13 ms·
Why your brain is 3 milion more times efficient than GPT-4
- xqcgrek2 2y agoThe caloric need of a monkey typing, or a cat, is much lower than even a human. But it doesn't mean the results are good.
- Synaesthesia 2y agoYeah because humans are really special. Monkeys and cats can still solve physical problems though which are quite complex, and make decisions.
- exe34 2y agocats are wiser than a lot of people. heck people think they're more intelligent than dolphins because they invented taxes and built new York while dolphins just hang out all day doing nothing, and dolphins think they are more intelligent for the same reason.
- mati365 2y agoMy is not
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- lukan 2y agoI was expecting a simple trivial calculation with comparing energy demand for LLMs and energy demand of the brain and lots of blabla around it.. But it rather seems a good general introduction into the realm aimed at beginners. Not sure if it gets everything right and the author clearly states he is not an expert and would like correction where he is wrong, but it seems worth checking out, if one is interested in understanding a bit about the magic behind it.
- proneb1rd 2y agoCall me lazy but I couldn’t get through the wall of text to learn what on earth vectored database is. Way too much effort spent talking about binary and how ascii works and whatnot - such basics that it feels that the article is for someone with zero knowledge about computers.
- swyx 2y agoindeed. its condescending and word vomity. i would flag it except that it doesnt break any rules, it is just badly written. as the author acknowledges it is a 4hr stream of consciousness word dump. title is clickbait relative to what it is, a vector db review piece with a long preamble to puff himself up
- lll-o-lll 2y agoMaybe, but I bet GPT-4 can spell million.
- kvdveer 2y agoI feel the author is comparing an abstract representation of the brain to a mechanical representation of a computer. This is not a fair or useful comparison. If a computer does not understand words, neither does your brain. While electromagnetic charge in the brain does not at all correspond with electromagnetic charge in a GPU, they do share an abstraction level, unlike words vs bits.
- Synaesthesia 2y agoNo, only a brain can "think" and be original. A computer is limited to what we input to it. An "AI" simply recapitulates what it was trained on.
- exe34 2y agothat's incredible! how did you put the confetti back in the canon?
- throwAGIway 2y agoI have heard this exact sentence so many times already. Are you sure? I'd take a good look inside myself now if I were in your shoes.
- jeffhuys 2y agoYou’re holding on to a lost battle. We are biological computers. Maybe there’s something deeper behind it, like what some call a soul, but that’s hard to impossible to prove.
- shinycode 2y agoIf some day AGI happens and can exists on its own, wouldn’t that prove that intelligence is a base requirement for intelligence to happen in the first place ? AGI can’t happen on its own, it needs our intelligence first to help it structure itself
- exe34 2y agoDo you think the same thing is required for flying? that aeroplanes can only be created by birds?
- shinycode 2y agoIntelligence and flying are different things, a leaf falling down a tree « fly » because of laws of nature.
- exe34 2y agoBeautiful analogy! Human intelligence is an extreme on the spectrum of animal intelligence, and evolution by natural selection is the law of nature that made it happen.
- wegfawefgawefg 2y agoIt would not prove that. It would be one observed example of a new intelligence which was created by an existing one.
- avereveard 2y agoOnly if we talk about trained intelligence. Likely the requirements for evolved intelligence are different and involve being embodied, edonistic, and the pressure of a selection mechanism
- shinycode 2y agoIf a spontaneous intelligence is 3 million times more efficient that one that one who took millions of hours of work from brains (there is so much effort that there is even more work put into AI that evolution who thinly spread changes through time, efforts diluted). We either have to define that AI will never be the same as HI and can’t compete with it or it’s of the same nature as some people say on HN and for me it brings the question of intelligence needed for an other one to appear. Because we have no other history of something that complex and intelligent ever emerging. The only thing that some of us consider as intelligent as us if not more, could ever emerge because of tremendous efforts and structure and will from our part (or from our intelligence)
- madsbuch 2y agoThere is an immensely strong dogma that, to my best knowledge, is not founded in any science or philosophy: First we must lay down certain axioms (smart word for the common sense/ground rules we all agree upon and accept as true). One of such would be the fact that currently computers do not really understand words. ... The author is at least honest about his assumptions. Which I can appreciate. Most other people just has it as a latent thing. For articles like this to be interesting, this can not be accepted as an axiom. It's justification is what's interesting,
- matwood 2y agoYeah, for axioms like the above my next question is define 'understand'. Does my dog understand words when it completes specific actions because of what I say? I'm also learning a new language, do I understand a word when I attach a meaning (often a bunch of other words to it) to it? Turns out computers can do this pretty well.
