Cambridge Analytica: Buzz, Buzz, Buzz
March 9, 2017
The idea that software can make sense of information is a powerful one. Many companies tout the capabilities of their business processes, analytical tools, and staff to look at data and get a sense of the future. The vast majority of these firms have tools and methods which provide useful information.
What happens when a person who did not take a course in analytics learns about the strengths and limitations of these systems?
Answer: You get some excitement.
I read “Big Data’s Power Is Terrifying. That Could Be Good News for Democracy.” The main idea is that companies with nifty analytic systems and methods can control life is magnetic. Lots of folks want to believe that a company’s analyses can have a significant impact on elections, public opinion, and maybe the stock market.
The write up asserts:
Online information already lends itself to manipulation and political abuse, and the age of big data has scarcely begun. In combination with advances in cognitive linguistics and neuroscience, this data could become a powerful tool for changing the electoral decisions we make. Our capacity to resist manipulation is limited.
My view is that one must not confuse the explanations from marketing mavens, alarmists, and those who want to believe that Star Trek is “real” with what today’s systems can do. Firms like Cambridge Analytica and others generate reports. In fact, companies have been using software to figure out what’s what for many years.
What’s interesting is that folks learn about these systems and pick up the worn ball and carry it down field while screaming, “Touchdown.”
Sorry. The systems don’t warrant that type of excitement. Reality is less exciting. Probabilities are useful, not reality. But why not carry the ball. It is easier than learning what analytics firms do.
Stephen E Arnold, March 9, 2017
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
IBM and Root Access Misstep?
March 2, 2017
Maybe this is fake news? Maybe. Navigate to “Big Blue’s Big Blunder: IBM Accidentally Hands Over Root Access to Its Data Science Servers.” When I read the title, my first reaction was, “Hey, Yahoot is back in the security news.” Wrong.
According to the write up, which I assume to be exposing the “truth”:
IBM left private keys to the Docker host environment in its Data Science Experience service inside freely available containers. This potentially granted the cloud service’s users root access to the underlying container-hosting machines – and potentially to other machines in Big Blue’s Spark computing cluster. Effectively, Big Blue handed its cloud users the secrets needed to potentially commandeer and control its service’s computers.
IBM hopped to it. Two weeks after the stumble was discovered, IBM fixed the problem.
The write up includes this upbeat statement, attributed to the person using a demo account which exposed the glitch:
I think that IBM already has some amazing infosec people and a genuine commitment to protecting their services, and it’s a matter of instilling security culture and processes across their entire organization. That said, any company that has products allowing users to run untrusted code should think long and hard about their system architecture. This is not to imply that containers were poorly designed (because I don’t think they were), but more that they’re so new that best practices in their use are still being actively developed. Compare a newer-model table saw to one decades old: The new one comes stock with an abundance of safety features including emergency stopping, a riving knife, push sticks, etc, as a result of evolving culture and standards through time and understanding.
Bad news. Good news.
Let’s ask Watson about IBM security. Hold that thought, please. Watson is working on health care information. And don’t forget the March 2017 security conference sponsored by those security pros at IBM.
Stephen E Arnold, March 2, 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
Upgraded Social Media Monitoring
February 20, 2017
Analytics are catching up to content. In a recent ZDNet article, Digimind partners with Ditto to add image recognition to social media monitoring, we are reminded images reign supreme on social media. Between Pinterest, Snapchat and Instagram, messages are often conveyed through images as opposed to text. Capitalizing on this, and intelligence software company Digimind has announced a partnership with Ditto Labs to introduce image-recognition technology into their social media monitoring software called Digimind Social. We learned,
The Ditto integration lets brands identify the use of their logos across Twitter no matter the item or context. The detected images are then collected and processed on Digimind Social in the same way textual references, articles, or social media postings are analysed. Logos that are small, obscured, upside down, or in cluttered image montages are recognised. Object and scene recognition means that brands can position their products exactly where there customers are using them. Sentiment is measured by the amount of people in the image and counts how many of them are smiling. It even identifies objects such as bags, cars, car logos, or shoes.
It was only a matter of time before these types of features emerged in social media monitoring. For years now, images have been shown to increase engagement even on platforms that began focused more on text. Will we see more watermarked logos on images? More creative ways to visually identify brands? Both are likely and we will be watching to see what transpires.
Megan Feil, February 20, 2017
Why Do We Care More About Smaller Concerns? How Quantitative Numbing Impacts Emotional Response
February 14, 2017
The affecting article on Visual Business Intelligence titled When More is Less: Quantitative Numbing explains the phenomenon that many of us have probably witnessed on the news, in our friends and family, and even personally experienced in ourselves. A local news story about the death of an individual might provoke a stronger emotional response than news of a mass tragedy involving hundreds or thousands of deaths. Scott Slovic and Paul Slovic explore this in their book Numbers and Nerves. According to the article, this response is “built into our brains.” Another example explains the Donald Trump effect,
Because he exhibits so many examples of bad behavior, those behaviors are having relatively little impact on us. The sheer number of incidents creates a numbing effect. Any one of Trump’s greedy, racist, sexist, vulgar, discriminatory, anti-intellectual, and dishonest acts, if considered alone, would concern us more than the huge number of examples that now confront us. The larger the number, the lesser the impact…This tendency… is automatic, immediate, and unconscious.
The article suggests that the only reason to overcome this tendency is to engage with large quantities in a slower, more thoughtful way. An Abel Hertzberg quote helps convey this approach when considering the large-scale tragedy of the Holocaust: “There were not six million Jews murdered: there was one murder, six million times.” The difference between that consideration of individual murders vs. the total number is stark, and it needs to enter into the way we process daily events that are happening all over the world if we want to hold on to any semblance of compassion and humanity.
