IBM Watson: Making AI Wizards Do Eye Ball Rolls
August 1, 2017
Three quotes from Wired’s analysis of IBM Watson in “Watson Won Jeopardy, but Is It Smart Enough to Spin Big Clue’s AI into Green?” My answer is, “No.” I circled these items.
- Reference to the IBM Watson ad in which Bob Dylan allegedly says, “I have never known love.”
- Quote from a wizard at the Allen Institute for AI, “The only intelligent thing about Watson is their [sic] PR department.” The Beyond Search grammatical goose notes that the phrase might be crafted by a person in the Harrod’s Creek general store as “The only thing about Watson is its PR department.” But the quip is an inspirational one.
- The MD Anderson Center pulled out of its $62 million dollar deal for the IBM Watson cancer treating marvel.
None of this info is new, but I found the assessment on point with some of my thoughts.
However, the major flaw in Watson is the time, cost, and Ask Jeeves-type approach to making Watson smart. There are better methods, but good old IBM is apparently not interested. There’s nothing like a big company which allows spokespeople to sign their blog posts with their first name, not a full identifier. What? You don’t know Vijay? Neither do we.
Stephen E Arnold, August 1, 2017
Doctors Fearful of Technology? Too Bad for Them and Maybe the Patients?
July 27, 2017
IBM, Google, and other outfits want doctors to get with the technology program. Sure, docs use mobile phones, but email and such wonderful innovations as selfies from the operating theatre have not yet caught on. Watching my doc fumble with the required online medical record system is interesting. Try it sometime. Puzzled expressions, eye squinting, and sloooow keyboarding are part of the show. One of my docs expressed interest in my Dark Web Notebook. I sent him a link so he could download a comp copy. Guess what? He couldn’t figure out how to download the book. Amazing expertise.
I read a Thomson Reuters’ article which seems to stray dangerously close to my view of technology in the medical profession. Mind you, here in Louisville sales people are in the operating room to provide information to a doc who may not be familiar with a new gadget. Get enough gadgets and peddlers in the facility and the patients may have to rest on gurneys in the hall.
But I digress. The write up i noticed was “Doctors View Technology as Largely Problematic.” I highlighted this “real” news statement:
69 percent of the 100 doctors in the audience said increased reliance on technology and electronic health records only served to separate them from their patients….But the biggest problem stemming from technology for the doctors, and the bane of many doctors’ existence, is the electronic health record, also known as an EHR.
Now think about the over the top marketing from IBM about Watson’s ability in a narrow field like bladder cancer. Put that Anderson affair out of your main. Google continues to push forward with an even more interesting approach. I recall the phrase was “solving death.” And there are other outfits which believe that their technologists can make life so much better for doctors.
Seems like the revolution may take a bit more time. The good news is that since Google has not solved death, the doubting docs will die. Their replacements may be more into the IBM, Google, et al approach to health care.
No worries in Harrod’s Creek. We just use a mixture of black powder and bourbon to cure all manner of ills.
Stephen E Arnold, July 27, 2017
Banks Learn Sentiment Analysis Equals Money
July 26, 2017
The International Business Times reported on the Unicorn conference “AI, Machine Learning and Sentiment Analysis Applied To Finance” that discussed how sentiment analysis and other data are changing the financing industry in the article: “AI And Machine Learning On Social Media Data Is Giving Hedge Funds A Competitive Edge.” The article discusses the new approach to understanding social media and other Internet data.
The old and popular method of extracting data relies on a “bag of words” approach. Basically, this means that an algorithm matches up a word with its intended meaning in a lexicon. However, machine learning and artificial intelligence are adding more brains to the data extraction. AI and machine learning algorithms are actually able to understand the context of the data.
An example of this in action could be the sentence: “IBM surpasses Microsoft”. A simple bag of words approach would give IBM and Microsoft the same sentiment score. DePalma’s news analytics engine recognises “IBM” is the subject, “Microsoft” is the object and “surpasses” as the verb and the positive/negative relationships between subject and the object, which the sentiment scores reflect: IBM positive, Microsoft, negative.
