Daily Archives: May 4, 2017

Artificial intelligence has shown promise in helping doctors predict which patients may be susceptible to chronic diseases like Alzheimers.

But despite the rapid advances , the healthcare industry is still in the early days of rolling out AI-powered treatments and drugs, Morten Sogaard, Pfizers vice president and head of genome sciences and technologies, said at Fortunes Brainstorm Health conference in San Diego on Wednesday.

Pfizer ( pfe ) has been using AI techniques like machine learning for years to sift through data, help research new drug compounds (essentially the combination of multiple drugs), and determine the best participants for clinical trials, he said. In some cases, it is nothing new, Sogaard said about AI in healthcare.

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What is new, however, is the rising flood of information like genomic data and sensor data from medical devices, he explained. This influx has made it more difficult to understand key connections that could help researchers discover new treatments.

Currently, Pfizer is using deep learning , which Google ( goog ) helped to popularize as a way to train computers to recognize cats in photos, to mine electronic health records and lab data. By doing so, Pfizer can better understand how ailments like autoimmune and fatty liver diseases progress, he explained.

Sogaard said that these deep learning techniques have shown promise in finding disease patterns across large groups of people, but the ultimate goal is to eventually help individual patients.

Pfizer has also partnered with IBM ( ibm ) to use its Watson data-crunching technology in pharmaceutical research. But the company is also open to partnering with Google, Amazon ( amzn ) , and other cloud-computing providers to incorporate their own respective technologies.

Sogaard believes a handful of cloud computing providers will have AI technologies that drug companies could eventually use for research and development. However, it will not all happen the day after tomorrow, of course, he said.

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Federal regulations have not yet caught up to the rapid pace of innovation that could one day help predict and diagnose diseases using a combination of genomic, protein, and medical imaging data. But Sogaard is hopeful, and based on Pfizers meetings with regulators, he believes the Federal Drug Administration is open-minded to AI-assisted medical treatment.

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How Artificial Intelligence May Help Doctors Save Lives - Fortune

When IBM supercomputer Deep Blue defeated chess champion Garry Kasparov in May 1997, it was considered a monumental achievement for artificial intelligence. But in his new book, Deep Thinking, Kasparov shows how this discovery only set up a number of new hurdles for the artificial intelligence world to overcome.

"We realized after 1997 that it's very much back to square one because beating the world champion and making it to the top of Mount Everest in chess - we just discovered at that point that there are many, many high peaks ahead of us," Kasparov told NBC News' Chuck Todd in the latest edition of 1947: The Meet the Press Podcast. "The question of artificial intelligence still remained unanswered."

Kasparov, who has been a vocal critic of Vladimir Putin's regime in Russia, notes that there is an intersection between his interests in computing and democracy. Kasparov said that technological development and Russia's alleged cyber attacks on the free world go hand in hand.

Although the rise in networked computing has given foreign adversaries a tool with which to attack governments and undermine democracies, Kasparov says the same tools can be used against autocratic regimes.

"Cybersecurity and social media become front lines of this new conflict," he said. "So we're trying to play by the rules and they use our own technology, technology invented in the free world, against the very foundation of the free world."

The threat of foreign government conflicts was central to the evolution of the early internet, but Kasparov noted that the founding fathers of computer science, great minds like Alan Turing and Claude Shannon, believed that chess would be the ultimate test for artificial intelligence because winning a game of chess requires intelligence. What they were unable to anticipate was the dramatic growth of "brute force" computing.

Today, "a free chess app on the latest mobile phone is stronger than Deep Blue," Kasparov said.

With the rapid rate at which technology is developing today, there may yet be a new Holy Grail for artificial intelligence. According to Kasparov, part of the problem for AI rests in the fact that many Americans view technology as competition. Automation has long been viewed as a menace to the working class, but he suggests looking at the development from another perspective.

"What about looking for a positive side?" Kasparov said. "Now we have new intelligent machines, and they will be taking over I would say more menial aspects of cognition. So maybe it will help us to elevate our lives toward curiosity, creativity, beauty, joy so there are other things that we can do if we move to the next level of the development of our civilization."

