The Future of Visual and Voice Search

October 4, 2017

From the perspective of the digital marketers they are, GeoMarketing ponders,  “How Will Visual and Voice Search Evolve?” Writer David Kaplan consulted Bing Ads’ Purna Virji on what to expect going forward. For example, though companies are not yet doing much to monetize visual search, Virji says that could change as AIs continue to improve their image-recognition abilities. She also emphasizes the potential of visual search for product discovery—If, for example, someone can locate and buy a pair of shoes just by snapping a picture of a stranger’s feet, sales should benefit handsomely. Virji had this to say about traditional, voice, and image search functionalities working together:

A prediction that Andrew Ng had made when he was still with Baidu was that that ‘by 2020, 50 percent of all search will be image or voice.’ Typing will likely never go away. But now, we have more options. Just like mobile didn’t kill the desktop, apps didn’t kill the browser, the mix of visual, voice, and text will combine in ways that are natural extensions of user behavior. We’ll use those tools depending on the specific need and situation at the moment. For example, you could ‘show’ Cortana a picture of a dress in a magazine via your phone camera and say ‘Hey Cortana, I’d love to buy a dress like this,’ and she can go find where to buy it online. In this way, you used voice and images to find what you were looking for.

The interview also touches on the impact of visual search on local marketing and how its growing use in social media offers data analysts a wealth of targeted-advertising potential.

Cynthia Murrell, October 4, 2017

Oracle: Sparking the Database Fire

October 3, 2017

Hadoop? Er, what? And Microsoft .SQLServer? Or MarkLogic’s XML, business intelligence, analytics, and search offering? Amazon’s storage complex? IBM’s DB2? The recently-endowed MongoDB?

I thought of these systems when I read “Targeting Cybersecurity, Larry Ellison Debuts Oracle’s New ‘Self-Driving’ Database.”

For me, the main point of the write up is that the Oracle database is coming. There’s nothing like an announcement to keep the Oracle faithful in the fold.
If the write up is accurate, Oracle is embracing buzzy trends, storage that eliminates the guess work, and security. (Remember Secure Enterprise Search, the Security Server, and the nifty credential verification procedures? I do.)
The new version of Oracle, according to the write up, will deliver self driving. Cars don’t do this too well, but the Oracle database will and darned soon.

The 18c Autonomous Database or 18cad will:

  • Fix itself
  • Cost less than Amazon’s cloud
  • Go faster
  • Be online 99.995 percent of the time

And more, of course.

Let’s assume that Oracle 18cad works as described. (Words are usually easier to do than software I remind myself.)

The customers look to be big winners. Better, faster, cheaper. Oracle believes its revenues will soar because happy customers just buy more Oracle goodies.

Will there be  a downside?

What about database administrators? Some organizations may assume that 18cad will allow some expensive database administrator (DBA) heads to roll.
What about the competition? I anticipate more marketing fireworks or at least some open source “sparks” and competitive flames to heat up the cold autumn days.

Stephen E Arnold, October 3, 2017

Antitrust Legislation Insufficient for Information Marketplace

October 3, 2017

At his blog, Continuations, venture capitalist Albert Wenger calls for a new approach to regulating the information market in his piece, “Right Goals, Wrong Tools: EU Antitrust Case Against Google.” Citing this case against Google, he observes that existing antitrust legislation is not up to the task of regulating companies like Google. Instead, he insists, we need solutions that consider today’s realities. He writes:

We need alternative regulatory tools that are more in line with how computation works and why the properties of information tend to lead to concentration. We want networks and network effects to exist because of their positive externalities. Imagine as a counter factual a world of highly fragmented operating systems for smartphones – it would make it extremely difficult for app developers to write apps that work well for everyone (hard enough across iOS and Android). At the same time we want to prevent networks and network effect companies from becoming so powerful and extractive that they stifle innovation. For instance, I have written before about how the app store duopoly has prevented certain kinds of innovation. Antitrust is a sledge hammer that was invented at a time of large industrial companies that had no network effects. Using it now is a bad idea and doubly so because it goes only after Google which has by far the more open mobile operating system when compared to Apple.

Wenger suggests a solution could lie in a requirement for open standards, or in the “right to be represented by a bot.” He points to his 17 minute Ted talk, embedded in the article, for more on his public policy suggestions.

