Banks Learn Sentiment Analysis Equals Money

July 26, 2017

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

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

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

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

Whitney Grace, July 26, 2017

Online Ad Fraud? You Must Be Joking

July 25, 2017

Years ago a New York conference organizer who specialized in early morning breakfast meetings asked me, “Will you do an exposé about online advertising fraud?” My response to this question was, “No.”

Why did I drag my feet? Three reasons:

In the research for my first Google book “The Google Legacy,” which is now out of print but I sell pre-publication versions 13 years after I wrote it, I realized that online ads were easy to manipulate. Here’s one example. Write a script which visits a page and clicks on an ad. The poor advertiser’s account can be consumed in a nonce. No one paid much attention to this “feature,” and I had zero desire to get involved with ad types. Who wanted hassles when I was still working? Not me.

Second, explaining the who, what, why, and how involved imparting technical information to decidedly non tech type people. Sorry, that’s not for me. Leave that work to those who have the patience and personality to deal with jazzed Madison Avenue types.

Third, none of my contacts wanted to reveal that click fraud was a problem. The approach was similar to the memorable statement, “Android fragmentation? There’s no Android fragmentation.” Yeah, right.

Now there are some brave souls stepping forward in what may become a darned interesting interpersonal, intercompany, and legal battle. This possible dust up is one which I will watch far from the fray.,

To get a sense of what’s about the become either “real” or “fake” news, navigate to “Online Ad Fraud Is a Widespread Problem, Google and Other Big Ad Platforms Admit.” Now the “big” online ad platforms boil down to a two horse race. I suppose the smaller folks like the vendors of annoying weird links, annoying pop ups, and looped videos with raucous sound tracks may be keeping some secrets under a rock, there are people who just want to see those online ad accounts depleted by a software robot. Click, click, click, and the pre paid ad accounts goes down, down, down.

The write up points out:

Google-run tests found evidence of fake ad spaces sold on Google and Oath-owned programmatic ad platforms, as well as well as on PubMatic and AppNexus.

The point, I think, is that the vendors of online ads want to “prove” and “remove doubt” that online ads work. One should keep in mind that almost everything online is an ad. Amazon, for example, is one giant Sears catalog with manufacturers and sellers desperate for positive reviews and placement on the first page of Amazon’s result pages. Do you every look at page 14 when searching for cufflinks which hide USB drives?

The write up focuses on spoofing, offering:

The method is used to trick ad buyers into purchasing advertising space on websites that don’t exist, or that the sellers don’t have access to. Because of the speed and volume of advertising online when bought programmatically, it’s virtually impossible to check if an ad ran where sellers say it was supposed to run.

As long ago as 2003, i noticed that there are many ways and many reasons for fiddling with online ads.

Perhaps Facebook and Google, among others, will share their knowledge, concerns, and ideas. The thrill of losing ad revenue should make for some interesting PR and, possibly, legal activity.

Stephen E Arnold, July 25, 2017

The Secret to Success for Apple and Google

July 25, 2017

What makes them so special? As part of their 10 Lessons from 10 Years of The World’s Most Innovative Companies series, Fast Company explains “Why Apple and Google Are Titans.” The article examines what these two historically very different companies hold in common. In a nutshell, each was built around purposeful innovation at breakneck speeds. Writer Robert Safin observes:

Innovation is not a onetime activity. It is a philosophy and culture. The fruits of innovation do not unfold on schedule, in a single year, along a straight line. To stay up with—and ahead of—the changes in today’s world, you need to be always moving, trying new things, fueled by an internal restlessness. This is at the heart of both Apple and Google. …

The critical corollary, then, to that need-for-speed: a need for purpose. Having a clearly understood mission behind an enterprise allows everyone to prioritize, in real time, to quickly assess which changes are worth responding to and what lens to use in addressing them. Apple and Google have always had this framework, from Steve Jobs’s mission of ‘making tools for the mind that advance humankind’ to Google founders Larry Page and Sergey Brin’s pledge to ‘do no evil’ while ‘organizing the world’s information.’

It is easy to underestimate each of these companies, Safin notes. It can seem as though Apple has been simply riding the connectivity wave in its iPhone surfboard, but we’re reminded how Apple has had to evolve and pivot to get and stay, at the fore. As for Google, one might think it has simply been lucky that internet search has become ubiquitous. However, Google has actually taken risk after risk, many of which turned out to be valuable only for their lessons. See the article for more examples about each company.

Cynthia Murrell, July 25, 2017

Machine Learning Does Not Have the Mad Skills

July 25, 2017

Machine learning and artificial intelligence are computer algorithms that will revolutionize the industry, but The Register explains there is a problem with launching it: “Time To Rethink Machine Learning: The Big Data Gobble Is OFF The Menu.”  The technology industry is spouting that 50 percent of organizations plan to transform themselves with machine learning, but the real truth is that it is less than 15 percent.

