IBM Watson: A Joke?

May 10, 2017

I wanted to ask IBM Watson is it thought the article “IBM’s Watson Is a Joke, Says Social Capital CEO Palihapitiya.” No opportunity. Bummer.

I learned from the real journalism outfit CNBC, which has been known to sell advertising, that:

“Watson is a joke, just to be completely honest,” he said in an interview with “Closing Bell” on the sidelines of the Sohn Investment Conference in New York.

The Social Capital top dog added:

“I think what IBM is excellent at is using their sales and marketing infrastructure to convince people who have asymmetrically less knowledge to pay for something,” Palihapitiya added. “I put them and Oracle in somewhat of the same bucket.”

I like that “asymmetrically less knowledge.” It suggests that the PR firms, the paid consultants who flog the word “cognitive,” and the torrent of odd ball conference talks are smoke and mirrors.

Should one put one’s money into IBM? My reading of the article suggests that the CNBC expert believes that Jeff Bezos and Elon Musk are where the action is. What? No Alphabet Google thing?

Several observations:

  1. Describing something in marketing science fiction is fun and can be lucrative. The reality is that Lucene, home brew code, and acquired technology do not add up to a breakthrough in smart software. Sorry, cheerleaders.
  2. Reporting five years of declining revenue puts hyperbole in context. IBM is simply trying to hard to push Watson into everything from recipes to healthcare. The financial reports tell me that the bet is not working.
  3. Creating wild and crazy Super Bowl ads which suggest a maximum refund tips toward carnival marketing. Floating white cubes are just as incomprehensible to me as PT Barnum’s Feejee mermaid.

Perhaps IBM can roll out a TV spot with Mr. Barnum’s Chang and Eng as a spokes-people.

Stephen E Arnold, May 9, 2017

US Still Most Profitable for Alphabet

May 8, 2017

Alphabet, Inc., the parent company of Google generates maximum revenue from the US market. Europe Middle East and Africa combined come at second and Asia Pacific occupying the third slot.

Recode in its earnings report titled Here’s Where Alphabet Makes Its Money says:

U.S. revenue increased 25 percent from last year to $11.8 billion. Sales from the Asia-Pacific region rose 29 percent to $3.6 billion. Revenue from Europe, the Middle East, and Africa was up 13 percent to $8.1 billion.

Despite the fact that around 61% of world population is in Asia Pacific region, Google garnering most of the revenues from a mere 322 million people is surprising. It can be attributed to the fact that China, which forms the bulk of Asia’s population does not have access to Google or its services. India, another emerging market though is open, is yet to embrace digital economy fully.

While chances of Chinese market opening up for Google are slim, India seems to be high on the radar of not only Google but also for other tech majors like Apple, Amazon, Microsoft and Facebook.

Vishol Ingole, May 8, 2017

Salesforce Einstein and Enterprise AI

May 5, 2017

One customer-relationship-management (CRM) firm is determined to leverage the power of natural language processing within its clients’ organizations. VentureBeat examines “What Salesforce Einstein Teaches Us About Enterprise AI.” The company positions its AI tool as a layer within its “Clouds” that brings the AI magic to CRM. They vow that the some-odd 150,000 existing Salesforce customers can deploy Einstein quickly and easily.

Salesforce has invested much in the project, having snapped up RelatelQ for $390 million, BeyondCore for $110 million, Predicition IO for $58 million, and MetaMind for an undisclosed sum. Competition is fierce in this area, but the company is very pleased with the results so far. Writer Mariya Yao cites Salesforce chief scientist Richard Socher as she examines:

The Salesforce AI Research team is innovating on a ‘joint many-task’ learning approach that leverages transfer learning, where a neural network applies knowledge of one domain to other domains. In theory, understanding linguistic morphology should also accelerate understanding of semantics and syntax.

In practice, Socher and his deep learning research team have been able to achieve state-of-the-art results on academic benchmark tests for main entity recognition (identifying key objects, locations, and persons) and semantic similarity (identifying words and phrases that are synonyms). Their approach can solve five NLP tasks — chunking, dependency parsing, semantic relatedness, textual entailment, and part of speech tagging — and also builds in a character model to handle incomplete, misspelled, or unknown words.

