OpenText Goes Cognitive

January 17, 2017

The Canadian roll up of ageing information access products has a new angle for 2017. No, it is not the OpenText “innovation” tour. No, it is not adding Alexa to its mind boggling array of content access systems such as BRS Search and IDI Information Dimension’s Basis. Not even the historical Fulcrum product gets an Alexa skill.

The future for OpenText is … a next generation cognitive platform.

Even more fascinating is that OpenText has had a Christine Maxwell moment and named the product Magellan. Who remembers the Magellan search and content access system? Ah, no one. I am not surprised. I met a millennial who had never used a camera with roll film yesterday.

If you want to know a bit more about the exciting old-new products, navigate to this OpenText page or follow  this link to a video. Yes, a video because OpenText is definitely in step with the next generation of enterprise software customers.

Stephen E Arnold, January 17, 2017

How Google Used Machine Learning and Loved It

January 16, 2017

If you use any search engine other than Google, except for DuckDuckGo, people cringe and doubt your Internet savvy.  Google has a reputation for being the most popular, reliable, and accurate search engine in the US.  It has earned this reputation, because, in many ways, it is the truth.  Google apparently has one upped itself, however, says Eco Consultancy in the article, “How Machine Learning Has Made Google Search Results More Relevant.”

In 2016, Google launched RankBrain to improve search relevancy in its results.  Searchmatics conducted a study and discovered that it worked.  RankBrain is an AI that uses machine learning to understand the context behind people’s search.  RankBrain learns the more it is used, similar to how a person learns to read.  A person learning to read might know a word, but can understand what it is based off context.

This increases Google’s semantic understanding, but so have the amount of words in a search query.  People are reverting to their natural wordiness and are not using as many keywords.  At the same time, back linking is not as important anymore, but the content quality is becoming more valuable for higher page rankings.  Bounce rates are increasing in the top twenty results, meaning that users are led to a more relevant result than pages with higher optimization.

RankBrain also shows Google’s growing reliance on AI:

With the introduction of RankBrain, there’s no doubt that Google is taking AI and machine learning more seriously.  According to CEO, Sundar Pichai, it is just the start. He recently commented that ‘be it search, ads, YouTube, or Play, you will see us — in a systematic way — apply machine learning in all these areas.’  Undoubtedly, it could shape more than just search in 2017.

While the search results are improving their relevancy, it spells bad news for marketers and SEO experts as their attempts to gain rankings are less effective.

Whitney Grace, January 16, 2016

RAVN Flaps Amidst a Flurry of Feathers

January 12, 2017

I read “Abraaj Drives Innovation in Private Equity Market with Implementation of RAVN’s Cognitive Search Solution.” The main idea is that RAVN, a vendor of enterprise search, has snagged a customer. That’s good. What’s interesting about the write up is the language of the “news.” Here’s a rundown of the words I highlighted as flaps of the RAVN’s marketing department wings:

  • Access
  • Artificial intelligence and AI
  • Classify
  • Cognitive search
  • Collaborate
  • Component
  • Connect enterprise
  • Data mining
  • Deal flow
  • Differentiation
  • Drive innovation
  • Dynamic decisions
  • Engagement
  • Engine as in “cognitive engine”
  • Experts and expertise
  • Extract
  • Functional knowledge
  • Ground breaking
  • Growth markets organization
  • Highly distributed network
  • Internal and external content
  • Intelligently transforms
  • Interrelationships
  • Knowledge graph
  • Knowledge management
  • Knowledge sources
  • Leverage
  • Lifecycle
  • Monitoring
  • Multi geography
  • Navigate
  • Phases
  • Platform
  • Proprietary
  • Sector knowledge.
  • Sectoral
  • Secure
  • Solutions
  • Teams
  • Transformation
  • Unstructured
  • Visualize

What’s left out? No analytics, which is one of the must have functions for a modern search and content processing system. My hunch is that RAVN has numbers in its nest. In the millennial editing frenzy, counting visitors and other useful items was overlooked. Amazing stuff. No wonder some folks roll their eyes when enterprise search vendors trot out keyword search dressed in rhetoric honed by Sophists.

For more lingo which makes search seem more than it is, review the list of cacaphones at this link. Ah, the cacophony of search and retrieval vendors.

