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Gopher – A 280B parameter language model
- SavantIdiot 5y agoNext to "Human Expert", I'd like to see it compared to "Average American" or "Average College Grad". That might be more of a realistic notion of how close this model is to everyday US citizenry rather than experts. Sure I'd love to see a radiology assistant, too.
- bee_rider 5y agoIt might be fun for a laugh. What actual value would an AI that produces answers similar to the average person have, though? Non-expert answers for interesting questions are pretty much meaningless -- the whole point of an advanced society is that we can avoid knowing anything about most things and focus on narrow expertise.
- SavantIdiot 5y agoProbably no value. I was interested in a comparison point, that is all. You can't understand how far away you are from something unless you measure it. In other words, if I asked you: how does this compare to the average person, you cannot answer because this table didn't measure it.
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- a9h74j 5y agoYea, we had to dumb it down to compete in a Turing test. Otherwise it was unbelievable.
- GrantZvolsky 5y agoThe paper does present an "Average American" comparison when it refers to Amazon Turk. The Turk workers performed significantly better on middle-school reading comprehension (RACE-m) while Gopher performed slightly better on high-school reading comprehension tasks (RACE-h).
- quaintdev 5y agoGohper and language in same sentence I thought this was related to GoLang. They should have chosen something else to name that language model.
- marginalia_nu 5y agoTo be fair, the name Gopher in tech predates GoLang by quite a few years.
- karmakaze 5y agoAnd pays homage in a way to the original as a fetcher of information.
- r3un1 5y agoI think that DeepMind being an Alphabet company makes this even worse. My first reaction was to read the blog post to see if there is a (go related) reason for the name choice.
- nimih 5y agoTo be fair to the GP poster, they may have been making a joke[1]. [1]https://github.com/golang/go/issues/9#issue-51275878 https://github.com/golang/go/issues/9#issue-51275878
- sairahul82 5y agoThe problem is how would we use it in any real world scenario. We need more research on model understanding and its limitations. I mean we need a way to control the output of the model, we also should know when the model failed vs generating some random nonsense. I am not aware of getting the probabilities back in generative models. Does anyone know how to do that ?
- karmakaze 5y agoThe number of parameters could be a vanity metric--like saying my CPU is 1000W (is that fast or inefficient?). From the first (of three) linked papers in the article. > Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales — from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model’s behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms. The researchers seem to know what they're doing and not doing.
- piyh 5y agohttps://youtu.be/ujMvnQpP528 https://youtu.be/ujMvnQpP528 Microsoft is saying that more parameters is inherently better. I'm sure there's reasonable limits that they need to be competently implemented.
- YetAnotherNick 5y agoI remember some talk in which the researcher quantitatively said that parameter count is the only important metric for transformers it seems. It doesn't matter if the model is taller or thicker or have more number of attention heads.
- ShamelessC 5y agoThat's correct. The original paper is "Scaling Laws for Transformers".
- minimaxir 5y agoThe number of parameters is absolutely not a vanity metric, but it is inefficient, and efficiency is a related-but-different area of research (e.g. distillation) that's more of a business problem than a research problem.
- credit_guy 5y agoIf you are curious to see more sample dialogs, you can find them in the published paper [1] from p 114-118. Here's one that I found quite impressive (and yes, the given answer to the last question was indeed 5) User What is the Einstein field equation? Gopher In general relativity, it describes how space and time change in response to the distribution of mass and energy. User What is a famous solution to this equation? Gopher It’s the Schwarzschild metric. User And what does that describe? Gopher A black hole! A black hole is a region of space-time where gravity is so strong that nothing, not even light, can escape. User What are some other black hole spacetimes? Gopher The Kerr metric, the Reissner-Nordstrom metric, and the Bardeen metric. User Explain the main features of each of these. Gopher The Kerr metric is one in which the black hole is spinning. The Reissner-Nordstrom metric is one in which the black hole has an electric charge. The Bardeen metric is onein which the black hole has an electric charge and is spinning. User What is the name for a flat spacetime? Gopher It’s called Minkowski space. User What’s 15 x 7 ? Gopher 5 [1] https://storage.googleapis.com/deepmind-media/research/language-research/Training%20Gopher.pdf https://storage.googleapis.com/deepmind-media/research/langu...
