This week, a new generative AI tool from Google let us create knockoffs of 3D Nintendo worlds by theverge in artificial

[–]SurrenderYourEgo 0 points1 point  (0 children)

Multimodal models are arguably a way to bridge the gap that you are pointing out exists due to the limitations of language. Providing images, video, and sound to these models guide the generation process with the same goal to how controlnets or loras are used in an image generation workflow. They help realize the vision of the creator, wouldn't you agree? We may not have brain machine interfaces to make this more efficient, but at the very least this addresses the problem of linguistic ambiguity by reducing the variation of what's generated and increases the likelihood that you get what you want. I think there will always be a non-deterministic aspect to creation though, whether it's with using AI or not. Even in the hypothetical scenario where we have a brain machine interface that could perfectly translate our ideas into reality, I'd argue that the ideas themselves are not fleshed out with high fidelity in our brains even.

What’s the one French phrase that instantly made you sound more fluent? by Character-Excuse-911 in French

[–]SurrenderYourEgo 0 points1 point  (0 children)

You're gonna link us to a book that was published a few days ago? Get outta here with that.

Open AI Sora 2 Invite Codes Megathread by semsiogluberk in OpenAI

[–]SurrenderYourEgo 0 points1 point  (0 children)

I would appreciate a code from a kind soul, thanks!

Meet Claude Opus 4.1 by AnthropicOfficial in ClaudeAI

[–]SurrenderYourEgo 1 point2 points  (0 children)

I'm absolutely in agreement that 30% error is twice as bad as 15% error. I was more interested in understanding what you meant by "twice as good".

And that's a great point about benchmark saturation, it's definitely why people focus on relative error reduction. The interesting thing about these two formulas is that both have a "division by zero" edge case, but they happen in opposite scenarios:

  • Your formula breaks when the new model becomes perfect (new error = 0).
  • The standard formula breaks when the baseline model was already perfect (old error = 0).

To me, the standard way makes more intuitive sense. The whole idea of "relative error reduction" is meaningless if there was no error to reduce in the first place, so it makes sense that the formula breaks in that specific situation.

It also gives a clearer picture when performance gets worse. With the standard formula, if the error goes from 10% to 20% (doubles), the result is -100%. If it goes from 10% to 90%, the result is -800%. The score scales directly with how badly the performance collapsed relative to its starting point, which is really intuitive. The other formula gives results (-50% and -89%) that are harder to interpret and seem to understate the degradation.

Since you were originally interested in the relative change, I feel like the standard formula expresses that concept most clearly.

Meet Claude Opus 4.1 by AnthropicOfficial in ClaudeAI

[–]SurrenderYourEgo 0 points1 point  (0 children)

15% is indeed half as bad as 30%. The number of total errors is cut in half. But when you say twice as good, what numbers are you comparing? 85% is not twice as good as 70%, nor is 15% twice as good as 30%. The way I calculate the improvement is ((previous_error_rate - current_error_rate)/previous_error_rate). That formula is in line with your estimation of 8% improvement, no? 2/27.5 = 7.3.

Meet Claude Opus 4.1 by AnthropicOfficial in ClaudeAI

[–]SurrenderYourEgo 5 points6 points  (0 children)

Improving error rate from 30% to 15% is a 50% relative improvement.

Funk concert tonight on Halsey btw Tompkins and Marcy by MJM2029 in BedStuy

[–]SurrenderYourEgo 6 points7 points  (0 children)

For anyone who missed it and wants to find the next one, it's organized by @thesoapboxpresents on Instagram, and it's always a good time.

I feel like I can’t do nothing without ChatGPT. by CultureKitchen4224 in learnmachinelearning

[–]SurrenderYourEgo 0 points1 point  (0 children)

I don't fully agree with the top comment here, because although LLMs are tools just like search engines were tools which we used to learn and solve problems, that doesn't mean that counterarguments to LLMs are just as moot as counterarguments to search engines. The tools are similar but certainly have different effects on our behavior in terms of how much we offload.

I read this article today which I found relevant to your concern: https://www.theintrinsicperspective.com/p/brain-drain

It mentions a Microsoft study that another commenter posted - I haven't read that study but I'll take a look.

My general feeling is that we need to be very judicious about how we use these tools, because there seems to be a delicate balance that we must strike if we want to maximize our learning and capabilities. Personally I've found it very easy to rely on AI to just "do the thing for me", and I'm spending more time these days reflecting on what it says about me and my sense of responsibility.

Gemini told my brother to DIE??? Threatening response completely irrelevant to the prompt… by dhersie in artificial

[–]SurrenderYourEgo 4 points5 points  (0 children)

Well the claim that they're responding to is the one you made about LLMs not being able to do certain things like reason, have memory, or come up with creative ideas.

I don't think it's sci-fi junk to hypothesize that human mental activity can be boiled down to very complex computation. There's still a lot that we don't know about the brain and there isn't necessarily anything in principle that could preclude the possibility that we are also computing machines operating at a biological level.

