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You might not need machine learning

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Re: You might not need machine learning

#181

Post by vntok »

qayxc wrote: post #176
vntok wrote: post #174

Earlier quoted context omitted.

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 oscillat…

The datasheet of the A100 lists "remote intervention from an engineer's home or workstation" as a core feature of that box, using one of the dozens of hyperoptimized networking components, so... not sure what argument you are going for there.

These hyperconnected boxes are definitely (hopefully?) not where an AGI will be built.

Re: You might not need machine learning

#182

Post by leobakerhytch »

thotsBgone wrote: post #165
blackbear_ wrote: post #145

Earlier quoted context omitted.

> ... and then it keeps you swimming in the same pool. This is a consequence of the metrics that are being optimized, it's not a fault of the algorithm per se.

It's not a fault at all. If you're going to spend more time watching videos if you're recommended stuff Youtube knows you already like, that's what it's going to do. Youtube just wants you to watch more videos. They don't care whether you are exposed to a variety of content.

Except I think there’s convincing argument to make that engagement will go down over time, if the algorithm makes no attempt to prioritize or suggest novel content.

The rare occasions I discover a new channel, it’s almost always from some source other than the algorithm: a referral from a friend, this site, another YouTuber, etc. My viewership of the same repetitive roster of videos absolutely tails off until I find something new from elsewhere.

For example, in months of being subscribed to my mechanics [0] (who does incredibly engrossing and relaxing restorations of mechanical stuff), not once was I suggested a video from Baumgartner Restoration [1], an art conservator who produces videos with a similar attention to detail and high production value.

Thematically this should be an easy recommendation for YouTube to make, but evidently the content is just different enough that it scores as a false-negative. After finding the latter channel independently, my viewing time absolutely rose for a while.

In theory, YouTube ought to be able to detect and learn from this signal of non-algorithmic discovery of new content. Yet, here we are.

[0]: https://www.youtube.com/c/mymechanics

[1]: https://www.youtube.com/c/BaumgartnerRestoration

Re: You might not need machine learning

#184

Post by qayxc »

vntok wrote: post #181
qayxc wrote: post #176

Earlier quoted context omitted.

> 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 oscillat…

The datasheet of the A100 lists "remote intervention from an engineer's home or workstation" as a core feature of that box, using one of the dozens of hyperoptimized networking components, so... not sure what argument you are going for there. These hyperconnected boxes are definitely (hopefully?) not where an AGI will be built.

You are aware of what a symbol picture is? Also, yes, every server and super computer has network components, that still doesn't mean that the Lawrence Livermore National Laboratory's Sierra and its 4 GPUs per node are accessible from the internet, so your remark is kind of meaningless.

Re: You might not need machine learning

#185

Post by sdoering »

mlthoughts2018 wrote: post #141
sdoering wrote: post #99

Earlier quoted context omitted.

From big automotive clients to small-ish fashion eCommerce. From publishing to food-delivery (with upselling in the checkout process) - I found the gains in using rules -> simple ML techniques -> complex systems like NN in most cases not to warrant the costs. The quality of recommendations nearly always increased from a revenue as well as perceived quality standpoint. However, it almost never had a positive impact on…

Your comment is in wild and incredulous disagreement with widely published results and my own ~10 years of industry experience doing ML professionally in ecommerce, quant finance, education technology and quant advertising. In fact, I’ve always found even just plain cost per unit service goes down with the introduction of more complex ML models. Their greater training complexity and compute costs are much more than a…

> probably biased to go against ML solutions for preconceived reasons.

Wow. Nice ad hominem. Thanks a lot for that.

> Just reduction of operating costs alone is usually a reason to favor ML solutions

I have yet to see one solution in the industries I work in and the clients I work with, were a ML solution beats simpler systems in development and operation costs (given the current real world environemnt there).

And believe me I try to sell these projects to clients, as I strongly believe that in the long run they could gain something from that.

But that would also mean getting rid of a clusterfuck of different systems, different data definitions from department a to department b as well as market x to market y. Politically motivated data mangling (we do not want "central" to know everything so we do not send all data or data in the necessary format).

When you see that markets use technically the same CRM system for example, but they rename tables, drop columns, use same dimension names for different things and so on integrating one market into a central data lake becomes a daunting task, let alone 130 markets. And this is just CRM. Not sales. Not - given automotive - the data from retailer systems.

But this would nonetheless be the data you need for ML systems to learn from. And then there are legal issues. car dealerships are separate legal entities. They are not allowed to "just" send PII date to the central brand (at least not with European GDPR). There is also a lot of stuff central just isn't legally allowed to know like discounts given - just to name one example.

After you get all of this entagneld and cleaned up (and changing all necessary business processes that depend on said structures I strongly believe ML would probably be cheaper. And leading to better results.

Don't think that I am telling my clients otherwise.

Re: You might not need machine learning

#186

Post by didibus »

bastawhiz wrote: post #169
didibus wrote: post #166

> If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time. I might be pedantic here, but wouldn't this then be a machine learning algorithm? Since the machine is learning the most appropriate coefficient based on some heuristic (best of X random). Wouldn't it be better (and more honest) to say then that, simpler ML mod…

> I might be pedantic here, but wouldn't this then be a machine learning algorithm? I would argue no. Simply trying a bunch of inputs and choosing the most effective ones based on the output is like running a single round of training on a machine learning model. It's hardly machine learning by any stretch—there's no feedback loop, the machine doesn't "learn" anything. If I build a compiler and test a thousand constan…

> If I build a compiler and test a thousand constants for default configuration options and choose the ones that perform the best

What if that was automated in the build? I had assumed the quote meant that it would be, not that they'd manually run a few random cases and manually pick the best. I thought it be automated, like given a new course, you'd run some training routine where the machine would play the course say 100 times each time choosing random coefficients and at the end, it'll take the ones from the pass that resulted in the quickest playthrough, and those would say go in some config file and become the coefficient used by the AI CPU race cars for that course.

