[SemanticStrengh]

NNs are just glorified logistic regression. People should simply understand that neural networks cannot emulate a dumb calculator accurately, this simple fact is enough to realize being an universal approximator is in practice a fallacy, and true Causal NLU or AGI is essentially out of reach of neural networks, by design. Only a brain fidel architecture would have hope however C.elegans retro engineering is underfunded and spiking neural networks are untrainable.

[visarga]

> NNs are just glorified logistic regression.

2015 called, they want you back! Now seriously, "just" does an amazing amount of work for you. How do you "just" make logistic regression write articles on politics, convert queries in SQL statements? or draw a daikon radish in a tutu?

Humans are "just" chemistry and electricity, and the whole universe just a few types of forces and particles. But that doesn't explain our complexity at all.

[SemanticStrengh]

Neural networks do achieve impressive things but they also fail to achieve essential things that preclude them from an AGI or Causal NLU ambition, such as the inability to approximate a dumb calculator without significant accuracy loss.

[visarga]

It's a model mismatch, not an inherent impossibility. A calculator needs to have an adaptive number of intermediate steps. Usually our models have fixed depth, but in auto-regressive modelling the tape can become longer as needed by the stepwise algorithm. Recent models show LMs can do arithmetic, symbolic math and common sense chain-of-thought step by step reasoning and reach much higher accuracies.

In other words, we too can't do three digit multiplication in our heads reliably, but can do it much better on paper, step by step. The problem you were mentioning is caused by the bad approach - LMs need intermediate reasoning steps to get from problem to solution, like us. We just need to ask them to produce the whole reasoning chain.

- Chain of Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903

- Deep Learning for Symbolic Mathematics https://arxiv.org/abs/1912.01412

[Filligree]

I can’t approximate a dumb calculator without significant accuracy loss. Not without emulating symbolic computation, which current AI is perfectly capable of doing if you ask it the right way.

Whatever makes you think it’s necessary for AGI, when we don’t have it?

[SemanticStrengh]

NNs fails to do any algorithmy like pathfinding, sorting, etc The point is not that you have it it's that you can have it by learning and using a pen and paper. Natural language understanding require both neural network like pattern recognition abilities and advanced algorithmic calculations. Since neural networks are pathetically bad at algorithmy, we need neuro-symbolic software. However the symbolic part is rigid and program synthesis is exponential. Therefore the brain is the only technology on earth to be able to dynamically code algorithmic solutions. Neural networks have only solved a subset of the class of automated programs.

[visarga]

There are about 3,610 results for "neural network pathfinding" in Google Scholar since 2021. Try a search.

[SemanticStrengh]

And as you can trivially see it is outputting nonsense values https://www.lovebirb.com/Projects/ANN-Pathfinder?pgid=kqe249... (see last slide) At least in this implementation

Even if it had 80% accuracy (optimistic) it would still he too mediocre to be used at any serious scale.

[Der_Einzige]

Using gradient based techniques does a LOT to force neural network weights to resemble surfaces that they do not at all look like when using global optimization and gradient free techniques to optimize them.

Most of the stupid crap that people give about degenerate cases where deep learning doesn't work (cartpoll in reinforcement learning, sine/infinite unbounded functions) are showcasing how bad gradient based training is - not how bad deep learning is at solving these problems. I can within seconds solve cartpoll with neural networks using neuroevolution of weights....

[drdeca]

Do you mean that a network trained to imitate a calculator won’t do so accurately or that there is no combination of weights which would produce the behaviors of a calculator?

Because, with RELU activation, I’m fairly confident that the latter, at least, is possible.

(Where inputs are given using digits (where each digit could be represented with one floating point input), and the output is also represented with digits)

Like, you can implement a lookup table with neural net architecture. That’s not an issue.

And composing a lookup table with itself a number of times lets one do addition, etc.

... ok, I suppose for multiplication you would have to like, use more working space than what would effectively be a convolution, and one might complain that this extra structure of the network is “what is really doing the work”, but, I don’t think it is more complicated than the existing NN architectures?

[SemanticStrengh]

I am talking about training a neural network to achieve calculations. And yes look-up tables might be fit for addition but not for multiplication. The accuracy would be <90% which is a joke for any serious use.

[drdeca]

Well, the main issue I see is where to put the n^2 memory (where n is the number of digits) when doing multiplication. (Or, doesn’t need n^2 space, could do it in less, but might need to put more structure into the architecture?)

If the weights are designed, and the network architecture allows something to hold the information needed, then there is really no obstacle to having it get multiplication entirely (not just 90%).

Now, would that be learnable? I’m not so sure, at least with the architecture one would use if designing the weights.

But,

I see no reason a transformer model couldn’t be trained on multiplication-with-work-shown and produce text fitting all of those patterns, and successfully perform multiplication for many digits that way.

And, by “showing all work” I don’t necessarily mean “in a way a person would typically show their work”, but in a easier-for-machine way.

[hoseja]

>cannot emulate a dumb calculator accurately

Neither can people, for the most part.

[mpfundstein]

knock knock. some critic from the 70s arrived. hows gofai going?

[SemanticStrengh]

Oh yes it's not GOFAI that has won the ARC challenge it's neural networks, right? right? https://www.kaggle.com/c/abstraction-and-reasoning-challenge

I have more expertise in deep learning than anyone else here and the delusions of the incoming transformer winter will be painful to watch. In the meantime, enjoy your echo chamber.

[astrange]

Are you Schmidhuber's alt?

[rfrey]

> I have more expertise in deep learning than anyone else here

I... I guess it's possible?

[redredrobot]

> I have more expertise in deep learning than anyone else here

No, you don't. Looking at your experience, there is simply no way that you are the foremost expert in DL on HN.

[SemanticStrengh]

Haha what do you know of my experience? Here is a glimpse of my unique pedigree https://www.metaculus.com/notebooks/10677/substance-is-all-y...

[redredrobot]

I have no idea what you think that proves.

What I know of your experience shows a low number of years of experience, a lack of papers, and a lack of true hands-on experience at the small number of companies in the world that have the resources to truly investigate large models. How can you know so much about LLMs without ever having the resource to train one?

I'm obviously not going to dox you, so you can easily just dismiss what I'm saying. But even just reading through your HN comments shows arrogance in your own knowledge (across multiple domains).

A specifically memorable quote is:

> I frequently create unique on the internet [words]

This is very true. Your erudition is apparently only matched by the uniqueness of the words you use when on the internet.

[nuclearnice1]

> the delusions of the incoming transformer winter will be painful to watch

Meaning?

[SemanticStrengh]

Meaning that HN in ten years will mock current HN