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This AI Boom Will Also Bust

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Re: This AI Boom Will Also Bust

#171

Post by [deleted] »

noobermin wrote: post #61
sgt101 wrote: post #31

Earlier quoted context omitted.

You might be being really subtle about python there ! I realize that one can create lines of code like :_ = ( 255, lambda V ,B,c :c and Y(V V+B,B, c -1)if(abs(V) abs(V) -0.4)/i ) ;v, x=1500,1000;C=range(v x );import struct;P=struct.pack;M,\ j =' x 3+26,26,12,v,x,1,24))or C: i ,Y=_;j(P('BBB', (lambda T:(T 80+T 9 i-950 T 99,T 70-880 T 18+701 T 9 ,T i (1-T 45 2)))(sum( [ Y(0,(A%3/3.+X%v+(X/v+ A/3/3.-x/2)/1j) 2.5 /x -2.7…

You might want to do code formatting for that. Look at the help[0]. [0] https://news.ycombinator.com/formatdoc

[deleted]

Re: This AI Boom Will Also Bust

#172

Post by godmodus »

cerrelio wrote: post #37
godmodus wrote: post #18

Earlier quoted context omitted.

you'll be a programmer - that is what counts. How good of a programmer you will be will determine your success. never put your eggs in one basket (not saying you shouldn't become an ML expert though, that's pretty damn nice). as a Phd, you are probably good enough. as to ML, its adoption is hyped. it is powerful, but not as anyone really talks about. support vector machines and Bayesian learning have been around sinc…

> support vector machines and Bayesian learning have been around since the 70s/80s (ninja edit: SVM's since 1963! Markov Chains 1950s, Bayesian Learning/Pattern recognition sine the 1950's), but adoption has been slow due to the nature of business, which is now drooling over it since neural networks beat a few algorithms. This is one of the things I find hardest about convincing managers and leads of. They think thin…

I explain it using the infinitesimal method, which if done right using the hill climbing metaphor, often delivers. But it does take away the magic of "wooo, neural" :p

Re: This AI Boom Will Also Bust

#173

Post by lucidrains »

logicallee wrote: post #161
jpatokal wrote: post #149

Earlier quoted context omitted.

I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype. And you're trying to rebut this by referencing an AI-generated Christmas jingle . I think the author rests their case...

No, I just meant to reference my discussion from there (i.e. for people to read through my comments there, after clicking.) IOW I meant to transclude that discussion here. (Perhaps within that comment thread a good specific summary comment is: https://news.ycombinator.com/item?id=13090869 ) Obviously it is hard to know when that magic moment will happen that some kind of general AI is created that can learn in some s…

absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167

Re: This AI Boom Will Also Bust

#174

Post by rampage101 »

bogomipz wrote: post #86
rampage101 wrote: post #23

The more I get into machine learning and deep learning it seems like there is an incredible amount of configuration to get some decent results. Cleaning and storing the data takes a long time. And then you need to figure out exactly what you want to predict. If you predict some feature with any sort of error in your process the entire results will be flawed. There are a few very nice applications of the AI techniques…

>" however most data sets don't fit well with machine learning" Could you elaborate on why this is?

Your average data-set does not fit into 3 nice categories like the Iris data-set does. For example, with the Iris data set if you know the sepal length and the petal length, you can say with near certainty which type of flower it is. Even trying to classify other objects in nature is much harder than this dataset.

Now let's take sentiment analysis which tries to determine if some words are positive or negative. If someone said: 'that new machine learning algorithm is so sick'. The algorithm has no way of knowing that sick may be slang to mean good, because the system looks up 'sick' and finds that is a negative word. Sentiment analysis has no way of defining sarcasm or other natural language terms.

Re: This AI Boom Will Also Bust

#175

Post by rampage101 »

ffwd wrote: post #75
rampage101 wrote: post #23

The more I get into machine learning and deep learning it seems like there is an incredible amount of configuration to get some decent results. Cleaning and storing the data takes a long time. And then you need to figure out exactly what you want to predict. If you predict some feature with any sort of error in your process the entire results will be flawed. There are a few very nice applications of the AI techniques…

> In the real world, most things are in a maybe state rather than yes/no. Not to get too far afield, but I disagree with this on a certain philosophical level. All states are yes/no. All states of all things should result in a yes/no and be differentiable, with enough data. This doesn't speak to the practicality of that but as far as I can tell the theoretical potential is huge, almost infinite even.

