Accenture Makes Two Key Acquisitions

August 29, 2017

Whither search innovation? It seems the future of search is now about making what’s available work as best it can. We observe yet another effort to purchase existing search technology and plug it into an existing framework; DMN reports, “Accenture Acquires Brand Learning and Search Technologies.” Brand Learning is a marketing and sales consultancy, and Search Technologies is a technology services firm. Will Accenture, a professional-services firm, work to improve the search and analysis functionalities within their newly acquired tools? DMN’s Managing Editor Elyse Dupre reports:

A press release states that Brand Learning’s advisory team will join the management consulting and industry specialists within Accenture’s Customer and Channels practice. The partnership, according to the press release, will enhance Accenture’s offerings in terms of marketing and sales strategy, organizational design, industry-specific consulting, and HR and leadership.

It is unclear whether the “advisory team” includes any of the talent behind Brand Learning’s software. As for the Search Technologies folks, the article gives us more reason to hope for further innovation. Citing another press release, Dupre notes that company’s API-level data connectors will greatly boost Accenture’s ability to access unstructured data, and continues:

Search Technologies will join the data scientists and engineers within Accenture Analytics. According to the press release, this team will focus on creating solutions that make unstructured content (e.g. social media, video, voice, and audio) easily searchable, which will support data discovery, analytics, and reporting. Accenture’s Global Delivery Network will also add a delivery center in Costa Rica, the release states, which will serve as the home-base for the more than 70 Search Technologies big data engineers who reside there. This team focuses on customer and content analytics, the release explains, and will work with Accenture Interactive’s digital content production and marketing services professionals.


Furthermore, Kamran Khan, president and CEO of Search Technologies, will now lead a new content analytics team that will reside within Accenture Analytics.

Let us hope those 70 engineers are given the freedom and incentive to get creative. Stay tuned.

Cynthia Murrell, August 29, 2017

An Automatic Observer for Neural Nets

August 25, 2017

We are making progress in training AI systems through the neural net approach, but exactly how those systems make their decisions remains difficult to discern. Now, Tech Crunch reveals, “MIT CSAIL Research Offers a Fully Automated Way to Peer Inside Neural Nets.” Writer Darrell Etherington recalls that, a couple years ago, the same team of researchers described a way to understand these decisions using human reviewers. A fully automated process will be much more efficient and lead to greater understanding of what works and what doesn’t. Etherington explains:

Current deep learning techniques leave a lot of questions around how systems actually arrive at their results – the networks employ successive layers of signal processing to classify objects, translate text, or perform other functions, but we have very little means of gaining insight into how each layer of the network is doing its actual decision-making. The MIT CSAIL team’s system uses doctored neural nets that report back the strength with which every individual node responds to a given input image, and those images that generate the strongest response are then analyzed. This analysis was originally performed by Mechanical Turk workers, who would catalogue each based on specific visual concepts found in the images, but now that work has been automated, so that the classification is machine-generated. Already, the research is providing interesting insight into how neural nets operate, for example showing that a network trained to add color to black and white images ends up concentrating a significant portion of its nodes to identifying textures in the pictures.

The write-up points us to MIT’s own article on the subject for more information. We’re reminded that, because the human thought process is still largely a mystery to us, AI neural nets are based on hypothetical models that attempt to mimic ourselves. Perhaps, the piece suggests, a better understanding of such systems could inform the field of neuroscience. Sounds fair.

Cynthia Murrell, August 25, 2017

Analytics for the Non-Tech Savvy

August 18, 2017

I regularly encounter people who say they are too dumb to understand technology. When people tell themselves this, they are hindering their learning ability and are unable to adapt to a society that growing more dependent on mobile devices, the Internet, and instantaneous information.  This is especially harmful for business entrepreneurs.  The Next Web explains, “How Business Intelligence Can Help Non-Techies Use Data Analytics.”

The article starts with the statement that business intelligence is changing in a manner equivalent to how Windows 95 made computers more accessible to ordinary people.  The technology gatekeeper is being removed.  Proprietary software and licenses are expensive, but cloud computing and other endeavors are driving the costs down.

