IBM: Watson, What Happened?

October 18, 2018

I read “IBM Surprises Investors with Quarterly Revenue Decline.” The write up states:

The company broke its three-quarter string of revenue growth with a 2 percent drop in total revenue to $18.76 billion, down from $19.15 billion a year ago.

The article pointed out:

Most notably, Cognitive Solutions revenues fell 5 percent, to $4.15 billion, against analyst estimates of $4.3 billion. That division, which includes IBM’s analytics business as well as the Watson cognitive computing platform, was pulled down by weakness in some horizontal categories such as collaboration, commerce and talent management.

Watson, what happened?

But IBM pointed out that it is starting to see “green shoots.” I think this means that growth is evident in some sectors.

IBM is a consulting company which still sells mainframes. Enough said.

Stephen E Arnold, October 18, 2018

Google and IBM: Me Too Marketing or a Coincidence?

October 15, 2018

I noted this article: “Google AI Researchers Find Strange New Reason to Play Jeopardy.” What caught my attention was the introduction of the TV game show which featured IBM Watson stomping mere humans in a competition. I dismissed the human versus machine as a Madison Avenue ad confection. IBM wanted to convince the folks in West Virginia and rural Kentucky that Watson smart software was bigger than college basketball.

I think it worked. It allowed me to crank out write ups poking fun at the cognitive computing assertion, the IBM billion dollar revenue target, and the assorted craziness of IBM’s ever escalating assertions about the efficacy of Watson. I even pointed out that humans had to figure out the content used to “train” Watson and then fiddle with digital knobs and levers to get the accuracy up to snuff. The behind the scenes work was hidden from the Madison Avenue creatives; the focus was on the sizzle, not the preparatory work in the knowledge abattoir.

The Googlers have apparently discovered Jeopardy. I learned that Google uses Jeopardy to inform its smart software about reformulating questions. Here’s a passage I highlighted:

Active Question Answering,” or Active QA, as the TensorFlow package is called, will reformulate a given English-language question into multiple different re-wordings, and find the variant that does best at retrieving an answer from a database.

I am not going to slog through the history of query parsing. The task is an important one, and in my opinion, without providing precise indexing such as “company type” and other quite precise terms, queries go off base. The elimination of explicit Boolean has put the burden on query processors figuring out what humans mea when they type a query using the word “terminal” for instance. Is it a computer terminal or is it a bus terminal. No indexing? Well, smart software which looks up data in a dynamic table will do the job in a fine, fine way. What if one wants to locate a white house? Is it the DC residence of the president or is it the term for Benjamin Moore house paint when one does not know 2126-70?

Well, Google has embraced Jeopardy to make its smart software smarter and ignore the cost, time, and knowledge work of creating controlled term lists, assigning and verifying index accuracy, and fine grained indexing to deal with the vagaries of language.

So, Google seems to have hit upon the idea of channeling IBM Watson.

But I recalled seeing this article: “Google AI Can Spot Advanced Breast Cancer More Effectively Than Humans.” That reminded me of IBM Watson’s message carpet bombing about the efficacy of Big Blue cancer fighting. The only problem was that articles like “IBM Pitched Its Watson Supercomputer As a Revolution in Cancer Care. It’s Nowhere Close” Continue to Appear.”

Is Google channeling IBM’s marketing?

My hypothesis is that Google is either consciously or unconsciously tilling an already prepped field for marketing touch points. IBM did Jeopardy; Google does Jeopardy with the question understanding twist. IBM did cancer; Google does a specific type of cancer better than humans and, obviously, better than IBM Watson.

So what? My thought is that Google is shifting its marketing gears. In the process, the Google-dozer is dragging its sheep’s’ foot roller across the landscape slowly recovering from IBM’s marketing blitzes.

Will this work?

Hey, Google, like Amazon, wants to be the 21st century IBM. Who knows? I thank both companies for giving me some new fodder for my real live goats which can walk away from behemoth smart machines reworking the information landscape.

Here’s a thought? Google is more like IBM than it realizes.

Stephen E Arnold, October 15, 2018

Has Amazon Kicked IBM Watson into Action?

October 2, 2018

Amazon’s policeware, based on Sagemaker and other Amazon innovations, makes it easy to de4ploy machine learning applications. Amazon delivers, as we report in our DarkCyber video about Amazon’s machine learning approach, a Blue Apron approach to smart software intended to make sense of Big Data. You can view our four part series beginning later this month. Watch Beyond Search for details.

