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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#171

Post by danans »

ML is useful for domains where we don't have a good analytical model of real world behavior of systems. Of course, that is a lot of problems, including many useful things that biological intelligence appears to solve.

But it is true that ML - in particular neural networks - often gets thrown at problems that traditional analysis and modeling could serve more efficiently, and also more predictably. The other benefit of first trying traditional analysis is that it helps the implementer of the system better understand the domain first.

Pertinently, I've heard some scientists working on medical imaging describe to me how there has been a trend away from the basic science of understanding the medical phenomena behind the image being analyzed and instead relying excessively on ML based pattern recognition to make predictions/inferences.

Re: You might not need machine learning

#172

Post by vntok »

qayxc wrote: post #154
johnmoberg wrote: post #94

Earlier quoted context omitted.

Even without physical interaction, you can do a whole lot with just an internet connection. A superintelligence could identify 0-day exploits and quickly spread across thousands of computers and thus render itself immune to your idea of just switching it off. What do we do then? Sure, we can shut down the internet, but where is that going to leave us and how much damage has been done before that happens?

> A superintelligence could identify 0-day exploits and quickly spread across thousands of computers and thus render itself immune to your idea of just switching it off. What do we do then? How about simply pulling the plug of the computer or even just the network cable? More to the point, how would an AI even learn about such mysterious exploit if it doesn't have access to an external network in the first place? Eve…

How exactly do you intend to prove that an AI does not have access to an external network?

Re: You might not need machine learning

#173

Post by qayxc »

vntok wrote: post #172
qayxc wrote: post #154

Earlier quoted context omitted.

> A superintelligence could identify 0-day exploits and quickly spread across thousands of computers and thus render itself immune to your idea of just switching it off. What do we do then? How about simply pulling the plug of the computer or even just the network cable? More to the point, how would an AI even learn about such mysterious exploit if it doesn't have access to an external network in the first place? Eve…

How exactly do you intend to prove that an AI does not have access to an external network?

Network connectivity isn't magic: no physical connection, no network. Simple as that. Hard to imagine in this "always connected" world, but WAN is completely optional.

Re: You might not need machine learning

#174

Post by vntok »

qayxc wrote: post #173
vntok wrote: post #172

Earlier quoted context omitted.

How exactly do you intend to prove that an AI does not have access to an external network?

Network connectivity isn't magic: no physical connection, no network. Simple as that. Hard to imagine in this "always connected" world, but WAN is completely optional.

Did you know it is perfectly possible for a computer to exfiltrate data without physical connections and without even having a networking card?

A few examples: Using speakers or microphones to transmit arbitrary data via ultrasounds. Making the CPU/GPU fans vibrate in a way that sends encoded bits. Blinking the screen to emit electromagnetic waves. Transfering certain data patterns between RAM and CPU so fast that they produce oscillations, effectively converting the BUS into a GSM antenna that can emit arbitrary data over a regular cellular network. Turning the fans off to change the heat signature in a way that transmits information.... Or even simply bliking a light to send data through regular lightwaves?

Scientists discover new ways to exfiltrate data basically every other year, how can you be so certain you've thought of every possible way?

Re: You might not need machine learning

#175

Post by [deleted] »

JackFr wrote: post #131
mjburgess wrote: post #79

Earlier quoted context omitted.

That's an excellent approach -- and how I try to introduce people to NNs. NNs are just polynomial regression with polynomial activations; and piece-wise linear regression with relu activations (etc.). A NN is just a highly parameterized regression model -- for better, or worse.

That was an eye-opener for me. I had always thought of neural nets in terms of the massive connected graph, that in my head was somehow behaved like a machine. When I realized in the end its just a representation of a massive function, f:Rm->Rn, which needs to fitted to match inputs and outputs. I know this is not precisely correct and glosses over many, many details - but this change in viewpoint is what finally all…

[deleted]

Re: You might not need machine learning

#176

Post by qayxc »

vntok wrote: post #174
qayxc wrote: post #173

Earlier quoted context omitted.

Network connectivity isn't magic: no physical connection, no network. Simple as that. Hard to imagine in this "always connected" world, but WAN is completely optional.

