Whirlpool Snaps up Yummly, Recipe Search Engine
June 2, 2017
IBM Watson’s book or recipes may have been a harbinger for foodies. Now Whirlpool, the appliance manufacturer, has taken another step into the future with the acquisition of tech start-up company Yummly, a recipe search engine/shopping list creator with 20 million users. Terms of the deal have not been made public.
Techcrunch reports in Whirlpool Acquires Yummly, The Recipe Search Engine Last Valued At $100M:
Yummly basically can help extend the kinds of services that Whirlpool can offer … it can (generate) more recipes and other suggestions for your food items; Yummly has created a lot of specific parameters for recipe searches which help make results more specific to what users need.
Yummly will maintain its offices and act as a subsidiary of Whirlpool. The acquisition provides Whirlpool with new avenues into technology and Yummly with a source a revenue as it continues to grow.
As tech start-ups continue to spring up and established companies evolve, nothing remains the same. Whirlpool seems to agree with us at Beyond Search. IBM Watson’s recipes are more like kale sandwiches than a trucker’s special.
Mary Pattengill, June 2, 2017
IBM (The Great Innovator) Tells India: You Are Not Innovative
May 22, 2017
I don’t know much about India. I have interacted with a handful of Indian entrepreneurs over the years. I owned a bit of a company set up and managed by a fellow from India. He struck me as bright and, I suppose, the word “innovative” suits him. I also spent a little time with the entrepreneur who created Aglaya. This is an outfit which has some technology which struck me as innovative if you think performing wireless intercepts when a person of interest is going about their daily routine innovative. I have had other bump ups over the last 40 years. These ranged from bright nuclear engineers at Halliburton Nuclear to chipper MBAS with good idea when I worked at the fun factory Booz, Allen & Hamilton to the assorted engineers I encountered in my other work.
To sum up, Indian engineers are not much different from engineers from other countries. I assume that parental guidance, curiosity, and being intelligent were the common factor. Country of origin was not exactly a predictor in my experience.
Well, gentle reader, that’s not how IBM perceives innovation from an entire country if the data in “New Study Finds 90% Of Indian Startups Will Fail Because Of Lack Of Innovation” is on the money. IBM allegedly learned that because India (now that’s a generalization) is not innovative, Indian start ups will fail. Pretty remarkable finding from the company which has tallied five years of declining revenue and the wonky Watson Lucene-based confection.
Innovative? IBM and its researchers are convinced that their work is changing the world. Don’t believe me? Ask Watson. I would not ask a shareholder.
I learned from the report about IBM’s research:
India might have become the third largest startup ecosystem, but it lacks successful innovation.
India is a big country. Doesn’t it seem likely that some individuals would attempt to start new firms instead of trying to get a job at the local bank?
IBM and Oxford Economics found that
90% of Indian startups fail within the first five years. And the most common reason for failure is lack of innovation — 77% of venture capitalists surveyed believe that Indian startups lack new technologies or unique business models.
Yeah, but don’t startups have a high mortality rate? Don’t the business models track with legal ways to generate revenue widely used by other countries’ entrepreneurs? Heck, most patents are stuffed with references to prior art? The innovation is the cuteness of the wording in the claims in many cases, right?
You think this is innovative? You are uninformed. IBM’s study verifies the lack of innovation in India. Tear this allegedly innovative building down. Go with an IBM glass “instant building.”
Not only are those Indian entrepreneurs unimaginative when it comes to making money, IBM’s study reports:
Other reasons cited for failure include lack of skilled workforce and funding, inadequate formal mentoring and poor business ethics, according to the study. It’s well known that most Indian startups are prone to emulate successful global ideas, by and large fine tuning an existing model to serve the local need…
With more than a billion people, it seems logical to focus on the market at hand.
But IBM’s data seems to impugn India for other faults; for example:
India doesn’t have meta level startups such as Google, Facebook or Twitter….Unsurprisingly, in 2016, Asian Paints was the only Indian organization in Forbes’ 25 most innovative companies, and Gillette India was among Forbes Top 25 Innovative Growth companies.
Ah, ha. The capitalist tool Forbes includes only one company called by the surprisingly American moniker Gillette India (very creative indeed) is on the Forbes Top 25 innovative growth companies.
A guru may be the source of this insightful comment:
Even in evolving AI technology, Indian entrepreneurs are not pioneers.
