Why your brain is 3 milion more times efficient than GPT-4

158 206 2 years ago

mati3652• 2 years ago

My is not

lukan• 2 years ago

I 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• 2 years ago

Call 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• 2 years ago

indeed. 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• 2 years ago

Maybe, but I bet GPT-4 can spell million.

kvdveer• 2 years ago

I 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.

mati3652• 2 years ago

The brain translates words into a matrix of cortical column neurons activations. So there are similarities to our naive implementation of such "thinking".

Synaesthesia3• 2 years ago

No, 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.

exe344• 2 years ago

that's incredible! how did you put the confetti back in the canon?

throwAGIway• 2 years ago

I 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• 2 years ago

You’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.

ben_w4• 2 years ago

A brain is an electrochemical network made of cells; artificial neural networks are a toy model of these.

Each neurone is itself a complex combination of chemicals cycles; these can be, and have been, simulated.

The most complex chemicals in biology are proteins; these can be directly simulated with great difficulty, and we've now got AI that have learned to predict them much faster than the direct simulations on a classical computer ever could.

Those direct simulations are based on quantum mechanics, or at least computationally tractable approximations of it; QM is lots of linear algebra and either a random number generator or superdeterminism, either of which is still a thing a computer can do (even if the former requires a connection to a quantum-random source).

The open question is not "can computers think?", but rather "how detailed does the simulation have to be in order for it to think?"

gizmo• 2 years ago

And what gives brains this unique power? Do brains of lesser animals also have this unique “thinking” property? Is this “thinking” a result of how the brain is architected out of atoms and if so why can’t other machines emulate it?

Our brains are the product of the same dumb evolutionary process that made every other plant and animal and fungus and virus. We evolved from animals capable of only the most basic form of pattern recognition. Humans in the absence of education are not capable of even the most basic reasoning. It took us untold thousands of years to figure out that “try things and measure if it works” is a good way to learn about the world. An intelligent species would be able to figure things out by itself our ancestors, who have the same brain architecture we do, were not able to figure anything out for generation after generation. So much for our ability to do original independent thinking.

scotty795• 2 years ago

Do a little exercise for me. Try to be as creative as you can be and imagine how a space alien might look.

It's a combination of what you have already seen, read about or heard of, isn't it?

shkkmo6• 2 years ago

Becareful with mixing up 'can' and 'does'.

Computers right now do not understand language, but that does not mean that they cannot. We don't know what it takes to bridge the gap from stochastic parrot to understanding in computers, however from the mistakes LLMs make right now, it appears we have not found it yet.

It is possible that silicon based computer architecture cannot support the processing and information storage density/latency to support understanding. It's hard to guage the likelihood this is true given how little we know about how understanding works in the brain.

shinycode8• 2 years ago

If 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

wegfawefgawefg• 2 years ago

It would not prove that. It would be one observed example of a new intelligence which was created by an existing one.

avereveard4• 2 years ago

Only 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

shinycode8• 2 years ago

If 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)

exe344• 2 years ago

Do you think the same thing is required for flying? that aeroplanes can only be created by birds?

shinycode8• 2 years ago

Intelligence and flying are different things, a leaf falling down a tree « fly » because of laws of nature.

halayli• 2 years ago

No, that just proves that intelligence can create another intelligence. It does not rule out that intelligence can exist due to entropy.

orlp• 2 years ago

Ah, the good old chicken and chicken paradox. Which came first, the chicken, or the chicken?

raincole• 2 years ago

If 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 ?

No, it would not.

shinycode8• 2 years ago

Thank you for your powerful dogmatic argument

mensetmanusman3• 2 years ago

Intelligence first has to exist in the phase space of universal experiences possible.

dist-epoch3• 2 years ago

Our intelligence happened without an existing one. primordial cell -> intelligent human through Darwinian evolution.

ben_w4• 2 years ago

I would argue that evolution is a form of intelligence in its own right, albeit one very alien to us.