Did you know it is perfectly possible for a computer to exfiltrate data without physical connections and without even having a networking card? A few examples: Using speakers or microphones to transmit arbitrary data via ultrasounds. Making the CPU/GPU fans vibrate in a way that sends encoded bits. Blinking the screen to emit electromagnetic waves. Transfering certain data patterns between RAM and CPU so fast that th…

> Did you know it is perfectly possible for a computer to exfiltrate data without physical connections and without even having a networking card?

Yes.

> A few examples: ...

All these examples require active physical interaction of the machine with the world, which simply isn't possible for a server to do.

This is the environment an AGI will likely "live" in: https://bit.ly/33w7ySX

There's no speakers, no bus oscillations to pick up (from where? by what?) and you might notice that these ominous boxes don't have anything that can pick up signals (optical, vibrations, or otherwise).

Exfiltrating data from machines without touching them is completely unrelated to the capabilities of a program running in a box like this https://bit.ly/3l2hPfw

There are no sensors that can measure the outside and the floors these boxes are located at are heavily shielded and isolated from the outside anyway for various reasons (EM protection, physical security, etc.) so no potential target PC in sight.

Re: You might not need machine learning

#177

Post by RyanGoosling »

username90 wrote: post #97
mjlee wrote: post #18

Earlier quoted context omitted.

I think Arthur Samuel would agree. This approach has a loss function, parameters, and inputs that feed in to a model to optimise the parameters. The big difference between this and the other approach mentioned in the article is the model is a simple one that's easy to understand instead of a many layered neural network which is rather opaque. I think the article may be better titled "You might not need neural network…

Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. Everything done in this article was known and used before computers existed hence not machine learning. Doing automatically controlled systems was still possible before computers just that it required…

> Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning.

How does your evidence support your claim?

Re: You might not need machine learning

#178

Post by ccortes »

Flashtoo wrote: post #54
spratzt wrote: post #51

Earlier quoted context omitted.

Genuinely curious as to what you moved into after working as a data scientist. I'm a data scientist and desperate to get out.

As a data scientist at the start of my career: why?

For me it was unrealistic expectations. Tons of companies hire data scientists just because everyone else is doing it and/or they think somehow they'll make everything better just by being in the company.

If you are lucky enough to be in a truly "data driven" startup then you'll most likely have fun a learn a lot.

Re: You might not need machine learning

#179

Post by Delk »

karolkozub wrote: post #32
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.

In my understanding AI is an even broader term and means "any solution that imitates intelligent behavior". E.g. expert systems which are pretty much a bunch of if-then rules are also considered AI.

There have traditionally been different approaches and definitions for AI. Some emphasize behaviour while others emphasize the logic behind the behaviour. (In some sense, while expert systems of course were an attempt at getting practical results, they might also have been an attempt to implement what was seen as human reasoning, while e.g. black box machine learning could be more about just getting the behaviour we want.) Some approaches view agents as intelligent if their action resembles humans or other beings that we consider intelligent, while other approaches are merely interested in whether they perform well at a specified task, perhaps more so than humans.

So yes, "any solution that imitates intelligent behaviour" is probably right, but with nuances with regard to what that actually means.

Re: You might not need machine learning

#180

Post by mjburgess »

JackFr wrote: post #131
mjburgess wrote: post #79

Earlier quoted context omitted.

That's an excellent approach -- and how I try to introduce people to NNs. NNs are just polynomial regression with polynomial activations; and piece-wise linear regression with relu activations (etc.). A NN is just a highly parameterized regression model -- for better, or worse.

That was an eye-opener for me. I had always thought of neural nets in terms of the massive connected graph, that in my head was somehow behaved like a machine. When I realized in the end its just a representation of a massive function, f:Rm->Rn, which needs to fitted to match inputs and outputs. I know this is not precisely correct and glosses over many, many details - but this change in viewpoint is what finally all…

It's unclear that there is such a thing as an NN, and in any case, that it is graph-like.

What are the nodes and edges?

There is a computational graph which corresponds to any mathematical function -- but it is not the NN diagram -- and not very interesting (eg., addition would be a node).

NNs are neither neural nor networks.

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