Socher believes that AI researchers will achieve transfer learning capabilities in more comprehensive ways in 2017 and that speech recognition will be embedded in many more aspects of our lives. ‘Right now, consumers are used to asking Siri about the weather tomorrow, but we want to enable people to ask natural questions about their own unique data.’

That would indeed be helpful. The article goes on to discuss the potentials for NLP in the enterprise and emphasizes the great challenge of implementing solutions into a company’s workflow. See the article for more discussion. Based in San Francisco, Salesforce was launched in 1999 by a former Oracle executive.

Cynthia Murrell, May 5, 2017

Forrester Research Loses Ground with Customer Management Emphasis

May 3, 2017

Yikes, the Wave people may be swamped by red ink. The investor-targeted news site Seeking Alpha asks, “Forrester Research: Is Irony Profitable?”  Posted by hedge fund manager Terrier Investing, the article observes that Forrester has been moving away from studies on business technology and toward customer-management research. The write-up reports:

The definition of irony, for $500 please? Forrester’s customers… don’t like what they’re selling. This is unfortunate, because as I explain in my Gartner write-up, selling technology research is actually a great business model in general – the value proposition to clients is strong […] and the recurring annual contracts with strong cash flow characteristics make it a hard business to kill even if you really try. To wit, while Forrester’s revenue growth and margins haven’t been anywhere near their targets for quite some time, the business hasn’t imploded and still throws off strong cash flow despite sales force issues and the ongoing product transition.

Perhaps that strong cash flow will ease the way as Forrester either pivots back toward business technology or convinces their customers to want what they’re now selling. The venerable research firm was founded back in 1983 and is based in Cambridge, Massachusetts.

Cynthia Murrell, May 3, 2017

 

 

Voice Recognition Software Has Huge Market Reach

May 3, 2017

Voice recognition software still feels like a futuristic technology, despite its prevalence in our everyday lives.  WhaTech explains how far voice recognition technology has imbedded itself into our habits in, “Listening To The Voice Recognition Market.”

The biggest example of speech recognition technology is an automated phone system.  Automated phone systems are used all over the board, especially in banks, retail chains, restaurants, and office phone directories.  People usually despise automated phone systems, because they cannot understand responses and tend to put people on hold for extended periods of time.

Despite how much we hate automated phone systems, they are useful and they have gotten better in understanding human speech and the industry applications are endless:

The Global Voice Recognition Systems Sales Market 2017report by Big Market Research is a comprehensive study of the global voice recognition market. It covers both current and future prospect scenarios, revealing the market’s expected growth rate based on historical data. For products, the report reveals the market’s sales volume, revenue, product price, market share and growth rate, each of which is segmented by artificial intelligence systems and non-artificial intelligence systems. For end-user applications, the report reveals the status for major applications, sales volume, market share and growth rate for each application, with common applications including healthcare, military and aerospace, communications, and automotive.”

Key players in the voice recognition software field are Validsoft, Sensory, Biotrust ID, Voicevault, Voicebox Technologies, Lumenvox, M2SYS, Advanced Voice Recognition Systems, and Mmodal.  These companies would benefit from using Bitext’s linguistic-based analytics platform to enhance their technology’s language learning skills.

Whitney Grace, May 3, 2017

 

Thomson Reuters: Now the Answer Company

April 25, 2017

Earlier this year I saw a reference to “the answer company.” I ignored it. Yesterday I saw a link to a podcast with Casey Hall, who is the “head of social media for business communications” at Thomson Reuters. Thomson Reuters is a publicly traded company with revenues in the $14 billion range. Here’s a Google chart showing how the company has performed over the last few years:

image

To my untrained eye, it looks as if revenues are down and profits are up. Yikes. How were those cost savings achieved? Perhaps the podcast explains how “the answer company” will boost revenues and continue to generate sustainable returns for stakeholders and, of course, senior management.

The podcast addresses a number of Thomson Reuters’ themes. One, for instance, is the fact that the company has 45,000 employees and a “giant footprint.” As the podcast ground forward, I realized that “the answer company” wants its employees to embrace employee advocacy.