This technology is used for sentiment analytics to understand how consumers feel about brands. In turn, that data can determine a brand’s worth and even volatility of stocks. This translates to that sentiment analytics will shape financial leanings in the future and it is an industry to invest in
Whitney Grace, July 26, 2017
Watson Does Whiteboards
July 24, 2017
A write-up at Helge Scherlund’s eLearning News describes a very useful tool in, “World’s Smartest Active Virtual Meeting Assistant Ricoh.” The tool integrates the IBM Watson AI into an interactive whiteboard system. The press release positions the tool as the future of meetings, but we wonder whether small businesses and schools can afford these gizmos. The write-up includes a nine-minute promotional video that describes the system, so interested readers should check it out. We’re also given a list of key features.
*Easy-to-join meetings: With the swipe of a badge the Intelligent Workplace Solution can log attendance and track key agenda items to ensure all key topics are discussed.
*Simple, global voice control of meetings: Once a meeting begins, any employee, whether in-person or located remotely in another country, can easily control what’s on the screen, including advancing slides, all through simple voice commands.
*Ability to capture side discussions: During a meeting, team members can also hold side conversations that are displayed on the same Ricoh interactive whiteboard.
*Translation of the meeting into another language: The Cognitive Whiteboard can translate speakers’ words into several other languages and display them on screen or in transcript.
I suppose one feature here may also be a thorn in the side of some old-school business people—the system creates a transcript of everything said in each meeting, including side conversations, and sends it to each participant. Auto CYA. The process would take some getting used to, but we can see the advantages for many organizations. Headquartered in Tokyo, Ricoh’s history stretches back to 1936.
Cynthia Murrell, July 24, 2017
IBM Watson: Two Views of the Same Pile of Tinker Toys
July 19, 2017
I find IBM an interesting outfit to watch. But more entertaining is watching how the Watson product and service is perceived by smart people. On the side of the doubters is a Wharton grad, James Kisner, who analyzes for a living at Jeffries & Co. His report “Creating Shareholder Value with AI? Not So Elementary, My Dear Watson?” suggests that IBM is struggling to makes its big bet pay off. If not a Google moon shot, Mr. Kisner thinks the low orbit satellite launch is in an orbit which will result in Watson burring up upon re-entry to reality.
The Big Dog of artificial intelligence and smart software may be a Chihuahua dressed up like a turkey, not a very big dog, not much of a bark, and certainly not equipped to take a big bite out a Wharton trained analyst’s foot.
On the rah rah side is Vijay, a blogger who does not put his name on his blog or on his About page. (One of my intrepid researchers thinks this Vijay’s last name is “Vijayasankar?.” Maybe?) I assume he is famous, just not here in Harrod’s Creek. His most recent write up about Watson is “IBM Watson Is Just Fine, Thank You!” His motivation for the write up is that the attention given to the Jeffries’ report caught his attention. He is a straight shooter; for example:
I am a big fan of criticism of technology – and as folks who have known me over time can vouch, I seldom hold back what is in my mind on any topic. I strongly believe that criticism is healthy for all of us – including businesses, and without it we cannot grow. If you go through my previous blogs, you can see first hand how I throw cold water on hype.
I like the cold water on hype from a person who is an IBM executive, and one who has been involved in the IBM Watson health initiatives. (I think this includes the star crossed Anderson project in Houston. I hear, “Houston, we have a problem,” but you may not.) I highlighted these points in this blog post:
- Hey, world, IBM is an enterprise product, not a consumer product. This seems obvious, but apparently IBM’s ability to communicate what it is selling and to whom is not working at peak efficiency or maybe not working because everyone is confused about Watson?
- IBM does not do the data federation things with its customer data. That’s good. I know that IBM sells a mainframe that encrypts everything. Interesting but I am not sure how this addresses flat revenue growth, massive layoffs, and the baffling Watson marketing which recently had a white cube floating in a tax preparer’s office. A white cube?
- IBM Watson has lots of successes. That’s a great assertion. The problem is that Watson started out as the next big thing. There was a promise of billions in revenue. There was a big office commitment in Manhattan. Then there was the implosion at the Houston health center. “Watson, do you read me?” I once tracked some of the Watson craziness in a series called the “Weakly Watson.” I gave up. The actual examples struck me as a painful type of fake news. What’s interesting is that the “weakly” stories were “real.” Scary to me and to stakeholders.
- Watson is not a product. Watson is an API to the IBM ecosystem. Vendor lock in beckons. And, of course, lots of APIs. These digital tinker toys can be snapped together. The problems range from the cost and time required for system training, the consulting and engineering services price tag, and the massaging required to explain that Watson is something that requires a lot of work. For the Instagram crowd that’s a problem. “Houston. Houston. Do you copy? Tinker toys. Lego blocks. Do you copy?”