The machine learning revolution has supposedly started, but in reality, the cannon has only be fired and the technology has not been implemented.  The problem is that while companies want to use machine learning, they are barely getting off the ground with big data and machine learning is much harder.  Organizations do not have workers with the skills to launch machine learning and the tech industry as a whole has a huge demand for skilled workers.

Part of this inaction comes down to the massive gap between ML (and AI) myth and reality. As David Beyer of Amplify Partners puts it: ‘Too many businesses now are pitching AI almost as though it’s batteries included.’ This is dangerous because it leads companies to either over-invest (and then face a tremendous trough of disillusionment), or to steer clear when the slightest bit of real research reveals that ML is very hard and not something the average Python engineer is going to spin up in her spare time.

Organizations also do not have the necessary amount of data to make machine learning feasible and they also lack the corporate culture to do the required experimentation for machine learning to succeed.

This article shares a story that we have read many times before.  The tech industry gets excited about the newest shiny object, it explodes in popularity, then they realize that the business world is not ready for implementing the technology.

Whitney Grace, July 25, 2017

A Wonky Analysis of Search Today: The SEO Wizard View

July 24, 2017

I read what one of my goslings described as a “wonky” discussion of search. You will have to judge for yourself, gentle reader. In an era of fake news, I am not sure what to make of a semi factual, incomplete write up with the title “How Search Reveals the World.” Search does not reveal “the world”; search provides some — note the word “some” — useful information about the behaviors of individuals who run queries or make use of systems like the oh, so friendly Amazon Alexa.

I learned that there are three types of search, and I have to tell you that these points were not particularly original. Here they are:

  • Navigational search queries. Don’t think about Endeca’s “guided navigation.” Think about Google Maps, which is going to morph into a publishing platform, a fact not included in the write up which triggered ruffled gosling feathers
  • Information search queries. Ah, now we’re talking. A human types 2.4 words in a search box and feels lucky or just looks at the first few hits on the first search page. Could these hits be ads unrelated or loosely related to the user’s query? Sure, absolutely.
  • Transactional search queries. I am not sure what this phrase “transactional search queries” means, but that’s not too surprising. The confusion rests with me when I think of looking for a product like a USB C plug on Amazon versus navigating to my bank’s fine, fine Web site and using a fine, fine interface to move money from Point A to Point B. Close enough for horseshoes.


Skimming the surface is good for seaplanes but not a plus for an analysis of search and retrieval.

But the most egregious argument in the write up is that search becomes little more than a rather clumsy manipulative tool for “marketers, advertisers, and business owners.” Why clumsy? The write up is happily silent about Facebook’s alleged gaming of its system for various purposes. Filtering hate speech, for example, seems admirable until someone has to define “hate speech.” Filtering live streaming of a suicide or crime in progress is a bit more problematic. But search is a sissy compared with the alleged Facebook methods. With marketers looking to make a buck, Facebook seems to slip the pager mâché noose of the write up’s argument.

But there is a far larger omission. One of the most important types of search is “pervasive, predictive search.” The idea is a nifty one. Using various “signals” a system presents information automatically to a user who is online and looking at an output. No specific action on the part of the user is required. The user sees what he or she presumably wants. Search without search! The marketer’s Holy Grail.

There are some important components of this type of search.

Perhaps an SEO expert will explain them instead of recycling old information and failing to define 33 percent of the bedrock statements. But that may be a bridge to far for those who would try to manipulate the systems and methods of some of the providers of free, ad supported search systems. The longest journey begins with a single step. Didn’t an SEO expert say that too?

Stephen E Arnold, July 24, 2017

China Transwarp: Can This Be a Palantir Challenger?

July 24, 2017

One of my sources provided me with a link to a write up which may be translated as “Yujialong star ring technology common to build China Palantir” or “Yu Jialong together star ring technology together to build China’s Palantir.” The link to the original article is here. “Yu Jialong” is a subsidiary of Boone Group, which may no longer be in operation. The point of the write up is that a group of Chinese wizards is working to create a “Chinese Palantir. The group is hoodek up with Six Ring Technology. TenCent is providing some financing.


This may be the experts who are tackling the Palantir like system.

There is the challenge of seamlessly importing the file formats used by developers of cyINT eDiscovery systems. I have added it to mist of companies engaged in moving beyond Analyst’s Notebook and Gotham systems.

Stephen E Arnold, July 24,2017

Watson Does Whiteboards

July 24, 2017

A write-up at Helge Scherlund’s eLearning News describes a very useful tool in, “World’s Smartest Active Virtual Meeting Assistant Ricoh.” The tool integrates the IBM Watson AI into an interactive whiteboard system. The press release positions the tool as the future of meetings, but we wonder whether small businesses and schools can afford these gizmos. The write-up includes a nine-minute promotional video that describes the system, so interested readers should check it out. We’re also given a list of key features.

*Easy-to-join meetings: With the swipe of a badge the Intelligent Workplace Solution can log attendance and track key agenda items to ensure all key topics are discussed.