"Machines will be better at anything that we're doing now, but as long as we are capable of dreaming and creating new things - say moving to other planets or exploring oceans. There are many things that we can do where machines will need human qualities. The question is how we combine it, how we become proper operators of these massive brute force and also certain other new qualities," Kasparov said.

1947: The Meet the Press Podcast is available on iTunes, Google Play, or wherever you get your podcasts.

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Kasparov on Putin and the Future of Artificial Intelligence - NBCNews.com

Human character animation has gotten much better over the years, but its still one of the most recognizable issues when enjoying video games. Animations are normally a predetermined set of canned motions, and while real enough looking in the right setting, can totally break the immersive experience when they stray out of bounds. The uncanny valley is a particularly hard one to escape.

Now, a research team from the University of Edinburgh has developed a new way to animate game characters using neural net computing, which could help developers make more fluid and realistic animations, while decreasing the system resources and time involved. The video demonstrating the new technology is legit:

The minds behind this video, and an accompanying paper published in ACM Transactions, are Daniel Holden, Taku Komura and Jun Saito. When I caught the video, I immediately reached out to Holden for more info. What exactly makes this different from other animation methods, and whats happening in the neural net to make the magic happen?

Neural networks (or NNs) are a way to train a computer with millions of differently weighted data points. Using algorithms, a NN can create completely new outputs on its own, based on the data it has to reference. The emerging technology is commonly used in facial recognition, image processing, and stock market prediction applications.

The workings of the neural network are themselves quite abstract and hard to understand, Holden admitted in an email. But basically, what he and his colleagues have done is employ a system called a phase-functioned neural net, in which the variables controlling a characters movement and interaction within the environment can change on the fly. His aim was to develop intricate, human-like cyclical movement that reacts appropriately to user inputs. As players mash on their gamepads or move a mouse, the neural net learns and evolves over time to create a seamless animation.

We change the weights of the neural network depending on what point in time in the locomotion cycle the character is, explained Holden, those weights being the data that influences what the animation will be. For example, when the character puts their left foot down the weights of the neural network are different to when the character puts their right foot down.

The result? A drastic reduction in the amount of time and effort animators need to achieve that perfect gait. The video above only used about 1.5GB, or 2 hours, of motion capture data, and the animation doesnt need as much on-board processing power to render. Holden thinks it will take some of the burden off animation programmers maintaining the hugely complex animation systems.

It took 30 hours of NN training and 4 million data points to create the animation in the video and, to me, it looks pretty good. The character deftly navigates raised obstacles, appropriately speeds up and slows down based on the input and reacts accordingly to the walls and bridges. Obviously, third-person perspective games could benefit from the new approach, but VR experiences are also mediums where, for maximum immersion, were going to need hyper-realistic motion.

Neural networks have been used in gaming before, in developing opponent AIs, but this is a great example of how the technology can make developers lives easier. Holden just started a new R&D job at Ubisoft, and while unable to say if the tech is going to be used there, he looks forward to seeing more implementation in the future.

I hope that this technique does change gameplayhopefully these sort of technologies will allow game designers to be more adventurous with the kind of environments they create, said Holden.

Bryson is a freelance storyteller who wants to explore the universe with you.

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Artificial Intelligence Could Prevent the Next Video Game Animation ... - Gizmodo

During a recent visit to IBMs digs in the chic Astor Place section of Manhattan, I got a peek at how Watson the famous (and increasingly useful) artificial intelligence machine is being taught to look for signs of improper trading, fraudulent account openings and other employee misdeeds.

"We take all of traders' emails and chats and run them through our personality insights and tone analyzer and identify whether theres anger, are they happy, are they sad?" said Marc Andrews, vice president of Watson Financial Services Solutions. Were analyzing the behavioral patterns that are associated with misconduct: How do people start behaving right before they get involved in misconduct?

One thing Watson has discovered, according to Andrews, is that U.S. traders stop using profanity and angry language just before doing something they shouldnt.

