[HN Gopher] Rodney Brooks on limitations of generative AI
___________________________________________________________________
Rodney Brooks on limitations of generative AI
Author : doener
Score : 129 points
Date : 2024-06-30 07:02 UTC (15 hours ago)
(HTM) web link (techcrunch.com)
(TXT) w3m dump (techcrunch.com)
| gjvc wrote:
| Amara's law -- "We tend to overestimate the effect of a
| technology in the short run and underestimate the effect in the
| long run."
| gjvc wrote:
| and bard/gemini and chatgpt consistently given look-good-but-
| broken examples when asked for help with code.
|
| the bubble on this is going to make the .com crash look like
| peanuts.
| Viliam1234 wrote:
| Also, the author focuses on the fact that LLMs are not much
| better at things that robots already do, such as moving stuff
| in a store. Yeah. But they are surprisingly good at many things
| that robots already _didn 't_ do, such as writing texts and
| composing songs.
|
| It's like getting a flying car and saying "meh, on a highway
| it's not really much faster than the classical car". Or getting
| a computer and saying that the calculator app is not faster
| than actual calculator.
|
| A robot powered by some future LLM may not be much better at
| moving stuff, but it will be able to follow commands such as "I
| am going on a vacation, pack my suitcase with all I need"
| without giving a detailed list.
| portaouflop wrote:
| Let's call it machine learning, the AI term is just so far from
| what it actually is
| bubblyworld wrote:
| He's talking about control theory, and other kinds of
| optimisation systems too. I think AI is a fine blanket term for
| all of that stuff.
| DonHopkins wrote:
| Rodney Brooks is the Godfather of Out of Control Theory.
|
| FAST, CHEAP AND OUT OF CONTROL: A ROBOT INVASION OF THE SOLAR
| SYSTEM:
|
| https://people.csail.mit.edu/brooks/papers/fast-cheap.pdf
| bubblyworld wrote:
| "Out-of-Control Theory" is a great name, love it =) thanks
| for the link, although I guess the idea never really took
| off.
| DonHopkins wrote:
| It certainly did take off, it's called "Subsumption
| Architecture", and Rodney Brooks started iRobot, who
| created the Roomba, which is based on those ideas.
|
| https://en.wikipedia.org/wiki/Subsumption_architecture
|
| Subsumption architecture is a reactive robotic
| architecture heavily associated with behavior-based
| robotics which was very popular in the 1980s and 90s. The
| term was introduced by Rodney Brooks and colleagues in
| 1986.[1][2][3] Subsumption has been widely influential in
| autonomous robotics and elsewhere in real-time AI.
|
| https://en.wikipedia.org/wiki/IRobot
|
| iRobot Corporation is an American technology company that
| designs and builds consumer robots. It was founded in
| 1990 by three members of MIT's Artificial Intelligence
| Lab, who designed robots for space exploration and
| military defense.[2] The company's products include a
| range of autonomous home vacuum cleaners (Roomba), floor
| moppers (Braava), and other autonomous cleaning
| devices.[3]
| bubblyworld wrote:
| Okay, interesting, but I meant nobody actually ended up
| sending thousands of little robots to other planets. No
| doubt the research led to some nice things.
|
| Edit: the direct sensory-action coupling idea makes sense
| from a control perspective (fast interaction loops can
| compensate for chaotic dynamics in the environment), but
| we know these days that brains don't work that way, for
| instance. I wonder how that perspective has changed in
| robotics since the 90s, do you know?
| DonHopkins wrote:
| About four years before Rodney Brooks proposed
| Subsumption Architecture, some Terrapin Logo hackers from
| the MIT-AI Lab wrote a proposal for the military to use
| totally out-of-control Logo Turtles in combat, in this
| article they published on October 1, 1982 in ACM SIGART
| Bulletin Issue 82, pp 23-25:
|
| https://dl.acm.org/doi/10.1145/1056602.1056608
|
| https://donhopkins.com/home/TurtlesAndDefense.pdf
|
| >TURTLES AND DEFENSE
|
| >Introduction
|
| >At Terrapin, we feel that our two main products, the
| Terrapin Turtle (r), and the Terrapin Logo Language for
| the Apple II, bring together the fields of robotics and
| AI to provide hours of entertainment for the whole
| family. We are sure that an enlightened application of
| our products can uniquely impact the electronic
| battlefield of the future. [...]
|
| >Guidance
|
| >The Terrapin Turtle (r), like many missile systems in
| use today, is wire-guided. It has the wire-guided
| missile's robustness with respect to ECM, and, unlike
| beam-riding missiles, or most active-homing systems, it
| has no radar signature to invite enemy missiles to home
| in on it or its launch platform. However, the Turtle does
| not suffer from that bugaboo of wire-guided missiles,
| i.e., the lack of a fire-and-forget capability.