*Simple, global voice control of meetings: Once a meeting begins, any employee, whether in-person or located remotely in another country, can easily control what’s on the screen, including advancing slides, all through simple voice commands.


*Ability to capture side discussions: During a meeting, team members can also hold side conversations that are displayed on the same Ricoh interactive whiteboard.


*Translation of the meeting into another language: The Cognitive Whiteboard can translate speakers’ words into several other languages and display them on screen or in transcript.

I suppose one feature here may also be a thorn in the side of some old-school business people—the system creates a transcript of everything said in each meeting, including side conversations, and sends it to each participant. Auto CYA. The process would take some getting used to, but we can see the advantages for many organizations. Headquartered in Tokyo, Ricoh’s history stretches back to 1936.

Cynthia Murrell, July 24, 2017

AI Feeling a Little Sentimental

July 24, 2017

Big data was one of the popular buzzwords a couple years ago, but one conundrum was how organizations were going to use all that mined data?  One answer has presented itself: sentiment analysis.  Science shares the article, “AI In Action: How Algorithms Can Analyze The Mood Of The Masses” about how artificial intelligence is being used to gauge people’s emotions.

Social media presents a constant stream of emotional information about products, services, and places that could be useful to organizations.  The problem in the past is that no one knew how to fish all of that useful information out of the social media Web sites and make it a usable.    By using artificial intelligence algorithms and natural language processing, data scientists are finding associations between words, the language used, posting frequency, and more to determine everything from a person’s mood to their personality, income level, and political associations.

‘There’s a revolution going on in the analysis of language and its links to psychology,’ says James Pennebaker, a social psychologist at the University of Texas in Austin. He focuses not on content but style, and has found, for example, that the use of function words in a college admissions essay can predict grades. Articles and prepositions indicate analytical thinking and predict higher grades; pronouns and adverbs indicate narrative thinking and predict lower grades…’Now, we can analyze everything that you’ve ever posted, ever written, and increasingly how you and Alexa talk,’ Pennebaker says. The result: ‘richer and richer pictures of who people are.’

AI algorithms are able to turn a person’s online social media accounts and construct more than a digital fingerprint of a person.  The algorithms act like digital mind readers and recreate a person based on the data they publish.

Whitney Grace, July 24, 2017

Instagram Reins in Trolls

July 21, 2017

Photo-sharing app Instagram has successfully implemented DeeText, a program that can successfully weed out nasty and spammy comments from people’s feeds.

Wired in an article titled Instagram Unleashes an AI System to Blast Away Nasty Comments says:

DeepText is based on recent advances in artificial intelligence, and a concept called word embeddings, which means it is designed to mimic the way language works in our brains.

DeepText initially was built by Facebook, Instagram’s parent company for preventing abusers, trolls, and spammers at bay. Buoyed by the success, it soon implemented on Instagram.

The development process was arduous wherein a large number of employees and contractors for months were teaching the DeepText engine how to identify abusers. This was achieved by telling the algorithm which word can be abusive based on its context.

At the moment, the tools are being tested and rolled out for a limited number of users in the US and are available only in English. It will be subsequently rolled out to other markets and languages.

Vishal Ingole, July 21, 2017

Drugmaker Merk Partners with Palantir on Data Analysis

July 21, 2017

Pharmaceutical company Merk is working with data-analysis firm Palantir on a project to inform future research, we learn from the piece, “Merk Forges Cancer-Focused Big Data Alliance with Palantir” at pharmaceutical news site PMLive. The project is an effort to remove the bottleneck that currently exists between growing silos of medical data and practical applications of that information. Writer Phil Taylor specifies:

Merck will work with Palantir on cancer therapies in the first instance, with the aim of developing a collaborative data and analytics platform for the drug development processes that will give researchers new understanding of how new medicines work. Palantir contends that many scientists in pharma companies struggle with unstructured data and information silos that ‘reduce creativity and limit researchers’ corrective analyses’. The data analytics and sharing platform will help Merck researchers analyse real-world and bioinformatics data so they can ‘understand the patients who may benefit most’ from a treatment.

The alliance also has a patient-centric component, and according to Merck will improve the experience of patients using its products, improve adherence as well as provide feedback on real-world efficacy.

Finally, the two companies will collaborate on a platform that will allow improved global supply chain forecasting and help to get medicines to patients who need them around the world as quickly as possible. Neither company has disclosed any financial details on the deal.

This is no surprise move for the 125-year-old Merk, which has been embracing digital technology in part by funding projects around the world. Known as MSD everywhere but the U.S. and Canada, the company started with a small pharmacy in Germany but now has its headquarters in New Jersey.

Palantir has recently stirred up some controversy. The company’s massive-scale data platforms allow even the largest organizations to integrate, manage, and secure all sorts of data. Its founding members include PayPal alumni and Stanford computer-science grads. The company is based in Palo Alto, California, and has offices around the world.

Cynthia Murrell, July 21, 2017

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