It might have been because they were trying to hide things, Andrews said.

But in the U.K., traders use of profanity rises when they go rogue.

They were being proper beforehand, but then they let go of their emotions, he said.

Along with the communications, Watson is analyzing trading behaviors, volumes and frequencies, looking for suspicious trading sequences, abnormal order sizes or significant price changes. Watson will weigh it against other recent events and communication patterns for signs something might be off.

Seeing a suspicious trade sequence or price alert alone might not indicate a problem, because good traders are good at timing the market, Andrews said. What would be telling would be a communication with a company insider just beforehand.

One trader received a note that said, Hey man, I think its going to rain here in Seattle, youd better cover up before you get drenched. Watson recognized that it was from a company insider and that it had a warning tone and therefore flagged it.

Watson will also look to see if traders had other compliance violations in their history or if they had made angry remarks.

By observing such patterns, it is hoped, Watson can start to alert banks to possible insider trading, pump-and-dump schemes, collusion and other forms of misconduct.

To catch phony accounts, Watson starts off looking for an unusually large number of complaints, which might indicate something is awry. It also looks for dormant accounts, accounts where notifications have been suppressed, mismatched contact information, suspicious logins, enrollment reversal and odd login times.

Watson will look to see if an employee suddenly had a spike in sales or unusual customer distribution, such as targeting elderly customers. It looks for management emails that express undue sales pressure.

IBM is beginning to apply this to voice communications, too, to identify ethics violations and changes in tone and speed of speech, as well as language, Andrews said.

The staff of Promontory Financial Group, the compliance consulting firm IBM bought last fall, provides color around motives, culture and conduct risk.

One of the largest global banks IBM would not say which one is using a cloud-based version of Watson for employee surveillance.

The gift of hindsight

Andrews acknowledges that when it comes to rogue trading and fake accounts, IBM is training Watson with histories of known prior misconduct and hindsight is 20-20. In fact, if you know exactly what you are looking for and someone violates a policy or law, a rules-based system could catch it; you do not even need artificial intelligence. It is when you do not know what to look for that trade surveillance gets tough.

Human beings are never static; theyre never doing the same thing today that they did previously, said Marten Den Haring, chief product officer at Digital Reasoning, whose artificial intelligence technology has been analyzing trader activity and communications on exchanges that use Nasdaq technology for a year.

Den Haring takes Watsons conclusions about British and American traders use of profanity with a grain of salt.

There are cultural differences to any type of communication patterns, he pointed out. I would be cautious to think you could identify the types of patterns you just described.

Better signals of wrongdoing come through tracking behaviors over time across multiple channels and seeing people try to conceal their behavior, he said.

In trying to cover up, people make more mistakes and leave a lot more clues, Den Haring said. A good example is boasting. You completed something nefarious, youre happy, youre done, you dont realize that high-fiving each other digitally is leaving just as many clues behind as planning to do something together, he said.

Digital Reasoning also pays close attention to networks among people and sudden changes in behavior. Those are far more interesting and less based on emotion and cultural differences, he said.

Someone has to care

In addition to the technological difficulties of identifying patterns of bad behavior, there is the question of the culture and will of a company and its management.

In most banking scandals, the underlying bad behavior was visible to the human eye for some time. Seven hundred whistleblower complaints had been lodged about fake accounts at Wells Fargo by 2010, along with hundreds of employee and customer complaints. In the JPMorgan Chase "London Whale" case, the trader Bruno Iksil has said his dangerously large credit swap positions were part of a trading strategy that had been initiated, approved, mandated and monitored by the CIOs senior management.

In such cases, it is not that no one knows what is going on, and technology is needed to bring it to light. Management knows and may even be directing the bad behavior, through emails and calls pressuring employees to cross-sell more aggressively or by ordering traders to execute a high-risk strategy. No amount of software, no matter how intelligent, can force leaders to make ethical decisions.

What technology can do is help speed a compliance investigation when foul play is suspected.

