Artificial intelligence could be another business driver for chipmaker Broadcom (AVGO), RBC Capital Markets said Thursday.

Broadcom's custom application-specific integrated circuits could provide upside for the company's stock over the next few years, RBC analyst Amit Daryanani said in a note to clients.

"While investors tend to focus on Broadcom's wireless assets, we think their custom ASIC solutions could unlock further upside especially as we think about revenues over the next 2-4 years," Daryanani said.

Broadcom has been involved in co-designing ASICs for hyperscale enterprise systems to handle artificial intelligence and machine learning workloads. These include projects with Alphabet's (GOOGL) Google, Alibaba (BABA) and Cisco Systems (CSCO), he said.

Daryanani estimates that ASICs for the artificial intelligence market could be a revenue opportunity for Broadcom that's worth $2 billion to $3 billion over the next few years.

IBD'S TAKE:For the latest news on chip stocks, visit IBD's news page Chip Stocks To Watch And Semiconductor Industry News.

Broadcom has limited competition in the custom ASIC business, Daryanani said. Rivals include STMicroelectronics (STM) and GlobalFoundries.

Daryanani maintainedhis "top pick" buy rating and 270 price target on Broadcom.

Broadcom lost 2.9% to close at 234.04on the stock market today.

Elsewhere in the semiconductor sector Thursday, Oppenheimer reiterated its outperform rating on Monolithic Power Systems (MPWR) after meeting with management this week.

"Tone remains bullish as management outlined the long-term vision for continued outsized (organically driven) growth," Oppenheimer analyst Rich Schafer said in a note to clients.

"Monolithic Power Systems has delivered high-teens annual (percentage) growth the past several years," Schaefer said. "We believe this growth is poised to accelerate as new opportunities in four key vectors take root. These four pillars of growth are high-end notebook, server, game console, and auto. We believe each of these verticals represents about $100 million incremental revenue potential over the next 3 years."

Oppenheimer has a price target of 100 on Monolithic Power.

Monolithic stock fell 1.9% to close at97.12 Thursday.

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Artificial Intelligence Could Be Another Catalyst For Broadcom - Investor's Business Daily

The human brain is the most powerful supercomputer on Earth, and now researchers from the University of Southern California are taking inspiration from the structure of the human brain to make better artificial intelligence systems.

Artificial intelligence (or AI) is a system of computing that aims to mimic the power of the human brain.We have more than 100 trillion neurons, or electrically conducting cells in our brain, that give us the incredible computing power for which we are known.Computers can do things like multiply 134,341 by 989,999 really well, but they can't do things like recognize human faces or learn or change their understanding of the world.At least not yet, and that's the goal of AI:to devise a computer system that can learn, process images and otherwise be human-like.

Very good question! Part of this answer is:why not?AI istheholy grail for computerscientists who want to make a computer as powerful asthe human brain. Basically, they want to create a computer that doesn't need to be programmed with all the variables becauseit can learn them just like our brain does.

A six-foot-tall, 300-pound Valkyrie robot is seen at University of Massachusetts-Lowell's robotics center in Lowell, Mass. "Val," one of four sister robots built by NASA, could be the vanguard for the colonization of Mars by helping to set up a habitat for future human explorers. (Elise Amendola/Associated Press)

Another reason scientists are interested in AI is thatit could be used for things like surveillance and face recognition, and having computer systems that can learn new terrain or solve a new problem somewhat autonomously, which, in certain situations, could be very beneficial.

In order to fully mimic the power of our own cognitive capacity, we have to first understand how the brain works, which is a feat in and of itself.We have to re-engineer and re-envision the computer to be completely different from hardware to software and everything in between, and the reason we have to do this has to do with how our brains are powered.

"If we compare, for example, our brain to the super computers we have today,they run on megawatts, [which is] a huge amount of power that's equivalent to a few hundred households, while our brain only relies on water and sandwiches to function," saidartificial intelligence and computing expert Han Wang from the University of Southern California said."It consumes power that's equivalent to a light bulb."

So you see the incredible efficiency of millions of years of evolution on our brain means we have learned to work with limited resources and become so power-efficient that we can beat a supercomputer for complex processing without breaking the energy bank.

This is where the main difference between the brain and the computer lie.