To me this is machine learning. Especially if you crank up that 100 to 1 million or 1 billion. At that point, it's still something that only a machine could do, I couldn't realistically try 1 billion random coefficients for the ones that result in the fastest playthrough.

So in effect, I see the machine is learning which coefficients perform better for a given course. If it tried 1 billion, it learned that out of 1 billion different possible coefficients, some particular set was the best.

> there's no feedback loop, the machine doesn't "learn" anything

So that's interesting, because if my prior statement is not to be considered machine learning. My next question is what's the criteria to go from the above to machine learning proper? So it seems that it could be the learning has to be a feedback loop. So each attempt at learning must take something away from the previous.

I'd be okay with this definition. And now I'm thinking what's the most minimal modification I can make to meet this new definition.

What if as it tried random coefficients, it remembered the ones it tried prior? And what if it made sure that no new attempt at a random set of coefficients had already been attempted before? This isn't super refined, no heuristic to what are the best coefficients to try next given the ones tried prior like say what linear regression would accomplish. But it still meets the definition. It starts random on the first round, and the next round is no longer truly random, since it can't pick the previous's round coefficients again. So at least it's descending along the set of possible coefficients through a path that will eventually try all combinations.

Would this be enough to be considered machine learning?

I'm also thinking this can start to sound a lot like Evolutionary Computation. Oh boy, in all honesty, I've always been confused about differences between stochastic, metaheuristc, and machine learning optimization techniques.

Re: You might not need machine learning

#187

Post by sdoering »

logicslave wrote: post #150
sdoering wrote: post #99

Earlier quoted context omitted.

From big automotive clients to small-ish fashion eCommerce. From publishing to food-delivery (with upselling in the checkout process) - I found the gains in using rules -> simple ML techniques -> complex systems like NN in most cases not to warrant the costs. The quality of recommendations nearly always increased from a revenue as well as perceived quality standpoint. However, it almost never had a positive impact on…

Neural Nets really are better, just because either you, your clients, or the problem you are solving is simple, doesnt mean NNs dont work. They work absurdly well.

Not what I said. It is just that in the respective environments the costs of developing and operating these doesn't return a higher ROI than simpler systems.

Not because they do not work, but because simpler systems can be run comparatively cheap in environments that are very stratified and were the underlying data situation is a messed up clusterfuck to begin with.

Believe me I really, really wonder how these companies are able to make money given what they have in terms of underlying central data quality. It is unbelievable sometimes.

Re: You might not need machine learning

#188

Post by sdoering »

disgruntledphd2 wrote: post #113
mlthoughts2018 wrote: post #78

Earlier quoted context omitted.

> “ Regarding recommender systems I see many companies trying neural nets and so many other fancy ML stuff for things that - in AB-tests are always outperformed by basic rules.” I work on large scale recommender systems for an ecommerce company and in my career I’ve seen only the exact opposite. Don’t get me wrong, sometimes simpler ML models, like clustering LSA vectors or nearest neighbors, work better than complex…

It depends. Rule based systems are great if you have people with deep domain understanding developing the rules. Unfortunately, those people are rare, so most rule-based approaches fail to perform well. However, most recommendation systems suck unless you get someone who knows what they are doing to build them. In terms of business value, I would be very hesitant to make strogn statements like the above (in both case…

Well - I said I have seen and tested. I would love for positive ML cases to arise. I really would. That would make it way easier to sell my Data Science colleagues to the respective clients on terms other than hype and buzzwords.

I also believe that with a good situation in underlying data quality we could be talking about massively reduced costs in getting these systems up and running - and this would tip the scale in favor of said systems.

But what I see in terms of data quality makes me sometimes just want to run as fast as I can in the other direction.

Re: You might not need machine learning

#189

Post by didibus »

didibus wrote: post #186
bastawhiz wrote: post #169

Earlier quoted context omitted.

> I might be pedantic here, but wouldn't this then be a machine learning algorithm? I would argue no. Simply trying a bunch of inputs and choosing the most effective ones based on the output is like running a single round of training on a machine learning model. It's hardly machine learning by any stretch—there's no feedback loop, the machine doesn't "learn" anything. If I build a compiler and test a thousand constan…

> If I build a compiler and test a thousand constants for default configuration options and choose the ones that perform the best What if that was automated in the build? I had assumed the quote meant that it would be, not that they'd manually run a few random cases and manually pick the best. I thought it be automated, like given a new course, you'd run some training routine where the machine would play the course s…

Answering myself, I think wikipedia might have clarified my confusion:

> The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.

> Machine learning (ML) is the study of computer algorithms that improve automatically through experience.

So it seems optimizations are the techniques for which most ML is based on, but ML is more of the idea that an algorithm would improve automatically from experience. Later in the wikipedia page, it creates a very clear distinction:

> The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.

Re: You might not need machine learning

#190

Post by erichahn »

Der_Einzige wrote: post #62
erichahn wrote: post #50

Neural networks are just regressors. Yes you can learn the weights with genetic algorithms. Is this advised? Not so much : 99.99% of neural networks are trained with some variation of gradient descent on a specified loss function. I don't even know if I agree to the statement. Polynomial regression solves basically the same problem as neural networks but performs way, way, way worse on big datasets. But nonetheless I…

This post isn't about learning NN weights with GAs, Also the reinforcement learning community does a significant amount of it's work with neural networks which are not trained using gradients. Gradient free training is a highly active research area. More like 90% of neural networks are trained with gradient descent

No but this post is trying to generalize on an experiment where NNs are trained with GAs which is - in my opinion - not where NN shine.
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