Machine Learning Does Not Have All the Answers
November 25, 2016
Despite our broader knowledge, we still believe that if we press a few buttons and press enter computers can do all work for us. The advent of machine learning and artificial intelligence does not repress this belief, but instead big data vendors rely on this image to sell their wares. Big data, though, has its weaknesses and before you deploy a solution you should read Network World’s, “6 Machine Learning Misunderstandings.”
Pulling from Juniper Networks’s security intelligence software engineer Roman Sinayev explains some of the pitfalls to avoid before implementing big data technology. It is important not to take into consideration all the variables and unexpected variables, otherwise that one forgotten factor could wreck havoc on your system. Also, do not forget to actually understand the data you are analyzing and its origin. Pushing forward on a project without understanding the data background is a guaranteed fail.
Other practical advice, is to build a test model, add more data when the model does not deliver, but some advice that is new even to us is:
One type of algorithm that has recently been successful in practical applications is ensemble learning – a process by which multiple models combine to solve a computational intelligence problem. One example of ensemble learning is stacking simple classifiers like logistic regressions. These ensemble learning methods can improve predictive performance more than any of these classifiers individually.
Employing more than one algorithm? It makes sense and is practical advice why did that not cross our minds? The rest of the advice offered is general stuff that can be applied to any project in any field, just change the lingo and expert providing it.
Whitney Grace, November 25, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Big Data Is Just a Myth
August 1, 2016
Remember in the 1979 hit The Muppet Movie there was a running gag where Kermit the Frog kept saying, “It’s a myth. A myth!” Then a woman named Myth would appear out of nowhere and say, “Yes?” It was a funny random gag, but while it is a myth that frogs give warts, most of the myths related to big data may or not be. Data Science Central decided to explain some of the myths in, “Debunking The 68 Most Common Myths About Big Data-Part 2.”
Some of the prior myths debunked in the first part were that big data was the newest power word, an end all solution for companies, only meant for big companies, and that it was complicated and expensive. In truth, anyone can benefit from big data with a decent implementation plan and with someone who knows how to take charge of it.
Big data, in fact, can be integrated with preexisting systems, although it takes time and knowledge to link the new and the old together (it is not as difficult as it seems). Keeping on that same thought, users need to realize that there is not a one size fits all big data solution. Big data is a solution that requires analytical, storage, and other software. It cannot be purchased like other proprietary software and it needs to be individualized for each organization.
One myth that is has converted into truth is that big data relies on Hadoop storage. It used to be Hadoop managed a market of many, but bow it is an integral bit of software needed to get the big data job done. One of the most prevalent myths is it only belongs in the IT department:
“Here’s the core of the issue. Big Data gives companies the greatly enhanced ability to reap benefits from data-driven insights and to make better decisions. These are strategic issues.
You know who is most likely to be clamoring for Big Data? Not IT. Most likely it’s sales, marketing, pricing, logistics, and production forecasting. All areas that tend to reap outsize rewards from better forward views of the business.”
Big data is becoming more of an essential tool for organizations in every field as it tells them more about how they operate and their shortcomings. Big data offers a very detailed examination of these issues; the biggest issue users need to deal with is how they will use it?
Whitney Grace, August 1, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Business Intelligence Services Partnership Between Swedish Tech Companies Zinnovate and Yellowfin
November 25, 2015
The article titled Business Intelligence Vendor Yellowfin Signs Global Reseller Agreement with Zinnovate on Sys-Con Media provides an overview of the recent partnership between the two companies. Zinnovate will be able to offer Yellowfin’s Business Intelligence solutions and services, and better fulfill the needs that small and mid-size businesses have involving enterprise quality BI. The article quotes Zinnovate CEO Hakan Nilsson on the exciting capabilities of Yellowfin’s technology,
“Flexible deployment options were also important… As a completely Web-based application, Yellowfin has been designed with SaaS hosting in mind from the beginning, making it simple to deploy on-premise or as a cloud-based solution. Yellowfin’s licensing model is simple. Clients can automatically access Yellowfin’s full range of features, including its intuitive data visualization options, excellent Mobile BI support and collaborative capabilities. Yellowfin provides a robust enterprise BI platform at a very competitive price point.”
