Google: Innovation Desperation or Innovation Innovation

December 5, 2018

Google has an innovation problem. The company has tried 20 percent free time. Engineers were supposed to work on personal projects. Google tried creating investment units. Google has acquired companies, often in time frames that seemed compressed. Anyone remember buying Motorola Mobility in 2011? Google created a super secret innovation center because the ageing Google Labs was not up to the task of creating Loon balloons and solving death. There have been competitions to identity bright young sprouts who can bring new ideas to the Google. If I dig through my files, there are probably innovation initiatives I have forgotten. Google is either a forward looking outfit, or it is struggling to do more than keep the 20 year old system looking young.

Image result for archimedes eureka

Has Google tried thinking in the hot tub like Archimedes? Google has bean bags, volleyball courts, and Foosball. But real innovations like those AltaVista mechanisms or GoTo’s pay to play for search visibility? There is Web Accelerator, of course.

I read “An Exclusive look inside Google’s in-house incubator Area 120.” The write up reports that a wizard Googler allegedly said and may actually believe:

“We built a place and a process to be able to have those folks come to us and then select what we thought were the most promising teams, the most promising ideas, the most promising markets,” explains managing director Alex Gawley, who has spent a decade at Google and left his role as product manager for Google Apps (since renamed G Suite) to spearhead this new effort. Employees “can actually leave their jobs and come to us to spend 100% of their time pursuing something that they are particularly passionate about,” he says.

Okay, Area 120. That even more mysterious than the famous Area 51. I am thinking of the theme from “Outer Limits.”

The Googlers “pitch” ideas in the hope of getting funding. A Japanese management expert explained a somewhat similar approach to keeping smart employees innovating. See Kuniyasu Sakai’s explanations of the method in “To Expand We Divide.” You probably have this and his other management writings on your desk, right? Someone at Google seems to have brushed against these concepts. In Fast Company / Google speak, these new companies are “hatchlings.”

Several observations:

  1. Innovation is a problem as companies become larger. Google illustrates this problem.
  2. Google’s approach to innovation is bifurcated. Most of its “innovations” originated elsewhere; for example, IBM Clever, AltaVista technology, GoTo-Overture “pay to play” advertising. The company’s goal is to innovate using original ideas, not refinements of other innovators’ breakthroughs.
  3. Google faces an innovation free environment. A recent example may be found in the wild and crazy Amazon announcements at its Re:Invent conference. Somewhere in the jet blast of announcements, there were a couple of substantive innovations. Google does phones with problems and wraps search in layers of cotton wool. Amazon, its seems, is sucking search innovation from Google.

For these reasons Google is gasping. Even rah rah write ups about Google like the recent encomium to Jeff Dean and Sanjay Ghemawat (both AltaVista veterans) is a technical “You Can’t Go Home Again” description of the good old days.

On one hand, Google’s efforts to become innovative are admirable. Persistence, patience, investment—yada yada. On the other hand, Google remains trapped as a servant to its Yahoo (GoTo and Overture) business model for online advertising.

The PR will continue to flow, but innovations? Maybe.

Stephen E Arnold, December 5, 2018

A Reasonable Assertion: Google Is Dying

October 10, 2018

Nope, this is not the view in Harrod’s Creek. The idea that “Google Is Dying” comes from a write up in Vortex by Lauren, whom I assume is a real, living entity and not an avatar, construct, or VR thing.

google is dying

You can find the analysis at this link.

I am not going to push back against the entity Lauren’s ideas.

I want to point out that:

  1. Companies, like real living humans, have a lifespan. It does not matter that some Googlers are awaiting the opportunity to merge with a machine, save their brain (assuming that intelligence is indeed  the sole province of thought), and live a long time. Ideally? Forever. The death of Google, therefore, is hard wired, and, if I may offer a controversial idea, has already taken place. Today we are dealing with the progeny of Google.
  2. The missteps which have captured some Google embracers’ attention is the outright failure of Google’s ability to create a secure environment for management and for users of the descendent of Orkut. The lapses are not an indication that Google is dying. The examples are logical manifestations of the consequences of inbreeding. Imagine West Virginia’s isolated communities connected via a mobile system. That does not change the inbreeding for some individuals. If you are not up on inbreeding, here’s a handy reference. The key point is cognitive deterioration. Stated more clearly, stupid decision making, impaired analytic skills, etc.
  3. Google’s lab rat approach to innovation has not, so far, been able to disprove Steve Ballmer’s brilliant observation: “One trick pony.” But what few analysts care to remember is that the “one trick pony” was online advertising derived from the GoTo.com/Overture.com/Yahoo.com idea. My recollection is that prior to the Google IPO, a legal settlement was reached with Yahoo. This billion dollar deal kept good old Yahoo afloat for several years. Thus, Google’s big idea was a bit of a “me too.” One might argue that the failure to find a way to generate an equivalent amount of revenue is not surprising. Even the Android ecosystem is like a sucker fish on a shark. The symbiosis between online advertising, data harvesting, and revenue is difficult to disentangle. The key point: The big idea was GoTo.com, implemented in a Googley way.

After writing three monographs about the Google and adding comments to my research about the company, I could write more.

Read the alleged humanoid’s “real news” essay. Make your own decision.

I am not pushing back. I am just disappointed that 20 years after the Backrub folks morphed into Google, analyses continue to look at here-and-now events, not the broader trends the company manifests.

Maybe Generation Z will step forward and fill the void?

Stephen E Arnold, October 11, 2018

Machine Learning Frameworks: Why Not Just Use Amazon?

September 16, 2018

A colleague sent me a link to “The 10 Most Popular Machine Learning Frameworks Used by Data Scientists.” I found the write up interesting despite the author’s failure to define the word popular and the bound phrase data scientists. But few folks in an era of “real” journalism fool around with my quaint notions.

According to the write up, the data come from an outfit called Figure Eight. I don’t know the company, but I assume their professionals adhere to the basics of Statistics 101. You know the boring stuff like sample size, objectivity of the sample, sample selection, data validity, etc. Like information in our time of “real” news and “real” journalists, some of these annoying aspects of churning out data in which an old geezer like me can have some confidence. You know like the 70 percent accuracy of some US facial recognition systems. Close enough for horseshoes, I suppose.

miss sort of accurate

Here’s the list. My comments about each “learning framework” appear in italics after each “learning framework’s” name:

  1. Pandas — an open source, BSD-licensed library
  2. Numpy — a package for scientific computing with Python
  3. Scikit-learn — another BSD licensed collection of tools for data mining and data analysis
  4. Matplotlib — a Python 2D plotting library for graphics
  5. TensorFlow — an open source machine learning framework
  6. Keras — a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
  7. Seaborn — a Python data visualization library based on matplotlib
  8. Pytorch & Torch
  9. AWS Deep Learning AMI — infrastructure and tools to accelerate deep learning in the cloud. Not to be annoying but defining AMI as Amazon Machine Learning Interface might be useful to some
  10. Google Cloud ML Engine — neural-net-based ML service with a typically Googley line up of Googley services.

Stepping back, I noticed a handful of what I am sure are irrelevant points which are of little interest to a “real” journalists creating “real” news.

First, notice that the list is self referential with python love. Frameworks depend on other python loving frameworks. There’s nothing inherently bad about this self referential approach to shipping up a list, and it makes it a heck of a lot easier to create the list in the first place.

Second, the information about Amazon is slightly misleading. In my lecture in Washington, DC on September 7, I mentioned that Amazon’s approach to machine learning supports Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras. I found this approach interesting, but of little interest to those creating a survey or developing an informed list about machine learning frameworks; for example, Amazon is executing a quite clever play. In bridge, I think the phrase “trump card” suggests what the Bezos momentum machine has cooked up. Notice the past tense because this Amazon stuff has been chugging along in at least one US government agency for about four, four and one half years.

Third, Google brings up dead last. What about IBM? What about Microsoft and its CNTK. Ah, another acronym, but I as a non real journalist will reveal that this acronym means Microsoft Cognitive Toolkit. More information is available in Microsoft’s wonderful prose at this link. By the way, the Amazon machine learning spinning momentum thing supports the CNTK. Imagine that? Right, I didn’t think so.

