On the machine learning curve

There was an article in the New York Times today I thought I might mention. "Taking Baby Steps toward Software that Reasons like Humana" (below) by John Markoff is a an articulate look at what I call machine learning. .

The story considers what is going on these days as a re-vitalization of artificial intelligence, which bloomed in the 1980s and then faded from headlines, and I agree. I think the story conveys, in a way, that there are some similarities.

The story looks doesn’t use the term 'machine learning' – tho it does mention pattern recognition (somewhat synonymous), deep learning and deep neural nets  .. which are fairly similar. What I think that emphasizes is that today's 'machine learning' is basically a new take on neural networks.

And as such machine learning faces hurdles -because flaws that stymied AI, still remain to be addressed. As Markoff writes "generalized systems that approach human levels of understanding and reasoning have not been developed."

What he doesn’t say is that the people that sell these things today tend to gloss over that, same as their counterparts 'back in the day.' That is not to criticize this particular work, which necessarily has a limited objective.

The story doesn’t use the term 'cognitive computing' either. But it talks about things – Q&A systems, natural speech processing - that combine with 'deep learning' to create cognitive computing.


Taking Baby Steps Toward Software That Reasons Like Humans

By JOHN MARKOFF MARCH 6, 2016 

Richard Socher appeared nervous as he waited for his artificial intelligence program to answer a simple question: “Is the tennis player wearing a cap?” The word “processing” lingered on his laptop’s display for what felt like an eternity. Then the program offered the answer a human might have given instantly: “Yes.” ....

First Thought - When data goes mystic or, Data Myth

Thinking a bit about the past. And how we got here. Ruminating on the passing of Paul Kantner, and contemplating his dogged clutch to a futuristic transcended science fiction vision. And where a lot of my impressions of technology emanate initially from .. Shannon the telegraph messenger, Wiener the cyberneticist, the Alchemists, neural nets.

Have taken time too to track back and visit Lewis Mumford, who I probably have only read before but once or twice removed. His Myth of the Machine –while rich but scatter plotted – sets a backdrop for the present moment of machine learning and the rebirth of AI – much as Kantner’s Wooden Ships does.

Maybe by riffing on Mumford I can characterize my moody interpretation of technology better. I have been a mystical spin on technology for a very long time. And therefore I go back to another point in time to start over again via Lewis Mumford

Who's book is purply impenetrable – as much about science as Benedictine monks’ fermenting cheese or Micky Mouse’s sorcerer’s apprentice’s broom.

Mumford can see the days of yore that now escape us. He sees  the envy of the birds in the desire to conquer the air in the myth of Icarus, the flying carpet in the Arabian Nights, or the Peruvian  flying figure of Ayar Katsi. [The index to They Myth of Machine is like the debris of a cruise ship in the Sea of Saragossa.]

Mumford notes that literate monks like Bacon and Magnus (the ones on the cusp of alchemy and modern science- when clockwork elements began to show the path of automation) like da Vinci did visualize elements that are still fodder for the Astonishing Tale tokens of our day -  incredible flying machines, instantaneous communication, transmuting of the elements. He notes too how magically influential still the dynamo and the talking machine were as he wrote (late 1960s).

Mumford mentions Thomas More, and Utopia, Bacon and The New Atlantis, in depicting the machine itself as an alternative way of reaching heaven. Language for him is a disease with symptoms we see as dream symbols that become imposing metaphors that like myth rule. You can only filter what you see using the commanding metaphors of your age, he suggests. And the machine is that which bugs Mum.

What is the myth or master metaphor of today? The belly I labor bacteria like in is made of the myth of data. Data bears resemblance to penury as described [p.274] by the Mummer man [who by the way had not too kind comments for contemporary Marshall Mcluhan.] Ask the people who sued Netflix for using their data in an A/B machine learning contest.  What do they think of mystic data? Or, the myth of the machine? –J.V.

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