Watson Weekly: Revenue Wait Watcher?
January 9, 2016
I read another bit of IBM Watson public relations’ fluff. The story was “CES 2016: IBM Announces Watson as a Personal Fitness Coach.” I assume that IBM Watson’s ability to craft recipes with tamarind will go from the kitchen to the gym with aplomb.
According to the article:
The news signals the rapid adoption of Watson technology by consumers and to illustrate this, announced that Under Armour and IBM have developed a new cognitive coaching system. Watson, will serve as a personal health consultant, fitness trainer and assistant by providing athletes with timely, evidence-based coaching about health and fitness-related issues. Where Watson differs from other systems is that it determines outcomes achieved based on others “like you.” It integrates IBM Watson’s technology with the data from Under Armour’s Connected Fitness community – a vast digital health and fitness community of more than 160 million members.
I hope that IBM lifts the weight from the shoulders of IBM stakeholders who want to be buoyed on a rush of new revenues. Vast too. A consumer product?
Stephen E Arnold, January 9, 2016
IBM and Yahoo Hard at Work on Real-Time Data Handling
January 7, 2016
The article titled What You Missed in Big Data: Real-time Intelligence on SiliconAngle speaks to the difficulties of handling the ever-increasing volumes of real-time data for corporations. Recently, IBM created supplementary stream process services including a machine learning engine that comes equipped with algorithm building capabilities. The algorithms aid in choosing relevant information from the numerous connected devices of a single business. The article explains,
“An electronics manufacturer, for instance, could use the service to immediately detect when a sensor embedded in an expensive piece of equipment signals a malfunction and automatically alert the nearest technician. IBM is touting the functionality as a way to cut through the massive volume of machine-generated signals produced every second in such environments, which can overburden not only analysts but also the technology infrastructure that supports their work.”
Yahoo has been working on just that issue, and lately open-sourced its engineers’ answer. In a demonstration to the press, the technology proved able to power through 100 million vales in under three seconds. Typically, such a high number would require two and a half minutes. The target of this sort of technology is measuring extreme numbers like visitor statistics. Accuracy takes a back seat to speed through estimation, but at such a speed it’s worth the sacrifice.
Chelsea Kerwin, January 7, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
IBM Watson Will See Soon
January 6, 2016
I read “Watson to Gain Ability to See with Planned $1B Acquisition of Merge Healthcare.” This mid 2015 deal will, according to the IBM announcement:
Watson will gain the ability to “see” by bringing together Watson’s advanced image analytics and cognitive capabilities with data and images obtained from Merge Healthcare Incorporated’s medical imaging management platform.
Interesting. IBM has a number of content management platforms; for example, FileNet. Reconciling the different types of images within Watson’s content intake system will keep some folks busy at Big Blue. The last diagnostic test I had generated a live stream of video images of various body parts chugging along. Movies!
Watson is a capable system, right?
Stephen E Arnold, January 6, 2016
IBM Generates Text Mining Work Flow Diagram
January 4, 2016
I read “Deriving Insight Text Mining and Machine Learning.” This is an article with a specific IBM Web address. The diagram is interesting because it does not explain which steps are automated, which require humans, and which are one of those expensive man-machine processes. When I read about any text related function available from IBM, I think about Watson. You know, IBM’s smart software.
Here’s the diagram:
If you find this hard to read, you are not in step with modern design elements. Millennials, I presume, love these faded colors.
Here’s the passage I noted about the important step of “attribute selection.” I interpret attribute selection to mean indexing, entity extraction, and related operations. Because neither human subject matter specialists nor smart software perform this function particularly well, I highlighted in red ink in recognition of IBM’s 14 consecutive quarters of financial underperformance:
Machine learning is closely related to and often overlaps with computational statistics—a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. It is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Text mining takes advantage of machine learning specifically in determining features, reducing dimensionality and removing irrelevant attributes. For example, text mining uses machine learning on sentiment analysis, which is widely applied to reviews and social media for a variety of applications ranging from marketing to customer service. It aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state or the intended emotional communication. Machine learning algorithms in text mining include decision tree learning, association rule learning, artificial neural learning, inductive logic programming, support vector machines, Bayesian networks, genetic algorithms and sparse dictionary learning.
Interesting, but how does this IBM stuff actually work? Who uses it? What’s the payoff from these use cases?
More questions than answers to explain the hard to read diagram, which looks quite a bit like a 1998 Autonomy graphic. I recall being able to read the Autonomy image, however.
Stephen E Arnold, December 30, 2015
Weekly Watson: In the Real World
January 2, 2016
I want to start off the New Year with look at Watson in the real world. My real world is circumscribed by abandoned coal mines and hollows in rural Kentucky. I am pretty sure this real world is not the real world assumed in “IBM Watson: AI for the Real World.” IBM has tapped Bob Dylan, a TV game show, and odd duck quasi chemical symbols to communicate the importance of search and content processing.