- southernplaces7 2y agoOh please, enough with the semantics. It reminds me of a post modernist asking me to define what "is" is. The LLM does not understand words in the way a human understands them and that's obvious. Even the creators of LLMs implicitly take this as a given and would rarely openly say they think otherwise no matter how strong the urge to create a more interesting narrative. Yes, we attach meaning to certain words based on previous experience, but we do so in the context of a conscious awareness of the world around us and our experiences within it. An LLm doesn't even have a notion of self, much less a mechanism for attaching meaning to words and phrases based on conscious reasoning. Computers can imitate understanding "pretty well" but they have nothing resembling a pretty good or bad or any kind of notion of comprehension about what they're saying.
- logicallee 2y agoIt's the most incredible coincidence. Three million paying OpenAI customers spend $20 per month (compare: NetFlix standard: $15.49/month) thinking they're chatting with something in natural language that actually understands what they're saying, but it's just statistics and they're only getting high-probability responses without any understanding behind it! Can you imagine spending a full year showing up to talk to a brick wall that definitely doesn't understand a word you say? What are the chances of three million people doing that! It's the biggest fraud since Theranos!! We should make this illegal! OpenAI should put at the bottom of every one of the millions of responses it sends each day: "ChatGPT does not actually understand words. When it appears to show understanding, it's just a coincidence." You have kids talking to this thing asking it to teach them stuff without knowing that it doesn't understand shit! "How did you become a doctor?" "I was scammed. I asked ChatGPT to teach me how to make a doctor pepper at home and based on simple keyword matching it got me into medical school (based on the word doctor) and when I protested that I just want to make a doctor pepper it taught me how to make salsa (based on the word pepper)! Next thing you know I'm in medical school and it's answering all my organic chemistry questions, my grades are good, the salsa is delicious but dammit I still can't make my own doctor pepper. This thing is useless! /s
- covfefeblack 2y ago[dead]
- asah 2y agoFTFY: ONLY 3 million times. At the current pace of development, AI will catch-up in a decade or less.
- mikae1 2y agoHow does that math work out? The developments during the last year has been... Abysmal? The hype and marketing bull is increasing exponentially though.
- exitb 2y agoGroq, which appeared 4 months ago, was an abysmal development for efficiency?
- ben_w 2y agoLook at the price difference of tokens on their API between the first release of ChatGPT and the current one. • Current 3.5-family price is $1.5/million tokens • Was originally $20/million tokens based on this quote: "Developers will pay $0.002 for 1,000 tokens — which amounts to about 750 words — making it 10 times cheaper" - https://web.archive.org/web/20230307060648/https://digiday.com/media-buying/with-developer-apis-for-chatgpt-and-whisper-openai-is-opening-the-floodgates-with-a-familiar-playbook/ https://web.archive.org/web/20230307060648/https://digiday.c... (I can't find the original 3.5 API prices even on archive.org, only the Davinci etc. prices, the Davinci model prices were also $20/million). There's also the observation that computers continue to get more power efficient — it's not as fast as Moore's Law was, doubling every 2.6 years, or a thousand-fold every 26 years, or about 30% per year.
- LtWorf 2y ago> How does that math work out? He asked chatgpt to do the math.
- badgersnake 2y agoAnd they pretty much made up a number. It’s a pretty clickbaity headline for an article that is mostly about vector databases.
- cjk2 2y agoI think GPT-4 is way more than 3 million times more efficient than my brain. All it does is a lot of multiplication and adding and my brain is crap at that.
- makingstuffs 2y agoYour conscious brain, maybe, your subconscious brain, no chance. The maths which goes into something as seemingly simple as picking up a glass is far beyond the reach of GPT. Hell, it’s so complex that the world’s top robotics labs burn through immense resources just to get some jittery arm to replicate the action.
- cjk2 2y agoIt’s not really mathematics though. That’s an abstract concept which is my point.
- ben_w 2y agoJust because GPT-4 uses matrix multiplication doesn't mean it can perform matrix multiplication — lots of people complain how bad LLMs are at arithmetic. My brain uses quantum mechanics for protein folding, my mind cannot perform the maths of QM.
- cjk2 2y agoSurely it can, just slowly and with poor accuracy :)
- cynusx 2y agoThe comparison doesn't really hold. He is comparing energy spend during inference in humans with energy spend during training in LLM's. Humans spend their lifetimes training their brain so one would have to sum up the total training time if you are going to compare it to the training time of LLM's. At age 30 the total energy use of the brain sums up to about 5000 Wh, which is 1440 times more efficient. But at age 30 we didn't learn good representations for most of the stuff on the internet so one could argue that given the knowledge learned, LLMs outperform the brain on energy consumption. That said, LLM's have it easier as they are already learning from an abstract layer (language) that already has a lot of good representations while humans have to first learn to parse this through imagery. Half the human brain is dedicated to processing imagery, so one could argue the human brain only spend 2500 Wh on equivalent tasks which makes it 3000x more efficient. Liked the article though, didn't know about HNSW's. Edit: made some quick comparisons for inference Assuming a human spends 20 minutes answering in a well-thought out fashion. Human watt-hours: 0.00646 GPT-4 watt-hours (openAI data): 0.833 That makes our brains still 128x more energy efficient but people spend a lot more time to generate the answer. Edit: numbers are off by 1000 as I used calories instead of kilocalories to calculate brain energy expense. Corrected: human brains are 1.44x more efficient during training and 0.128x (or 8x less efficient) during inference.