What I mean is the Iris data set splits into 3 categories easily.

Right now, they are saying AI self driving cars can get their predictions right 95%+ of the time. However, the cases where they cannot classify the object is the problem. Those are the "maybe" cases I was referring to where they algorithm simply cannot classify the object no matter how much it has seen.

Re: This AI Boom Will Also Bust

#176

Post by Entangled »

It won't. It will get better.

In a couple of years you will be able to take a picture of a rash in your arm and get exact diagnosis with treatment instructions. You will be able to take a pic of a flower, a leaf or any tree and get accurate info about its species, plagues and best techniques for growing them. You will be able to take a pic of any insect, spider, snake, animal, or anything that moves, any mineral, element or anything at all and get accurate info about that.

AI is not only about robots thinking, it is about collecting information and making it available on demand. Agriculture and health will be the first beneficiaries, finances in a close second.

Data mining is where the first stage of AI is, and Google is moving ahead of everybody else with their search engine, maps, translation, and all the information collecting tools. Once you have enough data, knowledge is just a couple of programs away.

Re: This AI Boom Will Also Bust

#177

Post by mathperson »

eli_gottlieb wrote: post #139
dheera wrote: post #39

Earlier quoted context omitted.

And before you do mundane data science on your structured data, you should figure out if there is a better way to get cleaner raw data, more data, as well as more accurate data. For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough.…

>Better time-of-flight depth cameras will be here soon enough. It's just basic physics. I worked on one for my PhD research. You can get pretty clean depth data with some basic statistics and no AI algorithm wizardry. We're just waiting for someone to take it to a fab, build driver electronics, and commercialize it. You should talk to us at Leaflabs. Commercializing research-level tech in embedded electronics is what…

are you guys looking to hire anyone with compressed sensing/neuro-imaging experience? possibly someone with the username /mathperson lol

Re: This AI Boom Will Also Bust

#178

Post by rampage101 »

toomuchtodo wrote: post #21
whorleater wrote: post #8

Earlier quoted context omitted.

I don't actually think that's true, if the "AI bubble" bursts at some point in the near future, the people who'll be in trouble with be those without formal education to back them up.

> the people who'll be in trouble with be those without formal education to back them up. The people who can't hack it are those who'll be in trouble. Tech has never much been the place where credentials are necessary. Don't specialize and saddle yourself with years of college debt if you're unsure of the field's long term prospects.

Depends where you live. In the USA credentials matter a lot... in Europe or else where they could care less if you studied somewhere.

Re: This AI Boom Will Also Bust

#179

Post by IshKebab »

Entangled wrote: post #176

It won't. It will get better. In a couple of years you will be able to take a picture of a rash in your arm and get exact diagnosis with treatment instructions. You will be able to take a pic of a flower, a leaf or any tree and get accurate info about its species, plagues and best techniques for growing them. You will be able to take a pic of any insect, spider, snake, animal, or anything that moves, any mineral, ele…

That is a valid use of deep learning. I think the article is talking about people thinking that because deep learning is very impressive for some things, they should use it for everything - even problems where a much simpler solution works fine, or problems where deep learning doesn't apply.

I work for a consumer product company, and there are often people talking about using 'big data' and 'machine learning'. They're just following the hype; they don't really know what machine learning is and I've only heard two or three potential applications of it mentioned that make any kind of sense.

Re: This AI Boom Will Also Bust

#180

Post by bogomipz »

rampage101 wrote: post #174
bogomipz wrote: post #86

Earlier quoted context omitted.

>" however most data sets don't fit well with machine learning" Could you elaborate on why this is?

Your average data-set does not fit into 3 nice categories like the Iris data-set does. For example, with the Iris data set if you know the sepal length and the petal length, you can say with near certainty which type of flower it is. Even trying to classify other objects in nature is much harder than this dataset. Now let's take sentiment analysis which tries to determine if some words are positive or negative. If so…

Thanks for the explanation and why the Iris data set is so ubiquitous in ML tutorials.
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