The Gray Lady Grinds on Big Blue
July 20, 2021
The New York Times may not be successful in selling ad space to IBM in the next few months. The estimable “real” news outfit published an entertaining discussion of Watson. Navigate to “What Ever Happened to IBM’s Watson.” Pay up. Read the 2,000 word business school, essay, opinion piece. Then check your portfolio to verify that IBM stock is down again, has new executives in new roles, and the tenacity to keep on with the “little engine that could” approach to dealing with the likes of Amazon, Google, and Microsoft as well as start ups offering everything from i2 Analyst Notebook clones to federating data systems to consulting services at a discount.
Yikes. Big Blue. The New York Times.
The write up has a number of zingers; for example:
- “Beware what you promise” about the “future of knowing.”
- “Watson has not remade any industries. And it hasn’t lifted IBM’s fortunes.”
- [Watson] “was not realistic.”
- [Watson was] “a learning journey.”
- “… The grand visions of the past are gone.”
- “Watson is no longer the next big thing, but it may become a solid business for IBM.”
Yep, that is a conditional and instead of an Amazon AWS Sagemaker gusher of cash, a “solid business.” The wonderfulness of the NYT article omits a couple of minor points:
- The “cognitive computing” pitch. Baloney in my opinion.
- The manual effort required to train the mash up of home brew code, open source, and stuff acquired from outfits like Vivisimo takes time and subject matter experts. The result? Expensive stuff for sure. And once the system is trained, one has to keep on training whilst optimizing.
- The complexity of taking a bunch of parts and implementing them as “smart software” is very difficult. Amazon seems to be going for the “off the shelf” approach and “ready to roll” models.
Net net: Let’s ask Watson how about those AI start ups as acquisition targets. Marketing, not innovation, seems to be the go to strength of IBM. What do you say, Watson? Watson, are you there? Wow, that latency is a killer isn’t it?
Stephen E Arnold, July 20, 2021
China, Quantum Computing; IBM, Heifers
July 15, 2021
This is probably an unfair comparison but, why not? Navigate to “Chinese Quantum Computer Sets Record in Processing Test.” The write up states:
Scientists in China have claimed another benchmark in computing, saying their quantum device takes just 72 minutes to do a task that would take the most powerful supercomputer at least eight years.
Is this true? Maybe, maybe not. But the PR is fantastic. China, a technology leader. Beating Google and other US quantum wannabes. I can almost hear the applause in the Chinese National People’s Congress.
The write up added:
The Chinese scientists said they used random quantum circuit sampling as a metric to evaluate the power of their quantum processor.
Shortly after spotting this Chinese marketing-centric item, I read “Heifer International and IBM Work with Coffee and Cocoa Farmers in Honduras to Increase Access to Data and Global Markets.” That write up reports:
Heifer International and IBM, together with CATIE, an international organization focused on sustainable and inclusive human well-being in Latin America, are also working with farmers to deploy the Watson Decision Platform for Agriculture. The system combines predictive AI technology with geospatial, weather, environmental and IoT field data in a comprehensive dashboard tailored to a farmer’s land. It delivers weather alerts and other information, such as optimal planting patterns and expected yields linked to market pricing. These insights can help farmers and agribusinesses make more informed decisions for improving crop yield and value, as well as food safety and sustainability. The technology is expected to play an important role in increasing the incomes of coffee and cocoa farmers.
Quite a juxtaposition. A marketing push from China for quantum computing. IBM pushes with the Heifer tie up with good old Watson.
Stephen E Arnold, July 15, 2021
Predicting Behavior from Videos: A New Frontier for Touts
July 13, 2021
I spotted “AI Learns to Predict Human Behavior from Videos.” Sounds good, sounds promising, sounds like IBM. The idea is that Watson (open source software, home grown IBM code, and software from acquisitions) can foretell the future. Feed Watson videos, and Watson can figure out what happens next.
The write up states:
In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.
What’s the time horizon? Answer: Several minutes in the future.
What’s the accuracy? Answer: Uh, well.
What’s actually predicted? Answer: A higher level that links concepts.
What’s this means? Answer: Uh, well.
IBM, which like Google declared quantum supremacy-ness, is working overtime to demonstrate that Watson can deliver high value payoffs to those who embrace the IBM approach to smart software.
One of the researchers/students allegedly said, “Prediction is the basis of human intelligence.”
Okay, I will make a prediction: This watching videos angle smacks of marketing hoo hah based on the efforts of students with access to Watsony stuff and an environment which is hungry for evidence of the quantum supremacy-ness.