Once you have put your finger on an individual youre putting on a watchlist, were making the investigation capability far richer, more interesting for the financial institutions, Den Haring said. That quick 360-degree look-back gives you more clues into what seems out of the norm for a trader.

Andrews also describes the value of IBMs Watson this way.

Were providing augmented intelligence to banks to help them identify things more quickly, earlier on, and with less resources, he said. Were not making the decision, [but] were providing evidence to support a decision.

Valerie Bannert-Thurner, senior vice president and head of risk and surveillance at Nasdaq, says some of Nasdaqs bank clients have started integrating voice and electronic communications together with its SMARTS trade surveillance software and the Digital Reasoning AI engine, in order to watch everything traders say and do in all channels at once.

Customers want to know if traders are changing language, location or communication channels, or suddenly starting to communicate more rapidly or often, she said.

All that metadata around communications, overlaid with trading will flag unusual trade activity and any intent to manipulate markets, Bannert-Thurner said.

Artificial intelligence software could also uncover collusion. In late April, the Federal Reserve fined Deutsche Bank $156.6 million for, among other things, "using electronic chatrooms to communicate with competitors about their trading positions."

Technology from IBM, Digital Reasoning and Sybenetix could easily catch such known violations. A rules-based system probably could as well.

AI can help compliance officers do their jobs better and make traders more aware they are being watched.

Sybenetix teaches its AI engine the specifics of each job, so it can create a model of normal behavior. This is used to create intelligent alerts for compliance officers, which lets them ask smarter questions. In some cases, its replacing an Excel sheet and trade sampling.

Weve seen in a number of cases that traders are now coming to compliance officers before they trade and checking with them, said Wendy Jephson, co-founder and chief behavior scientist at Sybenetix. One of our clients said this is unheard-of behavioral change, for the front office to come in and talk to compliance. Theyre basically saying, Look, I know youre going to tap me on the shoulder, youre going to ask me questions, let me just tell you about it upfront and make sure youre fine.

And it helps with the classic problem of compliance: false positives.

If you go to large banks, their systems that are processing trades produce tens of thousands of alerts, and 99% will be false positives, said Richard Maton, chief marketing and strategy officer at Sybenetix.

At the same time, regulators are requiring surveillance on more asset classes and instruments. Software can help sift through the large amounts of data faster than humans, and layer in related communications and behavior, to isolate activity that is truly suspicious.

Editor at Large Penny Crosman welcomes feedback at penny.crosman@sourcemedia.com

Penny Crosman is Editor at Large at American Banker.

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Have rogue employees met their match in AI? - American Banker

Since a group of scientists coined the term at Dartmouth College (my alma mater) in 1956, artificial intelligence (AI) has taken the world by storm, with a market expected to be valued at $152 billion by 2020. While over 80% of executives believe this pioneering technology improves worker performance and creates jobs, others arent convinced. Any disruptive technology introduces new pros and cons, but the fear lies in the potential AI has to eliminate jobs across various industries, from manufacturing to telecom to retail.

AI is defined as intelligence exhibited by machines. Its potential for disruption is limitless. For marketers, AI technology represents an opportunity to harness and analyze data with speed, depth, and breadth that were previously unimaginable. But the benefits can only be seized if marketers have the full confidence of their superiors. Today, the role of the CMO has shifted and they have more money to spend and greater impact on the bottom line than ever before. CIOs are no longer the only members of the C-suite investing in tech. As we speak, marketers are embracing the potential of AI-driven data analytics to enhance brand reputation while still relying on their human instinct to reap the full rewards.

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Increasing data visibility and use

One of the biggest priorities for marketers is gaining visibility into the customer journey beginning with uncovering a problem to executing on a solution and all of the steps in between. Using artificial intelligence, CMOs can identify patterns and better understand who is converting, why, and how to optimize each interaction based on this insight. AI offers the ability to process data with unprecedented speed; even Stephen Hawking couldnt come close to sifting through the amounts of data that AI can process in mere seconds. Combined with the ability to adapt quickly and drive more velocity in the sales funnel, the end result is everybodys ultimate goal: generating more revenue.