It seems that “the answer company” is trying to communicate with its employees. According to the write up “How Thomson Reuters Earned the Brand as The Answer Company” accompanying the podcast told me:

Thomson Reuters encourages their employees to engage with their network of data scientists, finance, and accounting professionals by sharing the brand’s message. Leveraging their employees’ networks allows them to increase their reach and enhance the authenticity of the message since it’s coming from a real person, the employee. The employee advocacy program also helps with internal communications. Employees engage with each other and share what’s going on in their part of the organization.

Yeah, but, what about explaining “how” Thomson Reuters became “the answer company”? As it turns out, the podcast focused exclusively on “on boarding employees,” which I don’t really understand. Another topic was measuring the impact of the employee advocacy program. I think this means closing sales.

I suppose that Thomson Reuters just decided it needed a new tag line even thought its online services usually require a person to run a search, read a results list, and hunt for the needed information. That’s not answers. That’s work.

I believe that Thomson Reuters licensed the Palantir Technologies’ system in order to have tools which make sense of information. But if the podcast is any indication of how Thomson Reuters became “the answer company,” my thought is that the company is trying social media as a sales tool.

As for answers, one still has to hunt to find out what companies Thomson Reuters owns. One has to run queries on its online legal information systems and then hunt for answers.

Ah, PR. Love it. An article title which does not related to the content of the podcast OR the article.

Stephen E Arnold, April 24, 2017

The Big Dud

April 24, 2017

Marketers often need a fancy term periodically to sell technologies to large companies. Big Data and Hadoop was one such term. After years of marketing, adopters are yet to see any results, let alone any ROI.

Datamani recently published an article titled Hadoop Has Failed Us, Tech Experts Say in which the author says:

Many companies still run mainframe applications that were originally developed half a century ago. But thanks to better mousetraps like S3 (for storage) and Spark (for processing), Hadoop will be relegated to niche and legacy statuses going forward.

One of the primary concerns with Hadoop is that only handful of people know how to play it. For data scientists to make head and tail out of data, precise data queries and mining needs to be done. The dearth of experts, however, is hampering efforts of companies who want to make Big Data work for them. Other frameworks are trying to overcome problems put forth by Hadoop, but many companies have already adopted it and are stuck with it. And just like many fads, Big Data might fade into oblivion.

Vishal Ingole, April 24, 2017

Context: Are You Confused? What Is Your Context?

April 21, 2017

Human utterances can be difficult to figure out. When I departed the sunny climes of Washington, DC, to take a job with the Courier Journal & Louisville Times Company, I found myself in a new “context.” Working on money losing database products was a different context for me.

Obviously, Louisville was not the zip zip Right Coast. The shift from consulting to doing was a different context. And there were others. Each context shaped my talking and writing.

To most native speakers of English, in the database unit of the Courier Journal, the word “terminal” referred to one of the ever reliable gizmos that connected to the super user friendly DEC 20, TIPS typesetting, and, of course, to the home brew content management system used for the companies money losing databases.

The context of the work unit made clear to someone working with the DEC 20 that the word “terminal” did not mean the airport terminal, the relative who was dying of a rare blood disorder, or the weird little wire holding thingy on my model train’s Lionel transformer.

Language and understanding does depend on context.

I read “Bog Data Context: Targeting Relevant Data That’s Fit for Purpose.” Let me tell you that I was excited to find that context is getting some Big Data love. I learned:

Context is critical.

Well, I agree. It is 2017, and the context idea has been around for many years.

The write up includes a graphic to explain the challenge of context:

image

The idea is that an entity named John Doe appears in different databases and apparently uses a number of social media services. How does a human or smart software figure out what data goes with each John Doe.

Yep, this is a problem law enforcement and intelligence professionals have been considering for many years. Other people want to match up people with data pertinent to a specific entity; for example, financial institutions, online matchmakers, and government immigration officials.

Unfortunately putting a person in a context with pertinent data is a bit of a sticky wicket.

How does one solve this apparently tough problem? I learned from the write up:

There needs to be a focus on relevant data.”

No disagreement from me. But focus is not solving the context problem.

