thought about adding page yesterday.. adding today.. after adding page of Peter Levine..
imagine that machine learning.. isn’t for us to get better at ie: running.. transporting.. even mtn climbing.. i mean chris mccandless.. didn’t go into the wild because he was craving a better stride.. a better car.. faster data… he went because he couldn’t bear to stay
machine learning has to go beyond getting more/faster data.. imagine we are focusing on the wrong data..[too much ness…gets us to inspectors of inspectors.. and people creating tools to inspect the inspectors.. wrong data via machine learning.. could get you to forgoing the mountain .. for the steep decline on the treadmill.. because over the years.. of the machine learning algo/pattern ness.. for you to climb better.. will mostly likely cause you to lose your humane .. antifragility..]
imagine that machine learning.. at its best for humanity/world.. for your children.. for you…. is to help us all become indigenous..
the above is what i wrote.. directed at Peter.. (like how i talk to people in a world where no one can listen)
fitting with yesterday.. as i was running.. biking .. and walking inside.. and thinking.. this is machine exercising.. like for me.. for the walking and biking.. since i’ve not done those inside .. i do those outside.. but was below 0 outside..et al… but.. i had to close my eyes to do them.. and imagine i was outside.. the running.. i’ve been doing inside forever.. and am fine with that…. so i started thinking about that.. about what happens when you go to a machine.. to exercise.. i mean.. it’s nice being able to do it when you couldn’t outside.. naturally.. (and my mind went to sugata’s soles.. et al.. and part of the vision of hosting life bits.. that you aren’t confined to locals to find your tribe).. but.. over time.. the machine makes you less natural/agile/antifragile.. because machines aren’t imperfect like people.. and the earth.. so we get better.. but only for the machine.. not for real life.. not to mention .. all the opps we miss at being/becoming indigenous..
you aren’t learning to listen to the trees.. the wind.. the birds.. people… on an exercise machine..
i could go on.. i certainly have in my head..
point to me.. is that this is similar to machine learning.. winning a chess game.. isn’t what our souls crave.. it’s not making us more indigenous.. it’s not fulfilling our deepest needs.. ie: a and a.. us living wild..
oy.. maybe i’ll come back to this .. clean it up.. a machine/algo certainly would.. but then again.. that’s the point..
antifragile is what we want.. and we need to be indigenous ness for that.. not algo ing more/faster data.. we need indigenous ness (listening ness) to realize what data matters.. what matters..
i haven’t even read this yet.. so i guess now is a good time..
Machine learning is the subfield of computer science that “gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959). Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data
what if algos don’t work for life.. and what if we can’t predict.. unless we turn ourselves into machines.. then we can predict.. is that what we want..? machine working for us.. (ie: hosting life bits.. so we can dance.. improv).. or us working for machine.. (ie: we cog ourselves.. measure ourselves.. so that we become predictable)
– such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,
we are so messed up about what.. making decisions… is all about.. we need to redefine that..
through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,
what if all this malicious ness.. is because of machining ourselves.. ie: let’s try gershenfeld sel
optical character recognition (OCR), search engines and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.
Zeynep Tufekci (@zeynep) tweeted at 7:36 AM – 21 Apr 2017 :
@timoreilly @dweinberger We don’t have a Laplace’s equation for humans—never will—but just not the same “not understanding” as with machine intelligence, an alien. (http://twitter.com/zeynep/status/855414872196210688?s=17)