Net net: The machine learning framework list may benefit from a bit of refinement. On the other hand, just use Amazon and move down the road to a new type of smart software lock in. Want to know more? Write benkent2020 @ yahoo dot com and inquire about our for fee Amazon briefing about machine learning, real time data marketplaces, and a couple of other most off the radar activities. Have you seen Amazon’s facial recognition camera? It’s part of the Amazon machine learning imitative, and it has some interesting capabilities.

Stephen E Arnold, September 16, 2018

IBM Watson Workspace

August 6, 2018

I read “What Is Watson Workspace?” I have been assuming that WW is a roll up of:

  • IBM Lotus Connections
  • IBM Lotus Domino
  • IBM Lotus Mashups
  • IBM Lotus Notes
  • IBM Lotus Quickr
  • IBM Lotus Sametime

image

The write up explains how wrong I am (yet again. Such a surprise for a person who resides in rural Kentucky). The write up states:

IBM Watson Workspace offers a “smart” destination for employees to collaborate on projects, share ideas, and post questions, all built from the ground up to take advantage of Watson’s cognitive computing abilities.

Yeah, but I thought the Lotus products provided these services.

How silly of me?

The different is that WW includes cognitive APIs. Sounds outstanding. I can:

  • Draw insights from conversations
  • Turn conversations into actions
  • Access video conferencing
  • Customize Watson Workspace.

When I was doing a little low level work for one of the US government agencies (maybe it was the White House?) I recall sitting in a briefing and these functions were explained. A short time thereafter I had the thankless job of reviewing a minor contract to answer an almost irrelevant question. Guess what? The “workspace” did not contain the email nor the attachments I sought. The system, it was explained to me by someone from IBM in Gaithersburg, was that it was not the fault of the IBM system.

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Business Intelligence: What Is Hot? What Is Not?

July 16, 2018

I read “Where Business Intelligence is Delivering Value in 2018.” The write up summarizes principal findings from a study conducted by Dresner Advisory Services, an outfit with which I am not familiar. I suggest you scan the summary in Cloud Tweaks and then, if you find the data interesting, chase after the Dresner outfit. My hunch is that the sales professionals will respond to your query.

Several items warranted my uncapping my trusty pink marker and circling an item of information.

First, I noticed a chart called Technologies and Initiatives Strategic to Business Intelligence. The chart presents data about 36 “technologies.” I noticed that “enterprise search” did not make the list. I did note that cognitive business intelligence, artificial intelligence, t4ext analytics, and natural language analytics did. If I were generous to a fault, I would say, “These Dresner analysts are covering enterprise search, just taking the Tinker Toy approach by naming areas of technologies.” However, I am not feeling generous, and I find it difficult to believe that Dresner or any other knowledge worker can do “work” without being able to find a file, data, look up a factoid, or perform even the most rudimentary type of research without using search. The omission of this category is foundational, and I am not sure I have much confidence in the other data arrayed in the report.

Second, I don’t know what “data storytelling” is. I suppose (and I am making a wild and crazy guess here) that a person who has some understanding of the source data, the algorithmic methods used to produce output, and the time to think about the likely accuracy of the output creates a narrative. For example, I have been in a recent meeting with the president of a high technology company who said, “We have talked to our customers, and we know we have to create our own system.” Obviously the fellow knows his customers, essentially government agencies. The customers (apparently most of them) want an alternative, and realizes change is necessary. The actual story based on my knowledge of the company, the product and service he delivers, and the government agencies’ budget constraints. The “real story” boils down to: “Deliver a cheaper product or you will lose the contract.” Stories, like those from teenagers who lose their homework, often do not reflect reality. What’s astounding is that data story telling is number eight on the hit parade of initiatives strategic to business intelligence. I was indeed surprised. But governance made the list as did governance. What the heck is governance?

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What Has Happened to Enterprise Search?

June 28, 2018

I read “Enterprise Search in 2018: What a Long Strange Trip It’s Been.” I found the information presented interesting. The thesis is that enterprise search has been on a journey almost like a “Wizard of Oz” experience.

The idea of consolidation, from my point of view, boils down to executives who want to cash in, get out, and move on. The reasons are not far to seek: Over promising and under delivering, financial manipulations, and positioning a nuts and bolts utility as something that solves information problems.

lava flow fixed

Some, maybe many, licensees of proprietary enterprise search systems may have viewed their investment as an opportunity that delivered an unexpected but inevitable outcome. Where is that lush scenery? Where’s the beach?