But that doesn't change your point, as there's no reason to require an intelligence to create evolution.

mensetmanusman3• 2 years ago

All of reality following governing laws with arbitrary precision is certainly a type of intelligence plane that came before the universe’s big bang.

xqcgrek2• 2 years ago

The 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.

Synaesthesia3• 2 years ago

Yeah because humans are really special. Monkeys and cats can still solve physical problems though which are quite complex, and make decisions.

exe344• 2 years ago

cats 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.

madsbuch5• 2 years ago

There 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• 2 years ago

Yeah, 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.

southernplaces72• 2 years ago

Oh 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.

logicallee4• 2 years ago

It'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!

madsbuch5• 2 years ago

i am not sure where this comment fits as an answer to my comment.

Firstly, do understand that I am not saying that LLMs (or ChatGPT) do understand.

I am merely saying that we don't have any sound frameworks to assess it.

For the rest of your rant: I definitely see that you don't derive any value from ChatGPT. As such I really hope you are not paying for it - or wasting your time on it. What other people decide to spend their money on is really their business. I don't think any normal functioning people have the expectation that a real person is answering them when they use ChatGPT - as such it is hardly a fraud.

shkkmo6• 2 years ago

Maps are useful, but they don't understand the geography they describe. LLMs are maps of semantic structures and as such, can absolutely be useful without having an understanding of that which they map.

If LLMs were capable of understanding, they wouldn't be so easy to trick on novel problems.

mensetmanusman3• 2 years ago

It’s a reasonable axiom, because for many people understanding involves qualia. If you believe LLM have qualia, you also believe a very large Excel sheet with the right numbers has an experience of consciousness and feels pain or something where the document is closed.

madsbuch5• 2 years ago

As I wrote, I appreciate that the author wrote it out as they did. It might be reasonable in the context of the article. But fixing it as an axiom just makes the discussion boring (for me).

If you believe LLM have qualia, you also believe a ...

You use the word believe twice here. I am actively not talking about beliefs.

I just realise, that the author indeed gave themselves an out:

... currently computers do not really understand words.

The author might believe that future computers can understand words. This is interesting. Questions being _what_ needs to be in order for them to understand? Could that be an emergent feature of current architectures? That would also contradict large parts of the article.

shkkmo6• 2 years ago

Amusingly, the author does not appear to fully understand the meaning of "axiom".

While practice, axioms are often statements that we all agree on and accept as true, that isn't necessarily true and isn't the core of it's meaning.

Axioms are something we postulate as true, without providing an argument for its truth, for the purposes of making an argument.

In this case, the assertion isn't really used as part of a argument, but to bootstrap an explanation of how words are represented in LLMs.

Edit: I find this so amusing because it is an example of learning a word without understanding it.

LtWorf4• 2 years ago

Axioms are something we postulate as true, without providing an argument for its truth, for the purposes of making an argument.

Uhm… no?

They are literally things that can't be proven but allow us to prove a lot of other things.

asah• 2 years ago

FTFY: ONLY 3 million times.

At the current pace of development, AI will catch-up in a decade or less.

mikae1• 2 years ago

How does that math work out? The developments during the last year has been... Abysmal? The hype and marketing bull is increasing exponentially though.

exitb• 2 years ago

Groq, which appeared 4 months ago, was an abysmal development for efficiency?

ben_w4• 2 years ago

Look 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.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.

LtWorf4• 2 years ago

How does that math work out?

He asked chatgpt to do the math.

badgersnake• 2 years ago

And they pretty much made up a number. It’s a pretty clickbaity headline for an article that is mostly about vector databases.

cjk23• 2 years ago

I 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• 2 years ago

Your 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.

cjk23• 2 years ago

It’s not really mathematics though. That’s an abstract concept which is my point.

ben_w4• 2 years ago

Just 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.

cjk23• 2 years ago

Surely it can, just slowly and with poor accuracy :)

cynusx4• 2 years ago

The 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.

jryan492• 2 years ago

How 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.

Closi3• 2 years ago

Humans 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.

