Context: Are You Confused? What Is Your Context?

April 21, 2017

Human utterances can be difficult to figure out. When I departed the sunny climes of Washington, DC, to take a job with the Courier Journal & Louisville Times Company, I found myself in a new “context.” Working on money losing database products was a different context for me.

Obviously, Louisville was not the zip zip Right Coast. The shift from consulting to doing was a different context. And there were others. Each context shaped my talking and writing.

To most native speakers of English, in the database unit of the Courier Journal, the word “terminal” referred to one of the ever reliable gizmos that connected to the super user friendly DEC 20, TIPS typesetting, and, of course, to the home brew content management system used for the companies money losing databases.

The context of the work unit made clear to someone working with the DEC 20 that the word “terminal” did not mean the airport terminal, the relative who was dying of a rare blood disorder, or the weird little wire holding thingy on my model train’s Lionel transformer.

Language and understanding does depend on context.

I read “Bog Data Context: Targeting Relevant Data That’s Fit for Purpose.” Let me tell you that I was excited to find that context is getting some Big Data love. I learned:

Context is critical.

Well, I agree. It is 2017, and the context idea has been around for many years.

The write up includes a graphic to explain the challenge of context:

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The idea is that an entity named John Doe appears in different databases and apparently uses a number of social media services. How does a human or smart software figure out what data goes with each John Doe.

Yep, this is a problem law enforcement and intelligence professionals have been considering for many years. Other people want to match up people with data pertinent to a specific entity; for example, financial institutions, online matchmakers, and government immigration officials.

Unfortunately putting a person in a context with pertinent data is a bit of a sticky wicket.

How does one solve this apparently tough problem? I learned from the write up:

There needs to be a focus on relevant data.”

No disagreement from me. But focus is not solving the context problem.

The article meanders through a number of ideas which do not strike me as directly related to the problem of figuring out context and then the meaning of utterances of a particular person. My thought is that the write up is not really about context. The article wants to use buzzwords and jargon to give the impression that context is going to less of a problem if someone implements many processes and procedures. These range from figuring out how trustworthy a source of data is to matching “representational effectiveness” with a model of context.

I learned that data lakes must not become “data graveyards.”

Okay, good idea. But I thought the article was tackling the problem of context, figuring out the meaning from its particular location among key signals like geography, behavior, and the nitty gritty of language itself.

How confused was I? Pretty confused. Here’s the last paragraph of the context write up:

There are a lot of starting points, a lot of pathways, in managing information in this rapidly changing data landscape. As McKnight said, “beyond the mountain is another mountain,” and Patricio reflected that this is a “continuous cycle of processing and evaluation.” Our data lakes will not be static; cannot afford to become data graveyards. But keeping them from becoming so requires us to continually reflect on the business problems we are trying to solve, to ask questions of the data, to understand the context of the data, and to measure and evaluate the fitness of the data for our purposes. With Big Data context in mind, we can mature our organizations and make more effective data-driven business decisions.

No wonder context remains a challenge. What is easy is writing headlines for what is:

  1. Cooking up an earthworm of quotes as a post conference rah rah
  2. Making the write up fit the title
  3. Moving beyond the obvious.

Wow.

Stephen E Arnold, April 21, 2017

Watson and Block: Tax Preparation and Watson

April 19, 2017

Author’s Note:

Tax season is over. I am now releasing a write up I did in the high pressure run up to tax filing day, April 18, 2017, to publish this blog post. I want to comment on one marketing play IBM used in 2016 and 2017 to make Watson its Amazon Echo or its Google Pixel. IBM has been working overtime to come up with clever, innovative, effective ways to sell Watson, a search-and-retrieval system spiced with home brew code, algorithms which make the system “smart,” acquired technology from outfits like Vivisimo, and some free and open source search software.

IBM Watson is being sold to Wall Street and stakeholders as IBM’s next, really big thing. With years of declining revenue under its belt, the marketing of Watson as “cognitive software” is different from the marketing of most other companies pitching artificial intelligence.

