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?

Read more

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

Significant Others: Salesforce Einstein and IBM Watson

March 13, 2017

The flow of semi-smart software publicity continues. Keep in mind that most smart software is little more than search with wrappers performing special operations.

The proud parents of Einstein and Watson announced that one another’s smart software systems have become a thing. Salesforce has scripts and numerical recipes to make it easier to figure out if a particular client really wants to drop the service. Watson brings Jeopardy type question answering and lots of data training to the festive announcement party.

I enjoyed “Salesforce Will Be Using IBM Watson to Make its Einstein AI Service Even Smarter.” The write up strikes me as somewhat closer to the realities of the tie up than the inebriated best wishes emanating from many other “real” journalists. For example, the write up asserts:

By bringing its all-important Watson service to Salesforce and Einstein customers, IBM is determined to double-down on that huge Salesforce consulting market, not compete with it.

IBM cannot “become” Salesforce. But Salesforce generates a need for services in many large companies. The idea is that Einstein does its thing to help a sales professional close a deal, and IBM Watson can do its thing to make “sense” of the content related to the company paying Salesforce for an integrated sales prospecting and closing system.

My take is that this is not much more than a co-publicity set up with the hope that the ability of Salesforce to talk about its tie up with IBM will generate sales and buzz. IBM hopes that its PR capabilities will produce some mileage for the huffing and puffing Watson “solution.”

In my opinion, IBM is turning cartwheels to get substantial, evergreen revenue from the Watson thing. But IBM may be pushing another fantasy animal into the revenue race. Quantum computing as a service is the next big thing. Now is quantum computing something one can actually use?

Nah, but the point is that revenue is not news at IBM. Quantum computing gives the IBM marketers another drum to bang. Moving in with Salesforce provides a way to sell something, anything, maybe.

Stephen E Arnold, March 13, 2017

IBM Watson: Mixed Signals from the Real World and IBM Marketers

February 21, 2017

I read a write up which might be fake news for all I know. I live in rural Kentucky and the doings of folks in a big city like Houston are mysterious and far away. Out local doctor squeezes in humans after dealing with race horses and dogs.

I read in Forbes, the capitalist tool, this story: “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.”

The main idea is easy to grasp, even for folks like me sitting near the wood stove in Harrod’s Creek. As I understand it, IBM Watson was supposed to be helping the doctors at the número uno cancer treatment center in their quest to eradicate cancer. I assume the idea was to make more time available to physicians and other health care givers because IBM Watson would have had answers about patient treatment. IBM Watson knew the Jeopardy answers, right. Dealing with cancer-related questions seems to me to be easier: More narrow domain, more consistent terminology, smart people, etc etc.

The possibly fake news write up says:

The partnership between IBM and one of the world’s top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year. MD Anderson is actively requesting bids from other contractors who might replace IBM in future efforts. And a scathing report from auditors at the University of Texas says the project cost MD Anderson more than $62 million and yet did not meet its goals.

But there is good news, or at least face saving news. I like this statement in the capitalist tool:

The report, however, states: “Results stated herein should not be interpreted as an opinion on the scientific basis or functional capabilities of the system in its current state.”

The door is not locked. Perhaps IBM Watson will once again be allowed to dine in the MD Anderson cafeteria and spark the pixels on the MD Anderson computing devices. Every smart software cloud may have a silver lining. Right?

But the project seems to be on “hold.” If the news is fake, then the project is full steam ahead, but I think the truth is closer to something like this: The users found the system like other smart software. Sort of helpful sometimes. At other times, the smart software was adding work, time, and frustration to an already high pressure, high stakes environment.

The capitalist tool ventures this observation:

The disclosure comes at an uncomfortable moment for IBM. Tomorrow, the company’s chief executive, Ginni Rometty, will make a presentation to a giant health information technology conference detailing the progress Watson has made in health care, and announcing the launch of new products for managing medical images and making sure hospitals deliver value for the money, as well as new partnerships with healthcare systems. The end of the MD Anderson collaboration looks bad.

I have zero idea what giant conference is held “tomorrow.” But I did notice this write up, which may be a coincidence: “IBM Sees Watson As a Primary Care Provider’s Assistant.” This seems similar to what IBM Watson was going to do at the MD Anderson cancer center. The write up asserts:

IBM is prepping Watson to work alongside primary care physicians and streamline processes. The company also added features to its Watson-based health cloud services.