Closi3• 2 years ago

I think you would probably have to take into account the full functioning power of a human too.

We don't know how to fully operate a human brain when it's fully disconnected from eyes, a mouth, limbs, ears and a human heart.

wanderingmind• 2 years ago

Not just that the brain of a newborn comes pretrained with billions of years of evolution. There is an energy cost associated with that which must be taken into account

eloeffler• 2 years ago

Then you must also take that cost into account when calculating the cost of training LLMs, as well as the cost humans operating the devices and their respective individual brain development.

LLMs are always an additional cost, never more efficient because they add to the calculation, if you look at it that way.

coldtea• 2 years ago

Well, LLMs also pressupose humans and evolution, since they needed us to create them, so their tally is even higher by definition...

thfuran2• 2 years ago

Brains are only about half a billion years old.

eru5• 2 years ago

Also our brains and our language are co-optimised to be compatible.

ChatGPT has to deal with the languages we already created, it doesn't get to co-adapt.

AbstractH24• 2 years ago

Those are sunk costs

madhatter999• 2 years ago

Also take into consideration the speed of evolution. LLM training might be much faster because a lot of competition power is used for its training. Maybe if it was the same speed as evolution then it would take billions of years, too?

freehorse• 2 years ago

representations for most of the stuff on the internet

Yes we have learnt far more complex stuff, ffs.

londons_explore• 2 years ago

At age 30 the total energy use of the brain sums up to about 5000 Wh,

That doesn't sound right... 30 years * 20 Watts = 1.9E10 Joules = 5300 kWh.

cynusx4• 2 years ago

Where did you get the 20 Watt from?

My number is based on calorie usage

mirekrusin2• 2 years ago

Also you can't cp human brain.

phantompeace• 2 years ago

Not yet, anyway.

vasco• 2 years ago

We can clone humans at current level of technology, otherwise there wouldn't be agreements about not doing it due to the ethical implications. Of course its just reproducing the initial hardware and not the memory contents or the changes in connections that happen at runtime.

marginalia_nu• 2 years ago

You kinda can do a sort of LoRA though. Reading the right book can not only change what you hold true, but how you think.

Rinzler89• 2 years ago

The plot of The Matrix would beg to differ.

dist-epoch3• 2 years ago

For every calorie a human consumes, hundreds or thousands more are used by external support systems.

So yeah, you do use 2000 calories a day, but unless you live in an isolated jungle tribe, vast amounts of energy are consumed on delivering you food, climate control, electricity, water, education, protection, entertainment and so on.

unyttigfjelltol• 2 years ago

Including support from ChatGPT. It really is a comparison of calories without ChatGPT and calories with, and that gets to the real issue of whether ChatGPT justifies its energy intensity or not. History suggests we won't know until the technology exits the startup phase.

b112• 2 years ago

By that metric, the electricity is only part of it. The cost of building the harsware, the cost of building the roof and walls for the datacentre, the cost of clearing the land, cost of humans maintaining the hardware, the cost of all the labour making the linux kernel, libc6, etc, etc. Lots of additionals here too.

greenthrow2• 2 years ago

Are you going to include all the externalities to build and power the datacenters behind LLMs then? Because i guarantee those far outweigh what it takes to feed one human.

CuriouslyC7• 2 years ago

You're doing apples and oranges.

Humans who spend a long time doing inference have not fully learned the thing being inferred - unlike LLMs, when we are undertrained, rather than a huge spike in error rate, we go slower.

When humans are well trained, human inference absolutely destroys LLMs.

cheema33• 2 years ago

When humans are well trained, human inference absolutely destroys LLMs.

This isn't an apt comparison. You are comparing a human trained in a specific field to an LLM trained on everything. When an LLM is trained with a narrow focus as well, human brain cannot compete. See Garry Kasparov vs Deep Blue. And Deep Blue is very old tech.

cynusx4• 2 years ago

Depends on the person I guess, but yes. Humans are more accurate for now.

bamboozled15• 2 years ago

Humans spend their lifetimes training their brain

I don't think this is true personally, ideally as children, we spend out time having fun and learning about the world is a side effect. This borg like thinking applied to intelligence because we have LLMs is unusual to me.

