Archive for the ‘ARTIFICIAL INTELLIGENCE’ Category


In ARTIFICIAL INTELLIGENCE, WARNING on July 31, 2011 at 10:29 pm
An electronic medical record example



IF YOU READ THIS carefully, one finds that Johns Hopkins says this: “More technology, less doctor = less technology more doctor. War is peace and peace is war.  You figure if there is just a tiny little bit of a falsehood contained in the piece below?

Consult with your doctors early and often. That’s not just good advice for patients; it’s what Stephanie Reel, the top IT officer at Johns Hopkins Medicine, says healthcare technology leaders must do to master intelligent medicine.

Speaking at this week’s InformationWeek Healthcare IT Leadership Forum in New York, Reel, head of IT at Johns Hopkins Medicine since 1994 and Chief Information Officer at the University since 1999, said the institution’s success in technology innovation is directly attributable to its habit of involving clinicians in IT projects. That point was backed up by Dr. Peter Greene, Johns Hopkins’ Chief Medical Information Office, who joined a panel discussion I led exploring “What’s Next In Intelligent Medicine.”

Data-driven decision-making helps develop a competitive edge.

Learn how to use real-time analytics to make educated business decisions.

There have been plenty of innovations at Johns Hopkins Medicine, a $5 billion-a-year organization that includes a renowned medical school, five hospitals, a network of physician offices, and massive research operations. The institution was among the pioneers of electronic health records (EHRs) through a clinical information system deployed in the early 1990s. The effort succeeded, Reel says, because it was initially supported by half a dozen clinicians who worked with IT to develop the system.

This interdisciplinary group has since grown to include about 75 people, and it still meets every month to “listen to the people on the front lines who are trying to make a difference,” Reel said.

Johns Hopkins’ clinical information system has evolved to embrace the latest EHR technologies, and it has also become the foundation for what Johns Hopkins calls “smart order sets.” These order sets have built-in checks, balances, and analytics to ensure that appropriate procedures, tests, and protocols are followed as appropriate for each patient.

Among the hundreds of smart order sets now in use at Johns Hopkins, one guides decision on appropriate regimens for diabetics. Hundreds of variables and possible recommendations are preprogrammed into the order set, but the right regimen is determined though the combination of known patient history, up-to-the-moment clinical measures, and feedback provided by doctors on a series of questions conditionally asked by the system based on known patient data and the clinician’s answers to key questions.

Smart order sets are developed by specialists and extensively studied by peer-review groups before they are embedded into patient care workflows. “The challenge is that you have to do a lot of custom work that isn’t included in off-the-shelf EHR products, so you can’t take on everything,” said Greene.

Johns Hopkins has prioritized based on risk, developing smart order sets for high-morbidity scenarios such as diabetic management and anticoagulation management.

For example, the institution has been widely recognized for its work on preventing venus thromboembolism (VTE), a dangerous blood-clotting condition that has been decreased by embedding intelligent risk-factor algorithms into admissions, post-operative, and patient-transfer order sets.

The approach has raised VTE assessment rates significantly, and VTE incidents have dropped significantly among at-risk patients, which is a huge achievement when lives are at stake.

One big risk to all this work is alert fatigue — the common problem whereby so-called intelligent systems and devices fire off so many alerts they are simply ignored. To minimize this, Johns Hopkins has built role-based and history-driven rules into many of its smart order sets.

Cardiologists, for example, would be assumed to be aware of the dangers of ordering both Cumadin and Ameoderone for the same patient whereas an intern would be shown an alert. But if an intern had already cleared such an alert for a particular patient on a particular day, the system recognizes that history and won’t alert that intern again that day regarding that patient.

At the cutting edge of intelligent medicine is personalized care. Smart order sets are part of Johns Hopkins’ strategy on that front, and the institution also has at least half a dozen departments working on other forms of personalized care.

The Department of Oncology, for instance, is exploring the use of genomics — study of the DNA of individual patients and of cancer cells. Even today, the presence of specific genetic biomarkers can trigger patient-specific recommendations about using, or not using, certain drugs, tests and protocols.

In the future, the DNA of both the patient and his or her cancer will be “readily available and integrated into every decision we’re making about your care,” Greene said, though he acknowledged that might be years from now.

Given that there are 3 billion base pairs in the human genome, the most advanced work will involve big-data computing. Oncology researchers at Johns Hopkins are collaborating with the university’s Department of Astronomy, which has a data center with rack upon rack of graphical processing units (GPUs, not CPUs) that are routinely applied to large-scale computational astronomy calculations.