But IBM sees the sun peeking through the heavy Indian clouds:
The IBM report adds that while strong government promotion of entrepreneurship has strengthened the startup culture, India’s economic openness and large domestic market are significant advantages.
What’s with IBM and its somewhat negative discussion of India? Is there an IBM Watson skeleton in the Big Blue closet wearing an IBM Watson t shirt? Did IBM’s own initiatives in India fail? Did a senior IBM executive have a bad experience at the decidedly non creative Taj Mahal? Maybe an Indian rug did not match the interior designer’s vision for Armonk carpetland?
That odd ball digit zero. I had a math professor or maybe it was my half crazy relative who may have contributed some non creative ideas to the Kolmogorov Arnold Moser theorem who told me that some Indian number crunchers cooked up the idea of a zero. IBM’s report suggests that Brahmagupta’s use of computation with the zero was definitely not innovative. I assume that means my crazed relative was innovative, not autistic, anti social, and usually lost in mathematical wonderland.
IBM is familiar with zeros. That’s the symbol I associate with IBM Watson’s contribution to IBM financial future. IBM is, of course, more innovative. It has lots of patents. Revenue growth? Nah, just money to spend proving that India’s start ups work pretty much like any other country’s start ups. Lots of failures.
Final thought: Why didn’t IBM just ask Watson about India. Why involve humans at all? By the way, where’s IBM’s Alexa, its Pixel phone, or its Facebook social network? Watson, Watson, are you there or just pondering life as an non innovative zero?
Stephen E Arnold, May 22, 2017
Did IBM Watson Ask Warren Buffet about Value?
May 19, 2017
I read “$4 Billion Stock Sale Suggests Warren Buffett’s Love Affair with IBM Is Over.” The subtitle caught my eye. What would Watson think about this statement:
Berkshire Hathaway’s founder Warren Buffett has admitted that buying IBM shares was a mistake. He has sold 30 percent of his 81 million shares because the company failed to live up to the expectations it held in 2011.
If I had access to a fully functioning (already trained) IBM Watson, I would ask Watson that question directly.
Last night I was watching the NBA playoff game between the technically adept Houston team and the programming-crazed San Antonio team. There in the middle of a start and stop game was an IBM Watson commercial.
Let me tell you that the IBM Watson message nestled comfortably amidst the tats, the hysterical announcers, and the computer-literature crowd.
IBM has a knack for getting its message out to buyers with cash in their hands for a confection of open source, home brew, and acquired technology.
Why doesn’t Warren Buffet get the message?
According the the write up, Mr. Buffet explains what message he received about IBM:
… IBM “hasn’t done what, five or six years ago, I expected would happen – or what the management expected would happen, if you look back at what they were projecting, and how they thought the business would develop. “The earnings have been obviously disappointing. I mean, five or six years ago, I think they were earning $20+ billion pre-tax and maybe it’s $13 billion now, and I don’t think the quality of the earnings has improved. “It’s been a period when it’s been tougher than they thought and it’s been tougher than I thought. But I was wrong. I don’t blame them. I get paid to make my own decisions, and sometimes they’re right and sometimes they’re wrong.
Interesting but not quite as remarkable as smart software being advertised to NBA fans. Air ball.
Stephen E Arnold, May 19, 2017
Malware Infected USB Sticks on the Loose
May 18, 2017
Oops. We learn from TechRepublic that “IBM Admits it Sent Malware-Infected USB Sticks to Customers.”
The article cites the company’s support Advisory Post announcing the problem, a resource anyone who has received an IBM Storwize V3500, V3700 or V5000 USB drive should check for the models and serial numbers affected. The recommended fix—destroy the drive and, if you’d already inserted it, perform a malware purge on your computer.
Writer Conner Forrest describes:
So, what does the infected drive actually do to a system? ‘When the initialization tool is launched from the USB flash drive, the tool copies itself to a temporary folder on the hard drive of the desktop or laptop during normal operation,’ the IBM post said. Then, a malicious file is copied to a temporary folder called %TMP%\initTool on Windows or /tmp/initTool on Linux or Mac. It is important to note that, while the file is copied onto a machine, it isn’t actually executed during the initialization process, the post also said. As reported by ZDNet’s Danny Palmer, the malware was listed by Kaspersky lab as a member of the Reconyc Trojan malware family, which is primarily used in Russia and India.