|
| >Often ground troops are reluctant to use wire-guided
| antitank weapons because of the need for line-of-sight
| contact with the target until interception is
| accomplished. The Turtle requires no such human guidance;
| once the computer controlling it has been programmed, the
| Turtle performs its mission without the need of human
| intervention. Ground troops are left free to scramble for
| cover. [...]
|
| >Because the Terrapin Turtle (r) is computer-controlled,
| military data processing technicians can write
| arbitrarily baroque programs that will cause it to do
| pretty much unpredictable things. Even if an enemy had
| access to the programs that guided a Turtle Task Team (r)
| , it is quite likely that they would find them impossible
| to understand, especially if they were written in ADA. In
| addition, with judicious use of the Turtle's touch
| sensors, one could, theoretically, program a large group
| of turtles to simulate Brownian motion. The enemy would
| hardly attempt to predict the paths of some 10,000
| turtles bumping into each other more or less randomly on
| their way to performing their mission. Furthermore, we
| believe that the spectacle would have a demoralizing
| effect on enemy ground troops. [...]
|
| >Munitions
|
| >The Terrapin Turtle (r) does not currently incorporate
| any munitions, but even civilian versions have a
| downward-defense capability. The Turtle can be programmed
| to attempt to run over enemy forces on recognizing them,
| and by raising and lowering its pen at about 10 cycles
| per second, puncture them to death.
|
| >Turtles can easily be programmed to push objects in a
| preferred direction. Given this capability, one can
| easily envision a Turtle discreetly nudging a hand
| grenade into an enemy camp, and then accelerating quickly
| away. With the development of ever smaller fission
| devices, it does not seem unlikely that the Turtle could
| be used for delivery of tactical nuclear weapons. [...]
| kennyloginz wrote:
| This is too great. Thanks for the link!
| YeGoblynQueenne wrote:
| See why today's hackers aren't real hackers? Where are
| the mischievous hackers hacking Roombas to raise and
| lower a pen and scrawl dirty messages on their owners'
| clean floors? Instead what we get is an ELIZA clone that
| speaks like a Roomba sucked all the soul out of the
| entire universe.
| oska wrote:
| There will never be, and can never be, "artificial
| intelligence". (The creation of consciousness is impossible.)
|
| It's a fun/interesting device in science _fiction_ , just
| like the concept of golems (animated beings) are in folk
| tales. But it's complete nonsense to talk about it as a
| possibility in the real world so yes, the label of 'machine
| learning' is a far, far better label to use for this powerful
| and interesting domain.
| bubblyworld wrote:
| I'll happily engage in specifics if you provide an argument
| for your position. Here's mine (which is ironically self-
| defeating but has a grain of truth): single-sentence
| theories about reality are probably wrong.
| oska wrote:
| I just went back and added a parenthetical statement
| after my first sentence before seeing this reply (on
| refresh).
|
| > The creation of consciousness is impossible.
|
| That's where I'd start my argument.
|
| Machines can 'learn', given iterative training and some
| form of memory but they can not _think_ nor understand.
| That requires consciousness, and the idea that
| consciousness can be emergent (which it is my
| understanding that the 'AI' argument rests upon), has
| never been shown. It is an unproven fantasy.
| bubblyworld wrote:
| And I guess I'd start my retort by saying that it can't
| be impossible, because here we are talking about it =P
| oska wrote:
| No, you don't have any proof for the creation of
| consciousness, including human consciousness (which is
| what I understand you are referring to).
|
| In my view, and in the view of major religions (Hinduism,
| Buddhism, etc) plus various philosophers, consciousness
| is eternal and the only real thing in the universe. All
| else is illusion.
|
| You don't have to accept that view but you do have to
| prove that consciousness can be created. An 'existence
| proof' is not sufficient because existence does not
| necessarily imply creation.
| bubblyworld wrote:
| Look, I don't want to get into the weeds of this because
| personally I don't think it's relevant to the issue of
| intelligence, but here's a list of things I think are
| evident about consciousness:
|
| 1. People have different kinds of conscious experience
| (just talk to other humans to get the picture).
|
| 2. Consciousness varies, and can be present or not-
| present at any given moment (sleep, death, hallucinogenic
| drugs, anaesthesia).
|
| 3. Many things don't have the properties of consciousness
| that I attribute to my subjective experience (rocks,
| maybe lifeforms that don't have nerve cells, lots of
| unknowns here).
|
| Given this, it's obvious that consciousness can be
| created from non-consciousness, you need merely to have
| sex and wait 9 months. Add to that the fact that humans
| weren't a thing a million years ago, for instance, and
| you have to conclude that it's possible for an
| optimisation system to produce consciousness eventually
| (natural selection).