Cynthia Murrell, October 3, 2017

The Narrowing App Market

September 29, 2017

If you are thinking of going into app development, first take a gander at this write-up; Business Insider reports, “Half of Digital Media Time Is Spent in Five Apps.” Citing comScore’s 2017 US Mobile App Report , writer Laurie Beaver tells us:

Users spend 90% of their mobile app time in their top five apps, making up 51% of total digital time spent. Perhaps more alarming is that half of the time spent on smartphones is within just one app. That drops dramatically to 18% of time for the second most used app. This suggests that unless a brand’s or business’ app is the first or second most used (most likely Facebook- or Google-owned), it’s unlikely to get any meaningful share of users’ attention.

There are a few reasons for developers to take heart—the number of app downloads is picking up, and users have become more willing to allow push notifications. Most importantly, perhaps, is that users are making in-app purchases; that is where most apps make their money. Beaver continues:

Nevertheless, the report shows the astonishing influence Facebook and Google have over how US mobile app users spend their time. And given the increasingly large share the top five apps have, it’s likely to only become more difficult for brands and publishers to receive any share of users’ time. Alternate app experiences such as Apple’s iMessage apps, Google’s Instant Apps, and Facebook Messenger’s Instant Games could provide brands and publishers with new avenues to reach consumers where they’re spending their time. While these services are nascent, they do provide a promising option for businesses moving forward.

We’re reminded that apps have gained ground over browsers, and are now the main way folks get online. However, the trends toward app consolidation and app abandonment may lead to a “post-app” future. Never fear, though—Business Insider’s research service, BI Intelligence, offers a report titled “The End of Apps” ($495) that could help businesses and developers prepare for the future. Founded in 2007, Business Insider is headquartered in New York City.

Cynthia Murrell, September 29, 2017

Let the Tweets Lead Your Marketing, Come What May

September 14, 2017

It seems that sales and marketing departments just can’t keep up with consumer patterns and behaviors. The latest example of this is explained in a DMA article outlining how to utilize social media to reach target leads. As people rely more on their own search and online acumen and less on professionals (IRL), marketing has to adjust.

Aseem Badshah, Founder, and CEO of Socedo, explain the problem and a possible solution:

Traditionally, B2B marketers created content based on the products they want to promote. Now that so much of the B2B decision making process occurs online, content has to be more customer-centric. The current set of website analytics tools provide some insights, but only on the audience who have already reached your website. Intent data from social media can help you make your content more relevant. By analyzing social media signals and looking at which signals are picking up in volume over time, you can gain new insights into your audience that helps you create more relevant content.

While everything Badshah says may be true, one has to ask themselves, is following the masses always a good thing? If a business wants to maintain their integrity to their field would it be in their best interest to follow the lead of their target demographic’s hashtags or work harder at marketing their product/service despite the apparent twitter-provided disinterest?

Catherine Lamsfuss, September 14, 2017

IBM Cloud As a Rube Goldberg Machine

September 10, 2017

Navigate to AdAge. Select the IBM ad. Its title is “IBM Cloud: Cloud for Enterprise: Pinball.” I snapped this image from the video which seems to represent a pinball game. Does this look like a Rube Goldberg machine? I think so.

ibm rube






Stephen E Arnold, September 10, 2017

Smart Software: An AI Future and IBM Wants to Be There for 10 Years

September 7, 2017

I read “Executives Say AI Will Change Business, but Aren’t Doing Much about It.” My takeaway: There is no there there—yet. I noted these “true factoids” waltzing through the MIT-charged write up:

  • 20% of the 3,000 companies in the sample use smart software
  • 5% use smart software “extensively” (No, I don’t know what extensively means either.)
  • About one third of the companies in the sample “have an AI strategy in place.”

Pilgrims, that means there is money to be made in the smart software discontinuity. Consulting and coding are a match made in MBA heaven.

If my observation is accurate, IBM’s executives read the tea leaves and decided to contribute a modest $240 million for the IBM Watson Artificial Intelligence Lab at MIT. You can watch a video and read the story from Fortune Magazine at this link.

The Fortune “real” journalism outfit states:

This is the first time that a single company has underwritten an entire laboratory at the university.

However, the money will be paid out over 10 years. Lucky parents with children at MIT can look forward to undergrad, graduate, and post graduate work at the lab. No living in the basement for this cohort of wizards.