It might be understandable if this were the first time this had happened, but IBM also unwittingly distributed infected USB drives back in 2010, at the AusCERT conference in Australia. Let us hope there is not a third time; customers rightly expect more vigilance from such a prominent company.
Cynthia Murrell, May 18, 2017
Passion for the Work Is Key to Watson Team HR
May 17, 2017
Have you ever wanted to be on the IBM Watson team? Business Insider shares, “An IBM Watson VP Says He’s Hired Candidates Without Even Conducting an Interview—Here’s Why He’d Hire You on the Spot.” The brief write-up introduces Watson’s VP of HR Obed Louissant, who reveals that he has offered some folks a job they weren’t actually seeking after speaking with them. Writer Áine Cain specifies:
In certain conversations, Louissant says that he’s been blown away by the passion and engagement with which some individuals speak about their work. … ‘It was more about the experience and what types of places they like to work at,’ Louissant says. If the type of workplace happens to sound just like IBM Watson, the branch of the company that focuses on the question answering computer system, then Louissant says he’s willing to make a job offer right then and there.”
So, never underestimate the power of revealing a passion for your work. It could just land you a better job someday, with Louissant or other corporate leaders who, like him, are ready to snap up enthusiastic workers as soon as they recognize them.
Cynthia Murrell, May 17, 2017
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:
- 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.
- 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.
- 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
How to Use a Quantum Computer
April 20, 2017
It is a dream come true that quantum computers are finally here! But how are we going to use them? PC World discusses the possibilities in, “Quantum Computers Are Here—But What Are They Good For?” D-Wave and IBM both developed quantum computers and are trying to make a profit from them by commercializing their uses. Both companies agree, however, that quantum computers are not meant for everyday computer applications.
What should they be used for?
Instead, quantum systems will do things not possible on today’s computers, like discovering new drugs and building molecular structures. Today’s computers are good at finding answers by analyzing information within existing data sets, but quantum computers can get a wider range of answers by calculating and assuming new data sets. Quantum computers can be significantly faster and could eventually replace today’s PCs and servers. Quantum computing is one way to advance computing as today’s systems reach their physical and structural limits.
What is astounding about quantum computers are their storage capabilities. IBM has a 5-qubit system and D-Wave’s 2000Q has 2,000 qubit. IBM’s system is more advanced in technology, but D-Wave’s computer is more practical. NASA has deployed the D-Wave 2000Q for robotic space missions; Google will use it for search, image labeling, and voice recognition; and Volkswagen installed it to study China’s traffic patterns.
D-Wave also has plans to deploy its quantum system to the cloud. IBM’s 5-qubit computer, on the other hand, is being used for more scientific applications such as material sciences and quantum dynamics. Researchers can upload sample applications to IBM’s Quantum Experience to test them out. IBM recently launched the Q program to build a 50-qubit machine. IBM also wants to push their quantum capabilities in the financial and economic sector.
Quantum computers will be a standard tool in the future, just as the desktop PC was in the 1990s. By then, quantum computers will respond more to vocal commands than keyboard inputs.
Whitney Grace, April 20, 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?
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?
IBM: Recycling Old Natural Language Assertions
April 6, 2017
I have ridden the natural language processing unicycle a couple of times in the last 40 years. In fact, for a company in Europe I unearthed from my archive NLP white papers from outfits like Autonomy Software and Siderean Software among others. The message is the same: Content processing from these outfits can figure out the meaning of a document. But accuracy was a challenge. I slap the word “aboutness” on these types of assertions.
Don’t get me wrong. Progress is being made. But the advances are often incremental and delivered as the subsystem level of larger systems. A good example is the remarkable breakthrough technology of Madrid, Spain-based Bitext. The company’s Deep Linguistic Analysis Platform solves a very difficult problem when an outfit like a big online service has to figure out the who, what, when, and where in a flood of content in 10, 20, or 30 or more languages. The cost of using old-school systems is simply out of reach even for companies with billion in the bank.
I read “Your Machine Used to Crunch Numbers. Now It Can Chew over What They Mean, Too.” The write up appeared in the normally factual online publication “The Register.” The story, in my opinion, sucks in IBM marketing speak and makes some interesting assertions about what Lucene, home brew scripts, and acquired technology can deliver. In my experience, “aboutness” requires serious proprietary systems and methods. Language, no matter what one believes when Google converts 400 words of Spanish into semi-okay English.
