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It happens, great ideas are often thought of at the same time, my advice is connect with the other research group and setup a research collaboration, clearly you are both interested in the same things.

Oh, snap. Yeah. Thanks for pointing this out.

Hey guys! Do we have an AI to add some objects into an image, mainly related with house/constructions ones? For example "Add a window to this house" and send a specific house photo?

Google's BERT use is not a commercial, consumer product, it is an enterprise one (Google uses it and runs it on their hardware), they presumably use the large version or something even larger than the pretrained weights available on the internet and to achieve latencies they have they are using datacentres and non-trivial distribution schemes for it, not just consumer hardware.

Meanwhile, your average CPU will need anywhere from 1-4 seconds to do one inference pass in onnx runtime, of course much less on a GPU, but to be truly cross platform you're targetting JS in most cases, which means CPU and not a stack as mature as what Python/C++/CUDA have.

What I'm saying is:

• people have said no to paid services, they want free products
• consumer hardware has not scaled nearly as fast as DL
• even ancient models are still too slow to run on consumer hardware after years of improvement
• distilling, quantizing and optimizing them seems to get them to run just fast enough to not be a nuisance, but is often too tedious to work out for a free product

Bert is being used by Google for search under the hood. It's how theyve got that instant fancy extractive answers box. I don't disagree that LLMs are large. So was Saturn V.

If the preprint appeared after the CVPR submission deadline your paper cannot be rejected based on lack of novelty. For review purposes the other paper basically doesn’t exist.

Half is not enough. We're thinking in the order of 100x or even more. Do not forget that even ordinary BERT is not really commercially viable as-is.

I mean sure you can use them to get a nicer distribution for your dataset. But at the end of the day the API is too slow to train any "real" model, and you can already probably collect and generate data for smaller models yourself. So as a replacement for lazy people - sure, I think ChatGPT by itself probably has the potential to solve most repetitive questions people have on the internet. But it won't be used like that at scale so ultimately it is not useful.

If it wasn't clear enough by now, I'm not skeptic because of what LLMs are, but how they simply do not scale up to real-world requirements. Ultimately, people do not have datacenters at home, and OpenAI and other vendors do not have the hardware for any actual volume of need other than a niche, hobbyist one. And the investment to develop something like ChatGPT is too big to justify for that use.

All of this was ignoring the obvious legal risks from using ChatGPT generations commercially!

Nah, there's already work that can reduce generic LLM model size by a half and not lose any performance. And LLMs I think will be great as foundation models for training more niche smaller models for narrower tasks - people already use openAIs API to generate data to fine-tune their own niche models. I think we'll look back at current LLMs and realise just how inefficient they were - though a necessary evil to prove that something like this CAN be done.

Sure you could. But the cost is so much it probably outweighs the benefits. And that is even if you made training stable (we already know based on recurrent networks, GANs and even transformers that they're not particularly stable). Hooking it up to the repl would make the task essentially reinforcement learning. And if you know something about reinforcement learning, you know that it generally doesn't work because the environment the agent has to traverse is too difficult to learn anything - what Deepmind managed to achieve with their chess and go engines is truly remarkable, but these are THEIR achievements despite the hardships RL introduces. This is not the achievement of RL. Meanwhile ChatGPT is mostly an achievement of a nice dataset, a clever task and deep learning. It is not that impressive from an engineering standpoint (other than syncing up all the hardware to preprocess the data and train it)

Unless LLMs are extremely optimized in regards to latency and cost, or unless compute becomes even more cheaper (not likely), they have no practical future for the consumer.

So far, it's still a dick measuring contest, as if a larger model and dataset will make much of a difference. I do not see much interest in making them more usable or accessible, I see only effort in beating last year's paper and getting investors to dump more money into a bigger model for next year. I also see ChatGPT as being a cheap marketing scheme all the while it's being used for some pretty nefarious things, some of them being botted Russian or Ukrainian war propaganda.

So you can forget the repl idea. Who would it serve? Programmers have shown they are not willing to pay for something like GitHub Copilot. Large companies can always find people to hire and do programming for them. Unless these are strides in something very expensive, like formal verification, it's not something a large company, the one that has the resources to research LLMs, would go into.

Maybe the next step is training it on WolframAlpha. But at that point you're just catching up to almost 15 year old software. Maybe that "almost 15 year old" shows you how overhyped ChatGPT really is for commercial use.

I think they meant: it is conceivable that in the future it could. i.e. you hook an LLM up with a repl. https://youtu.be/pdSfgRYy8Ao take at look at 15 minutes in. I could easily see how you could fine tune using self appraisal by executing code.

I feel like you'd make a really bad research student

Dude that's why you ought to put everything into NLP find a way of producing better results for cheaper on less expensive hardware and you'll be the talk of the town. I think everyone would love to have an unrestricted local version of chatgpt on their phones. Do the research!

Why the fuck are you coding on an iPhone - if you're going to use a phone at least be android

Is it a good idea to use Confusion Matrix for a multi class problem? I'm talking about 7 classes. It just looks a bit confusing..

Tried it, returns blank audio on my mobile

Please do, post after review process!

Do I need to learn the mathematical foundations of AI/ML before learning ML?

If you're doing the preprocessing and feature selection manually (meaning without the use of a library), yeah that's a pain.

If you're using sklearn, generally if you do all your preprocessing and feature selection with their classes in a sklearn pipeline you should be good. For example, if your input data is a pandas dataframe you can use a ColumnTransformer to tell it which columns to preprocess in which ways, such as a OneHotEncoder on categorical columns. Then you can follow it up with feature selection before your model.

Sklearn's classes are implemented so that they only train the preprocessing and feature selection on the training data.

> I work in Astronomy, not in ML, but review first and arxiv later is how most people work in Europe. I typically don't find european arxiv papers that are not accepted for publication already.

I did work in an Astronomy adjacent field, and European researchers in our area all submitted to arxiv first, just like US groups.

While this may feel shitty at first it shouldn't be a bad thing. You got lucky that you submitted (albeit still in review) before theirs came out. This wasn't a flag plant and it didn't come out while your research was in progress. Theirs existing on arxiv shouldn't stop yours being accepted (if the reviewers are fair).

If I were you I'd focus more on the fact that there's an entire research team in a large well funded industry lab that came up with the same thing as you. This means that you on your own did the same intellectual work as them... AND they'd probably want to hire you! If you're not interested in that, then they may consider collaboration given that they publish.

If contacting them I'd put it in a positive light and don't ask for anything (i.e. don't ask them to cite you!) Mention that you saw that they came out with the same thing and say they may be interested to see you've done exactly the same method and submitted to CVPR. You could suggest that you both may benefit from a meeting to chat about the topic/method :)

I feel like everyone in the new generation has this happen once, learns from it, and has started pre-printing everything...

These are the things I used

1. AWS Spot instances. For now it's the optimal solution that I know of
2. Coreweave. I used them, they're pretty reliable but not sure if they are cheap
3. LambdaLabs. Not very cheap but reliable. From my usage the lag between keystrokes was significant, I think because they proxy SSH through cloudflare

If you are okay with notebooks then definitely checkout kaggle or colab

EDIT: Added Lambdalabs

Breathe. This happens a lot. You still achieved what you achieved. It validates that your approach was smart. Science is not about being first, it's about rigor. Two independent studies that show similar results is a good thing.

I hope he does. If I were the other I would be surprised than we had the same idea. And maybe you could working together on a improvement of it. Good luck!!