Chelsea Kerwin, February 14, 2017
Data Mining Firm Cambridge Analytica Set to Capture Trump White House Communications Contract and Trump Organization Sales Contract
February 13, 2017
The article titled Data Firm in Talks for Role in White House Messaging — And Trump Business on The Guardian discusses the future role of Cambridge Analytica in both White House communication and the Trump Organization as well. Cambridge Analytica is a data company based out of London that boasts crucial marketing and psychological data on roughly 230 million Americans. The article points out,
Cambridge’s data could be helpful in both “driving sales and driving policy goals”, said the digital source, adding: “Cambridge is positioned to be the preferred vendor for all of that.”… The potential windfall for the company comes after the Mercers and Cambridge played key roles in Trump’s victory. Cambridge Analytica was tapped as a leading campaign data vendor as the Mercers… The Mercers reportedly pushed for the addition of a few top campaign aides, including Bannon and Kellyanne Conway, who became campaign manager.
Robert Mercer is a major investor in Cambridge Analytica as well as Breitbart News, Steve Bannon’s alt-right news organization. Steve Bannon is also on the board of Cambridge Analytica. The entanglements mount. Prior to potentially snagging these two wildly conflicting contracts, Cambridge Analytica helped Trump win the presidency with their data modeling and psychological profiling that focuses on building intimate relationships between brands and consumers to drive action.
Chelsea Kerwin, February 13, 2017
Cambridge Analytica: Applied Big Data
February 9, 2017
Cambridge University, not Stanford or Carnegie Mellon, is one of the academic institutions responsible for some of the most interesting content processing innovations. I often point to Cambridge’s role in the second world war. The magic of Bayesian statistics was a bit of a specialty for the fuddy duddies trundling near the banks of the Cam. i2 Group, Autonomy, and a host of other next generation content processing outfits took root and grew. Silicon Valley did not notice.
I was reminded of Cambridge’s role in figuring out what insights can be weaseled from algorithmic content processing when I read “The Data That Turned the World Upside Down.” The focus in the article is on the victory of Donald Trump, the dark art of psychometrics, and an outfit called Cambridge Analytica. You can get more information about the firm at this link.
The write up focuses on the dangers of making sense of Big Data. That’s okay, but danger may be in the eye of the beholder. The most interesting part of the write up was the realization that Facebook actions could provide clues to behavior. Interesting. Because systems which make sense of Facebook and Twitter content have been around for years. Moreover, these systems have been integrated into larger analytical platforms in wide use by law enforcement and intelligence entities for a while.
I learned from the write up:
Our smartphone…is a vast psychological questionnaire that we are constantly filling out, both consciously and unconsciously.
There you go. Sudden insight.
To learn how Donald Trump and politicians for Brexit used outputs from Cambridge Analytica, check out the source article.
Keep in mind that this method is not new. Over and out. Don’t forget to twitch your mantle blue. Sorrowful, no.
Stephen E Arnold, February 9, 2017
Presenting Watson as a Service
February 9, 2017
Every now and then, interest in Watson re-emerges. Forbes published a long-read recently entitled How IBM Is Building A Business Around Watson. After gaining press during Watson’s victorious Jeopardy face-off with Ken Jennings, Watson’s first commercial applications took off. IBM sold it to Memorial Sloan Kettering Cancer Center and Wellpoint to design an advisory system for its medical staff. Other medical institutions have purchased it since then. The author asserts,
Still, the potentially is undeniable. Think about how much more effective an ordinary doctor can be with Watson as an assistant. First, even before the patient enters the room, it can analyze their personal medical history, which often runs to hundreds of pages. Then, it can compare the case history with the 700,000 academic papers published every year as well as potentially millions of other patient records. All of this is, of course, beyond the capabilities of human doctors, who typically only get a few minutes to prepare for each examination. So being able to consult with Watson will be enormously helpful.
The real value is offering Watson as a service by providing its API, so that developers in organizations can develop their own applications using its technology. Over 550 partners are utilizing this currently for everything from retail to geolocation to travel agencies. Certainly, with all the hype Watson receives, we can only expect usage to grow.
Megan Feil, February 9, 2017
How to Quantify Culture? Counting the Bookstores and Libraries Is a Start
February 7, 2017
The article titled The Best Cities in the World for Book Lovers on Quartz conveys the data collected by the World Cities Culture Forum. That organization works to facilitate research and promote cultural endeavors around the world. And what could be a better measure of a city’s culture than its books? The article explains how the data collection works,
Led by the London mayor’s office and organized by UK consulting company Bop, the forum asks its partner cities to self-report on cultural institutions and consumption, including where people can get books. Over the past two years, 18 cities have reported how many bookstores they have, and 20 have reported on their public libraries. Hong Kong leads the pack with 21 bookshops per 100,000 people, though last time Buenos Aires sent in its count, in 2013, it was the leader, with 25.
New York sits comfortably in sixth place, but London, surprisingly, is near the bottom of the ranking with roughly 360 bookstores. Another measure the WCCF uses is libraries per capita. Edinburgh of all places surges to the top without any competition. New York is the only US city to even make the cut with an embarrassing 2.5 libraries per 100K people. By contrast, Edinburgh has 60.5 per 100K people. What this analysis misses out on is the size and beauty of some of the bookstores and libraries of global cities. To bask in these images, visit Bookshelf Porn or this Mental Floss ranking of the top 7 gorgeous bookstores.
Chelsea Kerwin, February 7, 2017
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