April 21, 2017
Big data was the buzzword a few years ago, but now artificial intelligence is the tech jargon of the moment. While big data was a more plausible solution for companies trying to mine information from their digital data, AI is proving difficult to implement. Forbes discusses AI difficulties in the article, “Artificial Intelligence Is Powerful Stuff, But Difficult To Scale To Real-Life Business.”
There is a lot of excitement brewing around machine learning and AI business possibilities, while the technology is ready for use, workers are not. People need to be prepped and taught how to use AI and machine learning technology, but without the proper lessons, it will hurt a company’s bottom line. The problem comes from companies rolling out digital solutions, without changing the way they conduct business. Workers cannot just adapt to changes instantly. They need to feel like they are part of the solution, instead of being shifted to the side in the latest technological trend.
CIO for the Federal Communications Commission Dr. David Bray said that:
The growth of AI may shift thinking in organizations. ‘At the end of the day, we are changing what people are doing,; Bray says. ‘You are changing how they work, and they’re going to feel threatened if they’re not bought into the change. It’s almost imperative for CIOs to really work closely with their chief executive officers, and serve as an internal venture capitalist, for how we bring data, to bring process improvements and organizational performance improvements – and work it across the entire organization as a whole.
Artificial intelligence and machine learning are an upgrade to not only a company’s technology but also how a company conducts business. Business processes will need to be updated to integrate the new technology, but also how workers will use and interface it. Businesses will continue facing problems if they think that changing technology, but not their procedures are the final solution.
Whitney Grace, April 21, 2017
April 19, 2017
Houston, we (male, female, uncertain) have a problem. Bias is baked into some image analysis and just about every other type of smart software.
The first step in solving a problem is to acknowledge that a problem exists. The second step is more difficult.
I read “The Reason Why Most of the Images That Show Up When You Search for Doctor Are White Men.” The headline identifies the problem. However, what does one do about biases rooted in human utterance.
My initial thought was to eliminate human utterances. No fancy dancing required. Just let algorithms do what algorithms do. I realized that although this approach has a certain logical completeness, implementation may meet with a bit of resistance.
What does the write up have to say about the problem? (Remember. The fix is going to be tricky.)
Research from Princeton University suggests that these biases, like associating men with doctors and women with nurses, come from the language taught to the algorithm. As some data scientists say, “garbage in, garbage out”: Without good data, the algorithm isn’t going to make good decisions.
Okay, right coast thinking. I feel more comfortable.
What does the write up present as wizard Aylin Caliskan’s view of the problem? A post doctoral researcher seems to be a solid choice for a source. I assume the wizard is a human, so perhaps he, she, it is biased? Hmmm.
I highlighted in true blue several passages from the write up / interview with he, she, it. Let’s look at three statements, shall we?
Regarding genderless languages like Turkish:
when you directly translate, and “nurse” is “she,” that’s not accurate. It should be “he or she or it” is a nurse. We see that it’s making a biased decision—it’s a very simple example of machine translation, but given that these models are incorporated on the web or any application that makes use of textual data, it’s the foundation of most of these applications. If you search for “doctor” and look at the images, you’ll see that most of them are male. You won’t see an equal male and female distribution.
If accurate, this observation means that the “fix” is going to be difficult. Moving from a language without gender identification to a language with gender identification requires changing the target language. Easy for software. Tougher for a human. If the language and its associations are anchored in the brain of a target language speaker, change may be, how shall I say it, a trifle difficult. My fix looks pretty good at this point.
And what about images and videos? I learned:
Yes, anything that text touches. Images and videos are labeled to they can be used on the web. The labels are in text, and it has been shown that those labels have been biased.
And the fix is a human doing the content selection, indexing, and dictionary tweaking. Not so fast. The cost of indexing with humans is very expensive. Don’t believe me. Download 10,000 Wikipedia articles and hire some folks to index them from the controlled term list humans set up. Let me know if you can hit $17 per indexed article. My hunch is that you will exceed this target by several orders of magnitude. (Want to know where the number comes from? Contact me and we discuss a for fee deal for this high value information.)
How does the write up solve the problem? Here’s the capper:
…you cannot directly remove the bias from the dataset or model because it’s giving a very accurate representation of the world, and that’s why we need a specialist to deal with this at the application level.
Notice that my solution is to eliminate humans entirely. Why? The pipe dream of humans doing indexing won’t fly due to [a] time, [b] cost, [c] the massive flows of data to index. Forget the mother of all bombs.
Think about the mother of all indexing backlogs. The gap would make the Modern Language Association’s “gaps” look like weekend catch up party. Is this a job for the operating system for machine intelligence?
Stephen E Arnold, April 17, 2017
April 19, 2017
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?
April 17, 2017
Math is objective, right? Not really. Developers of artificial intelligence systems, what I call smart software, rely on what they learned in math school. If you have flipped through math books ranging from the Googler’s tome on artificial intelligence Artificial Intelligence: A Modern Approach to the musings of the ACM’s journals, you see the same methods recycled. Sure, the algorithms are given a bath and their whiskers are cropped. But underneath that show dog’s sleek appearance, is a familiar pooch. K-means. We have k-means. Decision trees? Yep, decision trees.