The article meanders through a number of ideas which do not strike me as directly related to the problem of figuring out context and then the meaning of utterances of a particular person. My thought is that the write up is not really about context. The article wants to use buzzwords and jargon to give the impression that context is going to less of a problem if someone implements many processes and procedures. These range from figuring out how trustworthy a source of data is to matching “representational effectiveness” with a model of context.

I learned that data lakes must not become “data graveyards.”

Okay, good idea. But I thought the article was tackling the problem of context, figuring out the meaning from its particular location among key signals like geography, behavior, and the nitty gritty of language itself.

How confused was I? Pretty confused. Here’s the last paragraph of the context write up:

There are a lot of starting points, a lot of pathways, in managing information in this rapidly changing data landscape. As McKnight said, “beyond the mountain is another mountain,” and Patricio reflected that this is a “continuous cycle of processing and evaluation.” Our data lakes will not be static; cannot afford to become data graveyards. But keeping them from becoming so requires us to continually reflect on the business problems we are trying to solve, to ask questions of the data, to understand the context of the data, and to measure and evaluate the fitness of the data for our purposes. With Big Data context in mind, we can mature our organizations and make more effective data-driven business decisions.

No wonder context remains a challenge. What is easy is writing headlines for what is:

  1. Cooking up an earthworm of quotes as a post conference rah rah
  2. Making the write up fit the title
  3. Moving beyond the obvious.

Wow.

Stephen E Arnold, April 21, 2017

Watson and Block: Tax Preparation and Watson

April 19, 2017

Author’s Note:

Tax season is over. I am now releasing a write up I did in the high pressure run up to tax filing day, April 18, 2017, to publish this blog post. I want to comment on one marketing play IBM used in 2016 and 2017 to make Watson its Amazon Echo or its Google Pixel. IBM has been working overtime to come up with clever, innovative, effective ways to sell Watson, a search-and-retrieval system spiced with home brew code, algorithms which make the system “smart,” acquired technology from outfits like Vivisimo, and some free and open source search software.

IBM Watson is being sold to Wall Street and stakeholders as IBM’s next, really big thing. With years of declining revenue under its belt, the marketing of Watson as “cognitive software” is different from the marketing of most other companies pitching artificial intelligence.

One unintended consequence of IBM’s saturation advertising of its Watson system is making the word “cognitive” shorthand for software magic. The primary beneficiaries of IBM’s relentless use of the word “cognitive” has been to help its competitors. IBM’s fuzziness and lack of concrete products has allowed companies with modest marketing budgets to pick up the IBM jargon and apply it to their products. Examples include the reworked Polyspot (now doing business as CustomerMatrix) and dozens of enterprise search vendors; for example, LucidWorks (Really?), Attivio, Microsoft, Sinequa, and Squirro (yep, Squirro). IBM makes it possible for competitors to slap the word cognitive on their products and compete against IBM’s Watson. I am tempted to describe IBM Watson as a “straw man,” but it is a collection of components, not a product.

Big outfits like Amazon have taken a short cut to the money machine. The Echo and Dot sell millions of units and drive sales of Amazon’s music and hard goods sales. IBM bets on a future hint of payoff; for example, Watson may deliver a “maximum refund” for an H&R Block customer. That sounds pretty enticing. My accountant, beady eyed devil if there ever were one, never talks about refunds. He sticks to questions about where I got my money and what I did with it. If anything, he is a cloud of darkness, preferring to follow the IRS rules and avoid any suggestion of my getting a deal, a refund, or a free ride.

Below is the story I wrote a month ago shortly after I spent 45 minutes chatting with three folks who worked at the H&R Block office near my home in rural Kentucky. Have fun reading.

Stephen E Arnold, April 18, 2017

IBM Watson is one of Big Blue’s strategic imperatives. I have enjoyed writing about Watson, mixing up my posts with the phrase “Watson weakly” instead of “Watson weekly.” Strategic imperatives are supposed to generate new revenue to replace the loss of old revenues. The problem IBM has to figure out how to solve is pace. Will IBM Watson and other strategic imperatives generate sustainable, substantial revenue quickly enough to keep the  company’s revenue healthy.