Confidence level: 99.999999
Stephen E Arnold, July 13, 2021
IBM: Watson, What Email Service Should Big Blue Use?
July 6, 2021
Watson, yes, you, IBM Watson. What mail system should IBM use? I am waiting… in the meantime:
This is a one liner offered at lunch by one of my DarkCyber researchers. This individual finds IBM amusing. I, on the other hand, feel for the company.
“IBM’s 18 Month Company Wide Email System Migration Has Been a Disaster, Sources Say” may not be 100 percent spot on. However, I believe it is indeed possible that the former Big Dog of computing may have itself swimming in an Olympic sized pool filled with Schwartzs Kosher Dill Pickles.
The write up reports:
“Outlook won’t work with the new system, IBM Notes won’t work and the online email called Verse has now gone down,” a tipster told us. “Everyone has been affected and no fix is in sight.”
The write up adds:
a blog post to IBM’s internal network w3 said the migration had been planned for 18 months and that everything should go fine provided everyone follows the instructions emailed to them. Evidently, this did not happen.
Now back to my question: Watson, what email service should Big Blue user?
Answer: Proton Mail. Are you sure?
Stephen E Arnold, July 6, 2021
IBM and Watson: A More Focused Journey to Rediscover Relevance and Find More Revenue
May 21, 2021
Nope, this is not an episode of “Star Trek.” Think of “IBM’s CEO Says It’s Leaving Consumer AI Like Facial Recognition to Other Companies, As It Doubles Down on Pitching Watson to Business” as a new version of the 1904 film “Le Voyage à travers l’impossible.” Here’s the idea: A vendor of services, mainframes, and intellectual property like a two nanometer chip concept blasts off to a new world. The mission is:
enterprise AI. Watson will be “learning from small amounts of data” and utilizing natural language processing to answer specific business questions for
customers.”
IBM will continue to embrace Watson. Granted Watson has an interesting track record. The write up states:
IBM shut down its AI tool for drug discovery in 2019 after sluggish sales, and reportedly encountered hesitancy from banks when trying to sell into the financial sector.
Learn from one’s mistakes, my grandmother used to say.
The article points out:
Using natural language processing to understand communications like chat and email, building an AI that is trustworthy and free from bias, and automating business
functions like customer service and IT operations.
With the cloud propelling Watson to the Land of Enchanted Revenues, Big Blue will rely on innovations like Turbonomic and Instana. If you don’t know, Turbonomic is one of the leaders in AIOps. You don’t need to wait for Forrester or Gartner to explain this very advanced concept of Artificial Intelligence Operations. Turbonomic is one of the leaders in this nova like market, and IBM acquired the company. Instana, now swallowed by a Big Blue gulp delivers APM. Okay, I will clue to you in: APM means Application Performance Monitoring. The idea is that when smart software behaves in a stupid way and the cloud centric app won’t work, IBM can help you out. The idea makes sense for the IBM cloud, but I am not sure how some of IBM’s cloud competitors will perceive this change. Thunderstorms perhaps?
When will the revenues be discovered? Yeah, probably in a Rometty (that’s a unit of time used by a former Big Blue pure bred to describe Watson’s earlier pursuit of revenues in Houston, pardner. No tar and feathers from the cancer docs in them there parts, but it was suggested that some of the IBM Watson leave town before sundown. And what about using IBM Watson to discover drugs applicable to the Covid pandemic? (Cough, cough.) Is there a metaphor to describe the bold new direction in which the IBM Watson starship is headed? Yes, there it. Electrification, specifically “at the turn of the last century” electrification.
Will stakeholders wait for 19th century time to mark what has to happen in the zip zip real time world of the 21st century? Sure. Keep in mind the plot of the 1904 film which told the story of a journey through mountains to the sun and ended up under water. Yikes. Underwater! This is not a good word for shareholders and stakeholders to hear in a metaphorical context.
Stephen E Arnold, May 21, 2021
What Happens when an AI Debates Politics?
April 20, 2021
IBM machine-learning researcher Noam Slonim spent years developing a version of IBM’s Watson that he hoped could win a formal debate. The New Yorker describes his journey and the results in, “The Limits of Political Debate.” We learn of the scientist’s inspiration following Watson’s Jeopardy win and his request that the AI be given Scarlett Johansson’s voice (and why it was not). Writer Benjamin Wallace-Wells also tells us:
“The young machine learned by scanning the electronic library of LexisNexis Academic, composed of news stories and academic journal articles—a vast account of the details of human experience. One engine searched for claims, another for evidence, and two more engines characterized and sorted everything that the first two turned up. If Slonim’s team could get the design right, then, in the short amount of time that debaters are given to prepare, the machine could organize a mountain of empirical information. It could win on evidence.”