Reel and Greene encouraged their peers to push the use of predictive analytics and use of data on the clinical side of their operations. Electronic health records and intelligent medicine aren’t where they should be, Reel said, in part because the financial incentives have favored administrative uses of the technology — reducing cost rather than improving diagnostics and clinical care.

“We talk often about productivity gains in medicine because of the introduction of technology, and systems tend to reward quantity rather than quality,” she said.

The next step in intelligent IT support for medicine, she said, would be to work with clinicians to minimize time-wasting usability, interoperability, and security hurdles so they can spend less time interacting with technology while still getting ever-smarter decision support. That, she said, will give doctors more time with their patients.


Software fingers fake entries

In ARTIFICIAL INTELLIGENCE on July 31, 2011 at 10:16 pm
View of the Gables Great Hall, Cornell Univers...


Just as some models do not work well, some forms of Artificial Intelligence does seem to work very well indeed. And sadly, we, flesh and blood people lose when models fail, and we lose when AI does work, because in both instances, we become a function of the machine.


If you’re like most people, you give yourself high ratings when it comes to figuring out when someone’s trying to con you. Problem is, most people aren’t actually good at it–at least as far as detecting fake positive consumer reviews.

Fortunately, technology is poised to make up for this all-too-human failing. Cornell University researchers have developed software that they say can detect fake reviews (PDF). The researchers tested the system with reviews of Chicago hotels. They pooled 400 truthful reviews with 400 deceptive reviews produced for the study, then trained their software to spot the difference.

The software got it right about 90 percent of the time. This is a big improvement over the average person, who can detect fake reviews only about 50 percent of the time, according to the researchers.

They say people fall into two camps. One type accepts too much at face value and doesn’t reject enough fake reviews. The second type is overly skeptical and rejects too many real McCoys. Despite their very different approaches, each camp is right about half the time.

The Cornell system is similar to software that sniffs out plagiarism. While the plagiarism software learns to spot the type of language a specific author uses, the Cornell software learns to spot the type of language people use when they’re being deceptive in writing a review, said Myle Ott, the Cornell computer science graduate student who led the research.

The software showed that fake reviews are more like fiction than the real reviews they’re designed to emulate, according to the researchers. In part, deceptive writers used more verbs than real review writers did, while the real writers used more punctuation than the deceptive writers. The deceptive writers also focused more on family and activities while the real writers focused more on the hotels themselves.

The research team’s next steps are to use the technique with other types of service reviews, like restaurant reviews, and eventually try it with product reviews. The idea is to make it harder for unscrupulous sellers to spam review sites with fictitious happy customers.

Of course, just about any technology can be used for good or evil. The Cornell fake review spotter “could just as easily be used to train people to avoid the cues to deception that we learned,” Ott said.

This could lead to an arms race between fake review producers and fake review spotters. Ott and his colleagues are gearing up for it. “We’re considering… seeing if we can learn a new set of deception cues, based on fake reviews written by people trained to beat our original system,” he said.

Answer: the one on the right is fake.

Cornell software fingers fake online reviews | Crave – CNET.


Crowd-simulating software OR the ability to kill masses of people FAST

LDA entrance in Palestra House, designed by Wi...


GIST OF IT: While crowd simulation software has been developed before, the Bath/Bournemouth team hopes to use modern advances in processing power to create a more sophisticated program that models hundreds or thousands of individuals’ movements.

WHY SCYNET CARES:  Again, we feel our sense of identity being stripped away. Welcome to efficiency in the machine. This is the way prisons used to be constructed. Soulless,  while buildings used to be something that is both art and culture. Not anymore. Now its pure simulation. And beware of these studies that models movement. One day this very technology can of course find its way into  based upon models of their probable movement. Maybe the robot ship can fire at random spots where humans will be in the next fraction of time. Crazy fantastical rubbish? I hope so. In the meantime the technology will be constructing the space where YOU live.



A new project that uses artificial intelligence to model how crowds move could help architects design better buildings.  Researchers from Bath and Bournemouth universities are working with engineering consultancy Buro Happold to create software that shows how a building’s design can enable or prevent large numbers of people moving easily through it.

The program will create a visual representation of a crowd, modelling it as a group of many individual ‘agents’ instead of as a single mass of people and giving each agent its own goals and behaviour.

