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This AI Boom Will Also Bust

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Re: This AI Boom Will Also Bust

#221

Post by morgante »

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…

> Sentiment analysis has no way of defining sarcasm or other natural language terms.

Of course it can. If humans are capable of detecting a given inflection, computers absolutely can as well (given enough data).

Any sentiment analysis algorithm which classifies "that new machine learning algorithm is so sick" as negative is not worth an ounce of consideration. Compared to other problems, that is absolutely trivial to classify, especially since you're typically training off data sets which already include such vernacular.

Re: This AI Boom Will Also Bust

#222

Post by ffwd »

jonathankoren wrote: post #210
ffwd wrote: post #152

Earlier quoted context omitted.

Yeah that's why I called it philosophical, because the idea is a little more involved, shall we say. I'm not a god of this, so speculation ahead bewarned. In cases that aren't clear cut, you would also need contextual data like bigger actual physical area, or over time dimension, really any data point that can help narrow down what the thing is. It wouldn't just be pure deep learning stuff, it would be some kind of m…

This is literally a philosophical problem. It's called ontology. And no amount of data solves this problem, because ultimately it's a labeling problem, and the border between things is ill defined, and additional data doesn't help resolve labeling ambiguity, if anything it finds out just how ill defined the world actually is. Think about it. Let's say you had a problem which was find the black squares. So you collect…

> if anything it finds out just how ill defined the world actually is.

Yeah I hear this but it seems only half-true to me. While for most intents and purposes the world is ill-defined, in another sense the world itself is "100% signal" and no noise. If we "zoom out" and take a grand view, imagining that we have a supercomputer and a huge database, and the algorithms are solved, I think every 'thing' in the universe has some unique features, and if you start to have them all in a database you may be able to uniquely identify any thing, at least those important to us. Everything one has excludes something else, but it also includes that specific thing. Every thing adds context to one thing and removes context from another. If you can draw a map of it, it seems to me like deep learning can, hypothetically, automatically differentiate it. Deep learning isn't just about one vector or one hierarchy of features, it's about how the world is ALL vectors like this, even if right now, the CS around it is pretty limited. It seems to me intuitively true at least. At the bare minimum, seeing as us humans are absurd about categorizing everything into objects, and it actually works very well functionally (we can manipulate, create and predict in the world)

Re: This AI Boom Will Also Bust

#223

Post by kennethcwilbur »

kevinchen wrote: post #159
ma2rten wrote: post #101

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they…

> I feel like this post is essentially someone from the first group criticizing the last group, saying their methods are not applicable to him. Probably more accurate to say that it's the first group criticizing others in the first group who try to act like people in the third group. Data scientists who use deep learning for everything, when a more interpretable model would do just as well.

Or, someone in the first group criticizing observers who fail to distinguish among groups before making predictions.

Re: This AI Boom Will Also Bust

#225

Post by eva1984 »

>> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data

Not nearly true. The simple counter-argument is that prior to DL, we don't have good approach to really 'clean' data like images.

The author states this fact as if cleaning data is a piece of cake. No, it is surely not. In fact, part of the DL's magic trick is the ability to automatically learn to generalize useful features from data. From another perspective, the whole DL frontend, prior the very last layer, can be viewed as a data cleaning pipeline, which is learnt during the training process, optimized to pick the useful signals.

The author clearly isn't an expert on the matters he trying to put claims on. Yet his statement comes with such big confidence or ignorance. This shows why this revolution will be a truly impactful one, for even some of the claimed intellectuals cannot understand its importance and divergence of its predecessors. They will be caught off-guard then left behind. It would be very enjoyable to watch what their reaction would be once it happens.

Re: This AI Boom Will Also Bust

#226

Post by bogomipz »

ma2rten wrote: post #101

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they…

This was a really nice breakdown, thanks.

You mentioned that that Deep learning was a method of choice for AI researched because Deep it has unlocked a lot of new application.

I have a question - is it also a "method of choice" for researchers because its not well understood yet why Deep Learning actually works?

Re: This AI Boom Will Also Bust

#228

Post by logicallee »

xamuel wrote: post #204
logicallee wrote: post #138

Earlier quoted context omitted.

It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of sup…

>if there is no pixie dust that makes humans intelligent [...] then neural net breakthroughs [...] have the potential [...] You're assuming neural nets are the right model. Like a 19th century person saying, "if there is no pixie dust ... then eventually Newtonian mechanics will explain these unexpected wobbles we see in the planets' orbits."

What do you mean "assuming" it's the right model - a known-good model is the model in human brains, which is definitely a series of neurons, no? what else would it be?

Re: This AI Boom Will Also Bust

#229

Post by pakl »

wildmusings wrote: post #155

Until the taboo on talking about consciousness is broken and we seek to understand what role this incredible phenomenon plays in human cognition, there will be no progress towards the holy grail: true general purpose AI. That is my falsifiable prediction.

Just define consciousness, and then maybe we can start to have a scientific discussion about it. ;)

Re: This AI Boom Will Also Bust

#230

Post by jostmey »

ma2rten wrote: post #126
vonnik wrote: post #123

[Disclosure: I work for a deep-learning company.] Robin's post reveals a couple fundamental misunderstandings. While he may be correct that, for now, many small firms should apply linear regression rather than deep learning to their limited datasets, he is wrong in his prediction of an AI bust. If it happens, it will not be for the reasons he cites. He is skeptical that deep learning and other forms of advanced AI 1)…

This is somewhat tangental, but what do you think about Ladder Networks? ( https://arxiv.org/abs/1507.02672 , https://arxiv.org/abs/1511.06430 ) I was really impressed by their results, but I haven't seen it being applied successfully to other applications.

Disclaimer, I haven't looked at their work to closely.

I think the process they use to select the hyperparameters overfits on the labels in the validation set. The true size of the labelled data includes the 100 from the training set and all labels in the validation set.

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