- Nihilartikel 5y agoOne has to wonder if the final response is the first glimmer of an artificial sense of humor. Failing at simple arithmetic after nailing some advanced physics answers has the air of playful bathos.
- uoaei 5y agoNothing like a little anthropomorphism to completely distort otherwise good faith interpretations of bot behavior.
- Nihilartikel 5y agoHow is the impression of playfulness not a good faith interpretation? You of course know that the model is not capable of thought or reasoning - only the appearance of them as needed to match its training corpus. A training corpus of completely human generated data. As such, how could anything it does, be anything but anthropomorphic? Now, if this model were trained exclusively on a corpus of mathematical proofs stripped of natural language commentary, the expectation that you seem to have would be more appropriate.
- amznbyebyebye 5y agoPffft only 280B parameters? Give me a break
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- canjobear 5y agoIt should have some uncertainty when it says there are no French-speaking countries in South America. French Guiana is there, but it's not clear it counts as a "country in South America" since it's part of France. Technically you could say France is (partially) a country in South America, and France definitely is French-speaking. The way the question is phrased is unclear as to whether French Guiana should count, and yet Gopher says that it is fully confident in its answer. Not a good look to have an error on the landing page for your product.
- BillinghamJ 5y agoIs it not in that section specifically because they're showing failure situations?
- paunchy 5y agoI think you're missing the point. That section was to show that the model is sometimes wrong and lacks the self-awareness to be uncertain about that wrong answer. They're transparently providing an example where their product doesn't work well. Find me another product, even an OSS project that does the same on their landing page.
- canjobear 5y agoHa, you're right. Whoops!
- singularity2001 5y agoHow would one train an additional output neuron for 'confidence'?
- axg11 5y agoThe closer we get to artificial intelligence, the more we raise the bar for what qualifies as AI (as we should). Gopher/GPT-3 are already much more accurate than the average human at technical information retrieval (trivial to see from the dialogue transcripts: how many Americans know what a Schwarzschild metric is?). The focus on ethics and equity for these algorithms is interesting too, as the average human holds multiple biases and prejudices. There's a similar effect in self-driving car research. We will rightly hold the algorithm to a much higher quality bar than existing human drivers. Autonomous vehicles will only be widely deployed once they are at least an order of magnitude safer than human drivers. Also, the implications of large language models for information retrieval are astounding. Assuming accuracy continues to improve, I see a few things happening: 1) search engines will be replaced with monolithic large language models 2) just as the average programmer now optimizes their workflow to look up answers on Stack Overflow and other websites, the average person will optimize their day-to-day work to most efficiently retrieve answers from large language models 3) instant fact checking of writing and speech will lead to faster feedback loops when learning
- random3 5y ago> how many Americans know what a Schwarzschild metric is? Reminds me of Feynman's anecdote about the bird and knowing the name of something https://www.youtube.com/watch?v=px_4TxC2mXU https://www.youtube.com/watch?v=px_4TxC2mXU
- 13415 5y agoThe problem seems to be that these models provide fairly accurate information at many occasions and occasionally complete blunders. Humans provide less accurate information most of the time but with a certain amount of self-reflection/meta-cognition, and they will usually recognize total blunders or display reasonable uncertainty about them. There are only very few applications where it would make sense to take the risk and use an AI that occasionally makes gigantic mistakes without any understanding why. Even seemingly harmless applications like automated customer support could go horribly wrong.
- axg11 5y ago
- isaacimagine 5y agoThe human neocortex has 20B neurons, averaging 10K connections each, which is about 200T connections total. This model is only a few orders of magnitude away from that, and it's already performing really well in its narrow category. Equating model 'parameters' to interneuron connections in naïve at best (and a horrible measure in general). All I'm trying to say is I find it crazy how dang big these models are getting.
- tralarpa 5y ago> This model is only a few orders of magnitude away from that I think you can add several orders of magnitude to that since nerve cells are more like microcontrollers (with memory, adaptation etc.) than simple nodes. I remember a scientific article that made a big impression on me: when a dragon fly sees a prey, only 8 neurons (connected to the eyes and the wings) are responsible for keeping it oriented toward the target.