"Our current computers, there's a very powerful corebut then you have a long queue of tasks [which]come in sequentially and are processed sequentially," Wang said. "While our brain, the computation of units, which are the neurons, are connected in highly parallel manner. It's this high level parallelism that has advantages in learning and recognition."

So it's the parallelism in the brain that allows us to use only what we need only when we need it, and to not waste energy on running background processes that we all know slow down our computing power.

It's this concept of running at low energy in parallel circuits. The key to this is to make computer circuits more complex in the messagesthey can send.

In a typical computer, we think that each node sends a one or a zero, and then there's a series of ones and zerosuntil a program is made.

In the brain, it's a very small circuit and they can send a onewhich means go, a zerowhich means no signal, and/ora two that says stop, or both a one and a twoat the same time.

Artificial intelligence could be beneficial in situations where robots need to make quick decisions, like how to maneuver unknown terrain. (Boston Dynamics)

In other words, our brains can send double the information in any given exchange compared to a computer, and that, coupled with smaller networks working in parallel, reduces the power strain.

What Wang and colleagues did was to create a system of wires that connect using tin selenite and black phosphate that can send, stop, go, do nothing, or do both signals, dependingon the voltage sent.

Nowthe plan is to re-engineer the computer from the ground up andbuild a computer that has the capacity for these low voltage decisions that aren't wired through these few cores that we see today, but instead with each circuit of messages working in parallel like the brain does.

Until recently, this was a theoretical concept because there was really no way to send as much information in a single transmission as we have now.

So, artificial intelligence is only a few incredible brilliant research careers away from a reality.

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Human brain structure inspires artificial intelligence - CBC.ca

Twice in the space of six weeks, the world has suffered major attacks of ransomware malicious software that locks up photos and other files stored on your computer, then demands money to release them.

It's clear that the world needs better defenses, and fortunately those are starting to emerge, if slowly and in patchwork fashion. When they arrive, we may have artificial intelligence to thank.

Ransomware isn't necessary trickier or more dangerous than other malware that sneaks onto your computer, but it can be much more aggravating, and at times devastating. Most such infections don't get in your face about taking your digital stuff away from you the way ransomware does, nor do they shake you down for hundreds of dollars or more.

Despite those risks, many people just aren't good at keeping up with security software updates. Both recent ransomware attacks walloped those who failed to install a Windows update released a few months earlier.

Watchdog security software has its problems, too. With this week's ransomware attack , only two of about 60 security services tested caught it at first, according to security researchers.

"A lot of normal applications, especially on Windows, behave like malware, and it's hard to tell them apart," said Ryan Kalember, an expert at the California security vendor Proofpoint.

HOW TO FIND MALWARE

In the early days, identifying malicious programs such as viruses involved matching their code against a database of known malware. But this technique was only as good as the database; new malware variants could easily slip through.

So security companies started characterizing malware by its behavior. In the case of ransomware, software could look for repeated attempts to lock files by encrypting them. But that can flag ordinary computer behavior such as file compression.

Newer techniques involve looking for combinations of behaviors. For instance, a program that starts encrypting files without showing a progress bar on the screen could be flagged for surreptitious activity, said Fabian Wosar, chief technology officer at the New Zealand security company Emsisoft. But that also risks identifying harmful software too late, after some files have already been locked up.

An even better approach identifies malware using observable characteristics usually associated with malicious intent for instance, by quarantining a program disguised with a PDF icon to hide its true nature.

This sort of malware profiling wouldn't rely on exact code matches, so it couldn't be easily evaded. And such checks could be made well before potentially dangerous programs start running.

MACHINE VS. MACHINE

Still, two or three characteristics might not properly distinguish malware from legitimate software. But how about dozens? Or hundreds? Or even thousands?

For that, security researchers turn to machine learning, a form of artificial intelligence. The security system analyzes samples of good and bad software and figures out what combination of factors is likely to be present in malware.

As it encounters new software, the system calculates the probability that it's malware, and rejects those that score above a certain threshold. When something gets through, it's a matter of tweaking the calculations or adjusting the threshold. Now and then, researchers see a new behavior to teach the machine.

AN ARMS RACE

On the flip side, malware writers can obtain these security tools and tweak their code to see if they can evade detection. Some websites already offer to test software against leading security systems. Eventually, malware authors may start creating their own machine-learning models to defeat security-focused artificial intelligence.