- Watson “some times needs consulting.” Talk about an understatement. Watson needs lots and lots of consulting, engineering services, training, configuring, tuning. and training. Because Watson is a confection of open source, acquired technologies, and home brew code—a lot of work is needed. That’s because Watson was designed to generate high margin services, not the trivial revenue from online ads or from people ordering laundry detergent by pressing a button on their washing machine.
- Watson has two things in its bag of tricks: “Great marketing” and “AI talent.” Okay, marketing and smart people. The basic problem IBM has to solve before investors get frisky is generating significant, sustainable revenues and healthy margins. Spending money buys marketing and people. Effective management orchestrates what can be bought into stuff that can be sold at a profit.
The Vijay write up ends with a question. Here you go: “So why is IBM not publishing Watson revenue specifically?” This Vijay fellow who assumes that I know his last name does not answer the question. In the deafening silence, we need an answer.
That brings me to the Jeffries & Co. report by James Kisner, who is certified to do financial analysis. The answer to Vijay’s question consumes 53 pages of verbiage, charts, and tables of numbers. The entire document was available on July 18, 2017, at this link, but it may disappear. Many analyst documents disappear for the average guy. (If the link is dead, head over to Investext or give Jeffries & Co. a quick call to see if that will get you the meaty document.
A Jeffries & Co. analyst with teeth bites into the IBM financial data and seems to be unsatisfied.
In a nutshell, the Jeffries’ report says that IBM Watson is a limp noodle. Among the Watson characteristics are unhappy customers, wild and crazy marketing, misfires on deep learning, and the incredibly difficult, time consuming, and expensive data preparation required to make the system say, “Woof, woof” or maybe “Wolf, wolf” when there is something important for a human to notice.
Net net: IBM’s explanations of Watson have not produced the revenues and profits stakeholders expect. Jeffries & Co. goes MBA crazy providing a wide range of data to support the argument that Watson is struggling.
That “woof, woof” is the sound of a Chihuahua barking with the help of IBM spokespeople and lots of PR and marketing minions. The Wharton guy is a larger dog, barks ferociously, and has a bite backed up by data. IBM has to prove that it can solve problems for clients, generate sustainable revenue, and keep the competition from chowing down on a Watson weighted down with digital tinker toys.
Stephen E Arnold, July 19, 2017
IBM Watson: Predicting the Future
July 12, 2017
I enjoy IBM’s visions of the future. One exception: The company’s revenue estimates for the Watson product line is an exception. I read “IBM Declares AI the Key to Making Unstructured Data Useful.” For me, the “facts” in the write up are a bit like a Payday candy bar. Some nuts squished into a squishy core of questionable nutritional value.
I noted this factoid:
80 percent of company data is unstructured, including free-form documents, images, and voice recordings.
I have been interested in the application of the 80-20 rule to certain types of estimates. The problem is that the ‘principle of factor sparsity” gets disconnected from the underlying data. Generalizations are just so darned fun and easy. The problem is that the mathematical rigor necessary to validate the generalization is just too darned much work. The “hey, I’ve got a meeting” or the more common “I need to check my mobile” get in the way of figuring out if the 80-20 statement makes sense.
My admittedly inept encounters with data suggest that the volume of unstructured data is high, higher that the 80 percent in the rule. The problem is that today’s systems struggle to:
- Make sense of massive streams of unstructured data from outfits like YouTube, clear text and encrypted text messages, and the information blasted about on social media
- Identify the important items of content directly germane to a particular matter
- Figure out how to convert content processing into useful elements like named entities and relate those entities to code words and synonyms
- Perform cost effective indexing of content streams in near real time.
At this time, systems designed to extract actionable information from relatively small chunks of content are improving. But these systems typically break down when the volume exceeds the budget and computing resources available to those trying to “make sense” of the data in a finite amount of time. This type of problem is difficult due to constraints on the systems. These constraints are financial as in “who has the money available right now to process these streams?” These constraints are problematic when someone asks “what do we do with the data in this dialect from northern Afghanistan?” And there are other questions.
My problem with the IBM approach is that the realities of volume, interrelating structured and semi structured data, and multi lingual content is that these bumps in the information super highway Watson seems to speed along are absorbed by marketing fluffiness.