Stephen E Arnold, January 12, 2017

Yahoo May Become Altaba

January 10, 2017

I read a US government filing which revealed that after Verizon allegedly buys the core assets of Yahoot. Sorry, I meant “Yahoo”, the remaining part of the Internet old timer will be called Altaba.

Image result for fred flintstone

Darn. I was hoping that the non core assets of Yahoot. Sorry, I meant Yahoo would have a more mellifluous name; for example:

  • Hooty
  • Marissa Ville
  • Yabba-dabba-doo.

My pick is “Yabba-dabba-doo” in a nice sans serif font. I would probably recall the new name as “Yabba-dabba-hoot.” As I age, my mind plays tricks on me. Kudos to the artist who designed a possible new logo for the company which should be named Yabba-dabba-hoo.

Stephen E Arnold, January 10, 2017

IBM and Its Five Year Vision: Nothing Like Vision Instead of Revenue

January 9, 2017

I read “IBM’s 5 Year Vision Focuses on New Technology for Visualizing the World.” The author is a Kevin Murnane who is the author of Nutrition for Cyclists: Eating and Drinking Before, During and After the Ride. Seems like excellent preparation for the low fat approach to IBM technology, doesn’t it?

The write up in the capitalist tool Forbes Magazine recycles information from “IBM 5 in 5. Five Innovations That Will Help Change Our Lives within Five Years: The Invisible Made Visible.” Now that’s a title designed for Web search engines.

The IBM write up identifies these technologies as life changers:

  1. Artificial intelligence like IBM Watson
  2. Superhero vision via “hyperimaging”
  3. Macroscopes
  4. Medical labs on a chip
  5. Smart sensors.

What I found interesting was this comment from the nutritionist:

People would be wise to listen when IBM talks about future technology. Their past achievements include the invention of floppy discs and hard drives, the relational database and SQL, Fortran, DRAM, the virtual machine, the ATM machine, magnetic stripe cards and the Universal Bar Code. Their employees have won five Nobel Prizes, six Turing Awards, ten National Medals of Technology, and five National Medals of Science. IBM has a long history of looking forward, thinking big and accomplishing what they set out to do. If their future is like their past, IBM’s 5 in 5 will be more than pie in the sky.

Unfortunately the Nobel Prizes, the Turing Awards, the National Medals for Technology and the five National Medals of Science are not translating to top line revenue growth and juicy profits for stakeholders. IBM’s vision does not include expanding aggressively the i2-type technology at a time when IBM Federal Systems might be in for a a bit of Gotham shock.

Give me that old fashioned revenue growth, please. I am not sure that macroscopes and superhero vision can change how I see the company’s last fifteen quarterly reports. One does not get fit on a low calorie revenue diet, does one?

Stephen E Arnold, January 9, 2017

On-Demand Business Model Not Sure Cash Flow

December 23, 2016

The on-demand car service Uber established a business model that startups in Silicon Valley and other cities are trying to replicate.  These startups are encountering more overhead costs than they expected and are learning that the on-demand economy does not generate instant cash flow.  The LA Times reports that, “On-Demand Business Models Have Put Some Startups On Life Support.”

Uber uses a business model revolving around independent contractors who use their own vehicles as a taxi service that responds to individual requests.  Other startups have sprung up around the same on-demand idea, but with a variety of services.  These include flower delivery service BloomThat, on-demand valet parking Zirx, on-demand meals Spoonrocket, and housecleaning with Homejoy.  The problem these on-demand startups are learning is that they have to deal with overhead costs, such as renting storage spaces, parking spaces, paying for products, delivery vehicles, etc.

Unlike Uber, which relies on the independent contractor to cover the costs of vehicles, other services cannot rely on the on-demand business model due to the other expenses.  The result is that cash is gushing out of their companies:

It’s not just companies that are waking up to the fact being “on-demand” doesn’t guarantee success — the investor tide has also turned.  As the downturn leads to more cautious investment, on-demand businesses are among the hardest-hit; funding for such companies fell in the first quarter of this year to $1.3 billion, down from $7.3 billion six months ago.  ‘If you look in venture capital markets, the on-demand sector is definitely out of favor,’ said Ajay Chopra, a partner at Trinity Ventures who is an investor in both Gobble and Zirx.