Dmitri Alperovitch, co-founder and chief technology officer at the California vendor CrowdStrike, said that even if a particular system offers 99 percent protection, "it's just a math problem of how many times you have to deviate your attack to get that 1 percent."

Still, security companies employing machine learning have claimed success in blocking most malware, not just ransomware. SentinelOne even offers a $1 million guarantee against ransomware; it hasn't had to pay it yet.

A FUNDAMENTAL CHALLENGE

So why was ransomware still able to spread in recent weeks?

Garden-variety anti-virus software even some of the free versions can help block new forms of malware, as many are also incorporating behavioral-detection and machine-learning techniques. But such software still relies on malware databases that users aren't typically good at keeping up to date.

Next-generation services such as CrowdStrike, SentinelOne and Cylance tend to ditch databases completely in favor of machine learning.

But these services focus on corporate customers, charging $40 to $50 a year per computer. Smaller businesses often don't have the budget or the focus on security for that kind of protection.

And forget consumers; these security companies aren't selling to them yet. Though Cylance plans to release a consumer version in July, it says it'll be a tough sell at least until someone gets attacked personally or knows a friend or family member who has.

As Cylance CEO Stuart McClure puts it: "When you haven't been hit with a tornado, why would you get tornado insurance?"

Excerpt from:

How artificial intelligence is taking on ransomware - ABC News

IBM is convinced that its Watson supercomputer is capable of doing a whole lot more than winning at Jeopardy--and the company wants to make sure it stays that way.

To that end, IBM is making a push this week to urge lawmakers not to fall victim to artificial intelligence fear mongering. David Kenny, IBM Watson's senior vice president, sent a letter to Congress on Tuesday stressing the importance of pushing A.I. forward instead of restricting it. According to Recode, he's meeting with a group of Representatives today to discuss the technology.

"When you actually do the science of machine intelligence," Kenny wrote in the letter, which IBM published Tuesday, "and when you actually apply it in the real world of business and society ... you understand that this technology does not support the fear-mongering commonly associated with the AI debate today."

Kenny argued that fears of "massive job loss, or even an eventual AI 'overlord' " are overblown. "I must disagree with these dystopian views," he wrote. "The real disaster would be abandoning or inhibiting cognitive technology before its full potential can be realized."

IBM has an interest in ensuring that the government chooses not to restrict the use of artificial intelligence. While the Watson system is perhaps most famous for beating Jeopardy champ Ken Jennings in 2011, it's since been applied to a variety of tasks. Watson is used to recommend treatments for patients in medical facilities including the Cleveland Clinic and New York's Sloan-Kettering Cancer Center. H&R Block has begun using Watson to prepare client's tax returns. In April, the software was applied to the Masters golf tournament, letting online viewers quickly see the most exciting highlights, which it selected automatically based on factors like crowd noise and player reactions.

Even so, in recent months, some in the A.I. world have expressed surprise that Watson isn't further along in its capabilities, given what it did six years ago. The company's ambitions for more widespread applications of its tech mean the company has a lot at stake.

As A.I.'s abilities expand to tasks like driving, reading X-rays, diagnosing illnesses, and performing paralegal work--all of which it's already capable of doing on some level--millions of jobs could be lost. Recent expert predictions on the number of jobs lost have ranged from from 6 percent by 2021 to 50 percent by 2035.

Yet IBM is the latest A.I. company to assure the public that its fears of the technology are overblown. Adam Cheyer, co-founder of Apple's Siri and virtual assistant A.I. startup Viv, compared the fears that A.I. will become too smart to worrying about overpopulation on Mars. "We're barely at the beginning of A.I.," he said. "There's nothing to even be done yet."

Last month, Jeff Bezos, whose popular Amazon Alexa relies heavily on A.I., said during a chat at the Internet Association that the problem with artificial intelligence is that we don't have more of it. "Basically," he said, "there's no institution in the world that cannot be improved with machine learning."

Google co-founders Larry Page and Sergey Brin have also spoken out in defense of A.I.

Meanwhile, there's also a vocal group within the tech industry that errs on the fear-mongering side. A recent survey of academics and industry leaders found they believe, on average, that A.I. will be capable of performing any task--from driving trucks to writing novels--better than humans by 2060.