Voice interaction is another way BI is coming to the masses:

Semantic intelligence-powered voice recognition is simply the next logical step in how we interact with technology. Already, interfaces like Apple’s Siri, Amazon Alexa and Google Assistant are letting us query and interact with vast amounts of information simply by talking. Although these consumer-level tools aren’t designed for BI, there are plenty of new voice interfaces on the way that are radically simplifying how we query, analyze, process, and understand complex data.


One important component here is the idea of the “chatbot,” a software agent that acts as an automated guide and interface between your voice and your data. Chatbots are being engineered to help users identify data and guide them into getting the analysis and insight they need.

I see this as the smart people are making their technology available to the rest of us and it could augment or even improve businesses.  We are on the threshold of this technology becoming commonplace, but does it have practicality attached to it?  Many products and services are common place, but it they only have flashing lights and whistles what good are they?

Whitney Grace, August 18, 2017

Social Intelligence a Nice Addition to Analytics, but Not Necessary

August 9, 2017

Social media is an ever-evolving tricky beast to tame when it comes to analytics which is why most companies do the best they can with the resources appointed to the job. Social intelligence gurus, however, are constantly pushing more ways to make sense of the mounting social data.

A recent CIO article exploring the growing field of social intelligence highlighted the role of Sally-Anne Kaminski, Global Social Media Strategy Manager, at Zebra Technologies. Her job was explained as:

When the sales enablement team approaches her about prospective clients, Kaminski taps Oracle’s Social Cloud, a social relationship management tool, to build a comprehensive dashboard to help the sales representative nail the sale. Kaminski loads Social Cloud’s Boolean search with keywords, phrases and topics to discover in conversations across Facebook, Twitter and LinkedIn, as well as message boards and blogs.

Is it effective though? Even Kaminski admits there is no data showing her role analyzing social media data (beyond what analytics alone can do) is benefiting anyone. At the end of the day, social intelligence is reliant on the human touch (think more money) and we must question the operational value it provides.

Catherine Lamsfuss, August 9, 2017

Banks Learn Sentiment Analysis Equals Money

July 26, 2017

The International Business Times reported on the Unicorn conference “AI, Machine Learning and Sentiment Analysis Applied To Finance” that discussed how sentiment analysis and other data are changing the financing industry in the article: “AI And Machine Learning On Social Media Data Is Giving Hedge Funds A Competitive Edge.”  The article discusses the new approach to understanding social media and other Internet data.

The old and popular method of extracting data relies on a “bag of words” approach.  Basically, this means that an algorithm matches up a word with its intended meaning in a lexicon.  However, machine learning and artificial intelligence are adding more brains to the data extraction.  AI and machine learning algorithms are actually able to understand the context of the data.

An example of this in action could be the sentence: “IBM surpasses Microsoft”. A simple bag of words approach would give IBM and Microsoft the same sentiment score. DePalma’s news analytics engine recognises “IBM” is the subject, “Microsoft” is the object and “surpasses” as the verb and the positive/negative relationships between subject and the object, which the sentiment scores reflect: IBM positive, Microsoft, negative.

This technology is used for sentiment analytics to understand how consumers feel about brands.  In turn, that data can determine a brand’s worth and even volatility of stocks.  This translates to that sentiment analytics will shape financial leanings in the future and it is an industry to invest in

Whitney Grace, July 26, 2017

A Potentially Useful List of Enterprise Search Engine Servers

July 20, 2017

We found a remarkable list at Predictive Analytics Today—“Top 23 Enterprise Search Engine Servers.” The write-up introduces its roster of resources:

Enterprise Search is the search information within an enterprise, searching of content from multiple enterprise-type sources, such as databases and intranets. These search systems index data and documents from a variety of sources including file systems, intranets, document management systems, e-mail, and databases. Enterprise search systems also integrate structured and unstructured data in their collections and also use access controls to enforce a security policy on their users.

Entries are logically presented under two categories, proprietary solutions and open source software. From Algolia to Xapian, the article summarizes pros and cons of each. See the post for details.

However, we have a few notes to add about some particular platforms. For example, the Google Search Appliance has been discontinued, though Constellio is still going… in Canada. SearchBlox is now Elasticsearch, and SRCH2 was originally designed for mobile searches. Also, isn’t Sphinx Search specifically for SQL data? Hmm. We suggest this list could make a good springboard, but server shoppers should take its specifics with a grain of salt, and be sure to do your own follow-up research.

