Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Predict the future with Machine Learning

zeroequalsfalse.press

Post reply on HN
Print view View original

21–30 of 46 posts

Re: Predict the future with Machine Learning

#21

Post by zEVE16Ug50tV »

tinkerdol wrote: post #8

We shouldn't ever confuse machine learning with predicting the future -- just because you've never encountered a black swan in the wild, doesn't mean they don't exist. That being said, the article otherwise seems like a great introduction. Not sure why they chose that title.

> just because you've never encountered a black swan in the wild, doesn't mean they don't exist.

Great point. We can't know that a machine learning algorithm used to make predictions won't be wrong if the future turns out to be significantly different from the past. A swan-classifier trained on images of white swans would fail hard if given pictures of black swans.

That said, people find it useful to use machine learning algorithms to predict the future, as the future tends to be similar to the past, at least in the limited domains to which machine learning is currently applied. As compute increases and we learn how to write machine learning architectures[0], we don't need to distinguish as much between 'machine learning' and plain old 'learning' and much of what philosophers have thought over the years about the problem of induction, and relevant domains of induction, becomes relevant to the topic.

[0] Or learn them. Jeff Dean mentions experimental success learning RNN architectures: https://www.youtube.com/watch?v=vzoe2G5g-w4

Re: Predict the future with Machine Learning

#22

Post by visarga »

lngnmn wrote: post #19
visarga wrote: post #18

Earlier quoted context omitted.

You don't need perfect simulation, just a good enough one. Simulation is like a dynamic, extensible dataset. Neural nets can learn from simulation not only in game play, but also in genetics, robotics and general reasoning. I think simulation is at the core of what will lead to AGI.

My comment was is about inevitable philosophical gap between reality and any model or a simulation, which cannot be bridged in principle. Yes, simulators as sources of sensory input to get similar experience are used, for example, to train airline pilots. Nevertheless, there is no airline which train its pilots on a simulator only. Algorithm, like a pilot, would learn a simulation, not reality.

That's true. I was thinking about using simulation as an addition to being embedded in the real world. Simulation is necessary to plan complex actions based on reinforcement learning (model based RL). When AI can do simulations, it can check a few steps ahead to see how things will go and select and act in a proactive way as to maximize rewards. When interacting with people, the AI will need to model people's state of mind and knowledge, in order to infer how they would act and react. When using a device, an AI would need to control it and know what to expect from it. In almost any non-trivial interaction, AI needs to simulate in order to adapt to situations. Since it is impossible to rewind reality (like a game) in order to try another action, the agent needs to do that in its imagination (simulator).

By the way, humans are good for simulating some things, but quite bad for others, and it still doesn't stop us from being the most intelligent agents. I think AI only needs to simulate a little ahead in order to act much more intelligently than today, because today, AI is mostly reactive, or feed-forward, like a simple reflex.

Re: Predict the future with Machine Learning

#23

Post by visarga »

stareatgoats wrote: post #20

To me as an AI novice this article seemed like a good overview. It did not address one problem I have with AI however, which is the inherent lack of transparency. That is, unlike normal programs, we have an input and an output, but the reasoning in between is a black box to human intelligence. This problem has to be solved before we can turn over any really vital tasks to AI with confidence IMO.

It's not a 'black box'. We know what goes in: matrix multiplication and simple math, in millions of similar units. We can also probe the net to know what a neuron does, or what a specific configuration means - but it might be disappointing - each individual neuron or weight might not have much meaning on its own and even if it were removed, the neural net world work just as well (we can remove almost 95% of the neurons and still make it work).

We can also perturb the inputs to study how the outputs depend on them. There are many things we can do to probe a neural net. We might not be able to say for sure what situation will make it fail, but we can try it with millions of tests to see how good it is. That's how neural nets are rated, actually - by testing on loads of new data, they haven't seen during training.

On the other hand, human brains are really black boxes. We know much less about how they work or how they reach a conclusion. The fact that we can self-report is not the same as knowing how a human would behave in any reasonable situation. We don't actually know how people will behave. And yet, a few people do have access to incredible power to do harm to humanity. We have had to live with that fear since the invention of atomics and genetic viruses.

So I don't think the argument that "neural nets are black boxes" is so powerful. As opposed to what, I would say? Even our dear president is such a black box. We have no idea how it works, or what he will do next.

Re: Predict the future with Machine Learning

#24

Post by zEVE16Ug50tV »

stareatgoats wrote: post #20

To me as an AI novice this article seemed like a good overview. It did not address one problem I have with AI however, which is the inherent lack of transparency. That is, unlike normal programs, we have an input and an output, but the reasoning in between is a black box to human intelligence. This problem has to be solved before we can turn over any really vital tasks to AI with confidence IMO.

That's a good and deep point. Let's consider the case of writing an algorithm to drive a car. There are some ideas people have had:

1) What really matters is what happens in the worst case, so we need explanations in the worst case, but not necessarily the rest of the time: when the car is choosing a slightly more efficient trajectory, we don't need an explanation of why it 'chose' to do that. In the case of a crash, though, we'd like to know precisely what went wrong. This suggests, perhaps, that we could have a simpler and more transparent fallback system that usually is uninvolved in driving, but that takes 'responsibility' and has the ability to control the vehicle at a minimum level of safety (rather than efficiency).