These new on-demand startups have had to change their business models in order to remain in business and that requires dismantling the on-demand service model.  On-demand has had its moment in the sun and will remain a lucrative model for some services, but until we invent instant teleportation most companies cannot run on that model.

Whitney Grace, December 23, 2016

More Watson Cheerleading from a Former IBMer

December 20, 2016

I love content marketing. It seems so fresh, insightful, and substantive. Consider a write up about IBM Watson by a former IBMer turned consultant. I wonder, “Is Frank Palermo working for IBM Watson now as a rental?” I know that when you read “IBM Watson Points the Way to Our Cognitive Business Future”, you will realize how darned wonderful IBM Watson is. I believe that Watson is ahead of its time. On the other hand, perhaps Watson lags Google DeepMind by a teeny tiny bit.

In the write up, which strikes me as a touchstone of intellectual and journalistic integrity, I learned:

In the five years since Jeopardy, Watson has become pervasive in the world around us.

Yes, pervasive. Just like Android or Amazon. Well, almost.

I learned:

IBM has invested more than $15 billion in Watson. IBM is betting its 105-year-old future on Watson.

Okay, that’s quite a bit of money. In order for IBM to recover that money, Big Blue will have to crank out the $15 billion, plus interest, plus the ongoing costs of staff, infrastructure, consultants, PR professionals, etc. That works out to IBM’s need to have Watson deliver something on the order of 2.5 the $15 billion in the next year or two to get within sniffing distance of a pile of break even cash before stakeholders lose patience. How close is IBM to having a $6 or $7 billion dollar per year revenue stream from Watson? I don’t have any idea, and IBM does not offer a fully loaded Watson cost and revenue breakdown in its remarkable financial reports.

I learned that the president of IBM who wants to assist President Elect Trump apparently said at the WOW conference (oh, wow, WOW, is World of Watson):

IBM president and CEO Ginni Rometty opened her World of Watson keynote proclaiming that, “in five years, there is no doubt in my mind that cognitive computing will impact every decision. Bringing cognitive capabilities to digital business will change the way we work and help solve the world’s biggest problems.”

Perhaps. But I think the focus will be on IBM Federal Systems and its ability to retain its government work. IBM, like several other big time technology outfits, is involved with many projects; for example, the DCGS Army search and discovery system. Mr. Trump may make some changes to that program, which might add some urgency to the Watson making money thing.

I learned:

Everywhere you turn, Watson is now impacting and — in many cases — transforming businesses. Hundreds of millions of people are now impacted by Watson. By the end of next year, it will hit 1 billion people. Watson is interacting with 200 million consumers in shopping, insurance, banking services, education and let’s not forget: the weather.

What’s “everywhere” is IBM Watson PR. I am not sure it has had much, if any, impact here in Harrod’s Creek. IBM had an operation in Lexington, but that went south and now the new owners are from a foreign land. IBM used to make hardware, but that too has gone away. Now IBM generates wordage about IBM Watson.

I remember Jeopardy. I wonder if IBMers know much about post production and the scandal that tarnished TV game shows. That’s a $64,000 question, isn’t it.

Now the author of this piece is described as a person who:

brings more than 22 years of experience in technology leadership across a wide variety of technical products and platforms. Frank has a wealth of experience in leading global teams in large scale, transformational application and product development programs.

I liked the fact that the bio did not mention this factoid:

Frank worked at IBM in the Advanced Workstations Division, and took part in the PowerPC consortium with IBM, Motorola and Apple. He was also involved in the design of PowerPC family of microprocessors as well as architecting and developing a massive distributed client/server design automation and simulation system involving thousand of high-end clustered servers. Frank received several patents for his work in the area of microprocessor design and distributed client/server computing.

Objectivity? Nope, just the stuff that dreams are made of. I cannot wait until my content management system is powered by Watson. That will be a dream, a treat, a great day, and highly useful.

