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So we now have models with 0.5 trillion parameters, each the weight of a connection in a neural network. Trillion-parameter models are surely within reach in t
by cs702 5y ago
So we now have models with 0.5 trillion parameters, each the weight of a connection in a neural network.
Trillion-parameter models are surely within reach in the near term -- and that's only within two orders of magnitude of the number of synapses in the human brain, which is in the hundreds of trillions, give or take. To paraphrase the popular saying, a trillion here, a trillion there, and pretty soon you're talking really big numbers.
I know the figures are not comparable apples-to-apples, but still, I find myself in awe looking at how far we've come in just the last few years, to the point that we're realistically contemplating the possibility of seeing dense neural networks with hundreds of trillions of parameters used for real-world applications in our lifetime.
We sure live in interesting times.
- rajnathani 5y agoIf retrieval based NLP [0] becomes a thing, then trillion plus parameters models will likely be less of a thing; as very likely, most of these tens to hundreds of billions of parameters are likely over-fitting (better word: memorized) on training data [text corpus] as seen in the case of GPT-3. [0] https://ai.stanford.edu/blog/retrieval-based-NLP/ https://ai.stanford.edu/blog/retrieval-based-NLP/
- cs702 5y agoYes, self-attention mechanisms are dense associative memories, so it might be possible to replace them in many cases with simpler storage mechanisms. Still, I would count the required storage space as part of a model's parameter size -- e.g., a model consisting of 1 trillion values in RAM and 99 trillion values in storage consists of... 100 trillion values.
- LuisMondragon 5y agoMy issue with this kind of reasoning is the comparison and reference to the human brain. The potential and reach of AI transcends the brain. We never had to master the "mystery" of how birds fly to invent aviation. It was never necessary to compare the number of turbine revolutions of early airplanes to the number of an eagle's feathers. Maybe birds were an inspiration or a metaphor, but thankfully aviation has not been limited to the means of propulsion of the beautiful yet humble pigeon. The potential of aviation has taken us into space exploration and massive international travel. I don't know where AI will take us, but I don't think it will be constrained by this temporary organ called 'human brain'.
- EvgeniyZh 5y agoNot all weights are born equal, different paradigms allow more parameters while being less parameter-efficient, e.g. https://openreview.net/forum?id=TXqemS7XEH https://openreview.net/forum?id=TXqemS7XEH
- codeulike 5y agoUnless we have misunderstood neurons, and microtubules are the fundamental computational unit in which case we are out by an order of magnitude
- eximius 5y agoThere was a result recently of modeling an organic neuron with 1000 digital neurons. And even if that result was perfect modeling of the neuron, that assumes perfect and exhaustive data readings on the organic neuron, which is, frankly, unlikely. (Not that I know how to estimate how much it's missing, but I don't think we fully understand a single neuron yet.)
- fspeech 5y agoA 10 trillion parameter model was mentioned here: https://mobile.twitter.com/ethancaballero/status/1445826862064840713 https://mobile.twitter.com/ethancaballero/status/14458268620...
- ImprobableTruth 5y agoThat's MoE.
- singularity2001 5y agoExcept that every synapse is not a dumb weight but a highly complex system connected to an even more complex system (aka neuron) which might each be a (super)computer on its own. Given how extremely bad we are at computing, there is hope (for ai) that the neurons or their circuits are not _that_ powerful after all.
- alecst 5y agoLast I heard (and I believe this could be wrong) my professor said that we basically understand how a single neuron works. That like basically if we do X input we get Y output, up to some accuracy. He used this to discuss the idea behind neural networks -- that each neuron is simple enough to model, all we need to worry about is the weights and the dynamics of the network as a whole. How much of a simplification is that? And how much does the accuracy of such a model matter, in the grand scheme of things?
- andyxor 5y agoThat’s correct understanding as of 1943 when the “artificial neural network“ model your professor is teaching was developed. There is a whole lot of new knowledge on how live neurons and networks of neurons work that had been collected in the last 75 years in the neuroscience domain but it’s mostly ignored by computer scientists.
- visarga 5y ago
- postalrat 5y agoCPU in kilohertz then megahertz then gigahertz then it stopped. RAM in kilobytes then megabytes then gigabytes then it stopped.
- manquer 5y agoThose are material science and physical limitations. Number of parameters in a neural network is not really limited that way, doing useful compute with it is a different matter
- sanxiyn 5y agoYes for CPU, no for RAM. You can buy a computer with terabytes of RAM just fine. It's just expensive.
- soganess 5y agoExpensive is a really relative term right about now... 64gb DDR4 LRDIMMs could be had for about 250$ each on eBay before the chip shortage. While that price is a "good-ish deal", it really wasn't unheard of. A search I just did returns more than a few hits... Now compare to single GPU prices...
- dustfinger 5y agoThe switch transformer has already achieved a trillion parameters. https://arxiv.org/abs/2101.03961 https://arxiv.org/abs/2101.03961
- ZoomerCretin 5y agoChina's Wu Dao 2.0 has 1.75 trillion parameters. https://towardsdatascience.com/gpt-3-scared-you-meet-wu-dao-2-0-a-monster-of-1-75-trillion-parameters-832cd83db484 https://towardsdatascience.com/gpt-3-scared-you-meet-wu-dao-...
- YeGoblynQueenne 5y agoI don't understand this kind of comment. To my mind what it amounts to is "look at how big it is". Alright. So it's big. So what? Is this an elephant pageant? Suppose a friend comes over and says "I went for dinner at a restaurant. Oh my god the portions were sooo big!". Wouldn't you want to know more information about the food and the restaurant, before you decided whether you're interested in it? I appreciate that "big" is in peoples' minds associated with "strong", but most of the work in making language models bigger and bigger goes against the normal trend in computer science [1] and also neural networks research in gneral where the trend is to constantly try to reduce the size of models and improve their data efficiency. What's worse, the trend to supersize language models is never justified, either theoretically (ha ha) or empirically in the relevant literature - and when rival teams make the obvious experiments the evidence is that size is not required to achieve good performance. For example: It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners https://aclanthology.org/2021.naacl-main.185/ https://aclanthology.org/2021.naacl-main.185/ ____________ [1] Imagine someone bragging that their mergesort implementation has a million LOC! People brag about implementations in few lines of code, not many.
- sanxiyn 5y ago> What's worse, the trend to supersize language models is never justified, either theoretically (ha ha) or empirically in the relevant literature What? That's absurd. Large language models are motivated by empirical scaling law. It is actually better justified than other ML research. Scaling Laws for Neural Language Models: https://arxiv.org/abs/2001.08361 https://arxiv.org/abs/2001.08361
- cs702 5y ago> I don't understand this kind of comment. To my mind what it amounts to is "look at how big it is". Alright. So it's big. So what? It's a good question. A while ago, Rich Sutton wrote a good answer for it.: http://incompleteideas.net/IncIdeas/BitterLesson.html http://incompleteideas.net/IncIdeas/BitterLesson.html -- I recommend reading the whole essay. Quoting him (emphasis mine): > The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation. > We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. > One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning. A key related question -- to which no one has the answer today -- is whether we must scale computation to match or exceed that of the human brain to be able to replicate or surpass its cognitive abilities. (Note that this question is independent of whether doing so would require future theoretical breakthroughs -- another question to which no one knows the answer today.) PS. See also sanxiyn's response: https://news.ycombinator.com/item?id=28838745 https://news.ycombinator.com/item?id=28838745