| oska wrote:
| Your responses indicate (at least to me) that you are,
| philosophically, a Materialist or a Physicalist. That's
| fine; I accept that's a philosophy of existence that one
| can hold (even though I personally find it sterile and
| nihilistic). However many, like me, do not subscribe to
| such a philosophy. We can avoid any argument between ppl
| who hold different philosophies but still want to discuss
| machine learning productively by using that term, one
| that we can all agree on. But if materialists insist on
| using 'artificial intelligence' then they are pushing
| their unproven theories and I would say fantasies on the
| rest of us and they then expose a divergent agenda, where
| one does not exist when we all just talk about what we
| all agree we already have, which is machine learning.
| bubblyworld wrote:
| If you find it sterile and nihilistic that's on you,
| friend =)
|
| I think pragmatic thinking, not metaphysics, is what will
| ultimately lead to progress in AI. You haven't engaged
| with the actual content of my arguments at all - from
| that perspective, who's the one talking about fantasies
| really?
|
| Edit: in case I give the mistaken impression that I'm
| angry - I'm not, thank you for your time. I find it very
| useful to talk to people with radically different world
| views to me.
| lucubratory wrote:
| Every scientist has to be a materialist, it comes from
| interacting with the world as it is. Most of them then
| keep their materialism in their personal life, but
| unfortunately some come home from work and stop thinking
| critically, embracing dualism or some other unprovable
| fantasies. If you want people engaging with the world and
| having correct ideas, you need to tolerate materialism
| because that's how that happens. It offending your
| religious worldview is unrelated.
| oska wrote:
| > Every scientist has to be a materialist
|
| Quite obviously not true (and also untrue in a documented
| sense in that many great scientists have not been
| materialists). Science is a method, not a philosophical
| view of the world.
|
| > it comes from interacting with the world as it is
|
| That the world is materialistic is unproven (and not
| possible to prove).
|
| You are simply propounding a materialist philosophy here.
| As I've said before, it's fine to have that philosophy.
| It's not fine to dogmatically push that view as 'reality'
| and _then extend on that_ to build and push fantasies
| about 'artificial intelligence'. Again, we avoid all of
| this philosophical debate if we simply stick to the term
| 'machine learning'.
| singpolyma3 wrote:
| I'm not sure generic terms like "artificial intelligence"
| which describe every enemy in a video game are bound by a
| particular philosophy.
| quonn wrote:
| The Buddha did not teach that only consciousness is real.
| He called such a view speculative, similar to a belief
| that only the material world is real. The buddhist
| teaching is, essentially, that the real is real and ever
| changing and that consciousness is merely a phenomenon of
| that which is reality.
| oska wrote:
| Cheers, I should have been more precise in my wording
| there. I should have referred to _some (Idealist) schools
| of thought_ in Buddhism & Hinduism, such as the
| Yogachara school in Buddhism.
|
| Particularly with Hinduism, its embrace of various
| philosophies is very broad and includes strains of
| Materialism, which appears to be the other person's
| viewpoint, so again, I should have been more careful with
| my wording.
| MattPalmer1086 wrote:
| I don't see why consciousness is necessary for thinking.
|
| I would define thinking as the ability to evaluate
| propositions and evidence and arrive at some potentially
| useful plan or hypothesis.
|
| I see no reason why a machine could not do this, but
| without any awareness ("consciousness") of itself doing
| it.
|
| I also see no fundamental obstacle to true machine
| awareness either, but given your other responses, we can
| just disagree on that.
| ilaksh wrote:
| You can start to refine your incorrect thinking on this
| by decomposing these associated characteristics and terms
| rather than conflating all of them.
|
| Thinking, understanding, self-aware, awake, integrated
| sensory stream, alive, emotional, visual, sensory,
| autonomy, adaptiveness, etc.
| meiraleal wrote:
| > There will never be, and can never be, "artificial
| intelligence".
|
| And machines can't learn.
| drzhouq wrote:
| Can machines fly by themselves?
| IanCal wrote:
| No.
|
| AI is what we've called things far simpler for a far longer
| time. It's what random users know it as. It's what the
| marketers sell it as. It's what the academics have used for
| decades.
|
| You can try and make _all of them_ change the term they use, or
| just understand it 's meaning.
|
| If you do succeed, then brace for a new generation fighting you
| for calling it learning and coming up with yet another new term
| that everyone should use.
| openmajestic wrote:
| I like using AI or generative AI. Each term we use signifies
| the era of the technology. When I was starting, expert systems
| were the old thing, AI was a taboo phrase that you avoided at
| all costs if you wanted funding, and ML was the term of the
| day. Then it was the deep learning era. And now we are in the
| generative AI era - and also an AI spring which makes the term
| AI appropriate (while the spring lasts).
| none_to_remain wrote:
| > Brooks explains that this could eventually lead to robots with
| useful language interfaces for people in care situations.
|
| I've been thinking, in Star Trek terms, what if it's not Lt. Cdr.
| Data, but just the Ship's Computer?
| DonHopkins wrote:
| Or Bomb 20.
