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Many Machine Learning Studies Don't Actually Show Anything Meaningful, But They Spread Fear, Uncertainty, and Doubt (theoutline.com)
Michael Byrne, writing for the Outline: Here's what you need to know about every way-cool and-or way-creepy machine learning study that has ever been or will ever be published: Anything that can be represented in some fashion by patterns within data -- any abstract-able thing that exists in the objective world, from online restaurant reviews to geopolitics -- can be "predicted" by machine learning models given sufficient historical data. At the heart of nearly every foaming news article starting with the words "AI knows ..." is some machine learning paper exploiting this basic realization. "AI knows if you have skin cancer." "AI beats doctors at predicting heart attacks." "AI predicts future crime." "AI knows how many calories are in that cookie." There is no real magic behind these findings. The findings themselves are often taken as profound simply for having way-cool concepts like deep learning and artificial intelligence and neural networks attached to them, rather than because they are offering some great insight or utility -- which most of the time, they are not.
without the fun part.
That's what Skynet wants you to think.
you could say this about every study that has ever been or will ever be published - they all do the same thing - find some correlation between two things and suggest but don't prove causation and recommend more studies be done
When AI can teach itself how to use a programming language from documents found on the internet or solve a long unsolved mathematical puzzle, that'll be something to talk about.
---Technology will liberate us if it doesn't enslave us first.
I was told that I as an idiot and would soon be replaced by a computer
Doesn't everyone here already know this? And why does he keep saying "way-cool?" Is this meant to be for children?
The human brain sees pattern everywhere it looks too.
I'm retired now but I've been doing a lot of reading and experimentation with decision tree based classification methods. I like these because the produce models you can examine critically, as opposed to so-called "deep learning" algorithms which produce results that you pretty much have to judge by their giving you the result you expect. It's not that that isn't useful in some cases, but I don't find it as interesting.
Post may contain irony: discontinue use if experiencing mood swings, nausea or elevated blood pressure.
Or at least headlines are trying to achieve it.
Maybe we need to come up with a new word to describe when computers are used to generate meaningless connections between events. Or maybe we can add to Pareidolia's definition the idea that it can also find meaning in random information as well as sights and sounds. Oooo! That's it. Meta-Pareidolia. Finding meaning in random stimuli or information.
Who's this Albert person we keep hearing about? And maybe he'll even introduce a font that has unambiguous uppercase/lowercase letters.
I've been hearing about Artificial Intelligence since I read it in BYTE Magazine in the 1980's.
In the past it used to be stuff like "Scientists find that painting your room yellow leads to cancer!", now it's the same with AI. Turns out flipping through large amounts of statistical data until you happen upon a correlation is easily automated, and trash scientists will soon have to worry about their jobs.
Creating FUD requires fairly high level AI.
love is just extroverted narcissism
From the summary: "Anything that can be represented in some fashion by patterns within data ... can be 'predicted' by machine learning models given sufficient historical data."
This is nothing to sneeze at! In many cases, humans are *not* able to do accurate prediction given sufficient historical data.
This article wrote by Artificial Intelligence.
AI has been ridiculously (and recklessly) hyped since its inception as an academic discipline. This is just more of the same. Unfortunately, the implication is that it is just as difficult today, as it was 60 years ago, to take AI seriously, despite what some fearmongers out there who, drunk with success in one area, seem to think that they are experts on everything, would like people to believe.
AI notices patterns that detect cancer. Woot! AI notices patterns that determines crime rates amongst certain population groups! Fuq no. (I can't wait until someone calls AI sexist for it noticing that females get pregnant much more often than males)
Studies simply exist to inform others of a topic of interest. The problem is not the studies being scary, the problem is that highly technical information is inappropriately being repackaged and pushed out to the general public who have no insight into topic. The problem isn't the message itself, it's the messengers.
Anons need not reply. Questions end with a question mark.
Bogosity, the unit of bogusness, is named after Douglas Lenat for his work in AI.
"AI" has simply joined "Web 2.0", "Cloud", and countless other terms that are meaningless buzzwords for use by marketing departments.
