Escape from the glass house - thoughts on Gates and VB

Sometimes, this blog will venture into the deep past... In 2006, the news that Bill Gates was about to retire made me think. His move to give away money to good causes and to gradually remove the heavy yoke of incredibly unbelievable wealth, had given pause to some of us. Gates was championed by many, and criticized by many too. My long-time colleague, Rich Seeley drolly summed it: "This may be like when Ali left boxing; software may never be as fun without Gates to kick around." At the time, I wrote: I'd been in the hardware trade press for 10 years when my boss assigned me to cover a Microsoft product rollout in Atlanta. Call it a simple twist of fate. It was 1991. Of course I'd heard of Bill Gates, but he was in the software business, and of just about no interest to us. If he'd been doing assembler, of course, that would have been of a whole lot of interest, but he was doing Basic, which "real men" didn't do, back in those hardware circles. But the company was on the rise, and the boss sent me. The product rolling out, in fact, was Visual Basic, which has just lately turned 15. There was tension in the air at the launch as we waited for the keynote speaker. Then, Bill Gates came out and just about everybody stood up and cheered clamorously. In those days hardware trade journalists didn't applaud (politely or otherwise) at the end of an industry executive's speech, much less stand up when they just appeared on stage. So I covered the story of the birth of Visual Basic, and had one eye on the rapt audience as I did so. Later on I caught on to the fact that Bill Gates had become the richest man in the world and people were fascinated by that mere fact. Of course, there was real excitement about Visual Basic and Microsoft because the software was enabling for people who grew up during the batch processing era, when gatekeepers in smocks stood between you and the problem you wanted to crunch on.

Read the rest of the story - Halcyon days of the VB scripters

JSON hasn't surpassed XML - nay, XML has merely fallen and it cant get backup

Themes that memed

http://dispatchtelegraph.tumblr.com/post/136406604144/the-themes-that-memed-for-me-in-2015-on

Looking back at 2015

• Watson in 2015
• Author Medina describes cybernetics experiment in ...
• TensorFlow makes news in 2014
• Data Decisions

• AmaMart meets Walzon
• Machine learning and science

• Talking Data Podcast ponders analytical algorithms...
• Architectures from different views
• Tensor
• Machine learning for better medical income on TAP

• AirBnB Where are you?
• Doting on Data on Sunday Sept 13
• NoSQL meets SLAs
• Hand-picked data ferment

• Check out out data preparation podcast
• Doting on Data Daily - Aug 31 2015
• Im going down to Stasiland behind a cloud
• Build, Ignite, Azure
• Dremel drill doodling
• Holes in Mass. Halo: Sitting on Public Records.
• Machine and learning, trial and error
• Momentous tweets for a week in June 2015
• Advance and quandry: Big Data and veteran's health...
• New and notable Week of Jun 8
• Molecular sugar simulations on Gene/Q
• Data Journalism Hackaton
• 5 minute history of the disintegration of applicat...
• Telling winds from the cybernetic past
• Spark stories

• Rah rah, Data, Go, go, go
• Behind the music – Spark and the PDP-11
• Give me Algorithmic Accountability Or

• Spark was nowhere in 2013 - in 2014 it blew by Sto...

• Some dirt on dirt data
• Momentous Tweets in the week of our Lord Jan 25 20...

• Big Data from Space
• Between the buttons
• Data tweets I have known during the Week of Januar...
• The best laid plans of the mice and the man

Watson in 2015

In 2015, APIs for IBM's Watson system were front and center as a means to bring cognitive computing applications to a broader corporate audience. "People don't necessarily want to buy a million-dollar system to run Watson," IDC's David Schubmehl said. "But PaaS is well-suited for cognitive platforms. People can use Bluemix services and start working with one or two APIs, rather than use the whole system." He added that IBM's March 2015 purchase of AlchemyAPI -- a deep-learning AI technology startup -- was also notable, as it brought to Big Blue a popular set of developer APIs that can help Watson in areas beyond machine learning applications. READ IBM Watson APIs hold key to broader cognitive computing use.

Author Medina describes cybernetics experiment in 1970s Chile

 Does a 1970s Utopian technology effort offer useful guides for those trying to assess the progress of new technology today? In one case, at least, yes. It is the story of Salvador Allende's attempt to build a working Socialist government in Chile with computer cybernetics.