The write up takes a different approach. In fact, the article begins with an interesting comment:
Computers are stupid.
There you go. A snazzy one liner.
The purpose of the reminder that a man made device is not quite the same as one’s faithful boxer dog or next door neighbor’s teen is startling.
The article summarizes an interview with a Watson wizard, Steven Abrams, director of technology for the Watson Ecosystem. This is one of those PR inspired outputs which I quite enjoy.
The write up quotes Abrams as saying:
“You debug Watson’s system by asking, ‘Did we give it the right data?'” Abrams said. “Is the data and experience complete enough?”
Okay, but isn’t this Dr. Mike Lynch’s approach. Lynch, as you may recall, was the Cambridge University wizard who was among the first to commercialize “learning” systems in the 1990s.
According to the write up:
Developers will have data sets they can “feed” Watson through one of over 30 APIs. Some of them are based on XML or JSON. Developers familiar with those formats will know how to interact with Watson, he [Abrams] explained.
As those who have used the 25 year old Autonomy IDOL system know, preparing the training data takes a bit of effort. Then as the content from current content is fed into the Autonomy IDOL system, the humans have to keep an eye on the indexing. Ignore the system too long, and the indexing “drifts”; that is, the learned content is not in tune with the current content processed by the system. Sure, algorithms attempt to keep the calibrations precise, but there is that annoying and inevitable “drift.”
IBM’s system, which strikes me as a modification of the Autonomy IDOL approach with a touch of Palantir analytics stirred in is likely to be one expensive puppy to groom for the dog show ring.
The article profiles the efforts of a couple of IBM “partners” to make Watson useful for the “real” world. But the snip I circled in IBM red-ink red was this one:
But Watson should not be mistaken for HAL. “Watson will not initiate conduct on its own,” IBM’s Abrams pointed out. “Watson does not have ambition. It has no objective to respond outside a query.” “With no individual initiative, it has no way of going out of control,” he continued. “Watson has a plug,” he quipped. It can be disconnected. “Watson is not going to be applied without individual judgment … The final decision in any Watson solution … will always be [made by] a human, being based on information they got from Watson.”
My hunch is that Watson will require considerable human attention. But it may perform best on a TV show or in a motion picture where post production can smooth out the rough edges.
Maybe entertainment is “real”, not the world of a Harrod’s Creek hollow.
Stephen E Arnold, January 2, 2016
IBM: There Are Doubters
December 31, 2015
Watson has its works cut out for itself in 2016. I read “IBM Set to Drop 13% in 2015.” When one is tossing around a $100 billion outfit, the thought of a share drop is disconcerting. Will Alibaba or Jeff Bezos step in. Fixing up the Washington Post may be trivial compared with an IBM scale challenge.
According to the write up:
Much of the disappointment in the tech company is because it has been unable to replace its hardware and software legacy products with new cloud-based and AI products — at least not at a rate that would pull IBM’s revenue up. Its major branded product in new age technology is Watson. While Watson has been the source of press releases and small customer alliances, outsiders have trouble seeing what it does to sharply increase IBM’s sales. Granted, Watson may be one of the most impressive product advances among large companies in the sector recently, but what it does for IBM may be very modest.
Somewhat of a downer I perceive.
The smart software thing is not new. In the last 18 months, awareness of the use of various numerical recipes has increased. Faster chips, memories, and interconnections have worked their magic.
The challenge for IBM is to make money, not just marketing hyperbole. The crunch is that expectations for certain technologies are often more robust than possible in a market.
Watson is, when one keeps its eye on the ball, is a search and content processing system. The wrappers make it possible to call assorted functions. Unlike Palantir, which has its own revenue fish to catch, IBM is a publicly traded company. Palantir does its magic as a privately held company, ingesting money at rates which would make beluga whale’s diet look modest.
But IBM has exposed itself. The Watson marketing push is dragged into the reality of IBM’s overall company performance. In 2016, IBM Watson will have to deliver the bacon, or some of the millennialesque PR and marketing folks will have an opportunity to work elsewhere. Talk about smart software is not generating sustainable revenue from smart software.
Stephen E Arnold, December 31, 2015
Another Good Reason for Diversity in Tech
December 29, 2015
Just who decides what we see when we search? If we’re using Google, it’s a group of Google employees, of course. The Independent reports, “Google’s Search Results Aren’t as Unbiased as You Think—and a Lack of Diversity Could Be the Cause.” Writer Doug Bolton points to a TEDx talk by Swedish journalist Andreas Ekström, in which Ekström describes times Google has, and has not, counteracted campaigns to deliberately bump certain content. For example, the company did act to decouple racist imagery from searches for “Michelle Obama,” but did nothing to counter the association between a certain Norwegian murderer and dog poop. Boldon writes:
“Although different in motivation, the two campaigns worked in exactly the same way – but in the second, Google didn’t step in, and the inaccurate Breivik images stayed at the top of the search results for much longer. Few would argue that Google was wrong to end the Obama campaign or let the Breivik one run its course, but the two incidents shed light on the fact that behind such a large and faceless multi-billion dollar tech company as Google, there’s people deciding what we see when we search. And in a time when Google has such a poor record for gender and ethnic diversity and other companies struggle to address this imbalance (as IBM did when they attempted to get women into tech by encouraging them to ‘Hack a Hairdryer’), this fact becomes more pressing.”