Elon Musk, whose Tesla vehicles rely on artificial intelligence, soon chimed in with the notion that this would happen closer to 2030. "I hope I'm wrong," he tweeted.

Other Silicon Valley giants have warned against the technology. Peter Thiel co-founded OpenAI, a non-profit to ensure A.I.'s safe use, along with Musk. Earlier this year, Bill Gates suggested that robot taxes could help slow the loss of jobs to automation. And Tim Berners-Lee, inventor of the world wide web, recently warned that A.I. could one day replace financial institutions and control the world economy.

The lobbying push from IBM comes about a month after the formation of the Congressional Artificial Intelligence Caucus, a group of Representatives that will study A.I. and seek to create policies related to its use and implementation. Congressman John K. Delaney of Maryland, one of the group's co-founders, recently met with Amazon and Google, according to CNBC. The meeting with IBM on Wednesday is the group's next step.

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IBM Is Trying to Convince Congress That Artificial Intelligence Isn't That Bad - Inc.com

A look at TurboPatents new RoboReview service. (TurboPatent Photo)

Seattle startup TurboPatent is releasing a pair of new products designed to improve the patent application process, with help from artificial intelligence.

TurboPatent, which raised $1.4 millionin funding earlier this year, focuses on corporations and law firms, automating taskslike formatting or document preparation, for example, freeing up people to work on more complex, high-value work. The service is designed to cut costs, save time and lead to more accurate patent documentation.

The new products are called RoboReview and RapidResponse. RoboReview uses AI and predictive analytics to automatically analyze and review draft patent applications. The company says this will reduce to seconds a process that can normally take several people multiple days to complete. RapidResponse helps speed upoffice actions,written correspondence between an applicant and patent examiner during the application process.

Like any procedure that involves people, the review process is subject to human error, said TurboPatent CEO James Billmaier. TurboPatent automates the most tedious and time-consuming parts of the process, which drastically cuts down on the likelihood of potential issues going unnoticed. That leaves humans to decide whether or not to submit a patent and if so, what alterations need to be made.

Formerly known as Patent Navigation, TurboPatent has an experienced team led by co-foundersBillmaier and Charles Mirho. Billmaier was previously CEO of Melodeo, a cloud-based media platform company that sold to HP in 2010. He also teamed up with Paul Allen in 1999 to launchhome-entertainment technology company Digeo, which was eventually sold in 2009 to ARRIS Group Inc.

Mirho, meanwhile, is a patent law veteran, having worked as a patent counsel at Intel and later as a managing partner of a patent law firm. He also has a computer science degree from Rutgers.

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TurboPatent aims to improve the patent process with new artificial ... - GeekWire

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

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

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

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

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

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

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

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

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

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

Updated 3 hours ago

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This blog was first published on Health Populi.

Excerpt from:

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

This article was originally published on The Conversation. Read the original article.

There is a lot of money to be made from Artificial Intelligence. By one estimate, the market is projected to hit US$36.8 billion by 2025. Some of this money will undoubtedly go to social good, like curing illness, disease and infirmity. Some will also go to better understanding intractable social problems like wealth distribution, urban planning, smart cities, and more efficient ways to do just about everything.

But the key word here is some.Theres no shortage of people touting the untold benefits of AI. But once you look past the utopian/dystopian and techno-capitalist hyperbole, what we are left with is a situation where various stakeholders want to find new and exciting ways to part you from your money. In other words: its business, not personal.

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While the immediate benefits of AI might be clear from a strategic business perspective, the longer term repercussions are not. Its not just that the future is impossible to predict, complex technologies are hard to control, and human values are difficult to align with computer code, its also that in the present its hard to hear the voices calling for temperance and judiciousness over the din of companies clamoring for market advantage.

This is neither a new nor recent phenomenon. Whether it was the social media boom, the smart phone revolution, or the commercialization of the world wide web, if theres money to be made, entrepreneurs will try and make it. And they should. For better or worse, economic prosperity and stability depends on what brilliance can be conjured up by scientific minds.

But thats only one side of the coin. The flipside is that prosperity and stability can only be maintained if equally brilliant minds work together to ensure we have durable ways to govern these technologies, legally, ethically, and for the social good. In some cases, this might mean agreeing that there are simply certain things we should not do with AI; some things that profit should not be derived from. We might call this conscious capitalismbut it is, in fact, now a societal imperative.

