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Advancements in machine learning for machine learning

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Re: Advancements in machine learning for machine learning

#91

Post by nonrandomstring »

fjkdlsjflkds wrote: post #34
smabie wrote: post #24

Earlier quoted context omitted.

And they generally get outcompeted sooner or latter. All disciplines evolve over time and those who fail or refuse to keep up will be left behind.

Just because you can have a robot/machine that can efficiently churn out 1000 frozen lasagnas a second doesn't necessarily mean that italian restaurants have been "outcompeted" or "left behind" by not using such a machine in their business. Sometimes quality and responsibility matter. Even if a machine is really good at producing bug-free code, often someone is going to have to read/understand the code that the machi…

> and take responsibility for it, since machines cannot take responsibility for things.

That's an interesting thought; people "taking responsibility" as a form of labour, for machines that stole their lunch. Probably be around zero applicants for that job.

Responsibility is a complex quality needing capacity and competence. Right now even the manufacturers of "AI" are unable to assert much about behaviour.

Where "responsibility" exists around robotics and ML it will more likely be a blunt legal instrument tied to ownership, like owning a dangerous animal.

Re: Advancements in machine learning for machine learning

#92

Post by samus »

fjkdlsjflkds wrote: post #34
smabie wrote: post #24

Earlier quoted context omitted.

And they generally get outcompeted sooner or latter. All disciplines evolve over time and those who fail or refuse to keep up will be left behind.

Just because you can have a robot/machine that can efficiently churn out 1000 frozen lasagnas a second doesn't necessarily mean that italian restaurants have been "outcompeted" or "left behind" by not using such a machine in their business. Sometimes quality and responsibility matter. Even if a machine is really good at producing bug-free code, often someone is going to have to read/understand the code that the machi…

AI can be used to support that activity too. Models can just as well used to explain existing code, possibly cranked out by another AI. I bet many companies are thinking about fine-tuning or LoRA-ing language models on their moldy codebases and outdated pile of documentation to make onboarding, refactoring, and routine extensions easier.

To interpret what AI models themselves are actually doing, researchers employ AI models as well.

Re: Advancements in machine learning for machine learning

#93

Post by vbezhenar »

valine wrote: post #35
euos wrote: post #26

Earlier quoted context omitted.

I’ve been programming since middle school. That would be 30 years. Nothing really changed much. C++ is incrementally more convenient but fundamentally the same. Code editors are same. Debugger are same. Shell is same. I am certain in 30 years everything will still be the same.

The way I write code was fundamentally altered in the last year by GPT4 and copilot. Try having GPT4 write your code, you won’t be so certain about the future of programming afterward I guarantee it.

GPT 4 does not produce code that I'm ready to accept. The time it takes to convince it to produce code that I'll accept significantly larger than the time it takes to write that code myself.

GPT 4 is fine for absolutely foreign tasks to me, like write a power shell script, because I know almost nothing about power shell. However those tasks are rare and I generally competent about things I need to do.

Re: Advancements in machine learning for machine learning

#94

Post by samus »

SirMaster wrote: post #42
smabie wrote: post #24

Earlier quoted context omitted.

And they generally get outcompeted sooner or latter. All disciplines evolve over time and those who fail or refuse to keep up will be left behind.

People do things because they enjoy doing them. And people will continue to do things they enjoy doing such as programming. I don’t think it has anything to do with competition or being left behind.

The point is that people won't get paid anymore to do it. It has happened before: many activities that have been replaced by technology have been almost forgotten (for example the newspaper reader in the factory) or are practiced as art or niche crafts only. Careers built on these are either wildly successful or have highly unsteady income since they are literally reliant on the whims, not on the needs, of people.

Re: Advancements in machine learning for machine learning

#95

Post by ChatGTP »

ben_w wrote: post #74
SirMaster wrote: post #11

Earlier quoted context omitted.

I don’t see why not. I like programming how I do now. I don’t plan to stop. People do lots of things manually that machines have been able to do for a long time.

We can't all run a YouTube channel for the programming equivalent of Primitive Technology, fun though that would be. 99.99% of us will have to adapt to AI being a coworker, who will probably eventually replace us. Right now we're still OK because the AI isn't good enough; when it gets good enough, doing things manually is as economically sensible as making your own iron by gathering a few times your mass in wood, bur…

who will probably eventually replace us.

no one is going to be using AI and then just have it 'replace them', they're going to use it to augment their abilities and avoid replacement.

Re: Advancements in machine learning for machine learning

#96

Post by vbezhenar »

Fbnkigffb66tfbj wrote: post #60
sublinear wrote: post #41

Earlier quoted context omitted.

Have you ever worked in tech and had to deal with the typical illiteracy and incompetence of management and execs? If LLMs got this good, the brick wall these orgs will hit is what will really ruffle feathers. Leadership will have to be replaced by their technical workers in order for the company to continue existing. There's simply not enough information in the very high level plain english requirements they're used…

> you very likely cannot feed that half-assed junk to any LLM no matter how advanced and expect useful results Why don't you think that a sufficiently advanced AI can do the same as what technical humans do today with vague directions from managers?