The article notes that only 18 percent of Google’s tech staff worldwide are women, and that it is just two percent Hispanic and one percent black. Ekström’s talk has many asking what unperceived biases lurk in Google’s algorithms, and some are calling on the company anew to expand its hiring diversity. Naturally, though, any tech company can only do so much until more girls and minorities are encouraged to explore the sciences.
Cynthia Murrell, December 29, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Watson Weekly: What? No Meth?
December 28, 2015
I read “What Can I eat in Pregnancy? App Aims to Answer with Help from IBM’s Watson?” Consider that folks with smartphones constitute a modest percentage of the world population. Health, as I understand the fine outputs of my health care providers, depends on socio-economic background. Also, a person with access to the Google can find out what foods are okay to eat when pregnant. Sure, there are ads, but Google presents reasonably useful information when one queries, “pregnant mother diet.” No app is needed for this type of research. Heck, one can just ask around.
Nevertheless, the potent cognitive computing outfit powered by the question answering Watson is delivering pregnant person diet advice via a smartphone app. There’s an app for that remains an accurate statement.
The write up points out:
The Nutrino app, powered by IBM’s supercomputer Watson, claims to guide women through pregnancy. For $15 (£10) for the duration of pregnancy, the app gives personalized meal recommendations and nutritional support by combining Nutrino’s nutrition database and Watson’s natural language capabilities.
If one is a pregnant and the owner of a smartphone, the price for the app is no problem. The pace of IBM innovation never slows. Now about the pregnant folks in Umgababa, South Africa? That’s in the DwaZulu Natal province.
The write up points out:
Nutrino is likely to appeal to women who already track their diet and exercise. The fitbit and Apple Watch generation may prefer to get their information about pregnancy by talking to their wrist rather than chatting to their mums. But even Watson may struggle to provide the common sense and personal experience that complements scientific knowledge.
“Struggle.” Yep, I would say that is a good word.
Stephen E Arnold, December 28, 2015
IBM Watson Competes for the Artificial Intelligence Crown
December 21, 2015
The article titled IBM Watson Vs. Amazon: Machine Learning Systems Presage the Future on Datamation dukes it out between IBM’s famous supercomputer and the Amazon Web Services platform. Both are at the forefront of the industry, but which is best? Unsurprisingly, the article offers no definitive answer beyond: it depends what you are using them for. The article states,
“Amazon offers a simplified platform for developers who want to start working with machine learning without a lot of stress or specialized tools or investment… What IBM is trying to establish with the Watson analytics engine is not just storing and acquiring data, but taking all that information and doing something meaningful with it as an AI service or Intelligence as a Service.”
Jack Gold, Principal Analyst for J.Gold Associates, emphasizes that the larger point is that the AI technologies these two companies are competing to lead will shortly be much more far-spread due to the ever increasing amounts of data. The article also discusses some of the more exciting uses of Watson and Amazon. The former, through a company called Fluid, is being put to use in the retail industry relying on Watson’s ability to “read” customer personalities (with his handy personality matrix). Amazon Machine Learning, in the meanwhile, has recently been used for predictive modeling of job-cost estimates for insurance companies and builders.
Chelsea Kerwin, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Watson Is Laying Startup Eggs
December 21, 2015
Incubators are warming stations for eggs. Without having to rely on an organism’s DNA donor, an incubator provides a warm, safe environment for the organism to develop, hatch, and eventually be ready to face the world. Watson has decided it is time for itself to propagate, but instead of knitting tiny computer cases Watson will invest its digital DNA in startups. The Chicago Tribune discusses Watson’s reproduction efforts and progeny in “Watson, IBM’s Big-Data Program Is Also A Startup Incubator.”
While IBM sells Watson’s ability to scan and understand terabytes of data, the company also welcomes developers to use Watson for new ideas. What is even more amazing is that IBM gives developers the ability to use Watson for free for a limited time.
“In Ecosystem, everyone is invited to play with Watson for free (for a limited time); some 77,000 developers have accepted. If your Watson-powered startup shows promise, it becomes a “partner,” often via a quasi-incubator model, and enjoys access to IBM business and technology advisers–and a shot at a capital infusion from the $100 million IBM is making available to Watson startups…”
Ecosystem has been used for startups that feature lifestyle coaching, personal shopping, infrastructure guards, veterinarian advice, fantasy sports calculator, 311 information, and even a hotel butler.
To quote the biblical justification for propagation: “Go forth and multiply the [Watson startups].”
Whitney Grace, December 21, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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