IBM Watson Deep Learning: A Great Leap Forward

August 16, 2017

I read in the IBM marketing publication Fortune Magazine. Oh, sorry, I meant the independent real business news outfit Fortune, the following article: “IBM Claims Big Breakthrough in Deep Learning.” (I know the write up is objective because the headline includes the word “claims.”)

The main point is that the IBM Watson super game winning thing can now do certain computational tasks more quickly is mildly interesting. I noticed that one of our local tire discounters has a sale on a brand called Primewell. That struck me as more interesting than this IBM claim.

First, what’s the great leap forward the article touts? I highlighted this passage:

IBM says it has come up with software that can divvy those tasks among 64 servers running up to 256 processors total, and still reap huge benefits in speed. The company is making that technology available to customers using IBM Power System servers and to other techies who want to test it.

How many IBM Power 8 servers does it take to speed up Watson’s indexing? I learned:

IBM used 64 of its own Power 8 servers—each of which links both general-purpose Intel microprocessors with Nvidia graphical processors with a fast NVLink interconnection to facilitate fast data flow between the two types of chips

A couple of questions:

  1. How much does it cost to outfit 64 IBM Power 8 servers to perform this magic?
  2. How many Nvidia GPUs are needed?
  3. How many Intel CPUs are needed?
  4. How much RAM is required in each server?
  5. How much time does it require to configure, tune, and deploy the set up referenced in the article?

My hunch is that this set up is slightly more costly than buying a Chrome book or signing on for some Amazon cloud computing cycles. These questions, not surprisingly, are not of interest to the “real” business magazine Fortune. That’s okay. I understand that one can get only so much information from a news release, a PowerPoint deck, or a lunch? No problem.

The other thought that crossed my mind as I read the story, “Does Fortune think that IBM is the only outfit using GPUs to speed up certain types of content processing?” Ah, well, IBM is probably so sophisticated that it is working on engineering problems that other companies cannot conceive let alone tackle.

Now the second point: Content processing to generate a Watson index is a bottleneck. However, the processing is what I call a downstream bottleneck. The really big hurdle for IBM Watson is the manual work required to set up the rules which the Watson system has to follow. Compared to the data crunching, training and rule making are the giant black holes of time and complexity. Fancy Dan servers don’t get to strut their stuff until the days, weeks, months, and years of setting up the rules is completed, tuned, and updated.

Fortune Magazine obviously considers this bottleneck of zero interest. My hunch is that IBM did not explain this characteristic of IBM Watson or the Achilles’ heel of figuring out the rules. Who wants to sit in a room with subject matter experts and three or four IBM engineers talking about what’s important, what questions are asked, and what data are required.

AskJeeves demonstrated decades ago that human crafted rules are Black Diamond ski runs. IBM Watson’s approach is interesting. But what’s fascinating is the uncritical acceptance of IBM’s assertions and the lack of interest in tackling substantive questions. Maybe lunch was cut short?

Stephen E Arnold, August 16, 2017

Tidy Text the Best Way to Utilize Analytics

August 10, 2017

Even though text mining is nothing new natural language processing seems to be the hot new analytics craze. In an effort to understand the value of each, along with the difference, and (most importantly) how to use either efficiently, O’Reilly interviewed text miners, Julia Silge and David Robinson, to learn about their approach.

When asked what advice they would give those drowning in data, they replied,

…our advice is that adopting tidy data principles is an effective strategy to approach text mining problems. The tidy text format keeps one token (typically a word) in each row, and keeps each variable (such as a document or chapter) in a column. When your data is tidy, you can use a common set of tools for exploring and visualizing them. This frees you from struggling to get your data into the right format for each task and instead lets you focus on the questions you want to ask.

The due admits text mining and natural language processing overlap in many areas but both are useful tools for different issues. They regulate text mining to statistical analysis and natural language processing to the relationship between computers and language. The difference may seem minute but with data mines exploding and companies drowning in data, such advice is crucial.

Catherine Lamsfuss, August 10, 2017

ArnoldIT Publishes Technical Analysis of the Bitext Deep Linguistic Analysis Platform

July 19, 2017

ArnoldIT has published “Bitext: Breakthrough Technology for Multi-Language Content Analysis.” The analysis provides the first comprehensive review of the Madrid-based company’s Deep Linguistic Analysis Platform or DLAP. Unlike most next-generation multi-language text processing methods, Bitext has crafted a platform. The document can be downloaded from the Bitext Web site via this link.