2) Humans seem to produce make decisions first and explanations later http://www.skepticink.com/tippling/2013/11/14/post-hoc-ratio...: 'To the question many people ask about politics — Why doesn’t the other side listen to reason? — Haidt replies: We were never designed to listen to reason. When you ask people moral questions, time their responses and scan their brains, their answers and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.' so perhaps we could also train an ML agent to produce explanations that we find persuasive?

Re: Predict the future with Machine Learning

#25

Post by stareatgoats »

zEVE16Ug50tV wrote: post #24
stareatgoats wrote: post #20

To me as an AI novice this article seemed like a good overview. It did not address one problem I have with AI however, which is the inherent lack of transparency. That is, unlike normal programs, we have an input and an output, but the reasoning in between is a black box to human intelligence. This problem has to be solved before we can turn over any really vital tasks to AI with confidence IMO.

That's a good and deep point. Let's consider the case of writing an algorithm to drive a car. There are some ideas people have had: 1) What really matters is what happens in the worst case, so we need explanations in the worst case, but not necessarily the rest of the time: when the car is choosing a slightly more efficient trajectory, we don't need an explanation of why it 'chose' to do that. In the case of a crash,…

Agree, the need for transparency occurs mainly when something goes wrong. But it is not also a question of control? Without human understanding there is no human control, with no human control we have HAL ... which sort of answers your second point: no, we should not let AI come up with it's own explanations that we just find persuasive.

Re: Predict the future with Machine Learning

#26

Post by pcmaffey »

tinkerdol wrote: post #8

We shouldn't ever confuse machine learning with predicting the future -- just because you've never encountered a black swan in the wild, doesn't mean they don't exist. That being said, the article otherwise seems like a great introduction. Not sure why they chose that title.

Any relevant prediction model should account for the probability of black swans existence, even if it may have no idea what a black swan might look like.

Re: Predict the future with Machine Learning

#27

Post by TimPC »

pcmaffey wrote: post #26
tinkerdol wrote: post #8

We shouldn't ever confuse machine learning with predicting the future -- just because you've never encountered a black swan in the wild, doesn't mean they don't exist. That being said, the article otherwise seems like a great introduction. Not sure why they chose that title.

Any relevant prediction model should account for the probability of black swans existence, even if it may have no idea what a black swan might look like.

This runs against the challenge that (almost?) all statistical methods train by fitting a model to some sort of data. If you have zero examples of a black swan in the data you can agree in principle they might exist but you'd expect a statistical model to get them wrong.

Re: Predict the future with Machine Learning

#28

Post by TimPC »

thousandpounds wrote: post #17
hal9000xp wrote: post #2

I would like to read mathematical foundations of machine learning written for those who are bad at calculus but good at discrete mathematics and algorithms. For example, I'm learning algorithms, participate at contests, quite comfortable with combinatorics and discrete probability theory but I'm absolute zero at calculus. I would like to read machine learning's math introduction which is friendly to my "discrete" bra…

I am in the same boat. I get the feeling that most Calculus books are just a compilation of tips and tricks. So I am suggesting you invest time into learning real analysis proper. Right now I am learning from [1]. It follows Rudin closely and as opposed to many other analysis books meant to "better explain" stuff, it goes deep into the trenches and actually tackles the subject. [1] https://www.amazon.com/Real-Analysi…

The majority of the stuff you need from calculus for deep learning doesn't rise to the level of real analysis. Real analysis is worth doing if their are benefits to Fourier transforms on your data sets in the domain you're working in but otherwise has good payoff for studying more math rather than studying more deep learning.

Re: Predict the future with Machine Learning

#29

Post by thousandpounds »

TimPC wrote: post #28
thousandpounds wrote: post #17

Earlier quoted context omitted.

I am in the same boat. I get the feeling that most Calculus books are just a compilation of tips and tricks. So I am suggesting you invest time into learning real analysis proper. Right now I am learning from [1]. It follows Rudin closely and as opposed to many other analysis books meant to "better explain" stuff, it goes deep into the trenches and actually tackles the subject. [1] https://www.amazon.com/Real-Analysi…

The majority of the stuff you need from calculus for deep learning doesn't rise to the level of real analysis. Real analysis is worth doing if their are benefits to Fourier transforms on your data sets in the domain you're working in but otherwise has good payoff for studying more math rather than studying more deep learning.

Broadly speaking, I want to read books like [1]. It looks like they use quite a bit of advanced nondiscrete probability. Since I prefer books written in definition - theorem - proof format anyway, I figured I might as well get analysis out the way :)

[1]http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pd... (Foundations of Data Science by Bloom/Hopcroft/Kannan)

Re: Predict the future with Machine Learning

#30

Post by TimPC »

shincert wrote: post #15
hprotagonist wrote: post #13

Fundamentally it is Software that works like our brain.. Stopped here. Moving on.

Why? Is it really that bad of an analogy for an absolute beginner?

It's not terrible for an absolute beginner but it's fairly harmful overall. People tend to use this analogy a lot to conflate specific AI with general AI and argue for regulatory capture based on things completely outside of evidence. The real brain is sparsely connected and has multiple activation networks that reuse nodes. We also have the ability to train from single examples to things we've never seen before so it seems unlikely that our brain operates exclusively by derivatives on error or other data-fitting techniques. Humans still seem more unreasonably effective than deep learning on many tasks and this is despite having a harder problem (Humans have more tasks with unlabelled data as far as I can tell).
Post reply on HN