Cynthia Murrell, July 20, 2017

IBM Watson: Two Views of the Same Pile of Tinker Toys

July 19, 2017

I find IBM an interesting outfit to watch. But more entertaining is watching how the Watson product and service is perceived by smart people. On the side of the doubters is a Wharton grad, James Kisner, who analyzes for a living at Jeffries & Co. His report “Creating Shareholder Value with AI? Not So Elementary, My Dear Watson?” suggests that IBM is struggling to makes its big bet pay off. If not a Google moon shot, Mr. Kisner thinks the low orbit satellite launch is in an orbit which will result in Watson burring up upon re-entry to reality.

Image result for chihuahua costume

The Big Dog of artificial intelligence and smart software may be a Chihuahua dressed up like a turkey, not a very big dog, not much of a bark, and certainly not equipped to take a big bite out a Wharton trained analyst’s foot.

On the rah rah side is Vijay, a blogger who does not put his name on his blog or on his About page. (One of my intrepid researchers thinks this Vijay’s last name is “Vijayasankar?.” Maybe?) I assume he is famous, just not here in Harrod’s Creek. His most recent write up about Watson is “IBM Watson Is Just Fine, Thank You!” His motivation for the write up is that the attention given to the Jeffries’ report caught his attention. He is a straight shooter; for example:

I am a big fan of criticism of technology – and as folks who have known me over time can vouch, I seldom hold back what is in my mind on any topic. I strongly believe that criticism is healthy for all of us – including businesses, and without it we cannot grow. If you go through my previous blogs, you can see first hand how I throw cold water on hype.

I like the cold water on hype from a person who is an IBM executive, and one who has been involved in the IBM Watson health initiatives. (I think this includes the star crossed Anderson project in Houston. I hear, “Houston, we have a problem,” but you may not.) I highlighted these points in this blog post:

  1. Hey, world, IBM is an enterprise product, not a consumer product. This seems obvious, but apparently IBM’s ability to communicate what it is selling and to whom is not working at peak efficiency or maybe not working because everyone is confused about Watson?
  2. IBM does not do the data federation things with its customer data. That’s good. I know that IBM sells a mainframe that encrypts everything. Interesting but I am not sure how this addresses flat revenue growth, massive layoffs, and the baffling Watson marketing which recently had a white cube floating in a tax preparer’s office. A white cube?
  3. IBM Watson has lots of successes. That’s a great assertion. The problem is that Watson started out as the next big thing. There was a promise of billions in revenue. There was a big office commitment in Manhattan. Then there was the implosion at the Houston health center. “Watson, do you read me?” I once tracked some of the Watson craziness in a series called the “Weakly Watson.” I gave up. The actual examples struck me as a painful type of fake news. What’s interesting is that the “weakly” stories were “real.” Scary to me and to stakeholders.
  4. Watson is not a product. Watson is an API to the IBM ecosystem. Vendor lock in beckons. And, of course, lots of APIs. These digital tinker toys can be snapped together. The problems range from the cost and time required for system training, the consulting and engineering services price tag, and the massaging required to explain that Watson is something that requires a lot of work. For the Instagram crowd that’s a problem. “Houston. Houston. Do you copy? Tinker toys. Lego blocks. Do you copy?”
  5. Watson “some times needs consulting.” Talk about an understatement. Watson needs lots and lots of consulting, engineering services, training, configuring, tuning. and training. Because Watson is a confection of open source, acquired technologies, and home brew code—a lot of work is needed. That’s because Watson was designed to generate high margin services, not the trivial revenue from online ads or from people ordering laundry detergent by pressing a button on their washing machine.
  6. Watson has two things in its bag of tricks: “Great marketing” and “AI talent.” Okay, marketing and smart people. The basic problem IBM has to solve before investors get frisky is generating significant, sustainable revenues and healthy margins. Spending money buys marketing and people. Effective management orchestrates what can be bought into stuff that can be sold at a profit.

The Vijay write up ends with a question. Here you go: “So why is IBM not publishing Watson revenue specifically?” This Vijay fellow who assumes that I know his last name does not answer the question. In the deafening silence, we need an answer.

