Time-off notice: no new posts for the time being
In “What Searchable Speech Will Do To You” , published in Nautilus, James Somers discuss some interesting aspects on the coming possibility of having all we say recorded. And then labeled, tagged, searched…
“We are going to start recording and automatically transcribing most of what we say. (…) It will happen by our standard combination of willing and allowing. It will happen because it can. It will happen sooner than we think.
(…) But would all of this help or hurt us? (…) The more we come to rely on a tool, the less we rely on our own brains.
(…) By offloading more of memory’s demands onto the Record (…) it might not be that we’re making space for other, more important thinking. We might just be depriving our brains of useful material. (…)
The worry, then, is twofold: If you stopped working out the part of your brain that recalls speech (…) your mind would become a less interesting place.Continue reading
Posted at MonkeyLearn
“Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed.
(…) some of the most common categories of practical Machine Learning applications:
Image Processing (…) : Image tagging (…) , Optical Character Recognition (…) , Self-driving cars (…)
Text Analysis(…) : Spam filtering, (…) Sentiment Analysis,(…) Information Extraction, (…)
Data Mining(…): Anomaly detection, (…) Grouping , (…), Predictions(…)
Video Games & RoboticsContinue reading
When self-driving cars hit the streets, among other features it is expected that they take pedestrian safety seriously. This hope is illustrated by this couple of anecdotal stories show Google self driving car reactions to a lady chasing ducks on her wheelchair or a cyclist making a track stand.
But what next? After self-driving is supposedly stablished and approved technology, people will probably get back to the trend on posing pedestrian the blame. Ravi Mangla “The secret history of jaywalking: The disturbing reason it was outlawed — and why we should lift the ban” shows it happened in the past. Adoption of self-driving driving cars may be the opportunity window to have people free to walk again.
A similar claim comes from “The end of walking” by Antonia Malchik. When it comes to getting around, sitting apes have the high ground on the standing ones. It should also be noticed that such anti-pedestrian behaviour is growing among cyclists as well. Even if cyclists are in general much more civilized than car drivers. But so were drivers in automobile early days as well…
About when people would seem enough to think of computing capacity in terms of FLOPS, supercomputers development makes the point that a better measure is TEPS. TEPS stand for Traversed edges per second, which is sort of FLOPS weighted by communication cost.
Anyway, fact is AI Impacts produced estimates for our Brain performance in TEPS. Next thing was the ubiquitous, of course. It would seem we can hire this computational power in the next decade by $ 100/hour. But for the time being this cost is estimated to be around $4,700 – $170,000/hour. So go to your boss and tell him he’s renting your brain for a bargain.
IF you do so, your odds are better if you skip the info below and make it simple. New studies show that our brains do consider cognitive effort when making choices. This ‘TLDR’ feature of brain wiring may be the culprit in preventing you to go through the paper “Separate and overlapping brain areas encode subjective value during delay and effort discounting” that says so.
Having similar applications, users, and backgound, at a distance Machine Learning may sometimes be confused with an application of Statistics.
A closer look reveal fundamental differences, as in “Why a Mathematician, Statistician, & Machine Learner Solve the Same Problem Differently” by Nir Kaldero.
One scientific field this difference comes to surface in a distinguished manner is economics, as Noah Smith’s “Economics Has a Math Problem” sensibly puts the emphasis on the way economics uses math.
Pushing science to new fields, scientists can now employ much more data and computational power than the time when a significant part of mainstream economics was developed. If econometric tools set the tone for neoclassic economic papers in the final decades of last century, could machine learning, Bayesian inference, and neural networks open new possibilities to economic theory?
One arguable example is “Mechanisms for Multi-unit Combinatorial Auctions with a Few Distinct Goods” by Piotr Krysta, Orestis Telelis, Carmine Ventre. Not a coincidence, researchers are not from Economics departments. Even if economists are stubborn enough to dismiss game theory as a non-fundamental field, message is clear: if economists don’t embrace new math, other scientists (human or not) could engulf economics less cerimoniously.
If this happens, will we find that Keynesian uncertainty and weight of arguments fits big data better than deterministic parameters of neoclassic mainstream?