Got anything solid to back that claim up that isn't just vague handwavy concerns about a "superintelligence" or AGI? You're acting as if what you're saying is fact when it's clearly just an opinion.

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It sounds like you have some serious misunderstandings about what AI is and what it can be used for, rooted in the same sci-fi plots that has misinformed the entire public.

> Frankly LLMs will be the leading edge of the field for the next 2 years imo.

(curious outsider) what do you see being the leading edge after that? or will NLP be more or less solved by then?

> In a fully-connected layer, the input to the matrix multiply is the output of everything in the previous layer, not just the output of a single unit.

But if the previous layer is 0 everywhere except for one unit, the result is the same, no?

My mental picture is that input layer 0 has V = <token vocabulary size> neurons, and layer 1 has E_d = <embedding dimension> neurons. Layer 0 is 1 in 1 neuron, 0 everywhere else, as one-hot encoding normally goes. The embedding layer 1 is then given by x@W, where x is the layer 0 as a row vector, and W is the weight matrix with dimensions V x E_d. The matrix multiplication then "picks out" the desired row. That would be a fully connected linear layer with no bias.

Yup this will work. But I'm looking for making a model of questioning - answer. Something like this.https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb

At this point ChatGPT seems to be quite good at getting answers to this kind of questions. What it does not provide is a personal assessment like "this book is inspiring" or "it was fun to read".

ChatGPT:

>There isn't a single reference that covers the entire history of neural networks in great detail, but there are a number of good books that you might find helpful. Here are a few that you might want to check out:
>
>"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This book provides a comprehensive introduction to deep learning, including a historical overview of the field. It covers the key ideas that have driven the development of neural networks from the early days up to the current deep learning era, and provides information on their main contributions and inspirations.
>
>"Neural Networks: A Comprehensive Foundation" by Simon S. Haykin: This book is considered a classic reference in the field of neural networks. It provides a detailed historical overview of the field, starting with the early perceptron models and moving through the development of more sophisticated architectures such as convolutional neural networks and recurrent neural networks.
>
>"The Deep Learning Revolution" by Terrence J. Sejnowski: This book provides a broad overview of the history of neural networks and deep learning, and discusses how these technologies have been applied to a wide range of applications, including image and speech recognition, natural language processing, and more. It also provides a glimpse into the future of deep learning and how it is likely to continue to impact a variety of fields.
>
>I hope these references are helpful! Let me know if you have any other questions.

So, I asked: Is "The Deep Learning Revolution" fun to read?

ChatGPT:

>"The Deep Learning Revolution" by Terrence J. Sejnowski is an engaging and informative read for anyone interested in the history and future of deep learning. It provides a broad overview of the field and discusses how deep learning has been applied to a wide range of applications, including image and speech recognition, natural language processing, and more. Sejnowski writes in an accessible style and does a good job of explaining complex concepts in a way that is easy to understand. Overall, I think most people would find it an enjoyable and educational read.

I still think that ChatGPT does not have a proper opinion, as it could think that a book is easy to read but be inapprehensible for a human. But the answer may be useful nonetheless. It, at least, can be used as a starting point to further investigate.

ChatGPT can be used to find answers just like Google is used to find an answer in StackOverflow.

I foresee that new rules may be needed in Reddit to what to do with ChatGPT copy/paste, like Stackoverflow already did.

While this is certainly a very good and comprehensive article, be aware that it is a piece in an ongoing conflict between several ML pioneers in their struggle of controlling their legacy in the narrative of the field.

You could try to break it up into chunks or try to extract the most important information first using summarization.

I think you are overestimating ChatGPT a lot. It hallucinates information, it fails adding numbers, it fails at solving problems and complex reasoning, and a lot more. ChatGPT is great, but it has not solved NLP.

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did you read the article?

So, I am very new to AI category, and just would like to learn all the basic things from the perspective of an AI user (not a developer). Then I found out the MLPerf AI benchmark v2.1 result here: mlcommons.org/en/training-normal-21/
But there are so many different numbers, which makes me confused. On the top of the table it says "benchmark results (minutes)" but what does it mean? Does it mean higher score equals better performance or is it the opposite?
The reason why I am confused with this data is because all the tech media said Nvidia H100 GPU outperforms everybody in this benchmark v2.1 result from MLPerf,
but the table from above website shows that the numbers for the rows of nvidia H100 are not the highest nor the lowest on many of the categories. Can someone tell me how to read the numbers properly and see which ones to look out for?

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Not every physicist can afford a particle accelerator, but that doesn't stop them from researching particle physics.

Chat gpt makes basic reasoning errors that even a child wouldn't make, which makes me think this is a weakness of the current approach. Maybe "more data" is not the solution to this problem. This is one direction I would consider.

https://arxiv.org/abs/1404.7828

edit: https://people.idsia.ch/~juergen/

ChatGPT will probably do a good summary

the issue of hidden variables is "solved' by perturbation approaches... which are often unfeasible... so as a proxy we accept this limitation and we do our best... I think this is also mentioned in the article

That’s an awesome library, thanks for sharing!

So the idea is to summarise the input to increase the information density? A sort of lossy input compression?

Hi,
I am currently pursuing my undergraduate degree in CS. I am very interested in ML and want to pursue a career in this field as a ML Engineer. I am currently learning ML and building few projects alongside.
However, I want to know how realistic it is to get a job as MLE after Bachelor's degree (as I certainly don't wanna stay jobless after graduation even though I love ML too much). I really look forward to learn about the experience of other members regarding this.

Google is your friend here. ChatGPT may even give a decent response.

Start by learning bagging, then learn boosting.

I find the following site fairly good: https://machinelearningmastery.com/essence-of-boosting-ensembles-for-machine-learning .

The explanations are usually approachable, or the author usually has another article on the topic at a simpler level of detail. He has good code samples, many of his articles also go quite in depth, so he caters to a broad range of audiences and is good at sticking with a certain level of depth in a topic in his articles. I've even bought some of his materials and they were fairly good, but his free articles are plenty.

There are lots of other sites you can find that will teach you. Read a few different sources. They're worth understanding well. Since you stated you've read papers on gradient descent, you might find some helpful papers by searching scholar.google.com.

This is also a good place to start: https://www.ibm.com/topics/bagging

Fine tuning? https://beta.openai.com/docs/guides/fine-tuning