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Jensen embraced AI as a way to recover TAM after ASICs took over crypto mining. You can see that between-period in NVidia revenue and profit graphs.

By that time, GP-GPU had been around for a long, long time. CUDA still doesn't have much to do with AI - sure, it supports AI usage, even includes some AI-specific features (low-mixed precision blocked operations).

> vertebrates across the board require less data to learn new things, by several orders of magnitude.

Sometimes I wonder if it’s fair to say this.

Organisms have had billions of years of training. We might come online and succeed in our environments with very little data, but we can’t ignore the information that’s been trained into our DNA, so to speak.

What’s billions of years of sensory information that drove behavior and selection, if not training data?

> vertebrates across the board require less data to learn new things

the human brain is absolutely inundated with data, especially from visual, audio, and kinesthetic mediums. the data is a very different form than what one would use to train a CNN or LLM, but it is undoubtedly data. newborns start out literally being unable to see, and they have to develop those neural pathways by taking in the "pixels" of the world for every millisecond of every day

Do you have, offhand, any names or references to point me toward why you think fish and lizards can make rapid common sense deductions about man made objects they couldn't have seen in their evolutionary histories?

Also, separately, I'm only assuming but it seems the reason you think these deductions are different from hard wired answers if that their evolutionary lineage can't have had to make similar deductions. If that's your reasoning, it makes me wonder if you're using a systematic description of decisions and of the requisite data and reasoning systems to make those decisions, which would be interesting to me.

> I think there is a slight disconnect here between making AI systems which are smart and AI systems which are useful. It’s a very old fallacy in AI: pretending tools which assist human intelligence by solving human problems must themselves be intelligent.

I have difficulties understanding why you could even believe in such a fallacy: just look around you: most jobs that have to be done require barely any intelligence, and on the other hand, there exist few jobs that do require an insane amount of intelligence.

Maybe we just collectively decided that it didn't matter whether the answer was correct or not.

Not baloney. The culture around data in 2005-2010 -- at least / especially in academia -- was night and day to where it is today. It's not that people didn't understand that more data enabled richer + more accurate models, but that they accepted data constraints as a part of the problem setup.

Most methods research went into ways of building beliefs about a domain into models as biases, so that they could be more accurate in practice with less data. (This describes a lot of PGM work). This was partly because there was still a tug of war between CS and traditional statistics communities on ML, and the latter were trained to be obsessive about model specification.

One result was that the models that were practical for production inference were often trained to the point of diminishing returns on their specific tasks. Engineers deploying ML weren't wishing for more training instances, but better data at inference time. Models that could perform more general tasks -- like differentiating 90k object classes rather than just a few -- were barely even on most people's radar.

Perhaps folks at Google or FB at the time have a different perspective. One of the reasons I went ABD in my program was that it felt industry had access to richer data streams than academia. Fei Fei Li's insistence on building an academic computer science career around giant data sets really was ingenius, and even subversive.

It's not quite so - we couldn't handle it, and we didn't have it, so it was a bit of a none question.

I started with ML in 1994, I was in a small poor lab - so we didn't have state of the art hardware. On the other hand I think my experience is fairly representative. We worked with data sets on spark workstations that were stored in flat files and had thousands or sometimes tens of thousands of instances. We had problems keeping our data sets on the machines and often archived them to tape.

Data came from very deliberate acquisition processes. For example I remember going to a field exercise with a particular device and directing it's use over a period of days in order to collect the data that would be needed for a machine learning project.

Sometime in the 2000's data started to be generated and collected as "exhaust" from various processes. People and organisations became instrumented in the sense that their daily activities were necessarily captured digitally. For a time this data was latent, people didn't really think about using it in the way that we think about it now, but by about 2010 it was obvious that not only was this data available but we had the processing and data systems to use it effectively.

Answering to people arguing against my comment: you guys do not seem to take into account that the technical circumstances were totally different thirty, twenty or even ten years ago! People would have liked to train with more data, and there was a big interest in combining heterogeneous datasets to achieve exactly that. But one major problem was the compute! There weren't any pretrained models that you specialized in one way or the other - you always retrained from scratch. I mean, even today, who's get the capability to train a multibillion GPT from scratch? And not just retraining once a tried and trusted architecture+dataset, no, I mean as a research project trying to optimize your setup towards a certain goal.

Pre-ImageNet was like pre-2010. Doing ML with massive data really wasn't in vogue back then.

In 2019, GPT-2 1.5B was trained on ~10B tokens.

Last week Hugging Face released SmolLM v2 1.7B trained on 11T tokens, 3 orders of magnitude more training data for the same number of tokens with almost the same architecture.

So even back in 2019 we can say we were working with a tiny amount of data compared to what is routine now.

Not really. This is referring back to the 80's. People weren't even doing 'ML'. And back then people were more focused on teasing out 'laws' in as few data points as possible. The focus was more on formulas and symbols, and finding relationships between individual data points. Not the broad patterns we take for granted today.

>I wonder what would have happened if we poured the same amount of money, talent and hardware into SVMs, random forests, KNN, etc.