I learned surfing through play and enjoyment, not through training like a robot.

We can train for something with intention, but I think that is mostly a waste of energy, albeit necessary on occasion.

Jensson13• 2 years ago

we spend out time having fun and learning about the world is a side effect

What do you think "play" is? Animals play to learn about themselves and the world, you see most intelligent animals play as kids with the play being a simplification of what they do as adults. Human kids similarly play fight, play build things, play cook food, play take care of babies etc, it is all to make you ready for an adult life.

Playing is fun since playing helps us learn, otherwise we wouldn't evolve to play, we would evolve to be like ants that just work all day long if that was more efficient. So the humans who played around beat those who worked their ass off, otherwise we would all be hard workers.

glenstein2• 2 years ago

we spend out time having fun and learning about the world is a side effect

I think the part of this that resonates as most true to me is how this reframes learning in a way that tracks truth more closely. It's not all the time, 100% of the time, it's in fits and starts, its opportunistic, and there are long intervals that are not active learning.

But the big part where I would phrase things differently is in the insistence that play in and of itself is not a form of learning. It certainly is, or certainly can be, and while you're right that it's something other than Borg-like accumulation I think there's still learning happening there.

pessimizer• 2 years ago

That's like saying that you eat because it tastes good.

greenthrow2• 2 years ago

The article is a bit of a stretch but this is even more of a stretch. Humans can do way more than an LLM, humans are never in only learning mode, our brains are always at least running our bodies as well, etc.

glenstein2• 2 years ago

Exactly right - we are obviously not persistently in all-out training mode over the course of our lifetimes.

I suppose they intended that as a back-of-the-envelope starting point rather than a strict claim however. But even so, gotta be accountable to your starting assumptions, and I think a lot changes when this one is reconsidered.

pama• 2 years ago

Thanks. So after your corrected energy estimate and more reasonable assumptions it appeaars that the clickbaity title of the article is off by more than 7 orders of magnitude. With the upcoming NVidia inference chips later this year it will be off by another log unit. It is hard for biomatter to compete with electrons in silicon and copper.

bognition• 2 years ago

If we’re going to exclude the cortical areas associated with vision, you also need to exclude areas involved in motor control and planning. Those also account for a huge percent of the total brain volume.

We probably need to exclude the cerebellum as well (which is 50% of the neurons in the brain) as it’s used for error correction in movement.

Realistically you probably just need a few parts of the lambic system. Hippocampus, amygdala, and a few of the deep brain dopamine centers.

philipov• 2 years ago

A lot of our cognition is mapped to areas that are used for something else, so excluding areas simply because they are used for something else is not valid. They can still be used for higher-level cognition. For example, we use the same area of the brain to process the taste of disgusting food as we do for moral disgust.

bufferoverflow• 2 years ago

Also, human brains come pre-trained by billions of years of evolution. It doesn't start as a randomly-connected structure. It already knows how to breathe, how to swallow, how to lean new things.

redka• 2 years ago

Seems 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• 2 years 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_massa2• 2 years ago

I agree.

But it also compares one human with the whole GTP-4. It's like comaring a limonade stand with Coca Cola Inc.

mordae• 2 years ago

That'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• 2 years ago

I 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.

mjburgess• 2 years ago

One 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.

bamboozled15• 2 years ago

As a user, it does feel like a search engine that contains an almost accurate snapshot of many potential results.

intended• 2 years ago

It is often a search engine without ads.

shinycode8• 2 years ago

I 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• 2 years ago

I can ask ChatGPT extremely specific programming questions and get working code solving it. This is not something I can do with a search engine.

Another thing a search engine cannot do that I use ChatGPT for on a daily basis is taking unstructured text and convert it into a specified JSON format.

mirpa• 2 years ago

I can ask ChatGPT extremely specific programming questions and get working code solving it.