One unintended consequence of IBM’s saturation advertising of its Watson system is making the word “cognitive” shorthand for software magic. The primary beneficiaries of IBM’s relentless use of the word “cognitive” has been to help its competitors. IBM’s fuzziness and lack of concrete products has allowed companies with modest marketing budgets to pick up the IBM jargon and apply it to their products. Examples include the reworked Polyspot (now doing business as CustomerMatrix) and dozens of enterprise search vendors; for example, LucidWorks (Really?), Attivio, Microsoft, Sinequa, and Squirro (yep, Squirro). IBM makes it possible for competitors to slap the word cognitive on their products and compete against IBM’s Watson. I am tempted to describe IBM Watson as a “straw man,” but it is a collection of components, not a product.

Big outfits like Amazon have taken a short cut to the money machine. The Echo and Dot sell millions of units and drive sales of Amazon’s music and hard goods sales. IBM bets on a future hint of payoff; for example, Watson may deliver a “maximum refund” for an H&R Block customer. That sounds pretty enticing. My accountant, beady eyed devil if there ever were one, never talks about refunds. He sticks to questions about where I got my money and what I did with it. If anything, he is a cloud of darkness, preferring to follow the IRS rules and avoid any suggestion of my getting a deal, a refund, or a free ride.

Below is the story I wrote a month ago shortly after I spent 45 minutes chatting with three folks who worked at the H&R Block office near my home in rural Kentucky. Have fun reading.

Stephen E Arnold, April 18, 2017

IBM Watson is one of Big Blue’s strategic imperatives. I have enjoyed writing about Watson, mixing up my posts with the phrase “Watson weakly” instead of “Watson weekly.” Strategic imperatives are supposed to generate new revenue to replace the loss of old revenues. The problem IBM has to figure out how to solve is pace. Will IBM Watson and other strategic imperatives generate sustainable, substantial revenue quickly enough to keep the  company’s revenue healthy.

The answer seems to be, “Maybe, but not very quickly.” According to IBM’s most recent quarterly report, Big Blue has now reported declining revenues for 20 consecutive quarters. Yep, that’s five years. Some stakeholders are patient, but IBM’s competitors are thrilled with IBM’s stratgegic imperatives. For the details of the most recent IBM financials, navigate to “IBM Sticks to Its Forecast Despite Underwhlming Results.” Kicking the can down the road is fun for a short time.

The revenue problem is masked by promises about the future. Watson, the smart software, is supposed to be a billion dollar baby who will end up with a $10 billion dollar revenue stream any day now. But IBM’s stock buybacks and massive PR campaigns have helped the company sell its vision of a bright new Big Blue. But selling software and consulting is different from selling hardware. In today’s markets, services and consulting are tough businesses. Examples of companies strugglling to gain traction against outfits like Gerson Lehrman, unemployed senior executives hungry for work, and new graduates will to do MBA chores for a pittance compete with outfits like Elastic, a search vendor which sells add ons to open source software and consulting for those who need it. IBM is trying almost everything. Still those declining revenues tell a somewhat dismal tale.

I assume you have watched the Super Bowl ads if not the game. I just watched the ads. I was surprised to see a one minute, very expensive, and somewhat ill conceived commercial for IBM Watson and H&R Block, the walk in store front tax preparer.

The Watson-Block Super Bowl ad featured this interesting image: A sled going downhill. Was this a Freudian slip about declining revenues?

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Does it look to you that the sled is speeding downhill. Is this a metaphor for IBM Watson’s prospects in the tax advisory business?

One of IBM’s most visible promotions of its company-saving, revenue-gushing dreams is IBM Watson. You may have seen the Super Bowl ad about Watson providing H&R Block with a sure-fire way to kill off pesky competitors. How has that worked out for H&R Block?

Read more

Forrester: Enterprise Content Management Misstep

April 14, 2017

I have stated in the past that mid tier consulting firms—that is, outfits without the intellectual horsepower of a McKinsey, Bain, or BCG—generate work that is often amusing, sometimes silly, and once in a while just stupid. I noted an error which is certainly embarrassing to someone, maybe even a top notch expert at mid tier Forrester. The idea for a consulting firm is to be “right” and to keep the customer (in this case Hyland) happy. Also, it is generally good to deliver on what one promises. You know, the old under promise, over deliver method.