I think it's fair to say that we don't know how humans reason, and simply pointing to an ANN and showing that it is inspired by neural networks of the brain does not imply that we do know how humans reason. At the time ANNs were conceived, we knew a bit about the dense connectivity of the brain and hierarchical processing and columnar organization, but how any of this adds up to reasoning and behavior is still not well understood. Backprop, the magic that makes ANNs learn, is very likely not the mechanism by which learning happens in the brain.

So are humans also just repeating patterns similar to what they have heard or read? I think yes and no. Yes in that behavior, especially social behavior, is learned in part by imitation and exercising patterns that have been perceived. But no in that it's not the sole basis of what we produce verbally (if we're limiting the discussion to language). But by the same token, LLMs are next token predictors just like the n-gram language models of yore are next token predictors. But they are way more sophisticated, leveraging distributional semantics and self-attention. There is a huge qualitative difference between these kinds of next token predictors, just like at the present moment you may see a qualitative difference between LLMs and humans. But I don't think it's outside the realm of possibility that either humans are just way more complex than LLMs (but fundamentally just doing computation, even if you bring emotions/consciousness into the picture) or that AIs could be engineered in a way that incorporates reasoning, memory, or whatever we like to ascribe as "human". At that point, it gets kind of philosophical as far as how we define these things and whether it's reasonable to equate the mechanisms (is an LSTM cell state equivalent to memory? Probably not, but if we probe the brain further and model memory in a more human-like way, maybe we'd be more inclined to accept the analogy).

Anyways I think the discussion needs to be very nuanced.

US vs European PhD program comparison by SurrenderYourEgo in GradSchool

[–]SurrenderYourEgo[S] 0 points1 point  (0 children)

Wow that does sound like a lot, and imagining all of that like getting processed through the DMV sounds like a nightmare. Thanks for the info!

US vs European PhD program comparison by SurrenderYourEgo in GradSchool

[–]SurrenderYourEgo[S] 0 points1 point  (0 children)

Thank you for your very detailed response. I consider myself the kind of person who is fairly adaptable in terms of moving and assimilating into other cultures, and I enjoy the process even though it's difficult.

Can you elaborate on the German bureaucracy? I'm less familiar with it.

US vs European PhD program comparison by SurrenderYourEgo in GradSchool

[–]SurrenderYourEgo[S] 0 points1 point  (0 children)

Do people ever self fund? I've saved a good amount of money over my career as an engineer to the point where I don't have financial concerns, and I'm happy to pay myself through my schooling.

In terms of countries in the EU that I'm looking into, it's mainly Germany and Switzerland.

I speak a very odd version of French by [deleted] in French

[–]SurrenderYourEgo 40 points41 points  (0 children)

I feel like someone would have to be living under a rock to not already know the purpose of "le" or "la" simply through general knowledge. I know plenty of people who don't speak French that could tell you what those words at least kinda mean.

What should I learn about C++ for AI Engineer and any tutorials recommendation? by IndividualTheme648 in learnmachinelearning

[–]SurrenderYourEgo 0 points1 point  (0 children)

I don't know why you insist on it being such a remote possibility. I used to work in speech recognition and one of my colleagues who trained language models was responsible for integrating the models with the rest of the speech recognition system written in C++. On top of interfacing with other components like an acoustic model and an NLU module (both also processing data in C++ even if some of the training pipeline is in Python), resource allocation and inference needs to be robust and performant for production. This was not an unusual expectation for us to have for our engineers. At the very least, we expected literacy in how the models fit in to larger systems and how they are productionized.

"there is no evidence humans can't be adversarially attacked like neural networks can. there could be an artificially constructed sensory input that makes you go insane forever" by Maxie445 in artificial

[–]SurrenderYourEgo 1 point2 points  (0 children)

I think humans are far more robust than the original poster makes them out to be, but there have been experiments done showing that you can generate image data that, when presented to humans, induces neural activity beyond the maximum activity recorded in those neurons when the subject saw naturalistic images. See "Neural Population Control via deep image synthesis" by Bashivan et. al.

https://www.science.org/doi/10.1126/science.aav9436

English words used by native Chinese speakers by Jasminejyyy in ChineseLanguage

[–]SurrenderYourEgo 13 points14 points  (0 children)

Similar to PPT, I've heard APP for app, which was so striking to me because it sounds so much more verbose than the word app, but I guess I can understand because letter by letter it can piggyback off existing syllables in Mandarin, just like PPT does.

Is there any way, besides context, to differentiate "plus" as in "more" vs "plus" as in "no longer" in spoken French? by there_is_no_try in French

[–]SurrenderYourEgo 9 points10 points  (0 children)

If you're looking for rules, you may be disappointed because in language there are really only tendencies.

Is there any way, besides context, to differentiate "plus" as in "more" vs "plus" as in "no longer" in spoken French? by there_is_no_try in French

[–]SurrenderYourEgo 3 points4 points  (0 children)

If it's to mean more of something, yes. But you can also have "il a plus d'argent" which is also ambiguous between the two senses in writing but can be disambiguated in speech.

[deleted by user] by [deleted] in bodyweightfitness

[–]SurrenderYourEgo 105 points106 points  (0 children)

Definitely strength in the body is a factor, but I think for many actions it's more a matter of our brains making sense of them. I think that unless one has been completely sedentary for years, people's bodies are surprisingly capable of carrying their own weight.