Based on information gathered by the study team, the Bitext DLAP system outputs metadata with an accuracy in the 90 percent to 95 percent range.
Most content processing systems today typically deliver metadata and rich indexing with accuracy in the 70 to 85 percent range.

According to Stephen E Arnold, publisher of Beyond Search and Managing Director of Arnold Information Technology:

“Bitext’s output accuracy establish a new benchmark for companies offering multi-language content processing system.”

The system performs in near real time, more than 15 discrete analytic processes. The system can output enhanced metadata for more than 50 languages. The structured stream provides machine learning systems with a low cost, highly accurate way to learn. Bitext’s DLAP platform integrates more than 30 separate syntactic functions. These include segmentation, tokenization (word segmentation, frequency, and disambiguation, among others. The DLAP platform analyzes more  than 15 linguistic features of content in any of the more than 50 supported languages. The system extracts entities and generates high-value data about documents, emails, social media posts, Web pages, and structured and semi-structured data.

DLAP Applications range from fraud detection to identifying nuances in streams of data; for example, the sentiment or emotion expressed in a document. Bitext’s system can output metadata and other information about processed content as a feed stream to specialized systems such as Palantir Technologies’ Gotham or IBM’s Analyst’s Notebook. Machine learning systems such as those operated by such companies as Amazon, Apple, Google, and Microsoft can “snap in” the Bitext DLAP platform.

Copies of the report are available directly from Bitext at https://info.bitext.com/multi-language-content-analysis Information about Bitext is available at www.bitext.com.

Kenny Toth, July 19, 2017

Bitext and MarkLogic Join in a Strategic Partnership

June 13, 2017

Strategic partnerships are one of the best ways for companies to grow and diamond in the rough company Bitext has formed a brilliant one. According to a recent press release, “Bitext Announces Technology Partnership With MarkLogic, Bringing Leading-Edge Text Analysis To The Database Industry.” Bitext has enjoyed a number of key license deals. The company’s ability to process multi-lingual content with its deep linguistics analysis platform reduces costs and increases the speed with which machine learning systems can deliver more accurate results.

bitext logo

Both Bitext and MarkLogic are helping enterprise companies drive better outcomes and create better customer experiences. By combining their respectful technologies, the pair hopes to reduce data’s text ambiguity and produce high quality data assets for semantic search, chatbots, and machine learning systems. Bitext’s CEO and founder said:

““With Bitext’s breakthrough technology built-in, MarkLogic 9 can index and search massive volumes of multi-language data accurately and efficiently while maintaining the highest level of data availability and security. Our leading-edge text analysis technology helps MarkLogic 9 customers to reveal business-critical relationships between data,” said Dr. Antonio Valderrabanos.

Bitext is capable of conquering the most difficult language problems and creating solutions for consumer engagement, training, and sentiment analysis. Bitext’s flagship product is its Deep Linguistics Analysis Platform and Kantar, GFK, Intel, and Accenture favor it. MarkLogic used to be one of Bitext’s clients, but now they are partners and are bound to invent even more breakthrough technology. Bitext takes another step to cement its role as the operating system for machine intelligence.

Whitney Grace, June 13, 2017

Bitvore: The AI, Real Time, Custom Report Search Engine

May 16, 2017

Just when I thought information access had slumped quietly through another week, I read in the capitalist tool which you know as Forbes, the content marketing machine, this article:

This AI Search Engine Delivers Tailored Data to Companies in Real Time.

This write up struck me as more interesting than the most recent IBM Watson prime time commercial about smart software for zealous professional basketball fans or Lucidworks’ (really?) acquisition of the interface builder Twigkit. Forbes Magazine’s write up did not point out that the company seems to be channeling Palantir Technologies; for example, Jeff Curie, the president, refers to employees at Bitvorians. Take that, you Hobbits and Palanterians.

image

A Bitvore 3D data structure.

The AI, real time, custom report search engine is called Bitvore. Here in Harrod’s Creek, we recognized the combination of the computer term “bit” with a syllable from one of our favorite morphemes “vore” as in carnivore or omnivore or the vegan-sensitive herbivore.

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Machine Learning Going Through a Phase

May 10, 2017

People think that machine learning is like an algorithm magic wand.   It works by some writing the algorithmic code, popping in the data, and the computer learns how to do a task.  It is not that easy.  The Bitext blog reveals that machine learning needs assistance in the post, “How Phrase Structure Can Help Machine Learning For Text Analysis.”