The reality is that enterprise search vendors were aced by Shay Banon. His Act II of Compass: A Finding Story was Elasticsearch and the company Elastic. Why not use free and open source software. At least the code gets some bugs fixed unlike old school proprietary enterprise search systems. Bug fixes? Yep, good luck with your Fast Search & Retrieval ESP platform idiosyncrasies.

The landscape today is a bit like the volcanic transformation of Hawaii’s Vacationland. Real estate agents will be explaining that the lava flows have created new beach views, promising that eruptions are a low probability event.

The write up does not highlight one simple fact: Enterprise search has given way to “roll up” services or what I call “meta-plays.” The idea is that search is tucked inside systems like Diffeo, Palantir Gotham, and other “intelligence” platforms.

Why aren’t these enterprise grade solutions sold as “enterprise search” or “enterprise business intelligence and discovery solutions”?

The answer is that the information retrieval nest has been marginalized by the actions of vendors stretching back to the Smart system and to the present with “proprietary” solutions which actually include open source technology. These systems are anchored in the past.

Consider Diffeo?

Why offer enterprise search when one can provide a solution that delivers information in context, provides collaboration tools, and displays information in different ways with a single mouse click?

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Picking and Poking Palantir Technologies: A New Blood Sport?

April 25, 2018

My reaction to “Palantir Has Figured Out How to Make Money by Using Algorithms to Ascribe Guilt to People, Now They’re Looking for New Customers” is a a sign and a groan.

I don’t work for Palantir Technologies, although I have been a consultant to one of its major competitors. I do lecture about next generation information systems at law enforcement and intelligence centric conferences in the US and elsewhere. I also wrote a book called “CyberOSINT: Next Generation Information Access.” That study has spawned a number of “experts” who are recycling some of my views and research. A couple of government agencies have shortened by word “cyberosint” into the “cyint.” In a manner of speaking, I have an information base which can be used to put the actions of companies which offer services similar to those available from Palantir in perspective.

The article in Boing Boing falls into the category of “yikes” analysis. Suddenly, it seems, the idea that cook book mathematical procedures can be used to make sense of a wide range of data. Let me assure you that this is not a new development, and Palantir is definitely not the first of the companies developing applications for law enforcement and intelligence professionals to land customers in financial and law firms.

baseball card part 5

A Palantir bubble gum card shows details about a person of interest and links to underlying data from which the key facts have been selected. Note that this is from an older version of Palantir Gotham. Source: Google Images, 2015

Decades ago, a friend of mine (Ev Brenner, now deceased) was one of the pioneers using technology and cook book math to make sense of oil and gas exploration data. How long ago? Think 50 years.

The focus of “Palantir Has Figured Out…” is that:

Palantir seems to be the kind of company that is always willing to sell magic beans to anyone who puts out an RFP for them. They have promised that with enough surveillance and enough secret, unaccountable parsing of surveillance data, they can find “bad guys” and stop them before they even commit a bad action.

Okay, that sounds good in the context of the article, but Palantir is just one vendor responding to the need for next generation information access tools from many commercial sectors.

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Taking Time for Search Vendor Limerance

April 18, 2018

Life is a bit hectic. The Beyond Search and the DarkCyber teams are working on the US government hidden Web presentation scheduled this week. We also have final research underway for the two Telestrategies ISS CyberOSINT lectures. The first is a review of the DarkCyber approach to deanonymizing Surface Web and hidden Web chat. The second focuses on deanonymizing digital currency transactions. Both sessions provide attendees with best practices, commercial solutions, open source tools, and the standard checklists which are a feature of  my LE and intel lectures.

However, one of my associates asked me if I knew what the word “limerance” meant. This individual is reasonably intelligent, but the bar for brains is pretty low here in rural Kentucky. I told the person, “I think it is psychobabble, but I am not sure.”

The fix was a quick Bing.com search. The wonky relevance of the Google was the reason for the shift to the once indomitable Microsoft.