But that's backwards from how new techniques and progress is made. What actually happens is somebody (maybe a student at a university) has an insight or new idea for an algorithm that's near $0 cost to implement a proof-of concept. Then everybody else notices the improvement and then extra millions/billions get directed toward it.

New ideas -- that didn't cost much at the start -- ATTRACT the follow on billions in investments.

This timeline of tech progress in computer science is the opposite from other disciplines such as materials science or bio-medical fields. Trying to discover the next super-alloy or cancer drug all requires expensive experiments. Manipulating atoms & molecules requires very expensive specialized equipment. In contrast, computer science experiments can be cheap. You just need a clever insight.

An example of that was the 2012 AlexNet image recognition algorithm that blew all the other approaches out of the water. Alex Krizhevsky had an new insight on a convolutional neural network to run on CUDA. He bought 2 NVIDIA cards (GTX580 3GB GPU) from Amazon. It didn't require NASA levels of investment at the start to implement his idea. Once everybody else noticed his superior results, the billions began pouring in to iterate/refine on CNNs.

Both the "attention mechanism" and the refinement of "transformer architecture" were also cheap to prove out at a very small scale. In 2014, Jakob Uszkoreit thought about an "attention mechanism" instead of RNN and LSTM for machine translation. It didn't cost billions to come up with that idea. Yes, ChatGPT-the-product cost billions but the "attention mechanism algorithm" did not.

>into SVMs, random forests, KNN, etc.

If anyone has found an unknown insight into SVM, KNN, etc that everybody else in the industry has overlooked, they can do cheap experiments to prove it. E.g. The entire Wikipedia text download is currently only ~25GB. Run the new SVM classification idea on that corpus. Very low cost experiments in computer science algorithms can still be done in the proverbial "home garage".

Transformers were made for machine translation - someone had the insight that when going from one language to another the context mattered such that the tokens that came before would bias which ones came after. It just so happened that transformers we more performant on other tasks, and at the time you could demonstrate the improvement on a small scale.

The best tool for the job is, I’d argue, the one that does the job most reliably for the least amount of money. When you consider how little expertise or data you need to use openai offerings, I’d be surprised if sentiment analysis using classical ML methods are actually better (unless you are an expert and have a good dataset).

KANs (Kolmogorov-Arnold Networks) are one example of a promising exploration pathway to real AGI, with the advantage of full explain-ability.

> I wonder what would have happened if we poured the same amount of money, talent and hardware into SVMs, random forests, KNN, etc.

From my perspective, that is actually what happened between the mid-90s to 2015. Neural netowrks were dead in that period, but any other ML method was very, very hot.

Deep learning is easy to adapt to various domains, use cases, training criteria. Other approaches do not have the flexibility of combining arbitrary layers and subnetworks and then training them with arbitrary loss functions. The depth in deep learning is also pretty important, as it allows the model to create hierarchical representations of the inputs.

And based on what though do you think that?

I think neural networks are fundamental and we will focus/experiment a lot more with architecture, layers and other parts involved but emerging features arise through size

> neural nets are just a subset of machine learning techniques.

Fact by definition

>I wonder what would have happened if we poured the same amount of money, talent and hardware into SVMs, random forests, KNN, etc.

people did that to horses. No car resulted from it, just slightly better horses.

>I saw some YouTube coders doing calls to OpenAI's o1 model for some very simple classification tasks. That isn't the best tool for the job.

This "not best tool" is just there for the coders to call while the "simple SVMs and KNN" would require coding and training by those coders for the specific task they have at hand.

This is such a terrible opinion, im so tired of reading the LLM deniers

Geoffrey Hinton (now a Nobel Prize winner!) himself did a summary. I think it is the single best summary on this topic.

  Our labeled datasets were thousands of times too small.
  Our computers were millions of times too slow.
  We initialized the weights in a stupid way.
  We used the wrong type of non-linearity.

As compute has outpaced memory bandwidth most recent stuff has moved away from ReLU. I think Llama 3.x uses SwiGLU. Still probably closer to ReLU than logistic sigmoid, but it's back to being something more smooth than ReLU.

I was a deep learning researcher. The problem is that accuracy (+ related metrics) were prioritized in research and funding. Factors like interpretability, extrapolation, efficiency, or consistency were not prioritized, but were clearly important before being implemented.

Dall-E was the only big surprising consumer model-- 2022 saw a sudden huge leap from "txt2img is kind of funny" to "txt2img is actually interesting". I would have assumed such a thing could only come in 2030 or earlier. But deep learning is full of counterintuitive results (like the NFL theorem not mattering, or ReLU being better than sigmoid).

But in hindsight, it was naive to think "this does not work yet" would get in the way of the products being sold and monetized.

I'm still very taken aback by how far we've been able to take prompting as somehow our universal language to communicate with AI of choice.

I wound not call it invent”, but it seems Nvidia defined the term GPU. See https://www.britannica.com/technology/graphics-processing-un... and https://en.wikipedia.org/wiki/GeForce_256#Architecture:

“GeForce 256 was marketed as "the world's first 'GPU', or Graphics Processing Unit", a term Nvidia defined at the time as "a single-chip processor with integrated transform, lighting, triangle setup/clipping, and rendering engines that is capable of processing a minimum of 10 million polygons per second"”

They may have been the first with a product that fitted that definition to market.