IBM seems to have noticed what Amazon has been doing for the last four or five years. With a bit of a late start, IBM is, it appears, emulating the Bezos buzz saw. Some information about the more pragmatic approach to rule based smart software is revealed in “IBM Launches Pretrained Watson Packs for Industries.”

I learned:

IBM outlined prepackaged Watson tools pretrained for various industries use cases such as agriculture, customer service, human resources, manufacturing and marketing.

One of Watson’s more amusing characteristics is that human subject matter experts have to figure out what questions Watson is to answer and then build a collection of text to instruct Watson on the who, what, why, etc.

Expensive, time consuming, and usually a surprise to licensees who assume that IBM Watson is a product. Ho ho ho.

Like Amazon, IBM wants to deliver ready to use packs for specific business sectors like marketing.

Now Amazon is delivering its Sagemaker meals ready to eat, microwavable data burritos, and the Blue Apron “any fool can fix dinner” smart software. IBM is sort of moving in that direction. I noted this passage:

Each Watson pack is in different states of release but take best practices and training knowledge from various IBM engagements. For instance, IBM said its Watson Decision Platform for Agriculture is generally available. IBM has integrated its weather data as well as Internet of things end points in agriculture and images to provide a “predictive view of a farm.” Farmers would get an app for real-time decision support.

Just think. Amazon is selling to a large covert government agency its smart software. IBM is working on similar initiatives but it has the farm thing nailed. Can IBM fix John Deere tractors?

Will IBM Watson beat Amazon Sagemaker?

I am not sure the two are in the same game.

Stephen E Arnold, October 2, 2018

IBM Watson: Chug, Chug, Chugging Along

September 12, 2018

Beyond Search does feel a little sad for IBM. You know. The Watson thing.

IBM really, really wants their AI to cure cancer. We’ve reported on recent findings that the famous AI is, as of yet, falling short of that goal; Information Magazine adds some details in its opinion piece, “IBM’s Watson Hasn’t Beaten Cancer, but AI May Win.” Writer Faye Flam points to the revelations from internal IBM documents as reported by Stat News (registration required) that the software had made some “unsafe and incorrect” recommendations for cancer patients. Flam seems sure this is a temporary state and that, eventually, some form of machine-learning AI will come to permeate healthcare because medicine is, at heart, a data problem. She also notes, however, why cancer treatment is a particularly tough area to master—for one thing, humans ourselves haven’t quite mastered it yet. She writes:

“Cancer treatment is more complicated, because humans are still figuring it out. Patients get multiple treatments that may result in remission periods, but whether there’s a remission and how long it lasts depends on a host of variables. ‘It’s something we struggle with a lot,’ Beam said. ‘You want a ground-truth gold-standard correct answer for a given patient, and 99 percent of the time that doesn’t exist.’ Only a small fraction of cancer patients have their information recorded in a systematic way, said Isaac Kohane, a doctor and chairman of the biomedical informatics program at Harvard Medical School. That’s now starting to change. ‘Those of us in the AI community are extremely optimistic about how these techniques are going to revolutionize medicine,’ he said. But with Watson, ‘it’s just unfortunate that the marketing arm got ahead of the capabilities.’”

That marketing issue right there is the problem in a nutshell; salespeople gotta sell. AI, even Watson, holds great promise for better medical outcomes, but we mustn’t get ahead of ourselves. In the meantime, IBM is pursuing some less consequential AI projects, like delivering coffee by drone at just the right moment. TechAcute reports, “IBM Files a Patent for Coffee Delivery Drone Which Knows You Want Your Coffee.” Reporter Kate Sukhanova tells us the service monitors users’ biometric data and delivers them their cup of joe before they even know they need it. Just what we (or, perhaps Watson’s public relations) needed!

Cynthia Murrell, September 13, 2018

Smart Software and Old School Technology

August 22, 2018

It feels strange to say that anything analog is a trend in artificial intelligence, but that certainly seems to be the case in one segment. According to reports, there’s actually a way for AI to get faster and more accurate by indulging in some old timey thinking. We learned more from a recent Kurzweil article, “IBM Researchers Use Analog Memory to Train Deep Neural Networks Faster and More Efficiently.”