To fully capitalize on AI tools, marketers shift their thinking to work more like analysts in order to process data effectively. The combination of the left brain to work with data and the right brain to imagine what comes next is key to taking full advantage of this technology. CMOs especially need to be aware of all of the AI-powered tools out there, like just-in-time A/B testing and personalization that optimizes for customer acquisition and retention. That real-time learning and feedback is an incredible advantage when addressing fickle, distracted buyers and test-driving new tactics.

The shifting role of the CMO

Understanding the critical combination of todays technology and human instinct, the question still remains: how many marketers will actually embrace AI in 2017? Historically, the CMOs role relied minimally on technology, with greater emphasis on traditional forms of marketing and advertising and data processing. This is a pivotal time for technology, and for the first time in the history of marketing, analysts predict that CMOs will outspend CIOs on technology this year.

While the power of AI technology is pushing the boundaries of whats possible in marketing, human instinct remains as critical as ever to ensure that customer interaction is still tailored to meet brand and customer standards. To stay relevant, CMOs must invest in AI technology ensuring that the data used to make key decisions is generated and maintained in the most efficient way possible. It is imperative that the friendship between marketers and their AI-powered analytical capabilities remains in balance. Without AI, todays CMOs and their brands are at risk of falling behind competitors who are willing to take the leap and embrace this monumental change.

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Artificial Intelligence Is A CMO's Best Friend 05/04/2017 - MediaPost - MediaPost Communications

Siri and Alexa already help you find your keys and remember friends' birthdays. So maybe its not such a stretch that they could also also manage a few hundred cattle.

Computer scientists and dairy experts at the University of Wisconsin-Madison and the UW Extension are collaborating to create a suite of computer programs that can help with dairy farming a virtual brain that uses artificial intelligence to help farmers with day-to-day decision-making.

According to Victor Cabrera, a UW-Madison dairy science professor and the projects principal investigator, modern farms are already equipped with sophisticated data-collecting technology. Cows wear sensors that can track heat and motion, giving farmers insight into whether a cow may be in heat or ill. Soil and crop monitoring systems influence feeding decisions. Milking robots keep track of which cows are fidgety, whether a cows udders are healthy, the composition of the milk a cow produces.

All of this information exists, said Cabrera. A big leap we want to do in this project is connect different sources of information.

Integrating the different streams of data would be the first step. Then, the challenge would be to fuse prior research on agricultural data with machine learning in other words, computer programs that have been taught to adapt based on new information to create artificial intelligence systems that can assess that data in real time.

That AI could provide insights to help farmers in all kinds of ways, said Cabrera. Consider reproductive decisionmaking, he said. When it comes to selecting animals to breed, farmers have to use their old-fashioned human brains to consider multiple sources of data, from semen quality to a cows market value to a heifers fertility.

In a farm brain, it should be able to do that on the fly, he said.

Same for milking parlor decisions, said Cabrera. A dairy farm AI could help farmers weed out cows that for whatever reason end up taking a longer time to milk, and adjust milking schedules accordingly.

Its simple, but its actually a great help to the farmers, said Cabrera. If you have a group being milked, that one cow can delay the whole group of animals.

Cabrera noted that this would ideally be a real-time system of applications that would ping farmers with prescriptive and predictive suggestions in real time.

When the farm comes into the office, they should be told by the system, this cow should not be bred anymore, he said. And for this cow, they should be bred with better semen.

The three-year research project to create the brain is still in its nascent stages. Cabrera said that the core group of scientists working on the brain are still putting together a team and looking at potential grants, but that the work should begin in earnest this summer. He said that they have found a progressive farm located in south-central Wisconsin that has volunteered to be the projects guinea pig.

The project is among 21 proposals that recently received money through the UW2020 awards initiative, which looks to give a boost to high-risk, innovative research ventures.

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'Open the barn doors, Hal!': Artificial intelligence could one day run a dairy farm - Madison.com