The IBM Watson system has been enhanced too. The write up reports:

That Watson-primary care provider connection is being rolled out in Central New York in a six-county region and more than 2,000 providers. Meanwhile, Atrius Health, based in Massachusetts, will embed IBM’s cognitive computing tools inside its electronic medical records workflow for primary care providers.

This sounds good. Perhaps this is the “real” IBM Watson news. Rapid adoption and new capabilities make IBM Watson a must have in the smart health care providers arsenal of disease fighting weapons.

But there is that MD Anderson situation.

What do I make of these apparently contradictory write ups, which I assume are fake news, of course?

  1. IBM Watson, like other end user smart software systems, is a disappointment in actual use. Humans have to learn how to use the system and then take time to figure out which of the outputs are the ones that are likely to be useful in a particular patient’s case. Instead of saving time, the smart software adds tasks to already stretched professionals.
  2. The marketing and sales pressure is great. As a result, the marketers’ explanations may not match up with the engineering realities of a search-based system. When the marketers have left the building, the users learn the reality. After normal bureaucratic jabbering, the users’ dissatisfaction become too much for administrators to deal with. Hasta la vista, Sr. Watson.
  3. IBM, like other outfits betting on smart software, continue to repeat the cycle of belief, hyperbolic marketing, and learning about the costs and problems the smart system triggers. So why did Fast Search & Transfer’s run to fame fall off a cliff? Why is Hewlett Packard annoyed with Autonomy Software? Why did Entopia fail? Why is Lexmark’s new owners trying to exit the search with smart software business? Answer: Hope does not make an end user facing smart system generate sustainable revenues.

Because this IBM Watson news is fake. Why worry? Smart software will lift IBM to heights not experienced since the mainframe was the go to solution to computing needs. If you have a z series, you can run IBM Watson on it. Now that’s something I wish I could experience. My hunch is that none of the docs at MD Anderson will buy a z series and load up Watson because it is so darned useful. Maybe that is the “real” reality?

How does IBM get this Watson thing under control and generating money and producing happy customers? Let’s ask Watson? On the other hand, I don’t think the outputs will be too helpful.

Stephen E Arnold, February 21, 2017

Kasperski Discovers Firmware-Level Spyware Linked to NSA

February 20, 2017

It looks like the NSA is hacking computers around the world by accessing hard-drive firmware, reports Sott in their article, “Russian Researchers Discover NSA Spying and Sabotage Software Hidden in Hard Drives.” We learn that Russian security firm Kaspersky Lab found the sneaky software lurking on hard drives in 30 countries, mostly at government institutions, telecom and energy companies, nuclear research facilities, media outlets, and Islamic activist organizations. Apparently, the vast majority of hard drive brands are vulnerable to the technique. Writer Joseph Menn reports:

According to Kaspersky, the spies made a technological breakthrough by figuring out how to lodge malicious software in the obscure code called firmware that launches every time a computer is turned on. Disk drive firmware is viewed by spies and cybersecurity experts as the second-most valuable real estate on a PC for a hacker, second only to the BIOS code invoked automatically as a computer boots up. ‘The hardware will be able to infect the computer over and over,’ lead Kaspersky researcher Costin Raiu said in an interview.

Though the leaders of the still-active espionage campaign could have taken control of thousands of PCs, giving them the ability to steal files or eavesdrop on anything they wanted, the spies were selective and only established full remote control over machines belonging to the most desirable foreign targets, according to Raiu. He said Kaspersky found only a few especially high-value computers with the hard-drive infections.

Kaspersky’s reconstructions of the spying programs show that they could work in disk drives sold by more than a dozen companies, comprising essentially the entire market. They include Western Digital Corp, Seagate Technology Plc, Toshiba Corp, IBM, Micron Technology Inc and Samsung Electronics Co Ltd.”

Kaspersky did not come right out and name the NSA as the source of the spyware, but did connect it to Stuxnet, a known NSA tool. We also learn that a “former NSA employee” confirmed Kaspersky’s analysis, stating these tools are as valuable as Stuxnet.

Menn notes that this news could increase existing resistance to Western technology overseas due to security concerns. Researcher Raiu specifies that whoever created the spyware must have had access to the proprietary source code for the drives’ firmware. While Western Digital, Seagate, and Micron deny knowledge, Toshiba, Samsung, and IBM remain mum on the subject. Navigate to the article to read more details, or to view the four-minute video (scroll down a bit for that.)