Several questions arise:

  1. Which institution will “own” the intellectual property of the wizards from MIT and IBM? What about the students’ contributions?
  2. How will US government research be allocated when there is a “new” lab which is funded by a single commercial enterprise? (Hello, MITRE, any thoughts?)
  3. Will young wizards who formulate a better idea be constrained? Might the presence or shadow of IBM choke off some lines of innovation until the sheepskin is handed over?
  4. Are Amazon, Facebook, Google, and Microsoft executives kicking themselves for not thinking up this bold marketing play and writing an even bigger check?
  5. Will IBM get a discount on space advertising in MIT’s subscription publications?

Worth monitoring because other big name schools might have a model to emulate? Company backed smart software labs might become the next big thing to pitch for some highly regarded, market oriented institutions. How much would Cambridge University or the stellar University of Louisville capture if they too “sold” labs to commercial enterprises? (Surprised at my inclusion of the University of Louisville? Don’t be. It’s an innovator in basketball recruiting and recruiting real estate mogul talent. Smart software is a piece of cake for this type of institution of higher learning.)

Stephen E Arnold

Old School Searcher Struggles with Organizing Information

September 7, 2017

I read a write up called “Semantic, Adaptive Search – Now that’s a Mouthful.” I cannot decide if the essay is intended to be humorous, plaintive, or factual. The main idea in the headline is that there is a type of search called “semantic” and “adaptive.” I think I know about the semantic notion. We just completed a six month analysis of syntactic and semantic technology for one of my few remaining clients. (I am semi retired as you may know, but tilting at the semantic and syntactic windmills is great fun.)

The semantic notion has inspired such experts as David Amerland, an enthusiastic proponent of the power of positive thinking and tireless self promotion, to heights of fame. The syntax idea gives experts in linguistics hope for lucrative employment opportunities. But most implementations of these hallowed “techniques” deliver massive computational overhead and outputs which require legions of expensive subject matter experts to keep on track.

The headline is one thing, but the write up is about another topic in my opinion. Here’s the passage I noted:

The basic problem with AI is no vendor is there yet.

Okay, maybe I did not correctly interpret “Semantic, Adaptive Search—Now That’s a Mouthful.” I just wasn’t expecting artificial intelligence, a very SEO type term.

But I was off base. The real subject of the write up seems to be captured in this passage:

I used to be organized, but somehow I lost that admirable trait. I blame it on information overload. Anyway, I now spend quite a bit of time searching for my blogs, white papers, and research, as I have no clue where I filed them. I have resorted to using multiple search criteria. Something I do, which is ridiculous, is repeat the same erroneous search request, because I know it’s there somewhere and the system must have misunderstood, right? So does the system learn from my mistakes, or learn the mistakes? Does anyone know?

Okay, disorganized. I would never have guessed without a title that references semantic and adaptive search, the lead paragraph about artificial intelligence, and this just cited bit of exposition which makes clear that the searcher cannot make the search systems divulge the needed information.

One factoid in the write up is that a searcher will use 2.73 terms per query. I think that number applies to desktop boat anchor searches from the Dark Ages of old school querying. Today, more than 55 percent of queries are from mobile devices. About 20 percent of those are voice based. Other queries just happen because a greater power like Google or Microsoft determines what you “really” wanted is just the ticket. To me, the shift from desktop to mobile makes the number of search terms in a query a tough number to calculate. How does one convert data automatically delivered to a Google Map when one is looking for a route with an old school query with 2.73 terms? Answer: You maybe just use whatever number pops out from a quick Bing or Google search from a laptop and go with the datum in a hit on an ad choked result list.

The confused state of search and content processing vendors is evident in their marketing, their reliance on jargon and mumbo jumbo, and fuzzy thinking about obtaining information to meet a specific information need.

I suppose there is hope. One can embrace a taxonomy and life will be good. On the other hand, disorganization does not bode well for a taxonomy created by a person who cannot locate information.

Well, one can use smart software to generate those terms, the Use Fors and the See Alsos. One can rely on massive amounts of Big Data to save the day. One can allow a busy user of SharePoint to assign terms to his or her content. Many good solutions which make information access a thrilling discipline.

Now where did I put that research for my latest book, “The Dark Web Notebook”? Ah, I know. In a folder called “DWNB Research” on my back up devices with hard copies in a banker’s box labeled “DWNB 2016-2017.”

