-Fu Lee is the chairman and chief executive of Sinovation Ventures, a venture capital firm, and the president of its Artificial Intelligence Institute.

Follow The New York Times Opinion section on Facebook and Twitter (@NYTopinion), and sign up for the Opinion Today newsletter.

A version of this op-ed appears in print on June 25, 2017, on Page SR4 of the New York edition with the headline: The Real Threat of Artificial Intelligence.

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The Real Threat of Artificial Intelligence – The New York Times – New York Times

Space probes of the future will have artificial intelligence, and it’s … – SYFY WIRE (blog)

When you think of artificial intelligence, you may think of Lt. Commander Data or C-3PO, but this AI will actually be the spacecraft rather than on board.

Exploring space has some far-out challengesand this is after weve sent robots to Mars and all manner of probes and orbiters to other planets, including Venus and Saturn. Future missions will venture deeper and deeper into unexplored star systems and galaxies that have only been observed via telescope. This is easier dreamed than done. Too many unforeseen obstacles could cause a craft to glitch or break down hundreds of thousands of miles away, which is why scientists developing these future missions need to be paranoid.

Space scientists Steve Chien and Kiri Wagstaff of NASAs Jet Propulsion Laboratory suggest that programming probes with advanced artificial intelligence will largely eliminate the need for prompts from the home planet that would have increasing difficulty reaching out to them the further they ventured into space. Not to mention that probes on more daring missions will have to be able to think for themselves, because they even more of them will be required and they will probably not be able to receive any intervention from Earth. It gets creepier with the realization that the capacity to learn will need to be wired into their computerized brains to make them adaptable.

“The goal is for A.I. to be more like a smart assistant collaborating with the scientist and less like programming assembly code,” said Chien, who collaborated with Wagstaff on an article recently published in the journal Science Robotics. “It allows scientists to focus on the ‘thinking’ thingsanalyzing and interpreting datawhile robotic explorers search out features of interest.”

Autonomous probes should be able to function on a hypersensitive level that includes understanding and carrying out mission requirements, recognizing geological phenomena and identifying differences between what passes for normal planetary conditions (depending on the planet) and extreme space weather. They should also be able to reprioritize if they eye something spontaneous, like ocean plumes erupting on watery worlds similar to Enceladus. Advancing the science of AI enough may even make them able to use their findings for future studies. Not having infinite fuel means the robo-brains will also need to make the call on which regions are worth delving into the most.

AI is already being prototyped for the Mars 2020 rover and could someday make once-impossible endeavors, like a mission to Alpha Centauri, possible, but even the researchers themselves admit it still has to level up.

“For the foreseeable future, there’s a strong role for high-level human direction,” Wagstaff said. “But A.I. is an observational tool that allows us to study science that we couldn’t get otherwise.”

(via Phys.org)

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Space probes of the future will have artificial intelligence, and it’s … – SYFY WIRE (blog)

Artificial Intelligence Is The Real Thing For Pharma And Medtech – Seeking Alpha

Artificial intelligence might seem more the preserve of computer nerds and tech giants than pharma companies. But according to Boehringer Ingelheim’s global chief data scientist, Philipp Diesinger, “the entire industry is looking at data science and AI”.

This increased focus on data could drastically change the way drugs are developed and paid for. For example, AI will be vital if outcomes-based healthcare is to be successfully implemented, pointed out Philips’ chief innovation & strategy officer, Jeroen Tas, who also stressed that AI really signaled a new way of handling data.

He described AI as “the way you interpret data. You constantly stream the data and add that data to the body of knowledge,” he told EP Vantage during the AI Summit in London in May. “That’s not the case today, because it’s all in the head of the doctor.”

Boehringer’s Mr. Diesinger believes that what is new is the “combination of AI, big data and new perceptions of these deep analytical methods”, as well as an increasing capacity for data storage and processing.

While some might question whether this marks a real change from existing approaches, Mr. Diesinger believes that “there is a perception now for data-driven decision making in businesses, and that has not been around before”. He pointed out how AI has transformed the financial industry “using theoretical physicists and mathematicians to optimise trading. We’re doing the same now with regards to decision-making within [Boehringer].”

The German company has been active in AI for around two years, and is using data to reduce the cost of drug development and enable earlier go/no-go decisions on pipeline candidates. According to Mr. Diesinger, the group wants to evolve from a pharma to a holistic healthcare company, with the help of AI.

Meanwhile, Philips has been narrowing its focus from technology in general to medtech alone – and has gone big on connected devices and data processing.

Improving cancer care

Oncology is one area where pharma companies are already employing AI. Notably, Novartis (NYSE:NVS), which has also been involved in AI for two or three years, recently signed a deal with IBM Watson to explore the technology’s use in breast cancer care.