Machine learning techniques used for text analysis are not that accurate.  The post explains that instead of learning the meaning of words in a sentence according to its structure, all the words are tossed into a bag and translated individually.  The context and meaning are lost.  A real world example is Chinese and Japanese because they use kanji (pictorial symbols representing words).   Chinese and Japanese are two languages, where a kanji’s meaning changes based on the context.  The result is that both languages have a lot of puns and are a nightmare for text analytics.

As you can imagine there are problems in Germanic and Latin-based languages too:

Ignoring the structure of a sentence can lead to various types of analysis problems. The most common one is incorrectly assigning similarity to two unrelated phrases such as Social Security in the Media” and “Security in Social Media” just because they use the same words (although with a different structure).

Besides, this approach has stronger effects for certain types of “special” words like “not” or “if”. In a sentence like “I would recommend this phone if the screen was bigger”, we don’t have a recommendation for the phone, but this could be the output of many text analysis tools, given that we have the words “recommendation” and “phone”, and given that the connection between “if” and “recommend” is not detected.

If you rely solely on the “bag of words” approach for text analysis the problems only get worse.  That is why it phrase structure is very important for text and sentiment analysis.  Bitext incorporates phrase structure and other techniques in their analytics platform used by a large search engine company and another tech company that likes fruit.

Whitney Grace, May 10, 2017

Enterprise Search and a Chimera: Analytical Engines

May 1, 2017

I put on my steam punk outfit before reading “Leading Analytical Engines for Enterprise Search.” Now there was one small factual error; specifically, the Google Search Appliance is a goner. When it was alive and tended to by authorized partners, it was not particularly adept at doing “analytical engine” type things.

What about the rest of the article? Well, I found it amusing.

Let me get to the good stuff and then deal with the nasty reality which confronts the folks who continue to pump money into enterprise search.

What companies does this “real journalism” out identify as purveyors of high top shoes for search. Yikes, sorry. I meant to say enterprise search systems which do analytical engine things.

Here’s the line up:

The Google Search Appliance. As noted, this is a goner. Yep, the Google threw in the towel. Lots of reasons, but my sources say, cost of sales was a consideration. Oh, and there were a couple of Google FTEs plus assorted costs for dealing with those annoyed with the product’s performance, relevance, customization, etc. Anyway. Museum piece.

Microsoft SharePoint. I find this a side splitter. Microsoft SharePoint is many things. In fact, armed with Visual Studio one can actually make the system work in a useful manner. Don’t tell the HR folks who wonder why certified SharePoint experts chew up a chunk of the budget and “fast.” Insider joke. Yeah, Excel is the go to analysis tool no matter what others may say. The challenge is to get the Excel thing to interact in a speedy, useful way with whatever the SharePoint administrator has managed to get working in a reliable way. Nuff said.

Coveo. Interesting addition to the list because Coveo is doing the free search thing, the Salesforce thing, the enterprise search thing, the customer support thing, and I think a bunch of other things. The Canadian outfit wants to do more than surf on government inducements, investors’ trust and money, and a key word based system. So it’s analytical engine time. I am not sure how the wrappers required to make key word search do analytics help out performance, but the company says it is an “analytical engine.” So be it.

Attivio. This is an interesting addition. The company emerged from some “fast” movers and shakers. The baseball data demo was nifty about six years ago. Now the company does search, publishing, analytics, etc. The shift from search to analytical engine is somewhat credible. The challenge the company faces is closing deals and generating sustainable revenue. There is that thing called “open source”. A clever programmer can integrate Lucene (Elasticsearch), use its open source components, and maybe dabble with Ikanow. The result? Perhaps an Attivio killer? Who knows.

Lucidworks (Really?). Yep, this is the Avis to the Hertz in the open source commercial sector. Lucidworks (Really?) is now just about everything sort of associated with Big Data, search, smart software, etc. The clear Lucid problem is Shay Bannon and Elastic. Not only does Elastic have more venture money, Elastic has more deployments and, based on information available to me, more revenue, partners, and clout in the open source world. Lucidworks (Really?) has a track record of executive and founder turnover and the thrill of watching Amazon benefit from a former Lucid employee’s inputs. Exciting. Really?