Call me old fashioned but the semantic, syntactic, artificially intelligent razzmatazz underscores the triumph of jargon over systems and methods which deliver on point results in response to a query from a person who knows that for which he or she seeks.

Plus, I have some capable research librarians to keep me on track. Yep, real humans with MLS degrees, online research expertise, and honest-to-god reference desk experience.

Smart software and jargon requires more than disorganization and arm waving accompanied by toots from the jargon tuba.

Stephen E Arnold, September 7, 2017

A New and Improved Content Delivery System

September 7, 2017

Personalized content and delivery is the name of the game in PRWEB’s, “Flatirons Solutions Launches XML DITA Dynamic Content Delivery Solutions.”  Flatirons Solutions is a leading XML-based publishing and content management company and they recently released their Dynamic Content Delivery Solution.  The Dynamic Content Delivery Solution uses XML-based technology will allow enterprises to receive more personalized content.  It is advertised that it will reduce publishing and support costs.  The new solution is built with the Mark Logic Server.

By partnering with Mark Logic and incorporating their industry-leading XML content server, the solution conducts powerful queries, indexing, and personalization against large collections of DITA topics. For our clients, this provides immediate access to relevant information, while producing cost savings in technical support, and in content production, maintenance, review and publishing. So whether they are producing sales, marketing, technical, training or help documentation, clients can step up to a new level of content delivery while simultaneously improving their bottom line.

The Dynamic Content Delivery Solution is designed for government agencies and enterprises that publish XML content to various platforms and formats.  Mark Logic is touted as a powerful tool to pool content from different sources, repurpose it, and deliver it to different channels.

MarkLogic finds success in its core use case: slicing and dicing for publishing.  It is back to the basics for them.

Whitney Grace, September 7, 2017


IBM Watson Performance: Just an IBM Issue?

September 6, 2017

I read “IBM Pitched its Watson Supercomputer As a Revolution in Cancer Care. It’s Nowhere Close.” Here in Harrod’s Creek, doubts about IBM Watson are ever present. It was with some surprise that we learned:

But three years after IBM began selling Watson to recommend the best cancer treatments to doctors around the world, a STAT investigation has found that the supercomputer isn’t living up to the lofty expectations IBM created for it. It is still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM’s goal of establishing dominance in a multibillion-dollar market. And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care.

The write up beats on the lame horse named Big Blue. I would wager that the horse does not like being whipped one bit. The write up ignores a problem shared by many “smart” software systems. Yep, even those from the wizards at Amazon, Facebook, Google, and Microsoft. That means there are many more stories to investigate and recount.

But I want more of the “why.” I have some hypotheses; for example:

Smart systems have to figure out information. Now on the surface, it seems as if Big Data can provide as much input as necessary. But that is a bit of a problem too. Information in its various forms is not immediately usable in its varied forms. Figuring out what information to use and then getting that information into a form which the smart software can process is expensive. The processes involved are also time consuming. Smart software needs nannies, and nannies which know their stuff. If you have ever tried to hire a nanny who fits into a specific family’s inner workings, you know that the finding of the “right” nanny is a complicated job in itself.

Let’s stop. I have not tackled the mechanism for getting smart software to “understand” what humans mean with their utterances. These outputs, by the way, are in the form of audio, video, and text. To get smart software to comprehend intent and then figure out what specific item of tagged information is needed to deal with that intent is a complex problem too.

IBM Watson, like other outfits trying to generate revenue by surfing a trend, has been tossed off its wave rider by a very large rogue swell: Riffing on a magic system is a lot easier than making that smart software do useful work in a real world environment.

Enterprise search vendors fell victim to this mismatch between verbiage and actually performing in dynamic conditions.

Wipe out. (I hear the Safaris’ “Wipe Out” in my mind. If you don’t know the song, click here.)

IBM Watson seems to be the victim of its own over inflated assertions.

My wish is for investigative reports to focus on case analyses. These articles can then discuss the reasons for user dissatisfaction, cost overruns, contract abandonments, and terminations (staff overhauls).

I want to know what specific subsystems and technical methods failed or cost so much that the customers bailed out.

As the write up points out:

But like a medical student, Watson is just learning to perform in the real world.

Human utterances and smart software. A work in progress but not for the tireless marketers and sales professionals who want to close a deal, pay the bills, and buy the new Apple phone.

Stephen E Arnold, September 6, 2017

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