Cynthia Murrell, February 20, 2017

Watson to the Future: The Mainframe

February 19, 2017

Hey, you love mainframes. You may have some. IBMs own. Hitachi-style plug compatibles. Whatever.

Want to run some zip zip stuff on them? Now you can load Watson and get cognitive computing for your airline reservations, your government accounting, or your bank’s back office process which no one knows how to port to Goggle-style servers.

The light shined in my mind’s dark rooms when I read “IBM Brings Machine Learning To The Private Cloud.” Nestled into the article is this statement:

BM has extracted the core machine learning technology from IBM Watson and will initially make it available where much of the world’s enterprise data resides: the z System mainframe, the operational core of global organizations where billions of daily transactions are processed by banks, retailers, insurers, transportation firms and governments.

The write up makes some bold assertions; for example, “any” language, popular machine learning framework, transaction data type, and “without the cost, latency, or risk of moving data off premise.”

The write up provides a snapshot of where IBM thinks mainframes and Watson will generate revenues; specifically:

  • Retail
  • Financial services
  • Healthcare.

My thought is that each of these markets may want to reduce their dependence on mainframes and the challenges of cost control, staffing, and rapid application development “chains.”

If Watson were selling like hot cakes, why chase mainframes? Answer: More revenue. Customer demand, in my opinion, might be the wrong answer.

Stephen E Arnold, February 19, 2017

IBM Watson PR Tax Excitement

February 10, 2017

In one eight hour period I noticed these rah rah write ups about IBM Watson doing taxes. How timely? What a coincidence that these publications ran stories about yet another Watson achievement. Everything it seems except sustainable revenue.

Here are the write ups I reviewed:

  • Fast Company, “H&R Block’s Watson-Powered Robots Are Here To Help With Your Taxes” stating “Block and IBM say Watson has digested 600 million “data points” from past filings to learn tips and tricks.” I bet those IRS analysts love those “tricks.”
  • TechCrunch, “H&R Block Is Now Using IBM Watson to Find Tax Deductions,” stating “Beginning Sunday, February 5th, H&R Block customers will be able to interact with the new system at the company’s retail locations.” Nifty. Foot traffic for those who want H&R Block to “do” their taxes. In short, no hands on yet, right?
  • New York Times, “IBM Gives Watson a New Challenge: Your Tax Return,” stating “For IBM, the collaboration with H&R Block underlines its strategy in the emerging market for artificial intelligence technology. Watson will touch consumers, but through IBM’s corporate clients.” You may have to pay to view this apparent chunk of marketing collateral. I love the “touch” thing.

You get the idea. A huge PR push for Watson, H&R Block, a promo for a super bowl commercial, and jargon about how smart Watson because it indexes text.

Revenues? Did anyone mention revenues? Cost? Did anyone mention cost? Competitive technology? Did anyone mention competitors? Editorial rigor? Are you nuts? Rigor. What’s that?

Nah. Watson. Weakly.

Stephen E Arnold, February 10, 2017

IBM on Cognitive Computing Safari in South Africa

February 9, 2017

The article on ZDNet titled IBM to Use AI to Tame Big Data in Its Second African Research Lab discusses the 12th global research unit IBM has opened. This one is positioned in South Africa for data analytics and cognitive computing as applied to healthcare and urban development. Dr. Solomon Assefa, IBM’s Director of Research for Africa, mentions in the article that the lab was opened in only 18 months. He goes on,

Assefa said the facility will combine industrial research with a startup incubator, working closely with Wits’ own entrepreneur accelerator in the same innovation hub, known as the Tshimologong Precinct. Tshimologong is part of a major urban renewal project by Wits and the City of Johannesburg.

Nowhere else in the world is there an innovation hub that houses a world class research lab,” Assefa said. “One thing we agreed on from the start is that we will make the lab accessible to startups and entrepreneurs in hub.

The lab is funded by a ten-year investment program of roughly $60M and maintains an open door policy with the University of the Witswatersrand (Wits), The Department of Trade and Industry, and the Department of Science and Technology. The immediate focuses of several early applications include Cape region forest fire prevention, disease monitoring, and virtual reality.

Chelsea Kerwin, February 9, 2017

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