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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#81

Post by mlthoughts2018 »

tgv wrote: post #15
snemvalts wrote: post #5

The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.

It might not be a hard boundary, but I think the perception of ML vs. optimization is how much of a model you have. If all you have is a black box, then it's ML; if you know how the system you are studying works, it's (parameter) optimization.

That’s a very unfair distinction, almost like a No True Scotsmam fallacy to say machine learning is only bad and other stuff is only good (in terms of transparency).

But machine learning has predated neural networks by hundreds of years. The core mathematical basis of all machine learning coursework linear regression and decision trees. Other models like SVMs, Bayesian models, nearest neighbor indexes, TFIDF text search, naive Bayes classifier, etc., are basically like machine learning 101, and they have many different properties regarding interpretability depending on the problem to solve.

Re: You might not need machine learning

#82

Post by mjburgess »

dmos62 wrote: post #34
lmilcin wrote: post #26

Earlier quoted context omitted.

Well... I guess most people equal ML with AI and use these terms interchangeably. If you just replace ML with AI everywhere in this article it is going to make sense. The article has other problems, one being the main premise. The problem isn't to drive a car around track (which is what the polynomials did) but rather write a program that can figure out how to drive a car without you knowing how to solve it.

Well that depends on your definition of AI. Which isn't well defined. We call AI what we perceive as "magic". Black box algorithms have a higher chance of being perceived that way (e.g. neural nets). When you get some insight into how an algorithm works (easier for transparent box algos, but same holds for black box algorithms), you start to see it less and less as "magic", and, consequently, you're less likely to re…

I disagree.

We don't find these systems intelligent because, on inspection, they arent.

We are intelligent. Not "magically", but actually nevertheless.

Our intelligence, and that of dogs (, mice, etc.) consists in the ability to operate on partial models of environments; dynamically responsive to them; and to skilfully respond to changes in them.

This sort of intelligence requires the environment to physically reconstitue the animal in order to non-cognitively develop skills.

It is skillful action we are interested in; and precisely what I missing in naive rule-based models of congition.

Re: You might not need machine learning

#83

Post by mlthoughts2018 »

dhruvdh wrote: post #13
snemvalts wrote: post #5

The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.

Isn't that just searching a space? Machine learning generally refers to a fancy way of searching a space. Here the guy searched randomly so I'd say not machine learning. But perhaps using a cost function or a loss function is enough to call it machine learning. A machine just used an algorithm to learn another algorithm after all.

Stochastic gradient descent and other things like genetic algorithms and simulated annealing are random search techniques specifically created and taught in the context of machine learning.

Re: You might not need machine learning

#84

Post by kgwgk »

mlthoughts2018 wrote: post #83
dhruvdh wrote: post #13

Earlier quoted context omitted.

Isn't that just searching a space? Machine learning generally refers to a fancy way of searching a space. Here the guy searched randomly so I'd say not machine learning. But perhaps using a cost function or a loss function is enough to call it machine learning. A machine just used an algorithm to learn another algorithm after all.

Stochastic gradient descent and other things like genetic algorithms and simulated annealing are random search techniques specifically created and taught in the context of machine learning.

Simulated annealing goes back to the seventies and was definitely not "specifically created in the context of machine learning". Many (most?) optimization techniques have their origin in Operations Research.

Re: You might not need machine learning

#85

Post by thesz »

karolkozub wrote: post #20

I love the article, but I don't agree with the premise that machine learning equals neural nets. In my understanding machine learning is a very broad term that just as well could be applied to the polynomial model if the constants were optimized algorithmically. I feel like the presented argument is more for transparent vs opaque models rather than machine learning vs something else. Also one could argue that the pol…

> Also one could argue that the polynomial model is just a perceptron

One also can argue otherwise [1].

[1] https://matloff.wordpress.com/2018/06/20/neural-networks-are...

Re: You might not need machine learning

#86

Post by cambalache »

luc4sdreyer wrote: post #66
cupofcoffee wrote: post #55

I simply do not understand the ML hype. It's absolutely ridiculous. On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. Each and every time I see neural networks on some "techies" blog, I wanna vomit.