|
| https://www.youtube.com/watch?v=h73PsFKtIck
| Animats wrote:
| That makes sense from Brooks' perspective. He's done his best
| work when he didn't try to overreach. The six-legged insect thing
| was great, but very dumb. Cog was supposed to reach human-level
| AI and was an embarrassing dud. The Roomba was simple, dumb, and
| useful. The military iRobot machines were good little remote-
| controlled tanks. The Rethink Robotics machines were supposed to
| be intelligent and learn manipulation tasks by imitation. They
| were not too useful and far too expensive. His new mobile carts
| are just light-duty AGVs, and compete in an established market.
| hdhshdhshdjd wrote:
| I've got some scuffed furniture that suggests roomba may be a
| little too dumb...
| roenxi wrote:
| > He uses the iPod as an example. For a few iterations, it did in
| fact double in storage size from 10 all the way to 160GB. If it
| had continued on that trajectory, he figured out we would have an
| iPod with 160TB of storage by 2017, but of course we didn't.
|
| I think Brooks' opinions will age poorly, but if anyone doesn't
| already know all the arguments for that they aren't interested in
| learning them now. This quote seems more interesting to me.
|
| Didn't the iPod switch from HDD to SSD at some point, and they
| focused on shrinking them rather than upping storage size? I
| think the quality of iPods have been growing exponentially, we've
| just seen some tech upgrades on other axises that Apple thinks
| are more important. AFAIK, looking at Wikipedia, they're
| discontinuing iPods in favour of iPhones where we _can_ get a 1TB
| model and the disk size trend is still exponential.
|
| The original criticism of the iPod was that it had less disk
| space than its competitors and it turned out to be because
| consumers were paying for other things. Overall I don't think
| this is a fair argument against exponential growth.
| ozgrakkurt wrote:
| How can it exponentially grow ever? It is a small device that
| plays music
| pyrale wrote:
| > Overall I don't think this is a fair argument against
| exponential growth.
|
| Do we still need to make a fair claim against unrestricted
| exponential growth in 2024? Exponential growth claims have been
| made countless times in the past, and never delivered. Studies
| like the Limits to Growth report (1972) have shown the
| impossibility of unrestricted growth, and the underlying
| science can be used in other domains to show the same. There is
| no question that exponential growth doesn't exist, the only
| interesting question is how to locate the inflexion point.
|
| Apparently the only limitless resource is gullible people.
| croes wrote:
| People thought we have FSD now because of the early success,
| but the last 20% are the hardest.
|
| The same is true for LLMs. What they can do is impressive but
| to fix what they can't will be hard or even impossible.
| lostmsu wrote:
| There was never universally recognized early success in FSD.
| albert_e wrote:
| > we would have an iPod with 160TB of storage by 2017, but of
| course we didn't.
|
| 160 Tera Bytes to store what?
|
| I probably would never listen to more than a few GBs of high
| fidelity music in my life time.
|
| Why would anyone keep investing in exponential growth or even
| linear growth beyond a point of utility and economic sense.
|
| This can be extrapolated to miniaturization also ... why is a
| personal computer not smaller than my fingertip already?
| roenxi wrote:
| Arguing from a lack of personal imagination is not a strong
| position. It is the people with ideas who are responsible for
| finding uses for resources. They've succeeded every other
| time they were given an excess of storage space; I'm still
| not sure how I manage to use up all the TB of storage I've
| bought over the years.
|
| Maybe we store a local copy of a personal universe for you to
| test ideas out in, I dunno. There'll be something.
|
| > I probably would never listen to more than a few GBs of
| high fidelity music in my life time.
|
| Well from that I'd predict that uses would be found other
| than music. My "music" folder has made it up to 50GB because
| I've taken to storing a few movies in it. But games can
| quickly add up to TB of media if the space is available.
| albert_e wrote:
| Storage did become cheaper and more compact. Both flash
| drives and SD cards offer this functionality and showed
| significant improvements.
|
| Whatever innovative use cases people could come up with to
| store TBs of data in physical portable format is served by
| these. And along the way the world has shifted to storing
| data more cheaply and conveniently on the cloud instead.
|
| IPod being a special purpose device , with premium pricing
| (for average global consumer) and proprietary connectors
| and software would not have made a compelling economic case
| to over-spec it in hopes that some unforseen killer use
| case might emerge from the market.
| roenxi wrote:
| > IPod being a special purpose device , with premium
| pricing...
|
| Well, yes but if we're literally talking the iPod, it has
| been discontinued. Because it has been replaced by
| devices - with larger storage, I might note - that do
| more. I'm working from the assumption that as far as
| Brooks was talking iPhones basically are iPods. This is
| why the argument that the tech capped out seems suspect
| to me. The tech kept improving exponentially and we're
| still seeing storage space in the iPod niche doubling
| every few years.
| hanche wrote:
| > I probably would never listen to more than a few GBs of
| high fidelity music in my life time.