- jryan49 2y agoHow about the fact that llm's don't work unless humans generate all that data in the first place. I'd say the llm's energy usage is the amount it takes to train plus the amount to generate all that data. Humans are more efficient at learning with less data.
- Closi 2y agoHumans also learn from other humans (we stand on the shoulders of giants), so we would need to account for all the energy that has gone into generating all of human knowledge in the 'human' scenario too. i.e. not many humans invent calculus or relativity from scratch. I think OP's point stands - these comparisons end up being overly hand-wavey and very dependent on your assumptions and view.
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- redka 2y agoSeems like the title here on HN is bait testing for people not reading the article - and most of you failed. I came here to see what people have to say about his vector DBs comparisons
- tromp 2y ago> run on the equivalent of 24 Watts of power per hour. In comparison GPT-4 hardware requires SWATHES of data-centre space and an estimated 7.5 MW per hour. power per hour makes no sense, since power is already energy (in Joule) per unit of time (second).
- gus_massa 2y agoI agree. But it also compares one human with the whole GTP-4. It's like comaring a limonade stand with Coca Cola Inc.
- mordae 2y agoThat's a whole lot of hand waving. Also, field effect transistors deal with potential, not current. Current consumption stems mostly from charging and discharging parasitic capacitance. Also, computers do not really process individual bits. They operate on whole words. Pun intended.
- assimpleaspossi 2y agoI don't care. I've come to the conclusion that gpt and gemini and all the others are nothing but conversational search engines. They can give me ideas or point me in the right direction but so do regular search engines. I like the conversation ability but, in the end, I cannot trust their results and still have to research further to decide for myself if their results are valid.
- bamboozled 2y agoAs a user, it does feel like a search engine that contains an almost accurate snapshot of many potential results.
- intended 2y agoIt is often a search engine without ads.
- mjburgess 2y agoOne amusing way to put this is that LLMs energy requirements arent self-contained, since they use the energy of the human prompter to both prompt and verify the output. Reminds me of a similar argument about correctly pricing renewable power: since it isnt always-on (etc.) it requires a variety of alternative systems to augment it which aren't priced in. Ie., converting entirely to renewables isnt possible at the advertised price. In this sense, we cannot "convert entirely to LLMs" for our tasks, since there's still vast amounts of labour in prompt/verify/use/etc.
- shinycode 2y agoI do agree that I rarely use Google now, I search into a chat to have a summary and this saves lot of aggregation from different sites. The same for Stack Overflow, no use if I find the answer quicker. It’s exactly that for me, a conversational search engine. And the article explains it right, it’s just words organized in very specific ways to be able to retrieve them with statistical accuracy and the transformer is the cherry on top to make it coherent
- Kiro 2y ago
- TheDong 2y agoThe vector db comparison is written so much like an advertisement that I cannot possibly take it seriously. > Shared slack channel if problems arise? There you go. You wanna learn more? Sure, here are the resources. Workshops? Possible. > wins by far [...] most importantly community plus the company values. Like, talking about "You can pay the company for workshops" and "company values" just makes it feel so much like an unsubtle paid-for ad I can't take it seriously. All the actual details around the vectorDB (for example a single actual performance number, a clear description of the size of dataset or problem) is missing, making this all feel like a very handwavy comparison, and the final conclusion is just so strong, and worded in such a strange way, it feels disingenuous. I have no way to know if this post is actually genuine, not a piece of stealth advertising, but it hits so many alarm bells in my head that I can't help but ignore its conclusions about every database.
- mihaic 2y agoGenuinely curious who upvoted this and why. The title is clickbait, the writing is long and rambling and it seems to me like the author doesn't have a profound understand of the concepts either, all just to recommend Qdrant as a vector database.
- imabotbeep2937 2y agoPosted article quality is not always very good here lately. Clickholes get too many votes.
- mihaic 2y agoYeah, it seems almost insulting that the author expects countless people to spend time reading their posts, while they haven't spent a lot of time to edit and streamline it, all with the excuse: "these are just my ramblings". To paraphrase, I will not excuse such a long letter, for you had more time to write a shorter one.