So what do I think of this article in CIO Review? Two things:

  1. It is not too helpful to me and those looking for search solutions in Harrod’s Creek, Kentucky. The reason? The GSA error and gasping effort to make key word search into something hot and cool. “Analytical engines” does not rev my motor. In fact, it does not turn over.
  2. CIO Review does not want me to copy a quote from the write up. Tip to CIO Review. Anyone can copy wildly crazy analytical engines article by viewing source and copying the somewhat uninteresting content.

Stephen E Arnold, May 1, 2017

Palantir Technologies: A Beatdown Buzz Ringing in My Ears

April 27, 2017

I have zero contacts at Palantir Technologies. The one time I valiantly contacted the company about a speaking opportunity at one of my wonky DC invitation-only conferences, a lawyer from Palantir referred my inquiry to a millennial who had a one word vocabulary, “No.”

There you go.

I have written about Palantir Technologies because I used to be an adviser to the pre-IBM incarnation of i2 and its widely used investigation tool, Analyst’s Notebook. I did write about a misadventure between i2 Group and Palantir Technologies, but no one paid much attention to my commentary.

An outfit called Buzzfeed, however, does pay attention to Palantir Technologies. My hunch is that the online real news outfit believes there is a story in the low profile, Peter Thiel-supported company. The technology Palantir has crafted is not that different from the Analyst’s Notebook, Centrifuge Systems’ solution, and quite a few other companies which provide industrial-strength software and systems to law enforcement, security firms, and the intelligence community. (I list about 15 of these companies in my forthcoming “Dark Web Notebook.” No, I won’t provide that list in this free blog. I may be retired, but I am not giving away high value information.)

So what’s caught my attention. I read the article “Palantir’s Relationship with the Intelligence Community Has Been Worse Than You Think.” The main idea is that the procurement of Palantir’s Gotham and supporting services provided by outfits specializing in Palantir systems has not been sliding on President Reagan’s type of Teflon. The story has been picked up and recycled by several “real” news outfits; for example, Brainsock. The story meshes like matryoshkas with other write ups; for example, “Inside Palantir, Silicon Valley’s Most Secretive Company” and “Palantir Struggles to Retain Clients and Staff, BuzzFeed Reports.” Palantir, it seems to me in Harrod’s Creek, is a newsy magnet.

The write up about Palantir’s lousy relationship with the intelligence community pivots on a two year old video. I learned that the Big Dog at Palantir, Alex Karp, said in a non public meeting which some clever Hobbit type videoed on a smartphone words presented this way by the real news outfit:

The private remarks, made during a staff meeting, are at odds with a carefully crafted public image that has helped Palantir secure a $20 billion valuation and win business from a long list of corporations, nonprofits, and governments around the world. “As many of you know, the SSDA’s recalcitrant,” Karp, using a Palantir codename for the CIA, said in the August 2015 meeting. “And we’ve walked away, or they walked away from us, at the NSA. Either way, I’m happy about that.” The CIA, he said, “may not like us. Well, when the whole world is using Palantir they can still not like us. They’ll have no choice.” Suggesting that the Federal Bureau of Investigation had also had friction with Palantir, he continued, “That’s de facto how we got the FBI, and every other recalcitrant place.”

Okay, I don’t know the context of the remarks. It does strike me that 2015 was more than a year ago. In the zippy doo world of Sillycon Valley, quite a bit can change in one year.

I don’t know if you recall Paul Doscher who was the CEO of Exalead USA and Lucid Imagination (before the company asserted that its technology actually “works). Mr. Doscher is a good speaker, but he delivered a talk in 2009, captured on video, during which he was interviewed by a fellow in a blue sport coat and shirt. Mr. Doscher wore a baseball cap in gangsta style, a crinkled unbuttoned shirt, and evidenced a hipster approach to discussing travel. Now if you know Mr. Doscher, he is not a manager influenced by gangsta style. My hunch is that he responded to an occasion, and he elected to approach travel with a bit of insouciance.

Could Mr. Karp, the focal point of the lousy relationship article, have been responding to an occasion? Could Mr. Karp have adopted a particular tone and style to express frustration with US government procurement? Keep in mind that a year later, Palantir sued the US Army. My hunch is that views expressed in front of a group of employees may not be news of the moment. Interesting? Sure.

What I find interesting is that the coverage of Palantir Technologies does not dig into the parts of the company which I find most significant. To illustrate: Palantir has a system and method for an authorized user to add new content to the Gotham system. The approach makes it possible to generate an audit trail to make it easy (maybe trivial) to answer these questions:

  1. What data were added?
  2. When were the data added?
  3. What person added the data?
  4. What index terms were added to the data?
  5. What entities were added to the metadata?
  6. What special terms or geographic locations were added to the data?