I can do the opposite.

wruza• 2 years ago

I’m a local LLM elite who stopped using chat mode whatsoever.

I just go into the notebook tab (with an empty textarea) and start writing about a topic I’m interested in, then hit generate. It’s not a conversation, just an article in a passive form. The “chat” is just a protocol of in a form of an article with a system prompt at the top and “AI: …\nUser: …\n” afterwards, all wrapped into a chat ui.

While the article is interesting, I just read it (it generates forever). When it goes sideways, I stop it and modify the text in a way that fits my needs, in a recent place or maybe earlier, and then hit generate again.

I find this mode superior to complaining to a bot, since wrong info/direction doesn’t spoil the content. Also you don’t have to wait or interrupt, it’s just a single coherent flow that you can edit when necessary. Sometimes I stop it at “it’s important to remember …” and replace it with a short disclaimer like “We talked about safety already. Anyway, back to <topic>” and hit generate.

Fundamentally, LLMs generate texts, not conversations. Conversations just happen to be texts. It’s something people forget / aren’t aware of behind these stupid chat interfaces.

moffkalast3• 2 years ago

Replace "gpt and gemini and all the others" with "people" and funny enough your statement is still perfectly accurate.

You have a rough mathematical approximation of what's already a famously unreliable system. Expecting complete accuracy instead of about-rightness from it seems mad to me. And there are tons of applications where that's fine, otherwise our civilization wouldn't be here today at all.

somenameforme2• 2 years ago

These anthropomorphizations are increasingly absurd. There's a difference between a human making a mistake, and an AI arbitrarily and completely confidently creating entirely new code APIs, legal cases, or whatever that have absolutely no basis in reality whatsoever, beyond being what it thinks would be an appropriate next token based on what you're searching for. These error modes are simply in no way, whatsoever, comparable.

And then you tell it such an API/case/etc doesn't exist. And it'll immediately acknowledge its mistake, and ensure it will work to avoid such in the future. And then literally the next sentence in the conversation it's back to inventing the same nonsense again. This is not like a human because even with the most idiotic human there's an at least general trend to move forward - LLMs are just coasting back on forth based on their preexisting training with absolutely zero ability to move forward until somebody gives them a training set to coast back and forth on, and repeat.

AlienRobot• 2 years ago

I wish someone could explain me Bing. If you search on Bing, the first result appears BELOW the ChatGPT auto-generated message, and this message takes 10 seconds to be "typed" out.

I can click the first result 1 billion times faster.

At this point it's just wasting people's times.

davidmurdoch2• 2 years ago

Just started using Gemini and it has never been correct. Literally not once. It's is just slightly better than a markov chain.

EForEndeavour2• 2 years ago

Which model? And could you share an example of some of the things you've asked it and gotten wrong answers for?

seunosewa• 2 years ago

You should not dismiss all LLMs unless you have tried the best one. Gemini is not the best LLM. Try Meta AI, which is free, and ChatGPT premium first.

TheDong• 2 years ago

The 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.

mihaic2• 2 years ago

Genuinely 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• 2 years ago

Posted article quality is not always very good here lately.

Clickholes get too many votes.

mihaic2• 2 years ago

Yeah, 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• 2 years ago

It'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• 2 years ago

The 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• 2 years ago

What 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

CuriouslyC7• 2 years ago

Understand that the genes that encode the structure of the brain do a lot of other things as well.

Reason0772• 2 years ago

I 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!

bamboozled15• 2 years ago

In The Matrix I think people are used as batteries not processors.

Reason0772• 2 years ago

That 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!

cainxinth• 2 years ago

Bicycles are much more efficient than trucks, but try using one to move a sofa…

southernplaces72• 2 years ago

Some 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• 2 years ago

Neuromorphic 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• 2 years ago

They 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-informati...

SubiculumCode• 2 years ago

I 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

richrichie• 2 years 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.

Why your brain is 3 milion more times efficient than GPT-4

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