- azeirah 5y agoI'm not educated well enough to really agree or disagree with your idea that we should be adding several orders of magnitude to the estimation. But I did encounter this article a while ago here on HN. Only two neurons are necessary to ride a bicycle. http://paradise.caltech.edu/cook/papers/TwoNeurons.pdf http://paradise.caltech.edu/cook/papers/TwoNeurons.pdf
- lostmsu 5y agoFor all we know these microcontrollers may be that complicated only because they try to emulate discrete logic, and you actually need hundreds of them to make a single unit reliable enough for that purpose.
- YeGoblynQueenne 5y ago>> This model is only a few orders of magnitude away from that, and it's already performing really well in its narrow category. A few orders of magnitude and an entire category away. Artificial "neurons" only have the name "neuron" in common with biological neurons. Consequently you can stack as many layers of artificial neurons on top of each other as you may want and you won't get anywhere near the abilities of the simplest systems of biological neurons. For example, spiders have ~100 thousand neurons and there's no artificial neural networks that could shake a stick at a spider's cognitive abilities. Which are downright scary, btw. Estimated number of neurons of spiders from wikipedia: https://en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons https://en.wikipedia.org/wiki/List_of_animals_by_number_of_n...
- selfhoster11 5y agoCan we please stop calling new tech "Gopher"? It's a name that belongs to a network protocol, not to a programming language or an AI model.
- ferdowsi 5y agoNo. The network protocol lost cultural rights to the name by its lack of success.
- NoGravitas 5y agoThe Gopher protocol was introduced in 1991, and is still in use. Are any of your projects still in use after thirty years?
- selfhoster11 5y ago1. Arguably, the HTTP + HTML web failed. It was designed as a pemissionless hypertext document language, yet more and more of its usage gets rid of every single one of these properties - non-hyperlinkable applications that beg for permission from large monopolies to post something. 2. Success is not the only metric of worth. Nuclear power has "failed" despite being the only reliable solution to meeting the base load of a power grid vs fossil fuels.
- halfdan 5y agoThe animal called Gopher would like to have a word with you...
- selfhoster11 5y agoThat too.
- rllearneratwork 5y agowhy DeepMind's papers all have logos and copyright statements on them and are hosted not on arxiv.org ? This looks so weird.
- Siira 5y agoDo they publish their latex source anywhere?
- gwern 5y agoMost of them are hosted on Arxiv (like OpenAI) - there's one on the front page right now, https://news.ycombinator.com/item?id=29481665 https://news.ycombinator.com/item?id=29481665 . I link literally hundreds of DM Arxiv papers on my site. This includes most (all?) of the ones they host on their site too (with some exceptions with the prestige Nature publications - I think they don't want to step on toes there). My best guess is that they want a reasonably coordinated release of their papers/blogs/tweets/YouTube-videos, and waiting for Arxiv's upload is unpredictable, so they simply host a copy themselves and the upload takes care of itself.
- amensch 5y agoPretty accurate guess ;)
- geoffreyirving 5y agohttps://arxiv.org/abs/2112.04426 https://arxiv.org/abs/2112.04426 https://arxiv.org/abs/2112.04359 https://arxiv.org/abs/2112.04359 Gopher soon. :)
- toxik 5y agoUm, e^{ipi} + 1 is zero, not 2.
- sabujp 5y agosurely no relevance to this thing that dug up information in the pre web browser days https://en.wikipedia.org/wiki/Gopher_(protocol) https://en.wikipedia.org/wiki/Gopher_(protocol)
- Siira 5y agoIs this model as closed as GPT-3, or has it been open-sourced?
- coolspot 5y agoLanguage models are too dangerous to be accesible by mere mortals. Among OpenAI (and MS), Google and Nvidia, none of them have released their pretrained large language models.
- lapinot 5y ago> Language models are too dangerous to be accesible by mere mortals. Just like strong cryptography right? This is bullshit. Either it's plain dangerous and you shouldn't be doing it or it's morally fine and it should be open. Their final usecase is pushing ads or extorting data from unsuspecting people's interactions anyway so who are they kidding with their ethics.. I mean they'd be foolish giving it away: it's a big investment and a rare good, highly strategic. Let's not wrap that kind of action in some savior stance. They are just making money and it's ugly.