|
| FWIW, I currently have about 100 GB of music on my phone. And
| that is in a fairly high quality AAC format. Converted to
| lossless, it might be about five times that size? I don't
| even think of my music collection as all that extensive. But
| still, 160 TB would be a different ball game altogether. For
| sure, there is no mass market for music players with that
| sort of capacity. (Especially now that streaming is taking
| over.)
| latexr wrote:
| > I think Brooks' opinions will age poorly, but if anyone
| doesn't already know all the arguments for that they aren't
| interested in learning them now.
|
| Or they are too young still, or they just got interested in the
| subject, or, or, or...
|
| https://xkcd.com/1053/
|
| Don't dismiss someone offhand because they disagree with you,
| they may really have never heard your argument.
|
| > AFAIK, looking at Wikipedia, they're discontinuing iPods in
| favour of iPhones where we can get a 1TB model and the disk
| size trend is still exponential.
|
| iPods were single-purpose while iPhones are general computers.
| While music file sizes have been fairly consistent for a while,
| you can keep adding more apps and photos. The former become
| larger as new features are added (and as companies stop caring
| about optimisations) while the latter become larger with better
| cameras and keep growing in number as the person lives.
| Sharlin wrote:
| Exponentials have a bad habit of turning into sigmoids due to
| fundamental physical and geometric, and also practical,
| constraints. There's the thing called diminishing returns.
| Every proven technology reaches maturity; it happened to iPods,
| it happened to smartphones, it happened to digital cameras and
| so on. There's still growth and improvement, but it's greatly
| slowed down from the early days. But that's not to say that
| there won't be other sigmoids to come.
|
| If you haven't noticed how growth in storage capacity of HDDs
| in general screeched to a relative halt around fifteen years
| ago? The doubling period used to be about 12-14 months; every
| three or four years the unit cost of capacity would decrease
| 90%. This continued through the 90s and early 2000s, and then
| it started slowing down. A lot. In 2005 I bought a 250 GB HDD;
| at the same price I'd now get something like a 15 EB drive if
| the time constant had stayed, well, constant.
|
| There is, of course, always a multitude of interdependent
| variables to optimize, and you can always say that growth of X
| slowed down because priorities changed to optimize Y instead.
| But _why_ did the priorities change? Almost certainly at least
| partislly because further optimization of X was becoming
| expensive or impractical.
|
| > quality has been growing exponentially
|
| That's an entirely meaningless and nonsensical statement unless
| you have some rigorous way to _quantify_ quality.
| globalnode wrote:
| i dont know much about machine learning but what i think i know
| is that its getting an outcome based on averages of witnessed
| data/events. so how's it going to come up with anything novel? or
| outside of normal?
| ben_w wrote:
| That's not what ML is.
|
| You have some input, which may include labels or not.
|
| If it does have labels, ML is the automatic creation of a
| function that maps the examples to the labels, and the function
| may be arbitrarily complex.
|
| When the function is complex enough to model the world that
| created the examples, it's also capable of modelling any other
| input; this is why LLMs can translate between languages without
| needing examples of the specific to-from language pair in the
| training set.
|
| The data may also be synthetic based on the rules of the world;
| this is how AlphaZero beats the best Go and chess players
| without any examples from human play.
| richrichie wrote:
| ML is (smooth) surface fitting. That's mostly it. But that is
| not undermining it in any way. Approximation theory and
| statistical learning have long history, full of beautiful
| results.
|
| The kitchen sinks that we can throw at ML are incredibly
| powerful these days. So, we can quickly iterate through
| different neural net architecture configurations, check
| accuracy on a few standard datasets and report whatever
| configuration that does better on Archiv. Some might call it
| 'p-hacking'.
| michaelt wrote:
| Hypothetically?
|
| Training data gives the model an idea what "blue" means, and
| what "cat" means.
|
| It can now generate sensible output about blue cats, despite
| blue cats not being normal.
| IshKebab wrote:
| It's predicting a function. You train it on known inputs (and
| potentially corresponding known outputs). You get a novel
| output by feeding it a novel input.
|
| For example asking an LLM a question that nobody has even asked
| it before. The degree to which it does a good job on those
| questions is called "generalisation".
| randcraw wrote:
| It can't. Without the ability to propose a hypothesis and then
| experimentally test it in the physical real world, no ML
| technique or app can add new information to the world. The only
| form of creativity possible using AI (as it exists today) is to
| recombine existing information in a new way -- as a musical
| composer or jazz artist creates a variation on an theme that
| already exists. But that can't be compared to devising
| something new that we would call truly creative and original,
| especially novel work that advances the frontier of our
| understanding of the world, like scientific discovery.
| raincole wrote:
| > i dont know much about machine learning but what i think i
| know is that its getting an outcome based on averages of
| witnessed data/events
|
| "Extrapolation" is the word you're looking for, for a
| superficial understanding of machine learning. "Average" isn't
| even remotely close.
| fxtentacle wrote:
| To me, this reads like a very reasonable take.