What Buro Happold wants to be able to understand is the impact of a space on the way people move,’ said Julian Padget, project supervisor and senior lecturer in computer science at Bath University.

‘There’s also the related question of what happens when a large volume of people are all trying to get somewhere rapidly, such as in an emergency situation.’

While crowd simulation software has been developed before, the Bath/Bournemouth team hopes to use modern advances in processing power to create a more sophisticated program that models hundreds or thousands of individuals’ movements.

The project will tackle the problems of simulating the crowds and rendering them in a believable way, from both a wide-angle and a close-up view, meaning the individuals have to appear realistic and show how their movements affect the rest of the group.

‘You don’t want it to look like a bunch of automatons wandering around — the reason being that it distracts the viewer, because they find it unnatural,’ said Padget. ‘They pay attention to that rather than what the picture overall is showing them.’

Instead of programming the computerised people with specific instructions, the computer will give them a destination and a range of actions to choose from and leave them to determine their own route, partly based on data gathered from observing real crowds.

But there are still limits to computational power and simulating greater numbers of people will require each individual character to have less intelligent programming, said Padget.

‘Our challenge is to work out what we can throw away from the sophisticated model and still get plausible-looking behaviour when we’ve got a large number of individuals.’

The simulation software will also need to be compatible with a suitable platform to render buildings designed by Buro Happold.

The four-year research project will be carried out by an engineering doctorate student through the universities’ Centre for Digital Entertainment, funded by the EPSRC.



Crowd-simulating software could improve building design | News | The Engineer.


Larry Page - Caricature




  • GIST OF IT:  The app analyzes data from around the Web to figure out what you will like, based on similarities with other people. 
  • WHY SCYNET CARES:  How about being monitored while you do not even participate; how about being stereotyped by circumstantial evidence about your way of life, by a thing on somebody’s phone. How about losing your identity; how about this technology being used to profile you? How about paying more for health care because you had one pizza to many at the unhealthy local coffee shop? The nightmare scenarios are endless. This is even worse than Orwell could have imagined. All the little sheep walking freely into the machine! 


Google CEO Larry Page and Microsoft CEO Steve Ballmer agree on one thing: the future of search is tied in with artificial intelligence.

Page has talked about the ideal search engine knowing what you want BEFORE you ask it, and Ballmer recently explained Microsoft’s multibillion dollar investment in Bing by saying that search research is the best way to progress toward artificial intelligence apps that help you DO things, not just find things. So both companies will probably be taking a very close look at CleverSense, which launches its first iPhone app, a “personal concierge” called Alfred (formerly Seymour), today.

The app analyzes data from around the Web to figure out what you will like, based on similarities with other people. It’s similar to the recommendation engines pioneered by Amazon — “other people who bought X also bought Y” — or the Music Genome Project that eventually grew into Pandora. Only it’s applied to the real world.

CleverSense CEO Babak Pahlavan explains that the company grew out of a research project into predictive algorithms that he was working on at Stanford three years ago. The technology crawls the Web looking for what users are saying about particular products, and is able to categorize the results into between 200 and 400 attributes and sentiments for each one.

For instance, if somebody visits a coffee shop and posts on Yelp “the cappuccino at X was awesome but salad was crap,” CleverSense understands the words “awesome” and “crap,” and also notes that “cappuccino” is a high-interest word for coffee shops.

This kind of analysis is performed millions of times per day. When it launches, Alfred will have a database of more than 600,000 locations with between 200 and 400 categories rated ON EACH ONE. As you rate places, the app will get even more accurate.

Alfred is focused on four categories — bars, restaurants, nightclubs, and coffee shops — but CleverSense plans to apply its technology to other areas as well. Pahlvan explains that CleverSense could work very well with daily deals services like Groupon, LivingSocial, or Google Offers — instead of having merchants throw deals out to the entire world, they could target them at the users who would be most likely to buy.

At launch, the data is anonymous, but CleverSense is going to add Facebook Connect integration so it can add social data into its recommendations — if it knows that a lot of your friends are saying positive things about a particular bar, it will weigh those recommendations more highly than statements from random strangers.

The company has been running on an investment of about $6 million from angel investors, but Pahlavan says the company is planning to raise further rounds later this year. That’s assuming it doesn’t get snapped up by a big company first.

Microsoft may have an inside shot — CleverSense is participating in the company’s BizSpark program, which gives discounted software and other aid to startups — but there are tons of other companies who should be interested in the technology as well.

This iPhone App Knows What You Like — Before You Ask It A Single Question.