How about being wrong, failing, and not delivering at all? Read on about Forrester and content management.

Context

I noted the flurry of news announcements about Forrester, a bigly azure-chip consulting firm. A representative example of these marketing news things is “Microsoft, OpenText, IBM Lead Forrester’s ECM Wave in Evolving Market.” The write up explains that the wizards at Forrester have figured out the winners and losers in enterprise content management. As it turns out, the experts at Forrester do a much better job of explaining their “perception” of content management that implementing content management.

How can this be? Paid experts who cannot implement content management for reports about content management? Some less generous people might find this a minor glitch. I think that consultants are pretty good at cooking up reports and selling them. I am not too confident that mid tier consulting firms and even outfits like Booz, Allen has dotted their “i’s” and crossed their “t’s.”

Let me walk you through this apparent failure of Forrester to make their reports available to a person interested in a report. This example concerns a Forrester reviewed company called Hyland and its OnBase enterprise content management system.

The deal is that Hyland allows a prospect to download a copy of the Forrester report in exchange for providing contact information. Once the contact information is accepted, the potential buyer of OnBase is supposed to be able to download a copy of the Forrester report. This is trivial stuff, and we are able to implement the function when I sell my studies. Believe me. If we can allow registered people to download a PDF, so can you.

The Failure

I wanted a copy of “The Forrester Wave: ECM Business Content Services.” May I illustrate how Forrester’s enterprise content management system fails its paying customers and those who register to download these high value, completely wonderful documents.

Step 1: Navigate to this link for OnBase by Hyland, one of the vendors profiled in the allegedly accurate, totally object Forrester report

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Step 2: Fill out the form so Hyland’s sales professionals can contact you in hopes of selling you the product which Forrester finds exceptional

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Note the big orange “Download Now” button. I like the “now” part because it means that with one click I get the high-value, super accurate report.

Step 3: Click on one of these two big green boxes:

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I tested both, and both return the same high value, super accurate, technically wonderful reports—sort of.

Read more

You Do Not Search. You Insight.

April 12, 2017

I am delighted, thrilled. I read “Coveo, Microsoft, Sinequa Lead Insight Engine Market.” What a transformation is captured in what looks to me like a content marketing write up. Key word search morphs into “insight.” For folks who do not follow the history of enterprise search with the fanaticism of those involved in baseball statistics, the use of the word “insight” to describe locating a document is irrelevant. Do you search or insight?

For me, hunkered down in rural Kentucky, with my monitors flickering in the intellectual darkness of Kentucky, the use of the word “insight” is a linguistic singularity. Maybe not on the scale of an earthquake in Italy or a banker leaping from his apartment to the Manhattan asphalt, but a historical moment nevertheless.

Let me recap some of my perceptions of the three companies mentioned in the headline to this tsunami of jargon in the Datanami story:

  • Coveo is a company which developed a search and retrieval system focused on Windows. With some marketing magic, the company explained keyword search as customer support, then Big data, and now this new thing, “insight”. For those who track vendor history, the roots of Coveo reach back to a consumer interface which was designed to make search easy. Remember Copernic. Yep, Coveo has been around a long while.
  • Sinequa also was a search vendor. Like Exalead and Polyspot and other French search vendors, the company wanted manage data, provide federation, and enable workflows. After a president change and some executive shuffling, Sinequa emerged as a Big Data outfit with a core competency in analytics. Quite a change. How similar is Sinequa to enterprise search? Pretty similar.
  • Microsoft. I enjoyed the “saved by the bell” deal in 2008 which delivered the “work in progress” Fast Search & Transfer enterprise search system to Redmond. Fast Search was one of the first search vendors to combine fast-flying jargon with a bit of sales magic. Despite the financial meltdown and an investigation of the Fast Search financials, Microsoft ponied up $1.2 billion and reinvented SharePoint search. Well, not exactly reinvented, but SharePoint is a giant hairball of content management, collaboration, business “intelligence” and, of course, search. Here’s a user friendly chart to help you grasp SharePoint search.