According to the story:

“IBM researchers used large arrays of non-volatile analog memory devices (which use continuously variable signals rather than binary 0s and 1s) to perform computations. Those arrays allowed the researchers to create, in hardware, the same scale and precision of AI calculations that are achieved by more energy-intensive systems in software, but running hundreds of times faster and at hundreds of times lower power…”

This is an intriguing development for AI and machine learning. Next Platform took a look at this news as well and found: “these efforts focused on integrating analog resistive-type electronic memories onto CMOS wafers, they also look at photonic-based devices and systems and how these might fit into the deep learning landscape.” We’re excited to see where this development goes and what companies will do with greater AI strength.

Patrick Roland, August 22, 2018

Wake Up Time: IBM Watson and Real Journalists

August 11, 2018

I read “IBM Has a Watson Dilemma.” I am not sure the word “dilemma” embraces the mindless hyperbole about Vivisimo, home brew code, and open source search technology. The WSJ ran the Watson ads which presented this Lego collection of code parts one with a happy face. You can check out the Watson Dilemma in your dead tree edition of the WSJ on page B1 or pay for online access to the story at www.wsj.com.

The needle point of the story is that IBM Watson’s push to cure cancer ran into the mushy wall composed of cancerous cells. In short, the system did not deliver. In fact, the system created some exciting moments for those trying to handcraft rules to make Dr. Watson work like the TV show and its post production procedures. Why not put patients in jeopardy? That sounds like a great idea. Put experts in a room, write rules, gather training data, and keep it update. No problem, or so the received wisdom chants.

The WSJ reports in a “real” news way:

…Watson’s recommendations can be wrong.

Yep, hitting 85 percent accuracy may be wide of the mark for some cognitive applications.

From a practical standpoint, numerical recipes can perform some tasks to spin money. Google ads work this magic without too much human fiddling. (No, I won’t say how much is “too much.”)

But IBM believed librarians, uninformed consultants who get their expertise via a Gerson Lehrman phone session, and from search engine optimization wizards. IBM management did not look at what search centric systems can deliver in terms of revenue.

Over the last 10 years, I have pointed out case examples of spectacular search flops. Yet somehow IBM was going to be different.

Sorry, search is more difficult to convert to sustainable revenues than many people believe. I wonder if those firms which have pumped significant dollars into the next best things in information access look at the Watson case and ask themselves, “Do you think we will get our money back?”

My hunch is that the answer is, “No.”

For me, I will stick to humanoid doctors. Asking Watson for advice is not something I want to do.

But if you have cancer, why not give IBM Watson a whirl. Let me know how that works out.

Stephen E Arnold, August 11, 2018

IBM Embraces Blockchain. Watson Watches

August 10, 2018

IBM recently announced the creation of LedgerConnect, a Blockchain powered banking service. This is an interesting move for a company that previously seemed to waver on whether it wanted to associate with this technology most famous for its links to cryptocurrency. However, the pairing actually makes sense, as we discovered in a recent IT Pro Portal story, “IBM Reveals Support Blockchain App Store.”

According to an IBM official:

“On LedgerConnect financial institutions will be able to access services in areas such as, but not limited to, know your customer processes, sanctions screening, collateral management, derivatives post-trade processing and reconciliation and market data. By hosting these services on a single, enterprise-grade network, organizations can focus on business objectives rather than application development, enabling them to realize operational efficiencies and cost savings across asset classes.”

This, in addition, to recent news that some of the biggest banks on the planet are already using Blockchain for a variety of needs. This includes the story that the Agricultural Bank of China has started issuing large loans using the technology. In fact, out of the 26 publicly owned banks in China, nearly half are using Blockchain. IBM looks conservative when you think of it like that, which is just where IBM likes to be. Watson, we believe, is watching, able to answer questions about the database du jour.

Patrick Roland, August 10, 2018

IBM Watson Workspace

August 6, 2018

I read “What Is Watson Workspace?” I have been assuming that WW is a roll up of:

  • IBM Lotus Connections
  • IBM Lotus Domino
  • IBM Lotus Mashups
  • IBM Lotus Notes
  • IBM Lotus Quickr
  • IBM Lotus Sametime

image

The write up explains how wrong I am (yet again. Such a surprise for a person who resides in rural Kentucky). The write up states:

IBM Watson Workspace offers a “smart” destination for employees to collaborate on projects, share ideas, and post questions, all built from the ground up to take advantage of Watson’s cognitive computing abilities.

Yeah, but I thought the Lotus products provided these services.

How silly of me?

The different is that WW includes cognitive APIs. Sounds outstanding. I can:

  • Draw insights from conversations
  • Turn conversations into actions
  • Access video conferencing
  • Customize Watson Workspace.

