Ibm what is watson

Watson leverages the power of machine learning to answer questions. It allows users to learn more with fewer data. With IBM Watson , users can protect their insights as it gives them control over what is important to them.

It also provides the ability to maintain ownership of their data and safeguard data insights and IP address. By incorporating IBM Watson, users can reimagine their workflows wherever they are, be it healthcare, finance, education, transportation or any other field. The supercomputer has a deep understanding of every business and industry and has vast domain knowledge that provides users with better and quicker decisions. IBM Watson can help enrich a variety of data without any additional and unnecessary integration.

It lets users access data from broad resources with ease. It is being used in a variety of domains, powering businesses and industries as a whole. Reportedly, Watson technology was first applied in healthcare. IBM Watson Health is changing the way healthcare is delivered by meeting business and clinical needs with cloud, data, analytics, and AI solutions.

IBM Watson also brings confident decision-making to oncology and provide care to patients after understanding millions of data. With countries racing to the top in supercomputing, the top fastest supercomputers in the world are run by China and Switzerland. In medical text documents, Bengio says, AI systems can't understand ambiguity and don't pick up on subtle clues that a human doctor would notice. But no AI built so far can match a human doctor's comprehension and insight.

IBM's work on cancer serves as the prime example of the challenges the company encountered. The effort to improve cancer care had two main tracks. Kris and other preeminent physicians at Sloan Kettering trained an AI system that became the product Watson for Oncology in MD Anderson got as far as testing the tool in the leukemia department, but it never became a commercial product.

Both efforts have received strong criticism. One excoriating article about Watson for Oncology alleged that it provided useless and sometimes dangerous recommendations IBM contests these allegations. Watson for Oncology was supposed to learn by ingesting the vast medical literature on cancer and the health records of real cancer patients. The hope was that Watson, with its mighty computing power, would examine hundreds of variables in these records—including demographics, tumor characteristics, treatments, and outcomes—and discover patterns invisible to humans.

It would also keep up to date with the bevy of journal articles about cancer treatments being published every day. To Sloan Kettering's oncologists, it sounded like a potential breakthrough in cancer care. To IBM, it sounded like a great product. Watson learned fairly quickly how to scan articles about clinical studies and determine the basic outcomes. But it proved impossible to teach Watson to read the articles the way a doctor would.

Watson's thinking is based on statistics, so all it can do is gather statistics about main outcomes, explains Kris. The drug was fast-tracked based on dramatic results in just 55 patients, of whom four had lung cancer. Several studies have compared Watson for Oncology's cancer treatment recommendations to those of hospital oncologists. The concordance percentages indicate how often Watson's advice matched the experts' treatment plans.

The realization that Watson couldn't independently extract insights from breaking news in the medical literature was just the first strike.

Researchers also found that it couldn't mine information from patients' electronic health records as they'd expected. At MD Anderson, researchers put Watson to work on leukemia patients' health records—and quickly discovered how tough those records were to work with.

Yes, Watson had phenomenal NLP skills. But in these records, data might be missing, written down in an ambiguous way, or out of chronological order. In a paper published in The Oncologist , the team reported that its Watson-powered Oncology Expert Advisor had variable success in extracting information from text documents in medical records. It had accuracy scores ranging from 90 to 96 percent when dealing with clear concepts like diagnosis, but scores of only 63 to 65 percent for time-dependent information like therapy timelines.

In a final blow to the dream of an AI superdoctor, researchers realized that Watson can't compare a new patient with the universe of cancer patients who have come before to discover hidden patterns.

Both Sloan Kettering and MD Anderson hoped that the AI would mimic the abilities of their expert oncologists, who draw on their experience of patients, treatments, and outcomes when they devise a strategy for a new patient. A machine that could do the same type of population analysis—more rigorously, and using thousands more patients—would be hugely powerful. But the health care system's current standards don't encourage such real-world learning.

If an AI system were to base its advice on patterns it discovered in medical records—for example, that a certain type of patient does better on a certain drug—its recommendations wouldn't be considered evidence based, the gold standard in medicine.

