Compare Trump to Lincoln with Watson Personality Insights
August 19, 2015
IBM’s Watson is employing its capabilities in a new and interesting way: BoingBoing asks, “What Does Your Writing Say About You? IBM Watson Personality Insights Will Tell You.” The software derives cognitive and social characteristics about people from their writings, using linguistic analytics. I never thought I’d see a direct, graphically represented comparison between speeches of Donald Trump and Abe Lincoln, but there it is. There are actually some similarities; they’re both businessmen turned politicians, after all. Reporter Andrea James shares Watson’s take on Trump’s “We Need Brain” speech from the recent Republican primary debate:
“You are a bit dependent, somewhat verbose and boisterous. You are susceptible to stress: you are easily overwhelmed in stressful situations. You are emotionally aware: you are aware of your feelings and how to express them. And you are prone to worry: you tend to worry about things that might happen. Your choices are driven by a desire for efficiency. You consider both independence and helping others to guide a large part of what you do. You like to set your own goals to decide how to best achieve them. And you think it is important to take care of the people around you.”
For comparison, see the write-up for the analysis of Lincoln’s Gettysburg Address (rest assured, Lincoln does come out looking better than Trump). The article also supplies this link, where you can submit between 3500 and 6000 words for Watson’s psychoanalysis; as James notes, you can submit writing penned by yourself, a friend, or an enemy (or some random blogger, perhaps.) To investigate the software’s methodology, click here.
Cynthia Murrell, August 19, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
How to Use Watson
August 17, 2015
While there are many possibilities for cognitive computing, what makes an idea a reality is its feasibility and real life application. The Platform explores “The Real Trouble With Cognitive Computing” and the troubles IBM had (has) trying to figure out what they are going to do with the supercomputer they made. The article explains that before Watson became a Jeopardy celebrity, the IBM folks came up 8,000 potential experiments for Watson to do, but only 20 percent of them.
The range is small due to many factors, including bug testing, gauging progress with fuzzy outputs, playing around with algorithmic interactions, testing in isolation, and more. This leads to the “messy” way to develop the experiments. Ideally, developers would have a big knowledge model and be able to query it, but that option does not exist. The messy way involves keeping data sources intact, natural language processing, machine learning, and knowledge representation, and then distributed on an infrastructure.
Here is another key point that makes clear sense:
“The big issue with the Watson development cycle too is that teams are not just solving problems for one particular area. Rather, they have to create generalizable applications, which means what might be good for healthcare, for instance, might not be a good fit—and in fact even be damaging to—an area like financial services. The push and pull and tradeoff of the development cycle is therefore always hindered by this—and is the key barrier for companies any smaller than an IBM, Google, Microsoft, and other giants.”
This is exactly correct! Engineering is not the same as healthcare and it not all computer algorithms transfer over to different industries. One thing to keep in mind is that you can apply different methods from other industries and come up with new methods or solutions.
Whitney Grace, August 18, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
How to Use Watson
August 7, 2015
While there are many possibilities for cognitive computing, what makes an idea a reality is its feasibility and real life application. The Platform explores “The Real Trouble With Cognitive Computing” and the troubles IBM had (has) trying to figure out what they are going to do with the supercomputer they made. The article explains that before Watson became a Jeopardy celebrity, the IBM folks came up 8,000 potential experiments for Watson to do, but only 20 percent of them.
The range is small due to many factors, including bug testing, gauging progress with fuzzy outputs, playing around with algorithmic interactions, testing in isolation, and more. This leads to the “messy” way to develop the experiments. Ideally, developers would have a big knowledge model and be able to query it, but that option does not exist. The messy way involves keeping data sources intact, natural language processing, machine learning, and knowledge representation, and then distributed on an infrastructure.
Here is another key point that makes clear sense:
“The big issue with the Watson development cycle too is that teams are not just solving problems for one particular area. Rather, they have to create generalizable applications, which means what might be good for healthcare, for instance, might not be a good fit—and in fact even be damaging to—an area like financial services. The push and pull and tradeoff of the development cycle is therefore always hindered by this—and is the key barrier for companies any smaller than an IBM, Google, Microsoft, and other giants.”
This is exactly correct! Engineering is not the same as healthcare and it not all computer algorithms transfer over to different industries. One thing to keep in mind is that you can apply different methods from other industries and come up with new methods or solutions.
Whitney Grace, August 7, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Hire Watson As Your New Dietitian
August 4, 2015
IBM’s supercomputer Watson is being “trained” in various fields, such as healthcare, app creation, customer service relations, and creating brand new recipes. The applications for Watson are possibly endless. The supercomputer is combining its “skills” from healthcare and recipes by trying its hand at nutrition. Welltok invented the CaféWell Health Optimization Platform, a PaaS that creates individualized healthcare plans, and it implemented Watson’s big data capabilities to its Healthy Dining CaféWell personal concierge app. eWeek explains that “Welltok Takes IBM Watson Out To Dinner,” so it can offer clients personalized restaurant menu choices.
” ‘Optimal nutrition is one of the most significant factors in preventing and reversing the majority of our nation’s health conditions, like diabetes, overweight and obesity, heart disease and stroke and Alzheimer’s,’ said Anita Jones-Mueller, president of Healthy Dining, in a statement. ‘Since most Americans eat away from home an average of five times each week and it can be almost impossible to know what to order at restaurants to meet specific health needs, it is very important that wellness and condition management programs empower smart dining out choices. We applaud Welltok’s leadership in providing a new dimension to healthy restaurant dining through its groundbreaking CaféWell Concierge app.’”