When I was doing a little low level work for one of the US government agencies (maybe it was the White House?) I recall sitting in a briefing and these functions were explained. A short time thereafter I had the thankless job of reviewing a minor contract to answer an almost irrelevant question. Guess what? The “workspace” did not contain the email nor the attachments I sought. The system, it was explained to me by someone from IBM in Gaithersburg, was that it was not the fault of the IBM system.

Read more

Doc Watson Says: Take Two Big Blue Pills and Call Me in the Morning… If You Are Alive

August 1, 2018

Oh, dear. AI technology has great potential for good, but even IBM Watson is not perfect, it seems. Gizmodo reports, “IBM Watson Reportedly Recommended Cancer Treatments that Were ‘Unsafe and Incorrect’.” The flubs were found during an evaluation of the software, not within a real-world implementation. (We think.) Still, it is a problem worth keeping an eye on. Writer Jennings Brown cites a report by Stat News that reviewed some 2017 documents from IBM Watson’s former deputy health chief Andrew Norden, reports that were reportedly also provided to IBM Watson Health’s management. We’re told:

“One example in the documents is the case of a 65-year-old man diagnosed with lung cancer, who also seemed to have severe bleeding. Watson reportedly suggested the man be administered both chemotherapy and the drug ‘Bevacizumab.’ But the drug can lead to ‘severe or fatal hemorrhage,’ according to a warning on the medication, and therefore shouldn’t be given to people with severe bleeding, as Stat points out. A Memorial Sloan Kettering (MSK) Cancer Center spokesperson told Stat that they believed this recommendation was not given to a real patient, and was just a part of system testing. …According to the report, the documents blame the training provided by IBM engineers and on doctors at MSK, which partnered with IBM in 2012 to train Watson to ‘think’ more like a doctor. The documents state that—instead of feeding real patient data into the software—the doctors were reportedly feeding Watson hypothetical patients data, or ‘synthetic’ case data. This would mean it’s possible that when other hospitals used the MSK-trained Watson for Oncology, doctors were receiving treatment recommendations guided by MSK doctors’ treatment preferences, instead of an AI interpretation of actual patient data.”

Houston, we have a problem. Let that be a lesson, folks—always feed your AI real, high-quality case data. Not surprisingly, doctors who have already invested in Watson for Oncology are unhappy about the news, saying the technology can now only be used to supply an “extra opinion” when human doctors disagree. Sounds like a plan or common sense.

Cynthia Murrell, August 1, 2018

IBM Turns to Examples to Teach AI Ethics

July 31, 2018

It seems that sometimes, as with humans, the best way to teach an AI is by example. That’s one key takeaway from VentureBeat’s article, “IBM Researchers Train Ai to Follow Code of Ethics.” The need to program a code of conduct into AI systems has become clear, but finding a method to do so has proven problematic. Efforts to devise rules and teach them to systems are way too slow, and necessarily leave out many twists and turns of morality that (most) humans understand instinctively. IBM’s solution is to make the machine draw conclusions for itself by studying examples. Writer Ben Dickson specifies:

“The AI recommendation technique uses two different training stages. The first stage happens offline, which means it takes place before the system starts interacting with the end user. During this stage, an arbiter gives the system examples that define the constraints the recommendation engine should abide by. The AI then examines those examples and the data associated with them to create its own ethical rules. As with all machine learning systems, the more examples and the more data you give it, the better it becomes at creating the rules. … The second stage of the training takes place online in direct interaction with the end user. Like a traditional recommendation system, the AI tries to maximize its reward by optimizing its results for the preferences of the user and showing content the user will be more inclined to interact with. Since satisfying the ethical constraints and the user’s preferences can sometimes be conflicting goals, the arbiter can then set a threshold that defines how much priority each of them gets. In the [movie recommendation] demo IBM provided, a slider lets parents choose the balance between the ethical principles and the child’s preferences.”

Were told the team is also working to use more complex systems than the yes/no model, ones based on ranked priorities instead, for example. Dickson notes the technique can be applied to many other purposes, like calculating optimal drug dosages for certain patients in specific environments. It could also, he posits, be applied to problems like filter bubbles and smartphone addiction.

Beyond Search wonders if IBM ethical methods apply to patent enforcement, staff management of those over 55 year old, and unregulated blockchain services. Annoying questions? I hope so.

Cynthia Murrell, July 31, 2018

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