The tale is told especially well, under the able hands of author and researcher Eden Medina. Medina rolls up the takeaways in a recent article in Jacobin magazine. It is a summary of some important lessons garnered during work on her 2013 book, The Cybernetic Revolutionaries. (Prose continues here.)

TensorFlow makes news in 2015

On the face of it, it would appear that TensorFlow received an inordinate amount of attention in 015 for just a machine learning engine. But publicity works that way. Google is a $66 billion-per-year company, and buzz automatically goes with that. But a series of pieces by Cade Metz in Wired were fairly illuminating.

You push the little valve down, and the music goes around, and it comes out.

It seems that Google open sourcing, to some extent, TensorFlow gave it some lift. My take would be that they would like others to write the Java and JavaScript Notebooks, and create long lists of libraries, to give it the panache of Apache Spark, which is provably hot, due to its metered Apache klingonage. Is it an attempt to take wind out of Spark's sails? Some people say that Spark is general-purpose, and thus not as good for machine learning as is TensorFlow, which has no other use in life but to do machine learning.

Yet another Wired piece by Metz discusses the upswing in use of GPUs for machine learning. One banner that TensorFlow seems to forward is the use of GPUs for machine learning. It seems to be a growing meme.

One wag opines that TensorFlow smells like something that might work autonomous car data. In the back of my mind I can hear the words of someone who told me the difference between IBM and Watson and Google and TensorFlow is that the former is about software to enable enterprises to make consumer products, while the latter is about making consumer products. [Just last week, Google said it would launch such a vehicle with Ford. Which could play to the idea that it doesnt want to make cars, it wants to get data on drivers.]

As described Metz's article deep learning is the same as machine learning. Metz points out that the new goop in the secret sauce is the increase in both available processing and available data – suggesting that the algorithms are not dramatically different than in the past. [Although the author goes on to say the algorithms are evolving, and that gifted individuals are behind that evolution.]

Author Metz and source Lukas Biewald of Crowdflower note that Google open sourced the machine learning software, but not the data. Others criticize the fact that, while you can run Tensorflow on your own machine (that could include a GPU board), they are keeping the distributed version to themselves.

Links
https://www.youtube.com/watch?v=ENZoY4mLgDE
http://www.wired.com/2015/11/google-open-sources-its-artificial-intelligence-engine/
http://www.kdnuggets.com/2015/11/google-tensorflow-deep-learning-disappoints.html
http://www.wired.com/2015/11/google-open-sourcing-tensorflow-shows-ais-future-is-data-not-code/


http://www.wired.com/2015/11/googles-open-source-ai-tensorflow-signals-fast-changing-hardware-world/
https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi-5K0GDYQsrw_oRhC_2hQ0lOp5xC0n1GRxtnpViaFNmriieL3FWkHQRf9QJS_5W7ZMPilxA-MxibJO4A9dygaRSqj0x15klMEstjZ7JDbVtg282JnG8IRd4wTTBEfS828paM2UEQi-Vk4/s1600/cifar10_2.gif
http://www.tensorflow.org/
http://googleresearch.blogspot.com/2015/11/tensorflow-googles-latest-machine_9.html

http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html
https://www.youtube.com/watch?v=46Jzu-xWIBk
https://www.youtube.com/watch?v=gY9DewL6Dqk
http://blogs.nvidia.com/blog/2015/03/18/google-gpu/

Data Decisions

Let's not paint big data too darkly. It brings hope -- not just of making money, but of improving social institutions, helping to cure disease and more. However, hope can be accompanied by fear. While privacy is the chief public concern in the new world of voluminous information, there are others, as well. The chance that bad decisions might be made based on misreading big data is one of them. - Jack Vaughan

read more at http://searchdatamanagement.techtarget.com/news/2240185076/Business-decision-making-must-progress-in-the-age-of-big-data

Talking Data Podcast ponders analytical algorithms to help save whales

A right whale and her right calf. Source: NOAA

In a recent Talking Data podcast guest Kristen Khan of NOAA said she and her colleagues have watched the growth of machine learning projects that identify images. They have wondered: If such analytical algorithms could trim time from what is now a very labor intensive whale identification task, could that free up staff for more proactive efforts to save the whales? A contest sponsored by MathWorks is trying to find the answer. Listen here.