Limerance, according to Bing’s summary of Wikipedia means “a state of mind which results from a romantic attraction to another person typically including compulsive thoughts and fantasies and a desire to form or maintain a relationship and have one’s feelings reciprocated.”

limerance

Upon reflection, I decided that limerance can be liberated from the woozy world of psychologists, shrinks, and wielders of water witches.

Consider this usage in the marginalized world of enterprise search:

Limerance: The state of mind which causes a vendor of key word search to embrace any application or use case which can be stretched to trigger a license to the vendor’s “finding” system.

 

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Speeding Up Search: The Challenge of Multiple Bottlenecks

March 29, 2018

I read “Search at Scale Shows ~30,000X Speed Up.” I have been down this asphalt road before, many times in fact. The problem with search and retrieval is that numerous bottlenecks exist; for example, dealing with exceptions (content which the content processing system cannot manipulate).

Those who want relevant information or those who prefer superficial descriptions of search speed focus on a nice, easy-to-grasp metric; for example, how quickly do results display.

May I suggest you read the source document, work through the rat’s nest of acronyms, and swing your mental machete against the “metrics” in the write up?

Once you have taken these necessary steps, consider this statement from the write up:

These results suggest that we could use the high-quality matches of the RWMD to query — in sub-second time — at least 100 million documents using only a modest computational infrastructure.

Image result for speed bump

The path to responsive search and retrieval is littered with multiple speed bumps. Hit any one when going to fast can break the search low rider.

I wish to list some of the speed bumps which the write does not adequately address or, in some cases, acknowledge:

  • Content flows are often in the terabit or petabit range for certain filtering and query operations., One hundred million won’t ring the bell.
  • This is the transform in ETL operations. Normalizing content takes some time, particularly when the historical on disc content from multiple outputs and real-time flows from systems ranging from Cisco Systems intercept devices are large. Please, think in terms of gigabytes per second and petabytes of archived data parked on servers in some countries’ government storage systems.
  • Populating an index structure with new items also consumes time. If an object is not in an index of some sort, it is tough to find.
  • Shaping the data set over time. Content has a weird property. It evolves. Lowly chat messages can contain a wide range of objects. Jump to today’s big light bulb which illuminates some blockchains’ ability house executables, videos, off color images, etc.
  • Because IBM inevitably drags Watson to the party, keep in mind that Watson still requires humans to perform gorilla style grooming before it’s show time at the circus. Questions have to be considered. Content sources selected. The training wheels bolted to the bus. Then trials have to be launched. What good is a system which returns off point answers?

I think you get the idea.

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Crime Prediction: Not a New Intelligence Analysis Function

March 16, 2018

We noted “New Orleans Ends Its Palantir Predictive Policing Program.” The interest in this Palantir Technologies’ project surprised us from our log cabin with a view of the mine drainage run off pond. The predictive angle is neither new nor particularly stealthy. Many years ago when I worked for one of the outfits developing intelligence analysis systems, the “predictive” function was a routine function.

Here’s how it works:

  • Identify an entity of interest (person, event, organization, etc.)
  • Search for other items including the entity
  • Generate near matches. (We called this “fuzzification” because we wanted hits which were “near” the entity in which we had an interest. Plus, the process worked reasonably well in reverse too.)
  • Punch the analyze function.

Once one repeats the process several times, the system dutifully generates reports which make it easy to spot:

  • Exact matches; for example, a “name” has a telephone number and a dossier
  • Close matches; for example, a partial name or organization is associated with the telephone number of the identity
  • Predicted matches; for example, based on available “knowns”, the system can generate a list of highly likely matches.

The particular systems with which I am familiar allow the analyst, investigator, or intelligence professional to explore the relationships among these pieces of information. Timeline functions make it trivial to plot when events took place and retrieve from the analytics module highly likely locations for future actions. If an “organization” held a meeting with several “entities” at a particular location, the geographic component can plot the actual meetings and highlight suggestions for future meetings. In short, prediction functions work in a manner similar to Excel’s filling in items in a number series.

heat map with histogram

What would you predict as a “hot spot” based on this map? The red areas, the yellow areas, the orange areas, or the areas without an overlay? Prediction is facilitated with some outputs from intelligence analysis software. (Source: Palantir via Google Image search)

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