I loved this passage:
Chatterjee highlighted Macy’s as an example of an IBM customer that’s using the company’s tools to better personalize customers’ shopping experiences using AI. The Macy’s On Call feature lets customers get information about what’s in stock and other key details about the contents of a retail store, without a human sales associate present. It uses Watson’s natural language understanding capabilities to process user queries and provide answers. Right now, that feature is available as part of a pilot in 10 Macy’s stores.
Yep, I bet that Macy’s is going to hit a home run against the fast ball pitching of Jeff Bezos’ Amazon Prime team. Let’s ask Watson. On the other hand, let’s ask Alexa.
Stephen E Arnold, July 12, 2017
Palantir Technologies: The Buzzfeed Beat
July 3, 2017
I read “There’s a Fight Brewing between the NYPD and Silicon Valley’s Palantir.” Two points about this story. Palantir Technologies, a vendor profiled in my CyberOSINT and Dark Web Notebook reports is probably going to keep its eye on the real journalistic outfit Buzzfeed. I don’t know much about “real” journalism, but my hunch is that if Palantir’s stakeholders find the Buzzfeed write up coverage interesting, some of those folks might spill their Philz coffee.
The other point is that the New York Police Department may find questions about its contractual dealings a bit of distraction from the quotidian tasks the force faces each day. I would not characterize “real” journalists asking questions “annoying,” but I would hazard the phrase “time consuming” or the word “distracting.”
“You want me to believe that?” asks Max, a skeptical show dog who knows that some owners will do anything to win.
The point of the “Fight Brewing” write up strikes me as a story designed to suggest that Palantir Technologies may be showing some signs of stress. When I read the story, I thought of the news which swirled around some of the defunct enterprise search companies when one of their client engagements went south. Vendors hit with these situations can do little but ride out the storm.
Hey, enterprise search was routinely oversold. When a system was up and running, the results were usually similar to the results generated by the previous “solution to all your information problems.” The search engineers who coded the systems knew that overpromising and under delivering were highly probable once the on switch was flipped. But the sales professional were going to say what was necessary to close the deal. In fact, most of the fancy promises about an enterprise search system set the company up for failure.
Is that what’s going on in the NYPD-Palantir “showdown”? To wit:
Palantir explained the system’s functions and outputs. The NYPD signed on. Then when the system was installed, additional work was needed to make the Palantir system meet the expectations set by the Palantir sales engineers.
The “Fight Brewing” story says:
The NYPD quietly began work last summer on its replacement data system, and in February it announced internally that it would cancel its Palantir contract and switch to the new system by the beginning of July, according to three people familiar with the matter. The new system, named Cobalt, is a group of IBM products tied together with NYPD-created software. The police department believes Cobalt is cheaper and more intuitive than Palantir, and prizes the greater degree of control it has over this system.
Keep in mind that I, before I retired in 2013, had been an adviser to the original i2 Group Ltd., the company which created in my opinion the analytic and visualization method which defines modern cyber eDiscovery in the 1990s.
The notion that IBM, which now owns i2’s Analyst’s Notebook, is working hard to close deals in key Palantir accounts from what I have heard in the general store in Harrod’s Creek.
I don’t have to go much farther than my own experience to get a sense that the “fight” may be a manifestation of how the world works when it comes to making sales for systems like Palantir’s Gotham or IBM’s i2. In my work career I have seen some interesting jabs and punches thrown to close a deal.
The NYPD, like any organization, wants systems which work and represent good value. Incumbent vendors have to find a way to retain a customer. Competitors have to find a way to get a licensee of one product to switch to a different product.
I noted this statement in the “Fight Brewing” story:
Palantir has struggled to expand its work with the police force, the emails show. As of March and April 2015, Palantir had had “little exposure to the top brass,” and although it wanted to add more business, “the door there clearly still remains closed given the larger political environment,” staffers wrote in emails. A staffer at one point invoked a phrase popularized by Thiel, author of Zero to One: Notes on Startups, or How to Build the Future, saying that Palantir still needed to get “from 0->1 at NYPD.”
Now how many police forces in the US can afford a comprehensive cyber eDiscovery system like Palantir Gotham or IBM Analyst’s Notebook? This is an important point because the number of potential customers is quite small. For example, after NY, LA, Chicago, Miami, and maybe three or four other cities, the sales professional runs out of viable prospects. How many counties can foot the bill for the software, the consultants, and the people required to tag and analyze the data? The number is modest. How many US states can afford the investment in high end cyber eDiscovery software? Again, the number is small, and you can count out Illinois because getting bills paid is an interesting challenge. The same market size problem exists for US government entities.