There are structural problems in how the AI industry is shaping up, and serious asymmetries in the work that is being done. Its all well and good for large companies invested in presenting themselves as the softer, cuddlier, but no less profitable, face of this new technological revolution to tout hashtags like #responsibleAI or #AIEthics. No rational person objects to either, but they should not distract from the fact that hashtags arent coherent policy.

Effective policy costs money to research, devise, and implementand right now, there is not enough time, cash, brainpower and undivided attention being devoted to building the robust governance infrastructure that will be required to compliment this latest wave of technological terraforming.

There are people out there thinking the things that need to be thought and implemented on the law, policy and governance side, but they are being drowned out by the PR, social media influencers and marketing campaigns that want to turn a profit from AI, or tell you how they can help your company do so.

Ultimately, our reach exceeds our grasp. We are far better at building new, exciting and powerful technologies than we are at governing them. To an extent, this has always been the case with new technologies, but the gap between what we create and the extent we can control it is widening and deepening.

Over the course of my PhD, where I researched long term strategies for AI governance and regulation, I was offered some sage advice: If you want to ensure youre remembered as a fool, make predictions about the future. While I try and keep that in mind, I am going to go out on a limb: AI will fundamentally remake society beyond all imagination.

Our commitment to ensuring safe and beneficial AI should amount to more than hashtags, handshakes and changing the narrative. It should be internalized into the ethos of AI development. Technical research must go hand in hand with law and policy research on both the public and private side. With great power comes great shared responsibility and its about time we recognise that this is the best business model we have for AI going forward.

Canadian Prime Minister Justin Trudeau high fives a robotic arm as he takes part in a robotics demonstration at Kinova Robotics in Boisbriand, Quebec, Canada March 24, 2017. REUTERS/Christinne Muschi

If we are going to try and socialize the benefits of AI across society as the familiar refrain goes we need to get serious about the distribution of money across the AI industry today. Public and private research and public engagement has a critical role to play in this, even if its easier (and cheaper) to co-opt it into in-house research. We need to build a robust government-led research infrastructure in the UK, Europe and beyond to meet head on the challenges AI and other tech will pose. This means we need to think about more than just about data protection, algorithmic transparency and bias.

We also need to get serious about how our legal and political institutions will need to adapt to meet the challenges of tomorrow. And they will need to adapt, just as they have proven able to do in the face of earlier technological changes, whether it was planes, trains, automobiles or computers. From legal personhood to antitrust laws, or criminal culpability to corporate liability, we are starting to confront the incommensurability of certain legal norms with the lived reality of the 21st century.

AI is a new type of beast. We cannot do governance as usual, which has meant waiting for the latest and greatest tech to appear and then frantically react to keep it in check. Despite protestations to the contrary, we must be proactive in engaging with AI development, not reactive. In the parlance of regulation, we need to think ex ante and not just ex post. The hands-off, we-are-just-a-platform-and-have-no-responsibility-here tone of Silicon Valley must be rejected once and for all.

If we are going to adapt our institutions to the 21st century we must understand how they have adapted before, and what can be done today to equip them for the challenges of tomorrow. These changes must be premised upon evidence; not fatalistic conceits about the machines taking over, not philosophical frivolity, not private interests. We need smart people on the law and policy side working with the smart people sitting at the keyboards and toiling in the labs at the companies where these engines of tomorrow are being assembled line by line. Some might see this as an unholy alliance, but it is, in fact, a noble goal.

Belgian Ian Frejean, 11, walks with "Zora" the robot, a humanoid robot designed to entertain patients and to support care providers, at AZ Damiaan hospital in Ostend, Belgium June 16, 2016. REUTERS/Francois Lenoir

The governance and regulation of AI is not a national issue; it is a global issue. The untold fortunes being poured into the technical side of AI research needs to start making its way into the hands of those devoted to understanding how we might best actualize the technology, and how we can in good conscience use it to solve problems where there is no profit to be made.

The risk we run is that AI research kick starts a new global arms race; one where finishing second is framed as tantamount to economic hari-kari. There is tremendous good that the AI industry can do to help change this, but so far these good intentions have not manifested themselves in ways conducive to building the robust law, policy and social-scientific infrastructure that must compliment the technical side. As long as this imbalance continues, be afraid. Be very afraid.