Without the strict controls of a scientific study, such a finding would be considered only correlation, not causation. Kohn, formerly of IBM, and many others think the standards of health care must change in order for AI to realize its full potential and transform medicine. Infrastructure must change too: Health care institutions must agree to share their proprietary and privacy-controlled data so AI systems can learn from millions of patients followed over many years.

According to anecdotal reports , IBM has had trouble finding buyers for its Watson oncology product in the United States.

Some oncologists say they trust their own judgment and don't need Watson telling them what to do. Others say it suggests only standard treatments that they're well aware of. But Kris says some physicians are finding it useful as an instant second opinion that they can share with nervous patients.

Many of these hospitals proudly use the IBM Watson brand in their marketing, telling patients that they'll be getting AI-powered cancer care. Illustration: Eddie Guy. In the past few years, these hospitals have begun publishing studies about their experiences with Watson for Oncology. In India, physicians at the Manipal Comprehensive Cancer Center evaluated Watson on breast cancer cases and found a 73 percent concordance rate in treatment recommendations; its score was brought down by poor performance on metastatic breast cancer.

Watson fared worse at Gachon University Gil Medical Center, in South Korea, where its top recommendations for colon cancer patients matched those of the experts only 49 percent of the time. Doctors reported that Watson did poorly with older patients, didn't suggest certain standard drugs, and had a bug that caused it to recommend surveillance instead of aggressive treatment for certain patients with metastatic cancer.

These studies aimed to determine whether Watson for Oncology's technology performs as expected. But no study has yet shown that it benefits patients. But they needed to show, fairly quickly, an impact on hard outcomes. Sloan Kettering's Kris isn't discouraged; he says the technology will only get better. It's a long haul, but it's worth it. Some success stories are emerging from Watson Health—in certain narrow and controlled applications, Watson seems to be adding value.

Take, for example, the Watson for Genomics product, which was developed in partnership with the University of North Carolina, Yale University, and other institutions. The tool is used by genetics labs that generate reports for practicing oncologists: Watson takes in the file that lists a patient's genetic mutations, and in just a few minutes it can generate a report that describes all the relevant drugs and clinical trials.

Watson has a relatively easy time with genetic information, which is presented in structured files and has no ambiguity—either a mutation is there, or it's not. The tool doesn't employ NLP to mine medical records, instead using it only to search textbooks, journal articles, drug approvals, and clinical trial announcements, where it looks for very specific statements.

For 32 percent of cancer patients enrolled in that study, Watson spotted potentially important mutations not identified by a human review, which made these patients good candidates for a new drug or a just-opened clinical trial. But there's no indication, as of yet, that Watson for Genomics leads to better outcomes. The U.

Department of Veterans Affairs uses Watson for Genomics reports in more than 70 hospitals nationwide, says Michael Kelley , the VA's national program director for oncology.

The VA first tried the system on lung cancer and now uses it for all solid tumors. But Kelley says he doesn't think of Watson as a robot doctor.

Most doctors would probably be delighted to have an AI librarian at their beck and call—and if that's what IBM had originally promised them, they might not be so disappointed today. The Watson Health story is a cautionary tale of hubris and hype. Everyone likes ambition, everyone likes moon shots, but nobody wants to climb into a rocket that doesn't work. IBM began its effort to bring Watson into the health care industry in Since then, the company has made nearly 50 announcements about partnerships that were intended to develop new AI-enabled tools for medicine.

Some collaborations worked on tools for doctors and institutions; some worked on consumer apps. While many of these alliances have not yet led to commercial products, IBM says the research efforts have been valuable, and that many relationships are ongoing. Here's a representative sample of projects.

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It turns out that you don't need a lot of hardware to make a flying robot. Flying robots are usually way, way, way over-engineered, with ridiculously over the top components like two whole wings or an obviously ludicrous four separate motors.

Maybe that kind of stuff works for people with more funding than they know what to do with, but for anyone trying to keep to a reasonable budget, all it actually takes to make a flying robot is one single airfoil plus an attached fixed-pitch propeller.

And if you make that airfoil flexible, you can even fold the entire thing up into a sort of flying robotic swiss roll. This type of drone is called a monocopter, and the design is very generally based on samara seeds, which are those single-wing seed pods that spin down from maple trees.