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Flash forward to this Datanami article and what do I learn? Here’s a paragraph I noted with a smiley face and an exclamation point:

Among the areas where natural language processing is making inroads is so-called “insight engines” that are projected to account for half of analytic queries by 2019. Indeed, enterprise search is being supplanted by voice and automated voice commands, according to Gartner Inc. The market analyst released it latest “Magic Quadrant” rankings in late March that include a trio of “market leaders” along with a growing list of challengers that includes established vendors moving into the nascent market along with a batch of dedicated startups.

There you go. A trio like ZZTop with number one hits? Hardly. A consulting firm’s “magic” plucks these three companies from a chicken farm and gives each a blue ribbon. Even though we have chickens in our backyard, I cannot tell one from another. Subjectivity, not objectivity, applies to picking good chickens, and it seems to be what New York consulting firms do too.

Are the “scores” for the objective evaluations based on company revenue? No.

Return on investment? No.

Patents? No.

IRR? No. No. No.

Number of flagship customers like Amazon, Apple, and Google type companies? No.

The ranking is based on “vision.” And another key factor is “the ability to execute its “strategy.” There you go. A vision is what I want to help me make my way through Kabul. I need a strategy beyond stay alive.

What would I do if I have to index content in an enterprise? My answer may surprise you. I would take out my check book and license these systems.

  1. Palantir Technologies or Centrifuge Systems
  2. Bitext’s Deep Linguistic Analysis platform
  3. Recorded Future.

With these three systems I would have:

  1. The ability to locate an entity, concept, event, or document
  2. The capability to process content in more than 40 languages, perform subject verb object parsing and entity extraction in near real time
  3. Point-and-click predictive analytics
  4. Point-and-click visualization for financial, business, and military warfighting actions
  5. Numerous programming hooks for integrating other nifty things that I need to achieve an objective such as IBM’s Cybertap capability.

Why is there a logical and factual disconnect between what I would do to deliver real world, high value outputs to my employees and what the New York-Datanami folks recommend?

Well, “disconnect” may not be the right word. Have some search vendors and third party experts embraced the concept of “fake news” or embraced the know how explained in Propaganda, Father Ellul’s important book? Is the idea something along the lines of “we just say anything and people will believe our software will work this way”?

Many vendors stick reasonably close to the factual performance of their software and systems. Let me highlight three examples.

First, Darktrace, a company crafted by Dr. Michael Lynch, is a stickler for explaining what the smart software does. In a recent exchange with Darktrace, I learned that Darktrace’s senior staff bristle when a descriptive write up strays from the actual, verified technical functions of the software system. Anyone who has worked with Dr. Lynch and his senior managers knows that these people can be very persuasive. But when it comes to Darktrace, it is “facts R us”, thank you.

Second, Recorded Future takes a similar hard stand when explaining what the Recorded Future system can and cannot do. Anyone who suggests that Recorded Future predictive analytics can identify the winner of the Kentucky Derby a day before the race will be disabused of that notion by Recorded Future’s engineers. Accuracy is the name of the game at Recorded Future, but accuracy relates to the use of numerical recipes to identify likely events and assign a probability to some events. Even though the company deals with statistical probabilities, adding marketing spice to the predictive system’s capabilities is a no-go zone.

Third, Bitext, the company that offers a Deep Linguistics Analysis platform to improve the performance of a range of artificial intelligence functions, is anchored in facts. On a recent trip to Spain, we interviewed a number of the senior developers at this company and learned that Bitext software works. Furthermore, the professionals are enthusiastic about working for this linguistics-centric outfit because it avoid marketing hyperbole. “Our system works,” said one computational linguist. This person added, “We do magic with computational linguistics and deep linguistic analysis.” I like that—magic. Oh, Bitext does sales too with the likes of Porsche, Volkswagen, and the world’s leading vendor of mobile systems and services, among others. And from Madrid, Spain, no less. And without marketing hyperbole.