IBM Bans Remote Work
June 22, 2017
The tech blog SiliconBeat reveals a startling development in tech-related employment in, “IBM: So Much for Working from Home.” Thousands of professionals who have built their lives around their remote-work arrangements are now being required to come into the office. For many, the shift would mean packing up and moving closer to one of the company’s locations. As writer Rex Crum puts it:
That’s right. Find your way to an office cubicle, or hit the bricks. The Wall Street Journal reported that IBM began instituting the new you-can’t-work-from-home policy this week, and that the company is ‘quietly dismantling’ the program that has been in place for decades. The Journal said the retrenchment on its employees working remotely was being done so that IBM could ‘improve collaboration and accelerate the pace of work.’ It also happens to be taking place not long after IBM reported its 20th-straight quarter of declining year-over-year revenue. Legendary all-time investor Warren Buffett also said this month that Berkshire Hathaway has cut its holdings in IBM by one-third from the 81 million shares the company owned earlier this year.
But will herding all their talent into their buildings really solve IBM’s financial woes? Not according to this Forbes article. Crum recalls that Yahoo made the same move in 2013, when Marissa Mayer put a stop to remote work at that company. (How has that been going?) Will more organizations follow?
Cynthia Murrell, June 22, 2017
Instantaneous Language Translation in Your Ear
June 21, 2017
A common technology concept in cartoons and science-fiction series is an ear device that acts as a universal translator. The wearer would be able to understand and speak any language in the world. The universal translator has long been one of the humanity’s pipe dream since the Tower of Babel and as technology improves we could be closer to inventing it. The Daily Mail shares, “The Earpiece That Promises To Translate Language In Seconds: £140 Will Be Available Next Month.”
International travelers’ new best friend might be Lingmo International’s One2One translator that is built on IBM Watson’s artificial intelligence system. Unlike other translation devices, it does not reply on WiFi or BlueTooth connectivity. It supports eight languages: English, Japanese, French, Italian, Spanish, Brazilian, Portuguese, German, and Chinese (does that include Mandarin and Cantonese?). If the One2One does not rely on the Internet, how will it translate languages?
Instead, it uses IBM Watson’s Natural Language Understanding and Language Translator APIs, which intuitively overcomes many of the contextual challenges associated with common languages, as well as understanding the nuances of local dialects…This allows it to translate what you’re saying, almost in real-time.
Lingomo might be relying on IBM Watson for its natural language API, they should also consider using Bitext, especially when it comes to sentimental analysis. Some languages have words with multiple meanings that change based on a voice’s inflection and tone.
The ramifications for this device are endless. Can you imagine traveling to a foreign country and being able to understand the native tongue? It is the dream of billions, but it could also end some serious conflicts.
Whitney Grace, June 21, 2017
Watson Enters Two New Fields
June 13, 2017
IBM’s Watson has been very busy, and it is no longer just generating recipes and curing cancer. A couple pieces from the company’s recent PR blitz illustrate two new hats the AI has donned: Endgadget shares, “Watson Could Be the Key to Smarter Manufacturing Robots,” while “IBM Watson Now Being Used to Catch Rogue Traders” appears at Silicon Republic. It looks like IBM is positioning Watson as the AI that can do anything.
Engadget reports that Watson is being tapped to perform quality-control for ABB, a firm that makes manufacturing robots and the software that runs them. Writer Rob LeFabvre describes:
Imagine an automotive assembly line, full of robots that build cars without any human intervention. Someone has to monitor and inspect the machinery for defects, ensuring their safe and efficient operation. ABB’s technology can gather real-time images and then get Watson to analyze them for potential problems, something a human previously needed to do.
Meanwhile, Watson now offers a tool for companies to catch rogue traders within their ranks. Reporter Colm Gorey writes:
Referred to as Watson Financial Services, the new product will become a monitoring tool within companies to search through every trader’s emails and chats, combining it with the trading data on the floor. The objective? To see if there are any correlations between suspicious conversations online and activity that could be construed as rogue trading.
While the service is being tested out on a few trading-sector companies, IBM intends to market it to the growing “RegTech” field.
IBM has pointed its famous AI in many directions, and will likely continue to work Watson into as many fields as possible. We ask, “Can she save IBM?”
Cynthia Murrell, June 13, 2017
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