R. Crumb's Sweet Shellac - American Black String Bands Of The 20's & 30's

One day, some strange transmissions from the 1930s wandered into the Data Data Data antennae.

L-R. Robert Johnson, Robert Johnson, Robert Johnson and Robert Johnson.

Dixieland Jug Blowers, Banjoreno. Chicago 1926

One day, some strange transmissions from the 1930s wandered into the Data Data Data antennae. Here, the longtime theme of late Ray Smith's Jazz Decades Radio Show.

Macon Ed & Tampa Joe- Warm Wipe Stomp

One day, some strange transmissions from the 1930s wandered into the Data Data Data antennae.I misheard it as Warm White Stomp, but I know better now.

Bats in the machine

Scene from the immortal serial "The Batmen of Africa.

A lot of the chatter about machine learning is ‘bright shiny thing’ chatter. Like Tim the Tool Man with a SnapOn Tools catalog, the proponents rush ahead of the reality, painting pictures of distributed server farms iterating blissfully. Aside from the low hanging fruit of the recommendation engine, what applications are there?

An October 2015 issue of the IEEE Spectrum (always a favorite publication) takes the time to look at machine learning from an applications point of view. And a practical application at that. No, its not about reaping in big profit. It is about doing science. And trying to improve on governments' response to Ebola, which in its last outbreak took more than 11,000 lives.

Researcher Barbara Han tells about her use of machine learning algorithms to try and predict which reservoir species- bats or others- could come to harbor a disease like Elbola. Let’s not sit around and wait for the next epidemic, she suggests, let’s instead use computer power to try and foresee the path ahead.

Han said she and her Cary Institute of Ecology colleagues used machine learning to go through vast mounds of unstructured data about wildlife - trying to identify the traits that might alert us to possible ebola-disease-type sources. Along the way she provides a pretty succinct description of the machine learning processs. In machine learning the steps are:

1. Obtain training data set
2. create an initial classification tree
3. split the data set into two groups using randomly selected features - in this case for example, body size, which for the little varmints she is parsing could be ‘under or over’ 1KG
4. Use algorithm to build a second tree that prioritizes misclassified species in the attempt to sort them correctly
5. Repeat. Iteratively. Generate thousands of trees. See the classification accuracy improve.
6. When you get the model performing well on training data, then you
7. Make predictions, using the rest of the data set.


The interesting thing is that you're not waiting for the outbreak to react. Your machine does not get bored with the rote tasks. With ecological systems that have many many variables this is especially useful. I wonder of course if we arent ultimately driven by catastrophe.(batastrophe?)  We have a lot of data on those African bats - the researchers have gathered 30,000 individuals from hundreds of thousands of samples. Of course, The Cary group's  work of prediction, which guesses at likely newcomer carriers, would at least give a leg up. Han says they can suggest areas 'where to look for trouble'' - a model that looked at 58 rodent species could become culprits, they say. And a possible trouble spot could be in the Great Plains of the U.S., spanning from Nebraska south to Kansas. - Jack Vaughan

Related
http://spectrum.ieee.org/biomedical/diagnostics/the-algorithm-thats-hunting-ebola

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.