In the article I was told:
This makes sense, because the branches of AI gaining most traction today – machine learning and deep learning – typically have non-deterministic outputs. They’re “fuzzy”, producing confidence scores relating to their inputs and outputs. This makes AI-based analytics systems good at analyzing the kind of data that has sprung up since the early 2000s; particularly social media posts.
Well, sort of. There are systems which can identify from unstructured text in many languages the actor, the action, and the outcome. In addition, these systems can apply numerical recipes to identify items of potential interest to an analyst or another software systems. The issue is error rate. Many current entity tagging systems stumble badly when it comes to accuracy.
But IBM has been nosing around NLP and smart software for a long time. Do you remember Data Fountain or Dr. Jon Kleinberg’s CLEVER system? These are important, but they too were suggestive, not definitive approaches.
The write up tells me via Debbie Landers, IBM Canada’s vice president of Cognitive Solutions:
People are constantly buying security products to fix a problem or get a patch to update something after it’s already happened, which you have to do, but that’s table stakes,” he says. Machine learning is good at spotting things as they’re happening (or in the case of predictive analytics, beforehand). Their anomaly detection can surface the ‘unknown unknowns’ – problems that haven’t been seen before, but which could pose a material threat. In short, applying this branch of AI to security analytics could help you understand where attackers are going, rather than where they’ve been. What does the future hold for analytics, as we get more adept at using them? Solutions are likely to become more predictive, because they’ll be finding patterns in empirical data that people can’t spot. They’ll also become more context-aware, using statistical modeling and neural networks to produce real-time data that correlates with specific situations.
My reaction to this write up is that IBM is “constantly” thrashing for a way to make Watson-type services a huge revenue producer for IBM. From recipes to cancer, from education to ever more spectacular assertions about what IBM technology can do—IBM is demonstrating that it cannot keep up with smart software embedded in money making products and mobile services.
Is this a promotional piece? Yep, The Reg even labels it as such with this tag:
See. A promo, not fake news exactly. It is clear that IBM is working overtime with its PR firm and writing checks to get the Watson meme in many channels, including blogs.
Beyond Search wants to do its part. However, my angle is different. Look around for innovative companies engaged in smart software and closing substantive deals. Compare the performance of these systems with that of IBM’s solutions, if you can arrange an objective demonstration. Then you will know how much of IBM’s content marketing carpet bombing falls harmlessly on deaf ears and how many payloads hit a cash register and cause it to pay out cash. (A thought: A breakthrough company in Madrid may be a touchstone for those who are looking for more than marketing chatter.)
Stephen E Arnold, April 6, 2017
The Human Effort Behind AI Successes
March 14, 2017
An article at Recode, “Watson Claims to Predict Cancer, but Who Trained It To Think,” reminds us that even the most successful AI software was trained by humans, using data collected and input by humans. We have developed high hopes for AI, expecting it to help us cure disease, make our roads safer, and put criminals behind bars, among other worthy endeavors. However, we must not overlook the datasets upon which these systems are built, and the human labor used to create them. Writer (and CEO of DaaS firm Captricity) Kuang Chen points out:
The emergence of large and highly accurate datasets have allowed deep learning to ‘train’ algorithms to recognize patterns in digital representations of sounds, images and other data that have led to remarkable breakthroughs, ones that outperform previous approaches in almost every application area. For example, self-driving cars rely on massive amounts of data collected over several years from efforts like Google’s people-powered street canvassing, which provides the ability to ‘see’ roads (and was started to power services like Google Maps). The photos we upload and collectively tag as Facebook users have led to algorithms that can ‘see’ faces. And even Google’s 411 audio directory service from a decade ago was suspected to be an effort to crowdsource data to train a computer to ‘hear’ about businesses and their locations.
Watson’s promise to help detect cancer also depends on data: decades of doctor notes containing cancer patient outcomes. However, Watson cannot read handwriting. In order to access the data trapped in the historical doctor reports, researchers must have had to employ an army of people to painstakingly type and re-type (for accuracy) the data into computers in order to train Watson.
Chen notes that more and more workers in regulated industries, like healthcare, are mining for gold in their paper archives—manually inputting the valuable data hidden among the dusty pages. That is a lot of data entry. The article closes with a call for us all to remember this caveat: when considering each new and exciting potential application of AI, ask where the training data is coming from.
Cynthia Murrell, March 14, 2017
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