Christopher Markou is aPhD Candidate at the Faculty of Law at theUniversity of Cambridge

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AI: Artificial Intelligence Could Start a Global Arms RaceWill We Be Able to Control It? - Newsweek

The brains behind IBMs Jeopardy-winning, disease-tracking, weather-mapping Watson supercomputer plan to embark on a lobbying blitz in Washington, D.C., this week, hoping to show federal lawmakers that artificial intelligence isnt going to kill jobs or humans.

To hear IBM tell it, much of the recent criticism around machine learning, robotics and other kinds of AI amounts to merely fear mongering. The companys senior vice president for Watson, David Kenny, aims to convey that message to members of Congress beginning with a letter on Tuesday, stressing the real disaster would be abandoning or inhibiting cognitive technology before its full potential can be realized.

Labor experts and reams of data released in recent months argue otherwise: They foretell vast economic consequences upon the mass-market arrival of AI, as entire industries are displaced not just blue-collar jobs like trucking, as self-driving vehicles replace humans at the wheel, but white-collar positions like stock trading too.

Others fear the privacy, security and safety implications as more tasks, from managing the countrys roads to reading patients X-ray results, are automated and the most dire warnings, from the likes of SpaceX and Tesla founder Elon Musk, include the potential arrival of robots capable of destroying mankind.

But as IBM seeks to advance and sell its AI-driven services, like Watson, the company plans to tell lawmakers those sort of concerns are fantasy. Along with a private meeting with some lawmakers near Capitol Hill on Wednesday, Kenny is urging Congress to avoid reacting out of fear and pursuing some proposals, like an idea from Bill Gates to tax robots, as regulators debate how to handle this fast-growing field.

The impact of AI is evident in the debate about its societal implications with some fearful prophets envisioning massive job loss, or even an eventual AI Overlord that controls humanity, Kenny wrote. I must disagree with these dystopian views.

For IBM, the stakes are high: Watson and the future of what it calls cognitive technology are critical to Big Blues business. Beyond Watsons existing work from aiding in cancer research to funnier tasks, like writing a cookbook IBM has sought to bring its famed supercomputer to tackle some of the sprawling, data-heavy tasks of the federal government.

In some ways, though, the most vexing challenges facing AI arent technological theyre political.

Self-driving cars, trucks and drones, for example, cant just take to the roads and skies without permission from local and federal regulators, which are only just beginning to loosen restrictions on those industries.

Others fear that automation might lead to discrimination: Under President Barack Obama, the White House spent months warning that highly powerful algorithms could share the biases of their authors, leading to unfair treatment of minorities or other disadvantaged communities in everything from obtaining a credit card to buying a house. Thats why his administration in October explicitly urged Congress to help it hire more AI specialists in key government oversight roles.

And more challenging still are the economic implications of AI. It will be up to federal officials including President Donald Trump or his successors to grapple with untold numbers of Americans who might someday find themselves out of a job and in need of training in order to find new careers. (Trumps own Treasury secretary, however, previously has said AI is more than 50 years away from causing such disruptions.)

Sensing potential political hurdles, companies like Apple, Facebook, Google and IBM chartered a new organization, the Partnership for Artificial Intelligence, last year. In doing so, they hoped to craft ethical standards around the safe and fair operation of machine learning and robotics before government regulators in the United States or elsewhere sought to more aggressively target AI with consumer protection regulations. AI now counts among some of those tech companies regular lobbying expenses.

And IBM, in particular, has spent years trying to tell a friendlier, less economically catastrophic story about AI in the nations capital a campaign that it will continue this week.

Technological advancements have always stoked fears and concern over mass job loss, Kenny wrote in his Tuesday letter to Congress. But history suggests that AI, similar to past revolutionary technologies, will not replace humans in the workforce.

In many cases, like cyber security and medicine, Kenny told lawmakers its still humans at the end of the day who can choose the best course of action when an AI system has identified a problem. He stressed that government should instead focus its attention on fixing a shortage of workers with the skills needed to work in partnership with AI systems.