The ability to spin slows the seeds' descent to the ground, allowing them to spread farther from the tree. It's an inherently stable design, meaning that it'll spin all by itself and do so in a stable and predictable way, which is a nice feature for a drone to have—if everything completely dies, it'll just spin itself gently down to a landing by default.

F-SAM stands for Foldable Single Actuator Monocopter, and as you might expect, it's a monocopter that can fold up and uses just one single actuator for control.

There may not be a lot going on here hardware-wise, but that's part of the charm of this design. The one actuator gives complete directional control: increasing the throttle increases the RPM of the aircraft, causing it to gain altitude, which is pretty straightforward.

Directional control is trickier, but not much trickier, requiring repetitive pulsing of the motor at a point during the aircraft's spin when it's pointed in the direction you want it to go.

F-SAM is operating in a motion-capture environment in the video to explore its potential for precision autonomy, but it's not restricted to that environment, and doesn't require external sensing for control.

While F-SAM's control board was custom designed and the wing requires some fabrication, the rest of the parts are cheap and off the shelf. If you look closely, you'll also see a teeny little carbon fiber leg of sorts that keeps the prop up above the ground, enabling the ground takeoff behavior without contacting the ground. You can find the entire F-SAM paper open access here , but we also asked the authors a couple of extra questions.

IEEE Spectrum: It looks like you explored different materials and combinations of materials for the flexible wing structure. Why did you end up with this mix of balsa wood and plastic? Shane Kyi Hla Win: The wing structure of a monocopter requires rigidity in order to be controllable in flight.

Although it is possible for the monocopter to fly with more flexible materials we tested, such as flexible plastic or polymide flex, they allow the wing to twist freely mid-flight making cyclic control effort from the motor less effective.

The balsa laminated with plastic provides enough rigidity for an effective control, while allowing folding in a pre-determined triangular fold. Can F-SAM fly outdoors? What is required to fly it outside of a motion capture environment? Yes it can fly outdoors. It is passively stable so it does not require a closed-loop control for its flight. The motion capture environment provides its absolute position for station-holding and waypoint flights when indoors.

For outdoor flight, an electronic compass provides the relative heading for the basic cyclic control. We are working on a prototype with an integrated GPS for outdoor autonomous flights. A camera can be added we have done this before , but due to its spinning nature, images captured can come out blurry. A conventional LiDAR system requires a dedicated actuator to create a spinning motion. Your paper says that "in the future, we may look into possible launching of F-SAM directly from the container, without the need for human intervention.

Currently, F-SAM can be folded into a compact form and stored inside a container. However, it still requires a human to unfold it and either hand-launch it or put it on the floor to fly off. In the future, we envision that F-SAM is put inside a container which has the mechanism such as pressured gas to catapult the folded unit into the air, which can begin unfolding immediately due to elastic materials used. The motor can initiate the spin which allows the wing to straighten out due to centrifugal forces.

F-SAM could be a good toy but it may not be a good alternative to quadcopters if the objective is conventional aerial photography or videography. However, it can be a good contender for single-use GPS-guided reconnaissance missions. As it uses only one actuator for its flight, it can be made relatively cheaply. It is also very silent during its flight and easily camouflaged once landed. Various lightweight sensors can be integrated onto the platform for different types of missions, such as climate monitoring.

F-SAM units can be deployed from the air, as they can also autorotate on their way down, while also flying at certain periods for extended meteorological data collection in the air. We have a few exciting projects on hand, most of which focus on 'do more with less' theme. This means our projects aim to achieve multiple missions and flight modes while using as few actuators as possible. This platform, published earlier this year in IEEE Transactions on Robotics , is able to achieve two flight modes autorotation and diving with just one actuator.

It is ideal for deploying single-use sensors to remote locations. For example, we can use the platform to deploy sensors for forest monitoring or wildfire alert system. The sensors can land on tree canopies, and once landed the wing provides the necessary area for capturing solar energy for persistent operation over several years. Another interesting scenario is using the autorotating platform to guide the radiosondes back to the collection point once its journey upwards is completed.