Is to digitally fingerprint everyone and make encryption useless. In five or ten years, "signing in" to a website will be a joke. The AI will know who you are just based on your browsing habits and hardware. Have you ever AC'ed on Slashdot but signed in on other issues? An AI in its current form could accurately pair those to you. Facefarm invests and profits while the world's governments collect insanely accurate biometrics. Encryption will become useless because of digital fingerprinting and perhaps it would be easier to make it illegal. This way, the AI can identify "who" did the encryption and the "what" part of the message wouldn't matter. However, in the right computer (quantum?), I don't see why an AI couldn't decrypt most communication in a timely manner. One of its father's was Alan Turing for goodness sake. They make out like AI is for troll prevention, medicine, future lawyer bots, etc., while all under the umbrella of "open source," but you really need to ask yourself where does most of the funding for AI come from?
The shitty, half-assed, weak-ass excuse for artificial intelligence that everyone keeps trotting out to us does not 'know' a single thing -- because words like 'know' imply cognition (i.e. ability to think) and none of these shitty machines 'thinks' at all, not any more than a flowchart on a piece of paper 'thinks'.
You want some real fear of so-called 'AI'? It's the mis-application of it by too many people who do not understand it's (limited!) capabilities; the fear that some nudnik will put one of these half-assed iron idiots in charge of something way too important and potentially dangerous, thinking it'll 'figure it out itself', then just walk away. Sometime later: KABOOM. That's what you should be afraid of. That's also why I'll never set foot into any so-called 'self driving car' either, because I AM NOT READY TO DIE JUST YET.
***MIC DROP***
I realize I'm going against the grain here, but isn't the whole power of AI the fact that it *can* make predictions based on historical data?
Imagine you went to 1950 and told them about features in Google Docs. They would lose their nut over some of the most basic stuff: you can delete a word and retype it in *seconds*! Wow! And they would be right to be amazed. What we take for granted now is huge in terms of what it does for productivity. Imagine having to correct errors on paper by hand.
So now we have AI and *all* it can do is examine data, find patterns, and extrapolate. How pedestrian. Or should I say, how absolutely, magically fucking fantastical.
By the way, I remember in the early 1990s people complaining about how useless computers were, because they were expensive and didn't do very much. Let's look back on this discussion in 2030 and see how it panned out.
And I have a program in LISP that I wrote 30 years ago that has been saying the human race won't live past this week, every week, for three decades.
This is proof that we live in a virtual universe, probably written in Brainfuck.
AI or machine learning is just computing. That's all it is. And with better and faster computing resources, we can do more with it which makes sense. However, for "real" AI or machine learning to occur, there will need to be a biological substrate upon which such systems are to be built. I think this is the case because I believe that intelligent life is the universe's only way to "truly" conserve information.
nobody is expecting Machine Learning to be put to use in making people's lives better. More productive, yes, but not better. We're expecting three things from Machine Learning:
a. Prices will go up as ML makes it easier to figure out the maximum you can get away with charging and control/constrict supply chains.
b. Wages will go down as ML increases productivity per working.
c. The 1% will use ML to skim even more off the economy leaving the rest of us with less and less
When I was a kid there were discounts and sales everywhere. Real ones. Bargin bins where just full of cheap Chinese junk, they were full of oversupply. Bags of Halloween candy that used to be 90% off are now 20-30% off and there's damn few of them. I don't see 'manager's specials' or those cheap bags (yes bags) of donuts I used love. Maybe if you didn't grow poor you didn't notice any of this. But for anyone living poor (which given America's $59k median household income is a lot of people) you notice.
ML brings massive increases in efficiency, but for most of us that's actually a bad thing...
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(Presented in autism for the emotionally impaired)
weyland-yutani nostromo 180924
subject chris reimer would have been approximately 14 years old living in silicon valley when byte magazine dated april 1984 (autism epoch: 449625600) was released.
analysis: it is highly unlikely that this was the first time chris dale reimer was exposed to the concept of artificial intelligence given his age, location, and the time of publication. access to byte magazine would also indicate prior exposure to artificial intelligence.
conclusion: christopher dale reimer has an autistic compulsion to hyperlink in posts regardless of monitization
Fixed it for you.
sed -e 's/Chuck Norris/Rajnikant/g' joke > fact
There are trainable models such AS neural networks/deep learning and statistical models. There are also models which habe to be configured. We can even combine them. In the end this is all just classification mechanisms. They are all good at detecting known issues. They are not able to come up with new stuff.