- EncomLab 2y agoIt's always going to be difficult to compare a carbon based, ion mediated, indirectly connected, reconfigurable network of neurons to a silicon based, voltage mediated, directly connected, fixed configuration transistors. The analogy works, but not very far.
- tonyoconnell 2y agoThe performance issues with pgvector were fixed when they switched HNSW. It’s now 30x faster. It’s wonderful to be able to store vectors with Postgres Row Level security, for example if someone uploads a document you can create a policy that it appears only to them in a vector search.
- kingsleyopara 2y agoWhat often gets overlooked in these discussions is how much of the human brain is hardwired as a consequence of millions of years of evolution. Approximately 85% of human genes are used to encode the structure of the brain [0]. I find this particularly impressive when I consider how complex the rest of the body is. To relate this to LLMs, I'm tempted to think this is more like pre-training rather than straightforward model design. [0] https://www.nature.com/articles/tp2015153 https://www.nature.com/articles/tp2015153
- CuriouslyC 2y agoUnderstand that the genes that encode the structure of the brain do a lot of other things as well.
- Reason077 2y agoI guess this explains why the machines in The Matrix went to so much effort to create the matrix and “farm” humans for their brain energy. It’s just so much more efficient than running their AI control software on silicon-based hardware!
- bamboozled 2y agoIn The Matrix I think people are used as batteries not processors.
- Reason077 2y agoThat explanation never made any sense to me. Plenty of much easier ways for the machines to generate vastly more energy with far less hassle than using humans as “batteries”. There must be more to it than that!
- bamboozled 2y agoIt's a movie.
- Jensson 2y agoThe original idea wasn't batteries, so they probably started out with humans as cpus but then went with batteries to make it easier to understand for people.
- LtWorf 2y agoIn dollhouse they put people through nightmare scenarios repeatedly, to make their brain evaluate scenarios.
- cainxinth 2y agoBicycles are much more efficient than trucks, but try using one to move a sofa…
- southernplaces7 2y agoSome of the comparisons here in the comments between LLMs and the human brain go into the territory of deep naval gazing and abstract justification. To use a phrase mentioned below, by Sagan "You can make an apple pie from scratch, but you'd have to invent the universe first". Sure, to the deepest level this may be somewhat true, but the apple pie would still just be an apple pie, and not a condensed version of all that the universe contains. The same applies to LLMs in a way. If you calculate their capabilities to some arbitrary extreme of back--end inputs and ability based on the humans building them and all that they can do, you can arrive at a whole range of results for how capable and energy-efficient they are, but it wouldn't change the fact that the human brain as its own device does enormously more with much less energy than any LLM currently in existence. Our evolutionary path to that ability is secondary to it, since it's not a direct part of the brain's material resources in any given context. The contortions by some to give equivalency between human brains and LLMs are absurd when the very blatantly obvious reality is that our brains are absurdly more powerful. They're also of course capable of self-directed, self-aware cognition, which by now nobody in their rational mind should be ascribing to any LLM.
- joehogans 2y agoNeuromorphic chips represent the future because they mimic the brain's neural architecture, leading to significantly higher energy efficiency and parallel processing capabilities. These chips excel in pattern recognition and adaptive learning, making them ideal for complex AI tasks. Their potential to drastically reduce power consumption while enhancing computational performance makes them a pivotal advancement in hardware technology.
- chx 2y agoThey are not comparable. There's a prevalent metaphor which imagines the brain as a digital computer. However, this is a metaphor and not actual facts. While we have some good ideas on how the brain works on higher levels (recommended reading Incognito: The Secret Lives of the Brain by David Eagleman) we do not really have any ideas on the lower levels. As the essay I link below mentions, for example, when attending a concert, our brain changes so that later it can remember it but two brains attending the same concert will not change the same way. This make modelling the brain really damn tricky. This complete lack of understanding is also why it's completely laughable to think we can do AGI any time soon. Or perhaps ever? The reason for the AI winter cycle is the framing of it, this insane chase of AGI when it's not even defined properly. Instead, we should set out tasks to solve -- we didn't make a better horse when we made cars and locomotives. No one complains these do not provide us with milk to ferment into kumis. The goal was to move faster, not a better horse... https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer https://aeon.co/essays/your-brain-does-not-process-informati...
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- SubiculumCode 2y agoI kept waiting for the 'milion' in the headline to be part of the explanation somehow. I guess it was misspelling rather than an allusion to the Roman stone pillars for distance measurement https://en.m.wikipedia.org/wiki/Milion https://en.m.wikipedia.org/wiki/Milion
- richrichie 2y ago> Computers do not understand words, they operate on binary language, which is just 1s and 0s, so numbers. That’s a bit like saying human brains do not understand words. They operate on calcium and sodium ion transport.