The collaboration’s aims include identifying better treatment sequences or predictors of response, Pascal Touchon, Novartis’ global head of oncology strategy, told EP Vantage.

The project will analyse data from existing electronic health records using Watson’s AI expertise. So what does Novartis bring to the table? “We understand what the key questions are and what to do with the answers,” Mr. Touchon replied.

The scope is not limited to patients receiving Novartis drugs as the company is interested in breast cancer generally. Mr. Touchon expects initial findings in less than a year and, if it is successful, “we believe this collaboration could then be applied to other cancers”.

Another application for AI that both Novartis and Watson are exploring is clinical trial matching. A study presented at the recent Asco meeting found that using the technology reduced the time required to screen patients for eligibility by 78%.

“If you’re better at scanning patients, this could lead to faster trial enrollment [and] faster development of innovation,” Mr. Touchon said.

At a stroke

As for Boehringer, Mr. Diesinger would only give one example of its AI projects: the Angels Initiative, a joint venture with the European Stroke Organisation that gathers anonymous time stamp data from hospitals to reveal patterns in stroke care and identify potential pinch points. This could lead to improvements aimed at speeding up stroke treatment, ultimately resulting in better outcomes for patients.

One change in practice involves identifying stroke patients in the ambulance and carrying out simple tests, so the stroke team is waiting at the hospital entrance. “That saves something like 10 minutes right away,” Mr. Diesinger said.

Also looking for patterns is London-based BenevolentAI, which hopes its machine-based learning approach to processing academic research, clinical studies and other health-related data will help identify correlations in data that could lead to new drugs and significantly speed up the process of drug development.

The company has already signed a deal worth up to $800m to develop two Alzheimer’s drugs for an undisclosed US pharma group. This is good progress, but Jackie Hunter, BenevolentAI’s chief executive, believes most big pharma companies, if they are doing anything in AI, are dabbling. “We need critical mass,” she said.

Ms. Hunter also believes that if big pharma continues to sit on the sidelines and not integrate AI into their mainstream activities it could find itself over taken by other industries. Speaking at the Prism Series conference in London earlier this month Ms. Hunter said: “It would not surprise me if one of the top 10 companies in healthcare in 10 years will be [Alphabet’s] Google or Vodafone.”

Hurdles

AI could come into its own in outcomes-based pricing, an increasing focus for cost-conscious healthcare systems. While several outcomes-based deals have been announced, the approach still faces barriers.

“You might ask, why is it not happening? One reason is that’s not the way care is being reimbursed today,” said Philips’ Mr. Tas.

Current practice involves paying for discrete events: “Consultation, procedure, medication”. In contrast, outcomes-based strategies rely on continuous care. “You continuously monitor and you intervene at the moment it’s needed, so you need another way to reimburse it.”

Mr. Tas concluded that outcomes-based pricing was “not going to happen overnight because it’s such a big shift. But it’s happening, and we see it everywhere.”

With plenty of other companies clamoring to get into healthcare, including tech giants like IBM Watson and Alphabet, how will medtech and pharma groups compete in the AI space?

“We’re at the point of care,” Mr. Tas said. “It’s not only that we have the devices; it’s that we’re on the floor. We’re working with clinicians on the ground, and they get the insight into what’s needed, which perhaps someone who’s set back from that is not going to be able to gain.”

Boehringer’s Mr. Diesinger agreed: “IBM Watson has some nice cases where it is diagnosing patients better than doctors, but to make it to a highly regulated traditional market there’s a long way to go. We’re not a technology company obviously, but we already have all this regulatory burden and access to healthcare figured out.”

There are still issues to be ironed out, including cybersecurity dangers, illustrated by the ransomware attack in May that hit the UK’s NHS as well as a recent report by the US Health Care Industry Cybersecurity Task Force highlighting the challenges the industry faces.

In AI we trust?

Even if cybersecurity is assured, others in the industry believe that one of the biggest hurdles AI in healthcare will have to overcome is patient trust.

Josh Sutton of Sapientrazorfish, a digital and AI consultancy group, says the big problem for health-based AI is that patients often want the answers about their health explained.

“In certain industries, like advertising for example, people don’t care how you came up with an answer. In healthcare people are passionately obsessed, justifiably so, with how a decision was made to diagnose someone with cancer or recommend they have heart surgery.”

This desire for transparency around diagnosis could require AI companies to give details of the algorithms used in their technology, something they might be reluctant to consider – or even enabling the technology to provide direct explanations to patients.

Mr. Sutton believes that this will become more of a focus as AI becomes more prevalent in the industry and could be a limiting step for the global adoption of the approach as a standalone outside of the human-plus-machine construct many see for the industry in the short term.