Ah, but evidence is just one part. Upon consulting with a debate champion, Slonim learned more about the very human art of argument. Wallace-Wells continues:
“Slonim realized that there were a limited number of ‘types of argumentation,’ and these were patterns that the machine would need to learn. How many? Dan Lahav, a computer scientist on the team who had also been a champion debater, estimated that there were between fifty and seventy types of argumentation that could be applied to just about every possible debate question. For I.B.M., that wasn’t so many. Slonim described the second phase of Project Debater’s education, which was somewhat handmade: Slonim’s experts wrote their own modular arguments, relying in part on the Stanford Encyclopedia of Philosophy and other texts. They were trying to train the machine to reason like a human.”
Did they succeed? That is (ahem) debatable. The system was put to the test against experienced debater Harish Natarajan in front of a human audience. See the article for the details, but in the end the human won—sort of. The audience sided with him, but the more Slonim listened to the debate the more he realized the AI had made the better case by far. Natarajan, in short, was better at manipulating his listeners.
Since this experience, Slonim has turned to using Project Debater’s algorithms to analyze arguments being made in the virtual public square. Perhaps, Wallace-Wells speculates, his efforts will grow into an “argument checker” tool much like the grammar checkers that are now common. Would this make for political debates that are more empirical and rational than the polarized arguments that now dominate the news? That would be a welcome change.
Cynthia Murrell, April 20, 2021
AI Suffers the Slings and Arrow of Outrageous Marketing
March 19, 2021
I read “Loose Lips Sink AI Ships.” Amusing. The write up begins with a sentence designed to catch my attention:
Cognitive computing is not an IBM fraud. [Emphasis added. Editor.]
Imagine. IBM and fraud in the same sentence. Even more tasty is the phrase “cognitive computing.” The phrase evokes zeros and ones which think. The implication is that smart computers are as good as a mere mortal, perhaps even better at some things.
Fraud. Hmmm.
The write up explains that one naysayer is missing the boat. The naysayer took umbrage as a marketing person’s characterization of IBM Watson artificial intelligence platform being able to “outthink human brains in areas where finding insights and connections can be difficult due to the abundance of data.”
My goodness. A marketing person exaggerating. Plus the “abundance” word evokes the image of a tsunami of information. That’s an original metaphor too.
The write up explains that AI is a whiz bang deal. The case example is Covid research. I was hoping that the author would explain how IBM Watson was lashed to a gurney and wheeled into the parking lot at a major Houston, Texas, hospital. But no. The example was Covid.
The write up explains that AI is better with bigger and faster computers. That’s good news for some companies. Also, computer reasoning is “increasing quickly.” I like increased reasoning.
There is some less than sunny news too. What a surprise. For example, neural networks are clever, not intelligent. Clever was good enough for the Google, but not enough for real AI yet. And AI systems mimic human intelligence; the systems are not quite like your next door neighbor. (I think computers are quite like my next door neighbor, but I live in rural Kentucky. That’s a consideration.)
The write up seems to strive for balance if one relates to big data, big computers, and big marketing.
Let’s ask Watson? Well, maybe not.
Stephen E Arnold, March 19, 2021
Who Should Watch Over Smart Software? No One. Self Regulation Is the Answer
March 11, 2021
I read an amusing academic paper article called “Someone to Watch Over AI and Keep It Honest – and t’s Not the Public!.” The idea is that self regulation works. Full stop. Ignoring the 737 Max event and Facebook’s legal move to get anti-trust litigation dumped, the write up reports:
Dr Bran Knowles, a senior lecturer in data science at Lancaster University, says: “I’m certain that the public are incapable of determining the trustworthiness of individual AIs… but we don’t need them to do this. It’s not their responsibility to keep AI honest.”
And what’s the smart software entity figuring prominently in the write up? Amazon, the Google, or Twitter?
Nope.
IBM.
The idea, at least in the construct of the cited article, is that trust is important. And whom does one trust?
IBM.
How do I know there’s an element of trust required to accept this fine scholarly article?
Here’s a clue:
The paper is co-authored by John T. Richards, of IBM’s T.J. Watson Research Center, Yorktown Heights, New York.