Restaurant menus are very vague when it comes to nutritional information. When it comes to knowing if something is gluten-free, spicy, or a vegetarian option, the menu will state it, but all other information is missing. In order to find a restaurant’s nutritional information, you have to hit the Internet and conduct research. A new law passed will force restaurants to post calorie counts, but that will not include the amount of sugar, sodium, and other information. People have been making poor eating choices, partially due to the lack of information, if they know what they are eating they can improve their health. If Watson’s abilities can decrease the US’s waistline, it is for the better. The bigger challenge would be to get people to use the information.
Whitney Grace, August 4, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Watson Based Tradeoff Analytics Weighs Options
July 13, 2015
IBM’s Watson now lends its considerable intellect to helping users make sound decisions. In “IBM Watson Tradeoff Analytics—General Availability,” the Watson Developer Community announces that the GA release of this new tool can be obtained through the Watson Developer Cloud platform. The release follows an apparently successful Beta run that began last February. The write-up explains that the tool:
“… Allows you to compare and explore many options against multiple criteria at the same time. This ultimately contributes to a more balanced decision with optimal payoff.
“Clients expect to be educated and empowered: ‘don’t just tell me what to do,’ but ‘educate me, and let me choose.’ Tradeoff Analytics achieves this by providing reasoning and insights that enable judgment through assessment of the alternatives and the consequent results of each choice. The tool identifies alternatives that represent interesting tradeoff considerations. In other words: Tradeoff Analytics highlights areas where you may compromise a little to gain a lot. For example, in a scenario where you want to buy a phone, you can learn that if you pay just a little more for one phone, you will gain a better camera and a better battery life, which can give you greater satisfaction than the slightly lower price.”
For those interested in the technical details behind this Watson iteration, the article points you to Tradeoff Analytics’ documentation. Those wishing to glimpse the visualization capabilities can navigate to this demo. The write-up also lists post-beta updates and explains pricing, so check it out for more information.
Cynthia Murrell, July 13, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Watson Still Has Much to Learn About Healthcare
July 9, 2015
If you’ve wondered what is taking Watson so long to get its proverbial medical degree, check out IEEE Spectrum’s article, “IBM’s Dr. Watson Will See You… Someday.” When IBM’s AI Watson won Jeopardy in 2011, folks tasked with dragging healthcare into the digital landscape naturally eyed the software as a potential solution, and IBM has been happy to oblige. However, “training” Watson in healthcare documentation is proving an extended process. Reporter Brandon Keim writes:
“Where’s the delay? It’s in our own minds, mostly. IBM’s extraordinary AI has matured in powerful ways, and the appearance that things are going slowly reflects mostly on our own unrealistic expectations of instant disruption in a world of Uber and Airbnb.”
Well that, and the complexities of our healthcare system. Though the version of Watson that beat Jeopardy’s human champion was advanced and powerful, tailoring it to manage medicine calls for a wealth of very specific tweaking. In fact, there are now several versions of “Doctor” Watson being developed in partnership with individual healthcare and research facilities, insurance companies, and healthcare-related software makers. The article continues:
“Watson’s training is an arduous process, bringing together computer scientists and clinicians to assemble a reference database, enter case studies, and ask thousands of questions. When the program makes mistakes, it self-adjusts. This is what’s known as machine learning, although Watson doesn’t learn alone. Researchers also evaluate the answers and manually tweak Watson’s underlying algorithms to generate better output.
“Here there’s a gulf between medicine as something that can be extrapolated in a straightforward manner from textbooks, journal articles, and clinical guidelines, and the much more complicated challenge of also codifying how a good doctor thinks. To some extent those thought processes—weighing evidence, sifting through thousands of potentially important pieces of data and alighting on a few, handling uncertainty, employing the elusive but essential quality of insight—are amenable to machine learning, but much handcrafting is also involved.”
Yes, incorporating human judgement is time-consuming. See the article for more on the challenges Watson faces in the field of healthcare, and for some of the organizations contributing to the task. We still don’t know how much longer it will take for the famous AI (and perhaps others like it) to dominate the healthcare field. When that day arrives, will it have been worth the effort?
Cynthia Murrell, July 9, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
What Watson Can Do For Your Department
July 6, 2015
The story of Justin Chen, a Finance Manager, is one of many “Stories by Role” now displayed on IBM. Each character has a different job, such as Liza Hay from Marketing, Donny Cruz from IT and Anisa Mirza from HR. Each job comes with a problem for which Watson, IBM’s supercomputer, has just the solution. Justin, the article relates, is having trouble deciding which payments to follow. Watson provides solutions,
“With IBM® Watson™ Analytics, Justin can ask which customers are least likely to pay, who is most likely to pay and why. He can analyze this information… [and] collect more payments more efficiently… With Watson Analytics, Justin can ask which customers are likely to leave and which are likely to stay and why. He can use the answers for analysis of customer attrition and retention, predict the effect on revenue and determine which customer investments will lead to more profitable growth.”
It seems that the now world-famous Watson has been converted from search to a basket containing any number of IBM software solutions. It isn’t stated in the article, but we can probably assume that the revenue from each solution counts toward Watson’s soon to be reported billions in revenue.
Chelsea Kerwin, July 6, 2014
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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