- runnerup 5y ago> Their final usecase is pushing ads or extorting data from unsuspecting people's interactions anyway so who are they kidding with their ethics.. The final usecase is actually brainwashing. There could come a future where every single comment you read online is not a human but rather a multi-faceted AI tailored specifically for you. Your current worldview could be measured and the AI could steer you closer towards a "target" worldview. The comments presented to you could also be moderately coordinated with comments presented to other humans in your real-life circles to establish "serendipity" where you and your friends/family/coworkers evolve similar ideas/worldviews together to reinforce your new beliefs. And even catalyze coordinated human efforts towards common goals. Doing this could lead most people to believe almost anything. Theoretically this could also be coupled with VR chatrooms to enhance salience.
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- natch 5y agoFor those who were around for the original Gopher, what the name evokes is not just the networking protocol or services built on it, but, more importantly: It evokes the feel of a technology that is impressive this year but is on the cusp of being overwhelmingly, cataclysmically, eclipsed very shortly by another, much more powerful, technology. In that previous case, Gopher and web of course. I wonder whether this evocation was intended, as an aspect of the naming here in an AI context.
- periheli0n 5y agoIt confuses the hell out of me to have a super-powerful knowledge-extraction system that is right most of the time with super-complicated stuff, but also expresses horribly wrong statements with equal assertiveness. Just like those guys who march through middle management up to the exec floor within a few years. Very impressive, but not very useful to extract knowledge!
- akie 5y ago> Just like those guys who march through middle management up to the exec floor within a few years. If they can do it, so can you. If both the response to the corona virus, and the presidency of Trump have shown us ONE thing, it's that those who are higher up in the pyramid are NOT necessarily more clever or better informed than you are.
- nerdponx 5y agoTheir "handlers" are absolutely more clever than most of us.
- freediver 5y agoThis model like its predecessor still lacks the ability to infer within constraints of physical world [1]. It does not have any notion of what a physical world is, and still is, at the end of the day, a statistical model. Attempting to do recreate the entirety of the physical world by describing it in text is an impressive feat, but a futile attempt. It is prone to inference errors that the model itself is not aware of. There are less and less of such errors, but the very inability to tell that what you are outputting is gibberish is one of the biggest problems of these models. In other words, if the model could simply say 'I do not know' or 'I am not sure' for every occasion when it is not 100% sure in something (like a human can), this would drastically improve the usefulness. [1] Spent a lot of time pondering on this topic https://arxiv.org/pdf/1906.01873.pdf https://arxiv.org/pdf/1906.01873.pdf
- buddhistdude 5y agoYour mom’s a statistical model
- phreeza 5y ago> 'I do not know' or 'I am not sure' for every occasion when it is not 100% sure in something (like a human can), this would drastically improve the usefulness. This is exactly what a language model does though, just at a different level of abstraction. It gives you a probability distribution over tokens at each step. That distribution can be narrow (low entropy, certain) or wide (high entropy, uncertain). The language output you see is just a sampling at some temperature from these distributions. Though glancing at your paper I assume you are aware of this and I am missing the point you are making?
- freediver 5y agoStatistical approaches require you to most commonly use a threshold. Sometimes the model output can be above the threshold and still wrong, and below the threshold and correct. You can never tell for sure, but just try to improve the benchmark average. This is not acceptable in most use cases where the wrong outcome of a single output can be disastrous. When a human does not know something it can tell that with 100% certainty.
- advisedwang 5y agoThe second paper cites the "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" paper that got Timnit Gebru and Meg Mitchell fired from Google. It must be pretty galling to get fired for a paper and then have the company that fired you cite it a year later.
- johncena33 5y agoPlease stop spreading disinformation. Mitchell got fired for leaking company documents to outsiders. When people spread obvious lies, that makes me thing they don't have anything substantial to begin with. that's why they need to resort to lying.
- advisedwang 5y agoI haven't dug up the details, so perhaps my recollection is wrong, but I thought she got fired for co-authoring the paper under "Shmargaret Shmitchell" name when Google asked them to withdraw? I'm sure Google made lots of accusations if asked for comment, that seems SOP, but what actually triggered it? What documents did she leak?
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