|
| He suggests to limit the scope of the AI problem, add manual
| overrides in case there are unexpected situations, and he
| (rightly, in my opinion) predicts that the business case for
| exponentially scaling LLM models isn't there. With that context,
| I like his iPod example. Apple probably could have made a 3TB
| iPod to stick to Moore's law for another few years, but after
| they reached 160GB of music storage there was no usecase where
| adding more would deliver more benefits than the added costs.
| aurareturn wrote:
| I'm still waiting for SalesForce to integrate an LLM into Slack
| so I can ask it business logic and decisions long lost. Still
| waiting for Microsoft to integrate an LLM into outlook so I can
| get a summary of a 20 email long chain I just got CCed into.
|
| I don't think the iPod comparison is a valid one. People only
| have so much time to listen to music. Past a certain point, no
| one has enough good music they like to put into a 3TB iPod.
| However, the more data you feed into an LLM, the smarter it
| should be in the response. Therefore, the scale of iPod storage
| and LLM context is on completely different curves.
| SideburnsOfDoom wrote:
| > Still waiting for Microsoft to integrate an LLM into
| outlook
|
| Given that Microsoft this year is all-in on LLM AI, this is
| surely coming.
|
| But perhaps it will be a premium, paid feature?
| ravelantunes wrote:
| This exists and is available for paid users. My company
| started experimenting with it recently and it can be fairly
| helpful for long threads: https://support.microsoft.com/en-
| us/office/summarize-an-emai....
| hhh wrote:
| It's an additional $20-30/user/mo license, and has already
| existed in production for several months.
| belter wrote:
| They will sooner do that than fix Teams....
| SideburnsOfDoom wrote:
| The teams discussion was 3 days ago
| https://news.ycombinator.com/item?id=40786640
|
| I am happy with my comments there, including "MS could
| devote resources to making MS Teams more useful, but they
| don't have to, so they don't."
| richrichie wrote:
| > the more data you feed into an LLM, the smarter it should
| be in the response
|
| This is not obvious though.
| aurareturn wrote:
| It's in theory. The more information you have, the better
| the decision in theory.
| threeseed wrote:
| It's quality not quantity.
|
| You need to have accurate, properly reasoned information
| for better decisions.
| coredog64 wrote:
| It's a good thing that SFDC and Slack are both well known
| for being a repository of high quality data.
|
| /sarc
| richrichie wrote:
| Not quite. There are bounds on capacity of learning
| machines.
|
| https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis
| _di...
| lispm wrote:
| > However, the more data you feed into an LLM, the smarter it
| should be in the response.
|
| Is it that way? For example if it lacks a certain reasoning
| capability, then more data may not change that. So far LLMs
| lack useful ideas of truth, it will easily generate untrue
| statements. We see lots of hacks how to control that, with
| unconvincing results.
| aurareturn wrote:
| That has not been my experience with GPT4 and GPt4o. Maybe
| you're using worse models?
|
| The point is that the more context an LLM or human has, the
| better decision it can make in theory. I don't think you
| can debate this.
|
| Hallucinations and LLM context scale are more engineering
| problems.
| akerr wrote:
| ChatGPT says, "Generally, yes, both large language models
| (LLMs) and humans can make better decisions with more
| context. ... However, both LLMs and humans can also be
| overwhelmed by too much context if it's not relevant or
| well-organized, so there is a balance to be struck."
| akerr wrote:
| "Yes, it is debatable. Here are some arguments for and
| against the idea that more context leads to better
| decisions..."
| theteapot wrote:
| I think the argument was, GPT4 can't learn to do Math
| from more data. I'd be surprised if that's not true.
| threeseed wrote:
| ChatGPT makes mistakes doing basic arithmetic or sorting
| numbers.
|
| Pretty sure we have enough data for these fundamental
| tasks.
| pestaa wrote:
| It's more than enough data for a specialized tool, yes.
|
| It's not even remotely enough data for a statistical
| language processor.
| derefr wrote:
| Why are young children able to quickly surpass state-of-
| the-art ML models at arithmetic tasks, from only a few
| hours of lecturing and a "training dataset" (worksheets)
| consisting of maybe a thousand total examples?
|
| What is happening in the human learning process from
| those few thousand examples, to deduce so much more about
| "the rules of math" per marginal datapoint?
| moffkalast wrote:
| Llama-1, 1T tokens, dumb as a box of rocks
|
| Llama-2, 2T tokens, smarter than a box of rocks
|
| Mistral-7B, 8T tokens, way smarter than llama-2
|
| Llama-3, 15T tokens, smarter than anything a few times its
| size
|
| Gemma-2, 13T synthetic tokens, slightly better than llama-3
|
| (for the same approximate parameter size)
|
| I think it roughly tracks that moar data = moar betterer.
| cerved wrote:
| but the OP was talking about the size of the context
| window, not the size of the training corpus
| moffkalast wrote:
| Hmm right, I read that wrong. Still, interesting data I
| think.