Why then are companies based on keyword indexing with a sprinkle of semantics and basic math repositioning themselves by chasing each new spun sugar-encrusted trend?

I have given a tiny bit of thought to this question.

In my monograph “The New Landscape of Search” I made the point that search had become devalued, a free download in open source repositories, and a utility like cat or dir. Most enterprise search systems have failed to deliver results painted in Technicolor in sales presentations and marketing collateral.

Today, if I want search and retrieval, I just use Lucene. In fact, Lucene is more than good enough; it is comparable to most proprietary enterprise search systems. If I need support, I can ring up Elastic or one of many vendors eager to gild the open source lily.

The extreme value and reliability of open source search and retrieval software has, in my opinion, gutted the market for proprietary search and retrieval software. The financial follies of Fast Search & Transfer reminded some investors of the costly failures of Convera, Delphes, Entopia, among others I documented on my Xenky.com site at this link.

Recently most of the news I see on my coal fired computer in Harrod’s Creek about enterprise search has been about repositioning, not innovation. What’s up?

The answer seems to be that the myth cherished by was that enterprise search was the one, true way make sense of digital information. What many organizations learned was that good enough search does the basic blocking and tackling of finding a document but precious little else without massive infusions of time, effort, and resources.

But do enterprise search systems–no matter how many sparkly buzzwords–work? Not too many, no matter what publicly traded consulting firms tell me to believe.

Snake oil? I don’t know. I just know my own experience, and after 45 years of trying to make digital information findable, I avoid fast talkers with covered wagons adorned with slogans.

Image result for snake oil salesman 20th century

What happens when an enterprise search system is fed videos, podcasts, telephone intercepts, flows of GPS data, and a couple of proprietary file formats?

Answer: Not much.

The search system has to be equipped with extra cost  connectors, assorted oddments, and shimware to deal with a recorded webinar and a companion deck of PowerPoint slides used by the corporate speaker.

What happens when the content stream includes email and documents in six, 12, or 24 different languages?

Answer: Mad scrambling until the proud licensee of an enterprise search system can locate a vendor able to support multiple language inputs. The real life needs of an enterprise are often different from what the proprietary enterprise search system can deal with.

That’s why I find the repositioning of enterprise search technology a bit like a clown with a sad face. The clown is no longer funny. The unconvincing efforts to become something else clash with the sad face, the red nose, and  worn shoes still popular in Harrod’s Creek, Kentucky.

Image result for emmett kelly

When it comes to enterprise search, my litmus test is simple: If a system is keyword centric, it isn’t going to work for some of the real world applications I have encountered.

Oh, and don’t believe me, please.

Find a US special operations professional who relies on Palantir Gotham or IBM Analyst’s Notebook to determine a route through a hostile area. Ask whether a keyword search system or Palantir is more useful. Listen carefully to the answer.

No matter what keyword enthusiasts and quasi-slick New York consultants assert, enterprise search systems are not well suited for a great many real world applications. Heck, enterprise search often has trouble displaying documents which match the user’s query.

And why? Sluggish index updating, lousy indexing, wonky metadata, flawed set up, updates that kill a system, or interfaces that baffle users.

Personally I love to browse results lists. I like old fashioned high school type research too. I like to open documents and Easter egg hunt my way to a document that answers my question. But I am in the minority. Most users expect their finding systems to work without the query-read-click-scan-read-scan-read-scan Sisyphus-emulating slog.

Image result for sisyphus

Ah, you are thinking I have offered no court admissible evidence to support my argument, right? Well, just license a proprietary enterprise search system and let me know how your career is progressing. Remember when you look for a new job. You won’t search; you will insight.

Stephen E Arnold, April 12, 2017

Which Beyond Search? Text Processing or Meet Market?

April 3, 2017

In Madrid last week, a person showed me a link to Beyond Search. Nope, not this Beyond Search but to an executive recruitment firm based in London. This outfit owns the url beyondsearch.net and had the good sense to piggyback on the semantic value created by my Kentucky thoughts about search, content processing, text analytics and related subjects.