What happens when developers feed content into Rube Goldberg machines constructed of mathematical procedures known and loved by math wonks the world over?
The answer appears in “Semantics Derived Automatically from Language Corpora Contain Human Like Biases.” The headline says it clearly, “Smart software becomes as wild and crazy as a group of Kentucky politicos arguing in a bar on Friday night at 2:15 am.”
Biases are expressed and made manifest.
The article in Science reports with considerable surprise it seems to me:
word embeddings encode not only stereotyped biases but also other knowledge, such as the visceral pleasantness of flowers or the gender distribution of occupations.
Ah, ha. Smart software learns biases. Perhaps “smart” correlates with bias?
The canny whiz kids who did the research crawfish a bit:
We stress that we replicated every association documented via the IAT that we tested. The number, variety, and substantive importance of our results raise the possibility that all implicit human biases are reflected in the statistical properties of language. Further research is needed to test this hypothesis and to compare language with other modalities, especially the visual, to see if they have similarly strong explanatory power.
Yep, nothing like further research to prove that when humans build smart software, “magic” happens. The algorithms manifest biases.
What the write up did not address is a method for developing less biases smart software. Is such a method beyond the ken of computer scientists?
To get more information about this question, I asked on the world leader in the field of computational linguistics, Dr. Antonio Valderrabanos, the founder and chief executive officer at Bitext. Dr. Valderrabanos told me:
We use syntactic relations among words instead of using n-grams and similar statistical artifacts, which don’t understand word relations. Bitext’s Deep Linguistics Analysis platform can provide phrases or meaningful relationships to uncover more textured relationships. Our analysis will provide better content to artificial intelligence systems using corpuses of text to learn.
Bitext’s approach is explained in the exclusive interview which appeared in Search Wizards Speak on April 11, 2017. You can read the full text of the interview at this link and review the public information about the breakthrough DLA platform at www.bitext.com.
It seems to me that Bitext has made linguistics the operating system for artificial intelligence.
Stephen E Arnold, April 17, 2017
April 14, 2017
Here we have an example of Alphabet Google’s organizational prowess. Business Insider describes how “DeepMind Organises Its AO Researchers Into ‘Strike Teams’ and ‘Frontiers’.” Writer Sam Shead cites a report by Madhumita Murgia as described in the Financial Times. He writes:
Exactly how DeepMind’s researchers work together has been something of a mystery but the FT story sheds new light on the matter. Researchers at DeepMind are divided into four main groups, including a ‘neuroscience’ group and a ‘frontiers’ group, according to the report. The frontiers group is said to be full of physicists and mathematicians who are tasked with testing some of the most futuristic AI theories. ‘We’ve hired 250 of the world’s best scientists, so obviously they’re here to let their creativity run riot, and we try and create an environment that’s perfect for that,’ DeepMind CEO Demis Hassabis told the FT. […]
DeepMind, which was acquired by Google in 2014 for £400 million, also has a number of ‘strike teams’ that are set up for a limited time period to work on particular tasks. Hassabis explained that this is what DeepMind did with the AlphaGo team, who developed an algorithm that was able to learn how to play Chinese board game Go and defeat the best human player in the world, Lee Se-dol.
Here’s a write-up we did about that significant AlphaGo project, in case you are curious. The creative-riot approach Shead describes is in keeping with Google’s standard philosophy on product development—throw every new idea at the wall and see what sticks. We learn that researchers report on their progress every two months, and team leaders allocate resources based on those reports. Current DeepMind projects include algorithms for healthcare and energy scenarios.
Hassabis launched DeepMind in London in 2010, where offices remain after Google’s 2014 acquisition of the company.
Cynthia Murrell, April 14, 2017
April 12, 2017
Algorithms have practically changed the way the world works. However, this nifty code also has its limitations that lead to failures.
It is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms.
The whitepaper touches four pain points of Deep Learning, which is based on algorithms. The authors propose remedial measures that possibly could overcome these impediments and lead to better AI.
Eminent personalities like Stephen Hawking, Bill Gates and Elon Musk have however warned against advancing AIs. Google in the past had abandoned robotics as the machines were becoming too intelligent. What now needs to be seen is who will win in the end? Commercial interests or unfounded fear?
Vishal Ingole, April 12, 2017
April 11, 2017
On a recent trip to Madrid, Spain, I was able to arrange an interview with Dr. Antonio Valderrabanos, the founder and CEO of Bitext. The company has its primary research and development group in Las Rosas, the high-technology complex a short distance from central Madrid. The company has an office in San Francisco and a number of computational linguists and computer scientists in other locations. Dr. Valderrabanos worked at IBM in an adjacent field before moving to Novell and then making the jump to his own start up. The hard work required to invent a fundamentally new way to make sense of human utterance is now beginning to pay off.
Dr. Antonio Valderrabanos, founder and CEO of Bitext. Bitext’s business is growing rapidly. The company’s breakthroughs in deep linguistic analysis solves many difficult problems in text analysis.