Wow, that is harsh. The quoted claim is in the middle of a very long article. The background of the author seems to be more on the scientific side, than the technical side. So throw out everything, because the author got one (not very important) date wrong?

Defining who "really" invented something is often tricky. For example I mentioned in the article that there is some dispute about who discovered backpropagation. A

According to Wikipedia, Nvidia released its first product, the RV1, in November 1995, the same month 3dfx released its first Voodoo Graphics 3D chip. Is there reason to think the 3dfx card was more of a "true" GPU than the RV1? If not, I'd say Nvidia has as good a claim to inventing the GPU as 3dfx does.

After actually having read the article i can say that your comment is unnecessarily negative and clueless.

The article is a very good historical one showing how 3 important things came together to make the current progress possible viz;

1) Geoffrey Hinton's back-propagation algorithm for deep neural networks

2) Nvidia's GPU hardware used via CUDA for AI/ML and

3) Fei-Fei Li's huge ImageNet database to train the algorithm on the hardware. This team actually used "Amazon Mechanical Turk"(AMT) to label the massive dataset of 14 million images.

Excerpts;

“Pre-ImageNet, people did not believe in data,” Li said in a September interview at the Computer History Museum. “Everyone was working on completely different paradigms in AI with a tiny bit of data.”

“That moment was pretty symbolic to the world of AI because three fundamental elements of modern AI converged for the first time,” Li said in a September interview at the Computer History Museum. “The first element was neural networks. The second element was big data, using ImageNet. And the third element was GPU computing.”

Possibly technically correct, but utterly irrelevant. The 3dfx chips accelerated parts of the 3d graphics pipeline and were not general-purpose programmable computers the way a modern GPU is (and thus would be useless for deep learning or any other kind of AI).

If you are going to count 3dfx as a proper GPU and not just a geometry and lighting accelerator, then you might as well go back further and count things like the SGI Reality Engine. Either way, 3dfx wasn't really first to anything meaningful.

Arguably the November 01981 launch of Silicon Graphics kickstarted GPU interest and OpenGL. You can read Jim Clark's 01982 paper about the Geometry Engine in https://web.archive.org/web/20170513193926/http://excelsior..... His first key point in the paper was that the chip had a "general instruction set", although what he meant by it was quite different from today's GPUs. IRIS GL started morphing into OpenGL in 01992, and certainly when I went to SIGGRAPH 93 it was full of hardware-accelerated 3-D drawn with OpenGL on Silicon Graphics Hardware. But graphics coprocessors date back to the 60s; Evans & Sutherland was founded in 01968.

I mean, I certainly don't think NVIDIA invented the GPU—that's a clear error in an otherwise pretty decent article—but it was a pretty gradual process.

Can confirm. I was playing Unreal on my dual Voodoo2 SLI rig back in 1998.

Expert systems did catch on and do see widespread use - they're just not called AI anymore. It's 'business logic' or 'rules engine' now.

The issue with SVMs is that they get intractably expensive for large datasets. The cost of training a neural network scales linearly with dataset size, while the kernel trick for SVMs scales with dataset size squared. You could never create an LLM-sized SVM.

I've been looking at SVMs for use with a binary classification experiment. Training and operating these models is quite cheap. The tooling is ubiquitous and will run on a toaster. A binary decision made well can be extremely powerful. Multiple binary decisions underly... gestures broadly.

Obvious contextual/attention caveats aside, a few thousand binary classifiers operating bitwise over the training & inference sets would get you enough bandwidth for a half-ass language model. 2^N can be a very big place very quickly.

in insurance we use older statistical methods that are easily interpretable, because we are required to have rates approved by departments of insurance

> rather than just scaling up with more data.

That was the key takeaway for me from this article. I didn't know of Fei-Fei Li's ImageNet contribution which actually gave all the other researchers the essential data to train with. Her intuition that more data would probably make the accuracy of existing algorithms better i think is very much under appreciated.

Key excerpt;

So when she got to Princeton, Li decided to go much bigger. She became obsessed with an estimate by vision scientist Irving Biederman that the average person recognizes roughly 30,000 different kinds of objects. Li started to wonder if it would be possible to build a truly comprehensive image dataset—one that included every kind of object people commonly encounter in the physical world.

How much easier was it compared to the next best method at the time?

And the compute power!

Also: look for fields that have stagnated, where progress is enabled by apparently-unrelated innovations elsewhere

Unfortunately, they’re usually right. We just don’t hear about all the time wasted.

The lesson is reality is not the dialectics and symbolic logic but all the stuff in it.

Study story problems and you end up with string theory. Study data computed from endless world of stuff, find utility.

What a shock building the bridge is more useful than a drawer full of bridge designs.

What makes you think AGI is either here or imminent?

For me the current systems still clearly fall short of that goal.

What do you think is next?

Downvoters are responding to a perceived arrogance. What does agi mean to you?