| SideburnsOfDoom wrote:
| > not smart > slightly smarter > way smarter > Last one,
| "slightly smarter"
|
| So, the the usual s-curve, that has an exponential phase,
| then topping out?
| moffkalast wrote:
| Pretty much, yep. There was definitely a more significant
| jump there in the middle where 7B models went from being
| a complete waste of time to actually useful. Then going
| from being able to craft a sensible response to 80% of
| questions to 90% is a much smaller apparent increase but
| takes a lot more compute to achieve as per the pareto
| principle.
| hdhshdhshdjd wrote:
| I see giant models like Intel chips over the last decade:
| big, powerful, expensive, energy hogs.
|
| Small models are like arm: you get much of the
| performance you actually _need_ for common consumer
| tasks, very cheap to run, and energy efficient.
|
| We need both but I personally spend most of my ML time on
| small models training and I'm very happy with the
| results.
| ant6n wrote:
| > Still waiting for Microsoft to integrate an LLM into
| outlook so I can get a summary of a 20 email long chain I
| just got CCed into.
|
| Still waiting Microsoft to add a email search to Outlook that
| isn't complete garbage. Ideally with a decent UI and
| presentation of results that isn't complete garbage.
|
| ...why are we hoping that AI will make these products better,
| when they're not using conventional methods appropriately,
| and have been enshittified to shit.
| cerved wrote:
| Why should the response be better just because there is "more
| data"?
|
| Should I be adding extra random tokens to my prompts to make
| the LLM "smarter"?
| aurareturn wrote:
| More context. Not random data.
| MattPalmer1086 wrote:
| Haha, I'd be happy if outlook just integrated a search that
| actually works.
|
| Most of outlook search results aren't even relevant, and it
| regularly misses things I know are there. Literally the most
| useless search I've ever had to use.
| grugagag wrote:
| That's exactly my thoughts. If search is broken, expecting
| LLMs to fix that sounds naive or just disingenious.
| Propelloni wrote:
| Most data around is junk and the internet produces junk data
| faster then useful data and current GPT AIs basically
| regurgitate what someone already did somewhere on the
| internet. So I guess the more data we feed into GPTs the
| worse the results will get.
|
| My take to improve AI output is to heavily curate the data
| you feed your AI, much the like expert systems of old (which
| were lauded as "AI" also.) Maybe we can break the vicious
| circle of "I trained my GPT on billions of Twitter posts and
| let it write Twitter posts to great sucess", "Hey, me too!"
| threeseed wrote:
| > My take to improve AI output is to heavily curate the
| data you feed your AI
|
| This is what OpenAI is doing with their relationships with
| companies like Reddit, News Corp etc:
|
| https://openai.com/index/news-corp-and-openai-sign-
| landmark-...
|
| Problem is that we have a finite amount of this type of
| information.
| surfingdino wrote:
| > integrate an LLM into Slack
|
| They are already training their models
| https://slack.com/intl/en-gb/trust/data-
| management/privacy-p...
|
| > Microsoft to integrate an LLM into outlook
|
| Unlikely to happen. Orgs that use MS products do not want
| content of emails leaking and LLMs leak. There is a real
| danger that an LLM will include information in the summary
| that does not come from the original email thread, but from
| other emails the model was trained on. You could learn from
| the summary that you are going to get fired, even though that
| was not a part of the original conversation. HR doesn't like
| that.
|
| > However, the more data you feed into an LLM, the smarter it
| should be in the response
|
| Not necessarily. At some point you are going to run out of
| current data and you might be tempted to feed it past data,
| except that data may be of poor quality or simply wrong.
| Since LLMs cannot tell good data from bad, they happily
| accept both leading to useless outputs.
| geoduck14 wrote:
| >>Microsoft to integrate an LLM into outlook
|
| Didn't they already do this? A friend of mine showed me his
| outlook where he could search all emails, docs, and video
| calls and ask it questions. To be fair, he and I asked it
| questions about a video call and a doc - but not any
| emails, we only searched emails.
|
| This was last week amd it worked "mostly OK," but having a
| q/a conversation with a long email feels inevitable
| derefr wrote:
| Asking questions about a document is one thing; asking
| questions that synthesize information across many
| documents -- the human-intelligent equivalent of doing a
| big OLAP query with graph-search and fulltext-search
| parts on your email database -- is quite another.
|
| Right now AFAICT the latter would require the full text
| of all the emails you've ever sent, to be stuffed into
| the context window together.
| surfingdino wrote:
| The definitely added it to the web version n LinkedIn,
| you can see it when you want to write or reply to a
| message and it gives you an option to "Write with AI".
| derefr wrote:
| > There is a real danger that an LLM will include
| information in the summary that does not come from the
| original email thread, but from other emails the model was
| trained on. You could learn from the summary that you are
| going to get fired, even though that was not a part of the
| original conversation. HR doesn't like that.
|
| There could be separate personal fine-tunes per user,
| trained (in the cloud) on the contents of that user's mail
| database, which therefore have knowledge of exactly the
| mail that particular user can access, and nothing else.