I took a quick look at the company’s Web site, which looks quite a bit like one of those Squarespace instant sites with sliders, large type, and zippy images. There were a couple of points I noted. Permit me to focus on the staff and the partners of the London-based “get you a new job, pal” store front.

First, the list of partners includes a link to a Brazilian executive recruitment company named Grupo Selpe. I used to live in Campinas, and I did a quick check of this company. The connection between Grupo Selpe and Beyond Search seems to be one of Beyond Search’s “directors.” There’s not much information about the executive directors, but we will continue to monitor the named entities. There was one link related to Grupo Selpe and Beyond Search, and it was dated 2005. Odd that in 12 years, there’s only one modest reference to the London shot house type company.

Second, we noted that the founder of Beyond Search is a person allegedly named James Davies. He too exists in a bit of an information vacuum. His LinkedIn page reports that he is a graduate of Keele University, and he has been the founder of two interesting Google-scale operations; specifically:

  • ScaleUp Works, a conference designed to raise investment funds
  • Walker Davies, an outfit described as “the UK’s pre-eminent startup and scale up hiring specialists”.

Walker Davies is interesting because it is listed as one of the “partners” of the Beyond Search recruitment outfit. It strikes me that Walker Davies and Beyond Search are in the same business: Headhunting, a colloquial terms popular in the US for moving a person to a new job.

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Headhunting refers to the practice of some indigenous people. Beyond Search, despite its aboriginal origins, consumes only geese. Beyond Search in London may consume the careers of certain individuals. Beyond Search is enjoyed by certain individuals familiar with our approach and work for certain government entities engaged in law enforcement. Beyond Search in London is familiar to the pay-to-play aspect of executive recruitment; for instance, this company, Not Actively Looking.

Third, one of the partners of the recruitment outfit is the Financial Times. It apparently had a Non Executive Directors’ Club. I clicked on the link to the Financial Times, a publication which I view as one which tries not to get embroiled in illegal, underhanded, and deceptive practices. (I could be incorrect of course.) What happens when I follow the link? I get a 404 error.

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This snippet from the headhunting Web site says that Beyond Search is proud to be partners with the Financial times Non Executive Director’s Club. Please, note the typographical error introduced between the logo and the executive placement service’s rendering of the identical text. Careless? No, just a bad link. I saw this when I clicked on the logo:

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It seems that the Financial Times does not want to be captured in the headhunters’ pot of boiling oil or the Beyond Search headhunting outfit does not have the ability to get details right. If that is indeed the case, I am not sure I would entrust my Beyond Search goose’s job search to those who might plop the dear bird into a pot and sit back and wait for goose with sauerkraut. “Sour” right?

Fourth, The OwenJames’s link is not active. But it seems to be given pride of place on the Beyond Search LinkedIn page. I find that interesting because even my LinkedIn page includes slightly more timely information. Compare the two entries and decide for yourself: The Arnold LinkedIn page vs. the James Davies’ page.

Beyond Search BeyondSearch
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Fifth, the Beyond Search partner Paradox is in the coaching business. No, not football in the Roman Abramovich school of management. (See “Ruthless Sacking Is the Hallmark of Roman Abramovich Empire.” The Paradox service strikes me as somewhat vague. As a former Booz, Allen & Hamilton lackey, I understand the value of vagueness. I did enjoy the quote from Niels Bohr: The opposite of a correct statement is a false statement.” But is that what Paradox is about? False statements. I know that folks in Harrod’s Creek are not as sharp as those from more sophisticated cities like London, but the paradox is that I don’t understand how paradox is the heart of leadership.

An outfit with the same name as this beloved blog may have some good qualities. Granted, the punctuation errors, Financial Times’s link which isn’t, and the fascinating grab bag of partners suggests that the headhunter outfit is an interesting operation.