Yep, the home of the game shown winner and arguably one of the few smart software systems to be put on a gurney and rolled out the door of a Houston, Texas medical facility.
But just in case the self regulation thing doesn’t work, the scholarly experts’ findings point to “a regulatory ecosystem.”
Yep, regulations. How’s that been working out in the last 20 years?
Why not ask IBM Watson?
Stephen E Arnold, March 11, 2021
IBM Watson and Health: Take Two Aspirin, Do Not Call Me in the Morning
March 8, 2021
IBM Watson was going to put cancer in the cupboard with AS/400 manuals. Then the billion dollar brainiac was going to deal with the Covid Rona thing. Neither worked out.
Ever since Watson blew the competition away on Jeopardy, IBM boasted that their supercomputer would enhance and/or repair industries. The biggest mountain IBM wanted Watson to scale was healthcare and MarketScreener shares: “International Business Machines: IBM’s Retreat From Watson Highlights Broader AI Struggles In Health.”
IBM speculated that AI and machine learning would revolutionize the healthcare industry, so they invested billions in Watson Health. Watson Health was a unit dedicated to developing an AI product that could diagnose and cure cancer. The unit was not profitable and IBM is now selling it.
Google’s DeepMind also invested in healthcare AI programs, but they too lost money and privacy on health data was a big concern.
The biggest roadblock, like all AI endeavors, is the lack of data and insights into the healthcare field:
“The stumbles highlight the challenges of attempting to apply AI to treating complex medical conditions, healthcare experts said. The hurdles include human, financial and technological barriers, they said. Having access to data that represents patient populations broadly has been a challenge, the experts say, as have gaps in knowledge about complex diseases whose outcomes often depend on many factors that may not be fully captured in clinical databases.
Tech companies also sometimes lack deep expertise in how healthcare works, adding to the challenge of implementing AI in patient settings, according to Thomas J. Fuchs, Mount Sinai Health System’s dean of artificial intelligence and human health.”
IBM has not given up on healthcare entirely. Watson Health did have some small successes, but in order to nab a profit IBM needs to sell its excess and concentrate on smaller initiatives.
IBM tried to make sweeping changes by casting a wide net, instead of focusing on smaller steps towards the big picture. Marketing is easier than building systems that live up to the collateral written by MBAs and art history majors it seems.
Whitney Grace, March 8, 2021
IBM Watson: Learn How to Build a Recommendation Engine with Watson NLP
February 17, 2021
I came across this IBM free lesson: “Build a Recommendation Engine with Watson Natural Language Understanding.”
The preliminary set up, according to the write up, takes about an hour. Once that hour has been invested, the IBM Watson Knowledge Studio service will allow you to whip up your own recommendation engine. Plus, with Watson, the system will understand what humans write.
What are the preliminary steps? No big deal. Get an IBM cloud account, then navigate to the IBM Cloud console. Pick a pricing plan. Just choose “free” otherwise the lesson is free, not building the recommendation solution, you silly goose.) Then follow the steps for provisioning a Watson Knowledge Studio instance. Choose “free” again.
Next you have an opportunity to work through six additio0nal steps:
- Define entity types and subtypes
- Create “Relation Types”
- Collect documents that describe your domain language
- Annotate Documents
- Generate a Machine Learning Model
- Deploy model to Natural Language Understanding service.
The system seems to enjoy documents which are no larger than 2,000 words, preferable smaller. And the documents must be in ASCII, PDF, DOC, and HTML. The IBM information says Zip files are supported, but zip files can contain non text objects and long text documents. (That’s why people zip long text files, right?) The student can also upload documents in the UIMA CAS XMI format. If you are not familiar with this file format, you can get oriented by looking at documents like this.)
Once you have worked through steps one through five (obviously without making an error), you will need you Natural Language Understanding API Key which “is located at The Natural Language Understanding API Key and URL can be found by navigating to your Watson Natural Language Understanding instance page and looking in the Credentials section.”
No problem.
But what if the customer support system relies on voice? What if the customer is asked to upload a screenshot or a file containing data displayed when a fault occurs? What if the customer has paid for “premier” support which features a Zoom session? What if the person who wants to learn about Watson recommendation engine for a small trucking company?
Good questions. You may want to set aside some time to work through steps one through five which encapsulate some specialized college courses and hands-on experience with smart software, search, indexing, etc.
Perhaps hiring an IBM partner to set up the system and walk you through its quirks and features is a more practical solution.
On the other hand, check out Amazon’s off the shelf machine learning systems.
Stephen E Arnold, February 17, 2021
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