Founded in 2008, the firm specializes in deep linguistic analysis. The systems and methods invented and refined by Bitext improve the accuracy of a wide range of content processing and text analytics systems. What’s remarkable about the Bitext breakthroughs is that the company support more than 40 different languages, and its platform can support additional languages with sharp reductions in the time, cost, and effort required by old-school systems. With the proliferation of intelligent software, Bitext, in my opinion, puts the digital brains in overdrive. Bitext’s platform improves the accuracy of many smart software applications, ranging from customer support to business intelligence.
In our wide ranging discussion, Dr. Valderrabanos made a number of insightful comments. Let me highlight three and urge you to read the full text of the interview at this link. (Note: this interview is part of the Search Wizards Speak series.)
Linguistics as an Operating System
One of Dr. Valderrabanos’ most startling observations addresses the future of operating systems for increasingly intelligence software and applications. He said:
Linguistic applications will form a new type of operating system. If we are correct in our thought that language understanding creates a new type of platform, it follows that innovators will build more new things on this foundation. That means that there is no endpoint, just more opportunities to realize new products and services.
Better Understanding Has Arrived
Some of the smart software I have tested is unable to understand what seems to be very basic instructions. The problem, in my opinion, is context. Most smart software struggles to figure out the knowledge cloud which embraces certain data. Dr. Valderrabanos observed:
Search is one thing. Understanding what human utterances mean is another. Bitext’s proprietary technology delivers understanding. Bitext has created an easy to scale and multilingual Deep Linguistic Analysis or DLA platform. Our technology reduces costs and increases user satisfaction in voice applications or customer service applications. I see it as a major breakthrough in the state of the art.
If he is right, the Bitext DLA platform may be one of the next big things in technology. The reason? As smart software becomes more widely adopted, the need to make sense of data and text in different languages becomes increasingly important. Bitext may be the digital differential that makes the smart applications run the way users expect them to.
Snap In Bitext DLA
Advanced technology like Bitext’s often comes with a hidden cost. The advanced system works well in a demonstration or a controlled environment. When that system has to be integrated into “as is” systems from other vendors or from a custom development project, difficulties can pile up. Dr. Valderrabanos asserted:
Bitext DLA provides parsing data for text enrichment for a wide range of languages, for informal and formal text and for different verticals to improve the accuracy of deep learning engines and reduce training times and data needs. Bitext works in this way with many other organizations’ systems.
When I asked him about integration, he said:
No problems. We snap in.
I am interested in Bitext’s technical methods. In the last year, he has signed deals with companies like Audi, Renault, a large mobile handset manufacturer, and an online information retrieval company.
When I thanked him for his time, he was quite polite. But he did say, “I have to get back to my desk. We have received several requests for proposals.”
Las Rosas looked quite a bit like Silicon Valley when I left the Bitext headquarters. Despite the thousands of miles separating Madrid from the US, interest in Bitext’s deep linguistic analysis is surging. Silicon Valley has its charms, and now it has a Bitext US office for what may be the fastest growing computational linguistics and text analysis system in the world. Worth watching this company I think.
For more about Bitext, navigate to the firm’s Web site at www.bitext.com.
Stephen E Arnold, April 11, 2017
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
March 24, 2017
I read “Opening a New Chapter of My Work in AI.” After working through the non-AI output, I concluded that money beckons the fearless leader, Andrew Ng. However, I did note one interesting quotation in the apologia:
The U.S. is very good at inventing new technology ideas. China is very good at inventing and quickly shipping AI products.
What this suggests to me is that the wizard of AI sees the US as good at “ideas”, and China an implementer. A quick implementer at that.
My take is that China sucks up intangibles like information and ideas. Then China cranks out products. Easy to monetize things, avoiding the question, “What’s the value of that idea, pal?”
Ouch. On the other hand, software is the new electricity. So who is Thomas Edison? I wish I “knew”.
Stephen E Arnold, March 24, 2017
March 23, 2017
The article titled Silicon Valley Hedge Fund Takes Over Wall Street With AI Trader on Bloomberg explains how Sentient Technologies Inc. plans to take the human error out of the stock market. Babak Hodjat co-founded the company and spent the past 10 years building an AI system capable of reviewing billions of pieces of data and learning trends and techniques to make money by trading stocks. The article states that the system is based on evolution,
According to patents, Sentient has thousands of machines running simultaneously around the world, algorithmically creating what are essentially trillions of virtual traders that it calls “genes.” These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data. The genes that are unsuccessful die off, while those that make money are spliced together with others to create the next generation… Sentient can squeeze 1,800 simulated trading days into a few minutes.
Hodjat believes that handing the reins over to a machine is wise because it eliminates bias and emotions. But outsiders wonder whether investors will be willing to put their trust entirely in a system. Other hedge funds like Man AHL rely on machine learning too, but nowhere near to the extent of Sentient. As Sentient bring in outside investors later this year the success of the platform will become clearer.
Chelsea Kerwin, March 23, 2017