The answer seems to be, “Maybe, but not very quickly.” According to IBM’s most recent quarterly report, Big Blue has now reported declining revenues for 20 consecutive quarters. Yep, that’s five years. Some stakeholders are patient, but IBM’s competitors are thrilled with IBM’s stratgegic imperatives. For the details of the most recent IBM financials, navigate to “IBM Sticks to Its Forecast Despite Underwhlming Results.” Kicking the can down the road is fun for a short time.

The revenue problem is masked by promises about the future. Watson, the smart software, is supposed to be a billion dollar baby who will end up with a $10 billion dollar revenue stream any day now. But IBM’s stock buybacks and massive PR campaigns have helped the company sell its vision of a bright new Big Blue. But selling software and consulting is different from selling hardware. In today’s markets, services and consulting are tough businesses. Examples of companies strugglling to gain traction against outfits like Gerson Lehrman, unemployed senior executives hungry for work, and new graduates will to do MBA chores for a pittance compete with outfits like Elastic, a search vendor which sells add ons to open source software and consulting for those who need it. IBM is trying almost everything. Still those declining revenues tell a somewhat dismal tale.

I assume you have watched the Super Bowl ads if not the game. I just watched the ads. I was surprised to see a one minute, very expensive, and somewhat ill conceived commercial for IBM Watson and H&R Block, the walk in store front tax preparer.

The Watson-Block Super Bowl ad featured this interesting image: A sled going downhill. Was this a Freudian slip about declining revenues?

image

Does it look to you that the sled is speeding downhill. Is this a metaphor for IBM Watson’s prospects in the tax advisory business?

One of IBM’s most visible promotions of its company-saving, revenue-gushing dreams is IBM Watson. You may have seen the Super Bowl ad about Watson providing H&R Block with a sure-fire way to kill off pesky competitors. How has that worked out for H&R Block?

Read more

Forrester: Enterprise Content Management Misstep

April 14, 2017

I have stated in the past that mid tier consulting firms—that is, outfits without the intellectual horsepower of a McKinsey, Bain, or BCG—generate work that is often amusing, sometimes silly, and once in a while just stupid. I noted an error which is certainly embarrassing to someone, maybe even a top notch expert at mid tier Forrester. The idea for a consulting firm is to be “right” and to keep the customer (in this case Hyland) happy. Also, it is generally good to deliver on what one promises. You know, the old under promise, over deliver method.

How about being wrong, failing, and not delivering at all? Read on about Forrester and content management.

Context

I noted the flurry of news announcements about Forrester, a bigly azure-chip consulting firm. A representative example of these marketing news things is “Microsoft, OpenText, IBM Lead Forrester’s ECM Wave in Evolving Market.” The write up explains that the wizards at Forrester have figured out the winners and losers in enterprise content management. As it turns out, the experts at Forrester do a much better job of explaining their “perception” of content management that implementing content management.

How can this be? Paid experts who cannot implement content management for reports about content management? Some less generous people might find this a minor glitch. I think that consultants are pretty good at cooking up reports and selling them. I am not too confident that mid tier consulting firms and even outfits like Booz, Allen has dotted their “i’s” and crossed their “t’s.”

Let me walk you through this apparent failure of Forrester to make their reports available to a person interested in a report. This example concerns a Forrester reviewed company called Hyland and its OnBase enterprise content management system.

The deal is that Hyland allows a prospect to download a copy of the Forrester report in exchange for providing contact information. Once the contact information is accepted, the potential buyer of OnBase is supposed to be able to download a copy of the Forrester report. This is trivial stuff, and we are able to implement the function when I sell my studies. Believe me. If we can allow registered people to download a PDF, so can you.

The Failure

I wanted a copy of “The Forrester Wave: ECM Business Content Services.” May I illustrate how Forrester’s enterprise content management system fails its paying customers and those who register to download these high value, completely wonderful documents.

Step 1: Navigate to this link for OnBase by Hyland, one of the vendors profiled in the allegedly accurate, totally object Forrester report

image

Step 2: Fill out the form so Hyland’s sales professionals can contact you in hopes of selling you the product which Forrester finds exceptional

image

Note the big orange “Download Now” button. I like the “now” part because it means that with one click I get the high-value, super accurate report.

Step 3: Click on one of these two big green boxes:

image

I tested both, and both return the same high value, super accurate, technically wonderful reports—sort of.

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