HP, Now Xerox, and Maybe Palantir?
February 1, 2016
HP, after its fascinating board room machinations, its acquisition of Autonomy, and the subsequent legal frou-frou, is two companies. How are these outfits doing? Don’t ask.
Now, if McPaper is on the money Xerox will become two outfits. “Xerox Makes Official Its Split into Two Companies.” (If this link is dead, buzz Gannett, not me, gentle reader.)
According to the write up:
Xerox will separate into two companies, a $11 billion document technology company and a $7 billion business services company…
I am not sure that the hardware side will thrive. Services? Maybe.
But what struck me as I read about Xerox is this:
Palantir Technologies can split its commercial sector out and take it public. The government work can remain private.
Everyone with a stake in the outfit becomes Willy Wonka happy. Think of the money. Why even the smallest Hobbit will be able to buy a house near the Shire.
How likely is this move?
Well, after ingesting $2 billion or so over the last six years, the economic pressure on some of the stakeholders might trigger a chit chat or two.
Taking the commercial side of Palantir public might explain the number of rumors swirling that two thirds of Palantir’s revenues come from non government work.
Check out your seeing stone for the big picture or better yet, plug the data into IBM i2 Analyst Notebook and see what connections you can discern.
Stephen E Arnold, February 1, 2016
Big Data Is so Last Year, Data Analysts Inform Us
February 1, 2016
The article on Fortune titled Has Big Data Gone Mainstream? asks whether big data is now an expected part of data analysis. The “merger” as Deloitte advisor Tom Davenport puts it, makes big data an indistinguishable aspect of data crunching. Only a few years ago, it was a scary buzzword that executives scrambled to understand and few experts specialized in. The article shows what has changed lately,
“Now, however, universities offer specialized master’s degrees for advanced data analytics and companies are creating their own in-house programs to train talent in data science. The Deloitte report cites networking giant Cisco CSCO -4.22% as an example of a company that created an internal data science training program that over 200 employees have gone through. Because of media reports, consulting services, and analysts talking up “big data,” people now generally understand what big data means…”
Davenport sums up the trend nicely with the statement that people are tired of reading about big data and ready to “do it.” So what will replace big data as the current mysterious buzzword that irks laypeople and the C-suite simultaneously? The article suggests “cognitive computing” or computer systems using artificial intelligence for speech recognition, object identification, and machine learning. Buzz, buzz!
Chelsea Kerwin, February 1, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Measuring Classifiers by a Rule of Thumb
February 1, 2016
Computer programmers who specialize in machine learning, artificial intelligence, data mining, data visualization, and statistics are smart individuals, but they sometimes even get stumped. Using the same form of communication as reddit and old-fashioned forums, Cross Validated is a question an answer site run by Stack Exchange. People can post questions related to data and relation topics and then wait for a response. One user posted a question about “Machine Learning Classifiers”:
“I have been trying to find a good summary for the usage of popular classifiers, kind of like rules of thumb for when to use which classifier. For example, if there are lots of features, if there are millions of samples, if there are streaming samples coming in, etc., which classifier would be better suited in which scenarios?”
The response the user received was that the question was too broad. Classifiers perform best depending on the data and the process that generates it. It is kind of like asking the best way to organize books or your taxes, it depends on the content within the said items.
Another user replied that there was an easy way to explain the general process of understanding the best way to use classifiers. The user directed users to the Sci-Kit.org chart about “choosing the estimator”. Other users say that the chart is incomplete, because it does not include deep learning, decision trees, and logistic regression.
We say create some other diagrams and share those. Classifiers are complex, but they are a necessity to the artificial intelligence and big data craze.
Whitney Grace, February 1, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
-
- Subscribe to Beyond Search
Feature archive
News archive
-