Stephen E Arnold, December 20, 2016

In Pursuit of Better News Online

December 20, 2016

Since the death of what we used to call “newspapers,” Facebook and Twitter have been gradually encroaching on the news business. In fact, Facebook recently faced criticism for the ways it has managed its Trending news stories. Now, the two social media firms seem to be taking responsibility for their roles, having joined an alliance of organizations committed to more competent news delivery. The write-up, “Facebook, Twitter Join Coalition to Improve Online News” at Yahoo News informs us about the initiative:

First Draft News, which is backed by Google [specifically Google News Lab], announced Tuesday that some 20 news organizations will be part of its partner network to share information on best practices for journalism in the online age. Jenni Sargent, managing director of First Draft, said the partner network will help advance the organization’s goal of improving news online and on social networks.

Filtering out false information can be hard. Even if news organizations only share fact-checked and verified stories, everyone is a publisher and a potential source,’ she said in a blog post. ‘We are not going to solve these problems overnight, but we’re certainly not going to solve them as individual organizations.

Sargent said the coalition will develop training programs and ‘a collaborative verification platform,’ as well as a voluntary code of practice for online news.

We’re told First Draft has been pursuing several projects since it was launched last year, like working with YouTube to verify user-generated videos. The article shares their list of participants; it includes news organizations from the New York Times to BuzzFeed, as well as other interested parties, like Amnesty International and the International Fact-Checking Network. Will this coalition succeed in restoring the public’s trust in our news sources? We can hope.

Cynthia Murrell, December 20, 2016

How the Future of Mobile Looks Like the past of TV for Advertising

December 15, 2016

The article titled How Mobile Today Is Like TV Six Decades Ago on The Atlantic explores the radical changes in advertising in the last five years. The era of advertising through newspapers, magazines, TV, and radio is effectively over, replaced by digital advertising, which is almost exclusively mobile. That mobile content is split between Facebook and Google. Those two giants account for half of all digital media advertising. The article explains what this means for news,

For newspapers, magazines, and websites, there are several paths forward. First, billionaires can rescue media organizations from the stormy seas of the mobile Internet and fund journalism that the ad market won’t support. Second, companies like Facebook may determine that it is in their own interest to preserve some news and entertainment publishers, and they will directly pay media companies, the same way cable companies pay carriage to television channels.

The article also considers a return to the subscription model, or companies shifting to event and marketing strategies for revenue. But any company that tries to ignore the seismic shifts in the news landscape is in for an abrupt and painful shock. The article preaches an optimistic approach based in the history of TV. News is here to stay, but how it is paid for and what the advertising looks like is going to change.

Chelsea Kerwin, December 15, 2016

IBM Thinks Big on Data Unification

December 7, 2016

So far, the big data phenomenon has underwhelmed. We have developed several good ways to collect, store, and analyze data. However, those several ways have resulted in separate, individually developed systems that do not play well together. IBM hopes to fix that, we learn from “IBM Announces a Universal Platform for Data Science” at Forbes. They call the project the Data Science Experience. Writer Greg Satell explains:

Consider a typical retail enterprise, which has separate operations for purchasing, point-of-sale, inventory, marketing and other functions. All of these are continually generating and storing data as they interact with the real world in real time. Ideally, these systems would be tightly integrated, so that data generated in one area could influence decisions in another.

The reality, unfortunately, is that things rarely work together so seamlessly. Each of these systems stores information differently, which makes it very difficult to get full value from data. To understand how, for example, a marketing campaign is affecting traffic on the web site and in the stores, you often need to pull it out of separate systems and load it into excel sheets.

That, essentially, has been what’s been holding data science back. We have the tools to analyze mountains of data and derive amazing insights in real time. New advanced cognitive systems, like Watson, can then take that data, learn from it and help guide our actions. But for all that to work, the information has to be accessible.”

The article acknowledges that progress that has been made in this area, citing the open-source Hadoop and its OS, Spark, for their ability to tap into clusters of data around the world and analyze that data as a single set. Incompatible systems, however, still vex many organizations.

The article closes with an interesting observation—that many business people’s mindsets are stuck in the past. Planning far ahead is considered prudent, as is taking ample time to make any big decision. Technology has moved past that, though, and now such caution can render the basis for any decision obsolete as soon as it is made. As Satell puts it, we need “a more Bayesian approach to strategy, where we don’t expect to predict things and be right, but rather allow data streams to help us become less wrong over time.” Can the humans adapt to this way of thinking? It is reassuring to have a plan; I suspect only the most adaptable among us will feel comfortable flying by the seat of our pants.

Cynthia Murrell, December 7, 2016

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