Rah, rah, to any company which wishes to hang on the webbed feet of the flying goose. Remember. When the Beyond Search goose lands, it can lay golden eggs. Sometimes, however, it can leave a deposit which can discolor paint with poo burn like this:

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The opposite of the truth is what again? Ah, right. The Beyond Search operation in the UK. Recruit on, I say.

Stephen E Arnold, April 3, 2017

Bitcoin Alternative Monero Accepted by AlphaBay

March 17, 2017

As institutions like banks and law enforcement come to grips with the flow of Bitcoin, another cyber currency is suddenly gaining ground. Bloomberg Technology reveals, “New Digital Currency Spikes as Drug Dealers Get More Secrecy.” The coin in question, Monero, has been around for a couple of years, but was recently given a boost by the marketplace AlphaBay, one of the most popular destinations for buyers of illicit drugs on the Dark Web. In the two weeks after the site announced it would soon accept Monero, the total worth of that currency in circulation jumped to over $100 million (from about $25 million the previous month). Writer Yuji Nakamura explains why a shift may be underway:

Bitcoin, the most popular digital currency in the world with a total value of $9.1 billion, also allows users to move funds discreetly and uses a network of miners to verify the authenticity of each trade. But its privacy has come under threat as governments and private investigators increase their ability to track transactions across the bitcoin network and trace funds to bank accounts ultimately used to convert digital assets to and from traditional currencies like U.S. dollars.

Monero similarly uses a network of miners to verify its trades, but mixes multiple transactions together to make it harder to trace the genesis of the funds. It also adopts ‘dual-key stealth’ addresses, which make it difficult for third-parties to pinpoint who received the funds.

For any two outputs, from the same or different transactions, you cannot prove they were sent to the same person,’ Riccardo Spagni, a lead developer of Monero, wrote by e-mail. Jumbling trades together makes it ‘impossible to tell which transaction, of a set of transactions, a particular input comes from. It appears to come from all of them.

Though Monero has yet to withstand the trials of AlphaBay-level volumes for long, its security features received praise from investor and prominent digital-currency-advocate Roger Ver. As of this writing, Monero is ranked fifth among digital currencies in overall market value. Click here for a list of digital currencies ranked, in real time, by market cap.

Cynthia Murrell, March 17, 2017

Dark Web Explosives Buyer Busted Through FBI Infiltration

March 9, 2017

Here is the story of another successful Dark Web bust. Motherboard reports, “Undercover FBI Agent Busts Alleged Explosives Buyer on the Dark Web.” The 50-year-old suspect was based in Houston, and reporter Joseph Cox examined the related documents from the Southern District of Texas court. We are not surprised to learn that the FBI found this suspect through its infiltration of AlphaBay.; Cox writes:

The arrest was largely due to the work of an undercover agent who posed as an explosives seller on the dark web marketplace AlphaBay, showing that, even in the age of easy-to-use anonymization technology, old-school policing tactics are still highly effective at catching suspects.

According to the complaint, on August 21, an FBI Online Covert Employee (OCE)—essentially an undercover agent—located outside Houston logged into an AlphaBay vendor account they were running and opened an unsolicited private message from a user called boatmanstv. ‘looking for wireless transmitter with detonator,’ the message read. ‘Everything I need to set of a 5 gallon can of gas from a good distance away [sic].’ The pair started a rapport, and boatmanstv went into some detail about what he wanted to do with the explosives.

One thing led to another, and the buyer and “seller” agreed to an exchange after communicating for a couple of weeks. (Dark Web sting operations require patience. Lots of patience.) It became clear that Boatmanstv had some very specific plans in mind for a very specific target, and that he’d made plenty of purchases from AlphaBay before. The FBI was able to connect the suspect’s email account to other accounts, and finally to his place of business. He was arrested shortly after receiving and opening the FBI’s package, so it would appear there is one fewer violent criminal on the streets of Houston.

It is clear that the FBI, and other intelligence organizations, are infiltrating the Dark Web more and more. Let the illicit buyer be wary.

Cynthia Murrell, March 9, 2016

Voice Recognition Software Has Huge Market Reach

March 3, 2017

Voice recognition software still feels like a futuristic technology, despite its prevalence in our everyday lives.  WhaTech explains how far voice recognition technology has imbedded itself into our habits in, “Listening To The Voice Recognition Market.”