I feel that issue with AI is similar to issues with AI cars.

AI car won't ever reach its destination in my city. Because you need to actively break the rules few times if you want to drive to the destination. There's a stream of cars and you need to merge into it. You don't have an advantage, so you need to wait until this stream of cars will end. However you can wait for that for hours. In reality you act aggressively and someone will allow you to join. AI will not do that. Every driver does that all the time.

So when AI will try to integrate into human society, it'll hit the same issues. You sent mail to manager and this mail got lost because manager does not feel like answering it. You need to seek him, you need to face him and ask your question, so he has nowhere to run. AI does not have physical presence, neither he have aggression necessary for this. He'll just helplessly send emails around, moving right into spam.

Re: Advancements in machine learning for machine learning

#97

Post by ChatGTP »

creer wrote: post #58
cornel_io wrote: post #55

Earlier quoted context omitted.

There was a very recent time when passing some version of the Turing Test would have been a fairly commonly accepted goalpost. Many experts thought that was 20+ years away, and were perfectly comfortable saying that it was their "bar", primarily because they thought generating convincing conversational text was so difficult that you'd basically have to solve All The Problems(TM) first. Notice how nobody is talking ab…

Exactly so. The posts have been moved safely waayyyy over there at AGI, and at "super-human" or at "critical thinking". And several stages or degrees of AGI have been hierarchized. There is a serious reluctance at accepting how dumb an algorithm can be and still compare with humans. But it is also true that numerous ground techniques are issue of the field of AI and generally called AI as they come out. It makes for…

On the other hand, we've passed the turing test, what's changed?

Re: Advancements in machine learning for machine learning

#98

Post by quickthrower2 »

F-Lexx wrote: post #39
quickthrower2 wrote: post #31

Earlier quoted context omitted.

Well for it to be Artificial and convey Intellgence. I think that goal has been met!

How do you define intelligence?

I don’t have a exact definition but I will claim that helping me to write code that usually works first time or has a minor bug based on natural language is intelligence.

Re: Advancements in machine learning for machine learning

#99

Post by phillengel »

potac wrote: post #83

Can anyone explain how conv works in that graph. You have a tensor of shape [2,4,16] and you convolve with a kernel of shape [4,16,8] and that gives you a [2,8] tensor? How's that possible?

Does this help?

*1. Input:*

* Tensor shape: [2,4,16] * `2`: This represents the *batch size*, meaning there are two independent data samples being processed. * `4`: This is the *input feature dimension*, indicating each sample has 4 features. * `16`: This is the *input channel dimension*, suggesting each feature has 16 channels of information.

*2. Kernel:*

* Shape: [4,16,8] * `4`: This is the *kernel size*, meaning the filter window used to convolve has a width of 4. * `16`: This matches the *input channel dimension*, ensuring the filter operates on the same number of channels as the input. * `8`: This is the *output channel dimension*, indicating the convolution produces 8 new channels of information per sample.

*3. Output:*

* Shape: [2,8] * `2`: This remains the *batch size* as the operation is applied to each sample independently. * `8`: This matches the *output channel dimension* of the kernel, signifying the final tensor has 8 new features extracted from the input.

*4. How is it possible?*

Despite the seemingly mismatched dimensions in the input and output, convolution on graphs works by leveraging the *neighborhood structure* of the graph. Here's a simplified explanation:

* The kernel slides across the graph, applying its weights to the features of the current node and its neighbors within a specific radius. * This weighted sum is then aggregated to form a new feature for the current node in each output channel. * As the kernel moves across the graph, it extracts information from the local neighborhood of each node, creating new features that capture relationships and patterns within the graph.

*Additional considerations:*

* The graph structure and edge weights likely play a role in how information propagates during the convolution process. * Specific details of the convolution implementation, including padding and stride, might also influence the output shape.

Re: Advancements in machine learning for machine learning

#100

Post by djmips »

ben_w wrote: post #74
SirMaster wrote: post #11

Earlier quoted context omitted.

I don’t see why not. I like programming how I do now. I don’t plan to stop. People do lots of things manually that machines have been able to do for a long time.

We can't all run a YouTube channel for the programming equivalent of Primitive Technology, fun though that would be. 99.99% of us will have to adapt to AI being a coworker, who will probably eventually replace us. Right now we're still OK because the AI isn't good enough; when it gets good enough, doing things manually is as economically sensible as making your own iron by gathering a few times your mass in wood, bur…

Nice analogy! I saw an estimate recently on the cost of programming and they predicted that automated coding will cost 10,000 times less than human coders. It was all back of the envelope and questionable but still it was food for thought. Will we be 10,000 times more productive or will we be out of work? I think a lot of people will be out of work.
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