“The full automation of work that is done in the industry today will take a significantly longer time than [in] other industries simply because of how critical it is we get it right, and our need, correctly in my opinion, to understand how the decisions get made and why they get made,” he said.

Mr. Diesinger of Boehringer agrees that overall, the pharma sector is a “couple of years behind other industries” in terms of using AI. But he feels that that could soon begin to change, particularly if healthcare spending comes under more pressure, forcing the sector to become more streamlined.

He said: “Managers are now much more interested in these new technologies and much more open to trying new things.”

Editor’s Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

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Artificial Intelligence Is The Real Thing For Pharma And Medtech – Seeking Alpha

Q&A: Pete Kane, CEO of Silicon Valley Artificial Intelligence – San Jose Inside (blog)

As the CEO of Silicon Valley Artificial Intelligence, Pete Kane has founded multiple startups such as Healthcare Minnesota and Startup Venture Loft, which led to his most recent collaborative creation Silicon Valley Artificial Intelligence. The community group uses machine learning (ML) and artificial intelligence (AI) to collaborate on research projects that can make landmark discoveries in science and healthcare. Silicon Valley AI will host the Genomics Hackathon fromFriday through Sunday at Google Launchpad in San Francisco. We spoke to Kane to get the skinny on what AI means for the future, and whether we should be afraid of the machines turning on us.

Why should people be excited about AI?

AI is exciting because were all exploring it at the same pace. Its possibilities have captured undivided attention of the world’s smartest and most innovative people. Its exciting because were early on in this field. Everyone can get involved. Everyone can dream up ways to use machine intelligence.

What are the biggest benefits of AI now, and in the future?

I think of AI in terms of healthcare, medicine and life sciences research. Right now there are fantastic algorithms for imaging analysis like radiology and dermatology. In the future, I believe AI will play a leading role in areas like drug discovery, personalized medicine and cancer genomics.

Should we fear singularity?

No.The singularity question is a bit overhyped. I feel like we should focus on using AI to increase our understanding of medicine and biology.

What intentions did the original founders have for Silicon Valley Artificial Intelligence?

Our original intention was to build community in the SF Bay Area AI scene. We wanted to build sustainable non-profit organization, where people could learn from one another and make meaningful connections on a regular basis.

What was the first thing that got you interested in AI?

When I realized the AI scene wanted healthcare data, I was all in. The previous organization I started was a healthcare innovation community in Minnesota (Healthcare.mn), so I knew I could add a lot to the emerging AI scene here.

What response has the group received from the Silicon Valley community?

Strong! Weve have built wonderful relationships with researchers, students, and industry. The gatherings we host draw a serious, motivated crowd and I think weve built a great culture.

How does genomics play into AI and affect everyday people?

Very little at the moment. The cost and accessibility of high-resolution genomic sequencing excludes the general population. Moreover, it is still largely exploratory how AI/ML and Deep Learning is being applied to genomics, and the interpretability of those results.

What results could be a product of the Genomics Hackathon on June 23?

Participants will be analyzing drug treatment pathways, creating mutation ranking algorithms and simulating drug interventions. When 150 of the smartest people in AI, Genomics, Bioinformatics and Computer Science come together to hack on a rare cancer (NF2) genomics dataset, amazing things are going to happen. Stay tuned.

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Q&A: Pete Kane, CEO of Silicon Valley Artificial Intelligence – San Jose Inside (blog)

Seeking the world’s new artificial intelligence gorilla maybe born in the RSA? – BizNews

We should all be blessed with friends like my pal Stafford Masie. Energetic, enthusiastic and hugely knowledgeable, on a scale of smartness hes the only one of my pals whose intellect is in the same league as Simon Marais, the late chairman of Allan Gray. Stafford is deeply plugged into the tech world, a great advantage for his friends as he willingly helps the rest of us understand the big trends shaping the world.

Our breakfast was a celebration of sorts. Staffords most recent venture, the multibillion mobile payments product Thumbzup, is a huge success with its Absa relationship expanded to now include clients like Mr Price and even Uber. After a substantial investment from Visa my pal has at last been able to take the foot off the accelerator. Which is good news for me, as we were able to spend a rare couple of relaxing, thoughtful hours together yesterday.

Too many takeaways to list. But the thing which stays with me is his assertion that South Africa is full of entrepreneurial talent, rough diamonds that with a bit of polish are sure to become world beaters. He has proved the point with Thumbzup while the Paddock brothers of Cape Town did likewise with their recent $133m sale of edutech business Getsmarter to Nasdaq-listed 2U. The next big global winner, Stafford reckons, will be the company that becomes the gorilla in artificial intelligence. With all that talent around, who says it wont be born in the RSA?

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Seeking the world’s new artificial intelligence gorilla maybe born in the RSA? – BizNews


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