As for the perks to Yellowfin, the Managing Director Peter Baxter explained that Zinnovate was positioned to help grow the presence of the brand in Sweden and in the global transport and logistics market. In the last few years, Zinnovate has developed its service portfolio to include customers in banking and finance. Both companies share a dedication to customer-friendly, intuitive solutions.
Chelsea Kerwin, November 25, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
No Mole, Just Data
November 23, 2015
It all comes down to putting together the pieces, we learn from Salon’s article, “How to Explain the KGB’s Aazing Success Identifying CIA Agents in the Field?” For years, the CIA was convinced there was a Soviet mole in their midst; how else to explain the uncanny knack of the 20th Century’s KGB to identify CIA agents? Now we know it was due to the brilliance of one data-savvy KGB agent, Yuri Totrov, who analyzed U.S. government’s personnel data to separate the spies from the rest of our workers overseas. The technique was very effective, and all without the benefit of today’s analytics engines.
Totrov began by searching the KGB’s own data, and that of allies like Cuba, for patterns in known CIA agent postings. He also gleaned a lot if info from publicly available U.S. literature and from local police. Totrov was able to derive 26 “unchanging indicators” that would pinpoint a CIA agent, as well as many other markers less universal but useful. Things like CIA agents driving the same car and renting the same apartment as their immediate predecessors. Apparently, logistics agents back at Langley did not foresee that such consistency, though cost-effective, could be used against us.
Reporter Jonathan Haslam elaborates:
“Thus one productive line of inquiry quickly yielded evidence: the differences in the way agency officers undercover as diplomats were treated from genuine foreign service officers (FSOs). The pay scale at entry was much higher for a CIA officer; after three to four years abroad a genuine FSO could return home, whereas an agency employee could not; real FSOs had to be recruited between the ages of 21 and 31, whereas this did not apply to an agency officer; only real FSOs had to attend the Institute of Foreign Service for three months before entering the service; naturalized Americans could not become FSOs for at least nine years but they could become agency employees; when agency officers returned home, they did not normally appear in State Department listings; should they appear they were classified as research and planning, research and intelligence, consular or chancery for security affairs; unlike FSOs, agency officers could change their place of work for no apparent reason; their published biographies contained obvious gaps; agency officers could be relocated within the country to which they were posted, FSOs were not; agency officers usually had more than one working foreign language; their cover was usually as a ‘political’ or ‘consular’ official (often vice-consul); internal embassy reorganizations usually left agency personnel untouched, whether their rank, their office space or their telephones; their offices were located in restricted zones within the embassy; they would appear on the streets during the working day using public telephone boxes; they would arrange meetings for the evening, out of town, usually around 7.30 p.m. or 8.00 p.m.; and whereas FSOs had to observe strict rules about attending dinner, agency officers could come and go as they pleased.”
In the era of Big Data, it seems like common sense to expect such deviations to be noticed and correlated, but it was not always so obvious. Nevertheless, Totrov’s methods did cause embarrassment for the agency when they were revealed. Surely, the CIA has changed their logistic ways dramatically since then to avoid such discernable patterns. Right?
Cynthia Murrell, November 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Google Express Pales in Comparison to Amazon Prime
October 5, 2015
The article on Business Insider titled Google Should Be Very Scared of What Amazon Built, According to Investor Bill Gurley, details Gurley’s comments. Amazon Prime, according to Gurley, is challenging Google’s top dog position by offering inventory in addition to search capabilities. Shopping on Google might seem like a waste of time to many Prime members, who go directly to Amazon to search for what they are looking for. The article explains,
“Over many years, Amazon has built up this logistics framework and their one click feature and their Prime program to the point where the consumer has zero anxiety about the quality of the product, immense trust about the deliverability, down to a day and a half for most people, less than a day for some items. They trust on price. That doesn’t mean they are the absolute lowest price, but people don’t think Amazon’s trying to get ’em.”
Gurley estimates that Amazon may have as many as 90 million Prime Members loyal to their search engine for shopping, and using Google only as a last resort. Google Express, which most of us have never heard of, was Google’s “lame” answer to Amazon Prime, but without the years of planning and creating worldwide distribution centers. However, the article does not address that people use Google for quite a bit more than shopping, and Amazon Prime is limited that way.
Chelsea Kerwin, October 5, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
-
- Subscribe to Beyond Search
Feature archive
News archive
-