> On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. The actual problem is that we don't know how long it'll be until we build an AGI. Experts put the range somewhere between 10 years and never. Building an unconstrained AGI is an existential risk, so it's important to try and narrow the confidence bands on these questions. That's one…

The answer is "Not any time soon", you could also worry about giant lasers on sharks. Trying to regulate now a supposedly imminent AGI is like Morse and Edison regulating the Internet.

We are doing an spectacularly lousy job of regulating what it already exists.

Re: You might not need machine learning

#87

Post by mlthoughts2018 »

cupofcoffee wrote: post #55

I simply do not understand the ML hype. It's absolutely ridiculous. On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. Each and every time I see neural networks on some "techies" blog, I wanna vomit.

Your reaction seems quite extreme and vitriolic.

My job is to be the director of machine learning at a medium-sized ecommerce company. My company uses machine learning to solve lots of problems in search & customer recommendation, image & text processing, time series forecasting, and a few “backend” support models for things like phishing / fraud detection and gaining efficiencies in customer support operations.

I am happy to answer any questions I can about why machine learning has been a continued growth and investment area for my company and how we thoroughly validate business value when deciding whether to adopt ML solutions.

We use modern neural networks in probably about 10-15% of the solutions we operate.

Re: You might not need machine learning

#88

Post by Retric »

lopmotr wrote: post #74
maweki wrote: post #36

Earlier quoted context omitted.

Oh, I was not defending neural networks. This was the cynical sales pitch for the case where you don't want to employ mathematicians or computer scientists, but just throw code and computational resources at the problem.

But isn't that an important part of the value of neural networks? Mathematicians are expensive so we'd like a computer to make a model for us, just like drivers are expensive so we want self-driving cars.

The issue with that is NN fail in some really interesting ways so you still need a lot of effort to get a robust solution. Remember, after some serious investments by many organizations self driving cars are still in development. At the same time a few people have demonstrated a basic system that seems close without nearly that much investment. Unfortunately, the difference between a demo and working solution can be several orders of magnitude.

Re: You might not need machine learning

#89

Post by username90 »

yters wrote: post #73

How is this not machine learning? I guess because there is no gradient descent to pick the parameters, just random space exploration?

Gradient decent has been known for hundreds of years, it isn't machine learning. Optimizing parameters is an entire field in itself. Calling all cases of parameter optimization "machine learning" is just a fad because it sounds cooler. Before computers people still optimized thing but by hand, doing the same calculations with a computer is no more machine learning than doing them by hand is human learning.

Re: You might not need machine learning

#90

Post by qayxc »

luc4sdreyer wrote: post #66
cupofcoffee wrote: post #55

I simply do not understand the ML hype. It's absolutely ridiculous. On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. Each and every time I see neural networks on some "techies" blog, I wanna vomit.

> On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. The actual problem is that we don't know how long it'll be until we build an AGI. Experts put the range somewhere between 10 years and never. Building an unconstrained AGI is an existential risk, so it's important to try and narrow the confidence bands on these questions. That's one…

> Building an unconstrained AGI is an existential risk

But why? So far no-one has been able to explain this to me.

An AGI in at of itself is nothing but a brain-in-a-vat. The exponential increase in scientific knowledge was based on the scientific method, which replaced the ancient Greece discussion-based epistemology with a cycle of observation-hypothesis->test->observation->...

An isolated AGI cannot test its predictions about the world. Since the unconstrained space of possible explanations is bigger than the space of explanations constrained by observational evidence, there is no way an AGI can gain useful knowledge without access to external observations.

This means we are in full control of how "intelligent" (by whatever metric) an AGI can even get by restricting its access to information. But even unlimited access to (passive) information only gets you so far, as some models require data that cannot be obtained passively (i.e. they require deliberate controlled experiments).

The final nail in the coffin of dangerous AGI is interaction with the physical world. Yes, even a toddler is a terrifying menace to millions of people if I place a button right next it that sets off a thermonuclear bomb in a city centre.

But how about we just don't do that? An AGI with limited or no physical interaction with the world (directly via robot body or indirectly through remote access) can't be any more harmful and menacing than the late Stephen Hawking.

There's no need to put AI on a leash, since there's a final naturally limiting factor: energy. Switch off the cooling system and your AGI has to throttle down lest it faces fiery death.

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