That brings me to the Jeffries & Co. report by James Kisner, who is certified to do financial analysis. The answer to Vijay’s question consumes 53 pages of verbiage, charts, and tables of numbers. The entire document was available on July 18, 2017, at this link, but it may disappear. Many analyst documents disappear for the average guy. (If the link is dead, head over to Investext or give Jeffries & Co. a quick call to see if that will get you the meaty document.

Image result for snarling guard dog

A Jeffries & Co. analyst with teeth bites into the IBM financial data and seems to be unsatisfied.

In a nutshell, the Jeffries’ report says that IBM Watson is a limp noodle. Among the Watson characteristics are unhappy customers, wild and crazy marketing, misfires on deep learning, and the incredibly difficult, time consuming, and expensive data preparation required to make the system say, “Woof, woof” or maybe “Wolf, wolf” when there is something important for a human to notice.

Net net: IBM’s explanations of Watson have not produced the revenues and profits stakeholders expect. Jeffries & Co. goes MBA crazy providing a wide range of data to support the argument that Watson is struggling.

That “woof, woof” is the sound of a Chihuahua barking with the help of IBM spokespeople and lots of PR and marketing minions. The Wharton guy is a larger dog, barks ferociously, and has a bite backed up by data. IBM has to prove that it can solve problems for clients, generate sustainable revenue, and keep the competition from chowing down on a Watson weighted down with digital tinker toys.

Stephen E Arnold, July 19, 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 Information about Bitext is available at

Kenny Toth, July 19, 2017

Darktrace Delivers Two Summer Sizzlers

July 17, 2017

Darktrace offers an enterprise immune system called Antigena. Based on the information gathered in the writing of the “Dark Web Notebook,” the system has a number of quite useful functions. The company’s remarkable technology can perform real time, in depth analyses of an insider’s online activities. Despite the summer downturn which sucks in many organizations, Darktrace has been active. First, the company secured an additional round of investment. This one is in the $75 million range. This brings the funding of the company to the neighborhood of $170 million, according to Crunchbase.

Details about the deal appear in this Outlook Series write up. I noted this statement:

The cyber security firm has raised a $75 million Series D financing round led by Insight Venture Partners, with participation from existing investors Summit Partners, KKR and TenEleven Ventures.

On another front, Darktrace has entered into a partnership with CITIC. This outfit plans to bring “next-generation cyber defense to businesses across Asia Pacific.” Not familiar with CITIC? You might want to refresh your memory bank. Beyond Search believes that this tie up may open the China market for Darktrace. If it does, Darktrace is likely to emerge as one of the top two or three cyber security firms in the world before the autumn leaves begin to fall.

Here in Harrod’s Creek we think about the promise of Darktrace against a background of erratic financial performance from Hewlett Packard. As you may recall, one of the spark plugs for Darktrace is Dr. Michael Lynch, the founder of Autonomy. HP bought Autonomy and found that its management culture was an antigen to its $11 billion investment. It is possible to search far and wide for an HP initiative which has delivered the type of financial lift that Darktrace has experienced.

Information about Darktrace is at A profile about this company appears in the Dark Web Notebook in the company of IBM Analyst’s Notebook, Google/In-Q-Tel Recorded Future, and Palantir Technologies Gotham. You can get these profile at this link:

Stephen E Arnold, July 17, 2107

The Big Problems of Big Data

June 30, 2017

Companies are producing volumes of data. However, no fully functional system is able to provide actionable insights to decision makers in real time. Bayesian methods might pave the way to the solution seekers.

In an article published by PHYS and titled Advances in Bayesian Methods for Big Data, the author says:

Bayesian methods provide a principled theory for combining prior knowledge and uncertain evidence to make sophisticated inference of hidden factors and predictions.

Though the methods of data collection have improved, analyzing and presenting actionable insights in real time is still a big problem for Big Data adopters. Human intervention is required at almost every step which defies the entire purpose of an intelligent system. Hopefully, Bayesian methods can resolve these issues. Experts have been reluctant to adopt Bayesian methods owing to the fact that they are slow and are not scalable. However, with recent advancements in machine learning, the method might work.

Vishal Ingole, June 30, 2017

« Previous PageNext Page »

  • Archives

  • Recent Posts

  • Meta