You get the idea. Palantir’s Gotham brings to intelligence analysis the type of audit trail I found some compelling in the Clearwell system and other legal oriented systems. Instead of a person in information technology saying in response to a question like “Where did this information come from?”, “Duh. I don’t know.”

Gotham gets me an answer.

For me, explaining the reasoning behind Palantir’s approach warrants a write up. I think quite a few people struggling with problems of data quality and what is called by the horrid term “governance” would find Palantir’s approach of some interest.

Now do I care about Palantir? Nah.

Do I care about bashing Palantir? Nah.

What I do care about is tabloidism taking precedence over substantive technical approaches. From my hollow in rural Kentucky, I see folks looking for “sort of” information.

How about more substantive information? I am fed up with podcasts which recycle old information with fake good cheer. I am weary of leaks. I want to know about Palantir’s approach to search and content processing and have its systems and methods compared to what its direct competitors purport to do.

Yeah, I know this is difficult to do. But nothing worthwhile comes easy, right?

I can hear the millennials shouting, “Wrong, you dinosaur.” Hey, no problem. I own a house. I don’t need tabloidism. I have picked out a rest home, and I own 60 cemetery plots.

Do your thing, dudes and dudettes of “real” journalism.

Stephen E Arnold, April 27, 2017

Voice Search and Big Data: Defining Technologies for 2017

April 20, 2017

I read “Voice Search and Data: The Two Trends That Will Shape Online Marketing in 2017.” If the story is accurate, figuring out what people say and making sense of data (lots of data) will create new opportunities for innovators.

The article states:

Advancements in voice search and artificial intelligence (AI) will drive rich answers that will help marketers understand the customer intent behind I-want-to-go, I-want-to-know, I-want-to-buy and I-want-to-do micro-moments. Google has developed algorithms to cater directly to the search intent of the customers behind these queries, enabling customers to find the right answers quickly.

My view is that the article is correct in its assessment.

Where the article and I differ boils down to search engine optimization. The idea that voice search and Big Data will make fooling the relevance algorithms of Bing, Google, and Yandex a windfall for search engine optimization experts is partially true. Marketing whiz kids will do and say many things to deliver results that do not answer my query or meet my expectation of a “correct” answer.

My view is that the proliferation of systems which purport to understand human utterances in text,and voice-to-text conversions will discover that the the error rates of 60 to 75 percent are not good enough. Errors can be buried in long lists of results. They can be sidestepped if a voice enabled system works from a set of rules confined to a narrow topic domain.

Open the door to natural language parsing, and the error rates which once were okay become a liability. In my opinion, this will set off a scramble among companies struggling to get their smart software to provide information that customers accept and use repeatedly. Fail and customer turnover can be a fatal knife wound to the heart of an organization. The cost of replacing a paying customer is high. Companies need to keep the customers they have with technology that helps keep paying customers smiling.

What companies are able to provide higher accuracy linguistic functions? There are dozens of companies which assert that their systems can extract entities, figure out semantic relationships, and manipulate content in a handful of languages.

The problem with most of these systems is that certain, very widely used methods collapse when high accuracy is required for large volumes of text. The short cut is to use numerical tricks, and some of those tricks create disconnects between the information the user requests or queries and the results the system displays. Examples range from the difficulties of tuning the Autonomy Digital Reasoning Engine to figuring out how in the heck Google Home arrived at a particular song when the user wanted something else entirely.

Our suggestion is that instead of emailing IBM to sign a deal for that companies language technology, you might have a more productive result if you contact Bitext. This is a company which has been on my mind. I interviewed the founder and CEO (an IBM alum as I learned) and met with some of the remarkable Bitext team.

I am unable to disclose Bitext’s clients. I can suggest that if you fancy a certain German sports car or use one of the world’s most popular online services, you will be bumping into Bitext’s Digital Linguistic Analysis platform. For more information, navigate to Bitext.com.

The data I reviewed suggested that Bitext’s linguistic platform delivers accuracy significantly better than some of the other systems’ outputs I have reviewed. How accurate? Good enough to get an A in my high school math class.

Stephen E Arnold, April 20, 2017

Image Search: Biased by Language. The Fix? Use Humans!

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 culprit?

Numerical recipes.

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.)

I learned:

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

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