The biggest example of speech recognition technology is an automated phone system.  Automated phone systems are used all over the board, especially in banks, retail chains, restaurants, and office phone directories.  People usually despise automated phone systems, because they cannot understand responses and tend to put people on hold for extended periods of time.

Despite how much we hate automated phone systems, they are useful and they have gotten better in understanding human speech and the industry applications are endless:

The Global Voice Recognition Systems Sales Market 2017report by Big Market Research is a comprehensive study of the global voice recognition market. It covers both current and future prospect scenarios, revealing the market’s expected growth rate based on historical data. For products, the report reveals the market’s sales volume, revenue, product price, market share and growth rate, each of which is segmented by artificial intelligence systems and non-artificial intelligence systems. For end-user applications, the report reveals the status for major applications, sales volume, market share and growth rate for each application, with common applications including healthcare, military and aerospace, communications, and automotive.

Key players in the voice recognition software field are Validsoft, Sensory, Biotrust ID, Voicevault, Voicebox Technologies, Lumenvox, M2SYS, Advanced Voice Recognition Systems, and Mmodal.  These companies would benefit from using Bitext’s linguistic-based analytics platform to enhance their technology’s language learning skills.

Whitney Grace, May 3, 2017

Fake News Is Old News. Fake Research Is Old Too.

March 1, 2017

I read “Crossfire” on the Andrewgelman.com site. I liked the write up. I noted the introduction’s quotation from 1967. I had heard something similar from one of my college instructors in 1962. My recollection is that one of his professors told him about crazy research, fiddled experiments, and lousy math in the late 1930s. My hunch is that this declaration of “a crisis” has been pointed out since folks gathered for lectures about mathiness and rational thought.

I did highlight several passages from the write up:

  • A comment about a flawed study: “The basic problem here is not the results, but the basic implausibility of the methods combined with the results.”
  • On getting published in an academic journal: “Everything will get published, if you just keep submitting it to journal after journal.”
  • On the state of “real” research: “The real problem is that this sort of work is standard operating practice in the field of psychology, no better and no worse (except for the faked data) than the papers on himmicanes, air rage, etc., endorsed by the prestigious National Academy of Sciences. As long as this stuff is taken seriously…”

There’s interest in fake news. A British newspaper staffed with “real” journalists has been banned as a source for Wikipedia. What about “real” scholars who crank out fake research? Oh, right, it takes expertise to identify some academic baloney. Who has time for academic research when watching Facebook videos is the better way to become a critical thinker. Marketing for tenure: Great idea.

Stephen E Arnold, March 1, 2017

Finding Meaning in Snapchat Images, One Billion at a Time

February 27, 2017

The article on InfoQ titled Amazon Introduces Rekognition for Image Analysis explores the managed service aimed at the explosive image market. According to research cited in the article, over 1 billion photos are taken every single day on Snapchat alone, compared to the 80 billion total taken in the year 2000. Rekognition’s deep learning power is focused on identifying meaning in visual content. The article states,

The capabilities that Rekognition provides include Object and Scene detection, Facial Analysis, Face Comparison and Facial Recognition. While Amazon Rekognition is a new public service, it has a proven track record. Jeff Barr, chief evangelist at AWS, explains: Powered by deep learning and built by our Computer Vision team over the course of many years, this fully-managed service already analyzes billions of images daily. It has been trained on thousands of objects and scenes. Rekognition was designed from the get-go to run at scale.

The facial analysis features include markers for image quality, facial landmarks like facial hair and open eyes, and sentiment expressed (smiling = happy.) The face comparison feature includes a similarity score that estimates the likelihood of two pictures being of the same person. Perhaps the most useful feature is object and scene detection, which Amazon believes will help users find specific moments by searching for certain objects. The use cases also span vacation rental markets and travel sites, which can now tag images with key terms for improved classifications.

Chelsea Kerwin, February 27, 2017

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