For all the scrutiny facing the industry, however, some in Congress are still getting up to speed. Thats why lawmakers like Rep. John Delaney, D-Md., co-founded the Congressional Artificial Intelligence Caucus, an informal organization of Democrats and Republicans studying the issue. His group is joining IBMs Kenny and other tech leaders at a private, off-record event at the Capitol Hill Club on Wednesday.

Delaney told Recode he comes to AI from the perspective that the sky is not falling that even if industries change, old jobs might still be replaced by new ones in emerging fields. With the caucus, he said, the goal is to make sure Congress is as informed on this issue as possible, so when it inevitably has some sort of knee-jerk reactions on AI, the final response from Capitol Hill is more measured in scope.

Asked if the tech industry similarly appreciates the economic implications of its inventions, the congressman replied: I think the [tech] industry focuses, as it should, on being at the cutting edge of innovation and creating products and services that enhance productivity and improve peoples lives.

So when theyre thinking about driverless cars, do they spend more time thinking about how this will enhance productivity, and how it will [protect] safety, than the jobs that will be affected? I think the answer is probably yes, but I dont think they do it in some of kind of nefarious way, Delaney said.

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IBM is telling Congress not to fear the rise of an AI 'overlord' - Recode

Nvidia (NVDA) and Volkswagen (OTCPK:VLKAY) have just announced a promising collaboration in developing a viable AI and deep learning software offering for cars, traffic systems, and other applications. This intensifies the cooperation of the two companies, which revealed their first strategic partnership earlier this year.

The first collaboration framework included integrating Nvidias digital cockpits in the cars of the entire VW group, while also developing autopilot technology with Audi (OTCPK:AUDVF), owned by VW. The first autonomous prototype based on the Audi Q7 model was revealed during CES 2017. The technology was claimed to be built on the basis of Nvidias Drive PX2 processing unit.

This initial agreement was already considered to be very beneficial for both corporations. Thus, Nvidia gained a large customer (the largest automaker in 2016), which among other benefits would allow Nvidia generate consistent and recurring revenue over time from the automotive industry. In turn, VW got a possibility to enhance its vehicles in terms of technology, thus regaining customers attention after the dieselgate scandal hit VWs reputation.

The new cooperation goes beyond just the auto industry. It is stated (the link is in German) the two corporations are going to work together in their own data lab, while also attracting start-up companies to collaborate. Notably, it is stated by Techcrunch the first data lab of VW was established in 2014 to develop AI for such areas as robotic enterprise. The technologies by Nvidia will provide a sound basis for further development.

The words of Volkswagen Chief Information Officer Martin Hofmann are provided by Reuters:

Artificial intelligence is the key to the digital future of the Volkswagen GroupWe want to develop and deploy high-performance AI systems ourselves. This is why we are expanding our expert knowledge required. Cooperation with Nvidia will be a major step in this direction.

Will the collaboration improve Nvidias position in the auto sector?

Nvidias automotive revenue increased 24% YoY in Q1 2017 reaching $140 million. Taking into consideration VW groups revenue amounted to more than $215 billion in 2016, it is clear Nvidia has room to grow in automotive. This is also supported by the prediction of David Pahl from Texas Instruments (TXN), who projects at least 2% of a cars price will be represented by electronics. In the case of VW, 2% from $215 billion lead to $4.3 billion of potential revenue.

In addition, it is interesting to see how Nvidia expands its operations further to Europe, especially to Germany, where the auto sector is one of the key industries. This shows the company bets heavily on the automotive sector as a profit driver in the future, since some players, like AMD (AMD), try to beat Nvidia in the gaming industry.

(Source: Nvidia website)

Volkswagen also actively hires specialists for the research lab.

(Source: jobs.automobilwoche.de)

Conclusion

Overall, the new collaboration is set to intensify the cooperation of the two corporations, which is likely to be beneficial for both VW and Nvidia. Nvidias stock has already grown by more than 200% over last year, and now it trades at a hefty forward P/E multiple of 43.5. However, if the company is able to exploit all the potential it has in the auto industry, this growth can continue.

(Source: Graphiq.com)

As regards VW, the new cooperation will improve its position in the field of AI for cars and also open possibilities to further expand the operations to enterprises, which is also able to bring the stock higher.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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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Nvidia To Cooperate With Volkswagen On Artificial Intelligence - Seeking Alpha

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

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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 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

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

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

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