|
| AFAICT this is essentially what Apple is claiming they're
| doing to power their own "on-device contextual querying."
| surfingdino wrote:
| > There could be separate personal fine-tunes per use
|
| Yes, but that contradicts the earlier claim that giving
| AI more info makes it better. If fact, those who send few
| emails or just joined may see worse results due to the
| lack of data. LLMs really make us hard to come up with
| ideas on how to solve problems that did not exist without
| LLMs.
| eysgshsvsvsv wrote:
| Is there a reason why memory was used and not compute power as
| an example? I don't understand how cherry picking random
| examples from past explain future of AI. If he think business
| needs does not exist he should explain how he arrived at that
| conclusion instead of a random iPod example.
| sigmoid10 wrote:
| This. The scaling of compute has vastly different
| applications than the scaling of memory. Shows once again
| that people who are experts in a related field aren't
| necessarily the best to comment on trendy topics. If e.g. an
| aeroplane expert critiques Spacex's starship, you should be
| equally vary, even though they might have some overlap. The
| only reason this is in the media at all is because negative
| sentiment to hype generates many clicks. That's why you see
| these topics every day instead of Rubik's cube players
| criticising the latest version of Mikado.
| cerved wrote:
| It's an analogy. He's making the point that even though
| something can scale at an exponential rate, it doesn't mean
| there is a business need for such scaling
| raincole wrote:
| > Apple probably could have made a 3TB iPod
|
| It's a very weird comparison, as putting more music tracks to
| your iPod doesn't make them sound better, while giving a LLM
| more parameters/computing power make it smarter.
|
| Honestly it sounds like a typical "I've drawn my conclusion,
| and now I only need an analogy that remotely supports my
| conclusion" way of thinking.
| derefr wrote:
| No, it makes sense: he's coming at it from the perspective of
| knowing exactly what task you want to accomplish (something
| like "fixing the grammar in this document.") In such cases, a
| model only has to be _sufficiently smart_ to work for
| 99.9999% of inputs -- at which point you cross a threshold
| where adding more intelligence is just making the thing
| bulkier to download and process, to no end _for your
| particular use-case_.
|
| In fact, you would then tend to go the other way -- once you
| get the ML model to "solve the problem", you want to then
| find the _smallest and most efficient such model_ that solves
| the problem. I.e. the model that is "as stupid as possible",
| while still being very good at this one thing.
| aetherson wrote:
| We'll see GPT-5 in a few months and that will be vastly more
| useful information to update your sense of whether the current
| approach will continue to work than anyone's speculation today.
| wrasee wrote:
| Right. Given we have so few data points at least GPT 5 will
| offer a completely new one. That will actually be new
| information.
| christianqchung wrote:
| In a few months, or in 18 months? Mira Murati said this month
| that the next "PhD level intelligence" models will be released
| in a year or a year and a half. Everything points to GPT-5 not
| happening this year, especially given that they've only started
| the training officially last month.
| locallost wrote:
| After using Copilot that is pretty bad at guessing what I exactly
| want to do, but still occasionally right on the money and often
| pretty close: AI is not really AI and it won't kill us all, but
| the realization is that a lot of work is just repetitive and
| really not that clever at all. If I think about all the work I
| did in my life it follows the same pattern: a new way of doing
| things comes along, then you start figuring out how to do it and
| how to use it, and once you're there you rinse and repeat. The
| real value in the work will be increasingly in why is it useful
| for people using it, although it probably was like this always,
| the geeks just didn't pay attention to it.
|
| Sorry for not commenting on the article directly.
| lostmsu wrote:
| Copilot can not read your mind: that is an impressive argument
| against AI!
| locallost wrote:
| I actually said it can read my mind which I interpret as my
| work being repetitive and not that clever. It can still read
| my mind poorly most of the time, although it's in the
| ballpark, but on occasion it hammers one home which feels
| like magic. Whether or not it can get good at reading my mind
| or not I don't know. But whether it can get good at doing non
| repetitive tasks that have been done a billion times, well,
| it feels like a no right now.
| lostmsu wrote:
| No, you literally said the opposite. Read your first
| sentence.
|
| > ... Copilot ... is pretty bad at guessing what I exactly
| want to do
| visarga wrote:
| > a new way of doing things comes along, then you start
| figuring out how to do it and how to use it, and once you're
| there you rinse and repeat
|
| This happens a billion times a month in chatGPT rooms. User
| comes with a task, maybe gives some references and guidance.
| The model responds. User gives more guidance. And this iterates
| for a while. The LLM gets tons of interactive sessions, it can
| learn how to rank the useful answers higher. This creates a
| data flywheel where people generate experience and LLMs learn
| and iteratively improve. LLMs have the tendency to make people
| bring the world to them, they interact with the real world
| through us.
| ilaksh wrote:
| Have you ever used gpt-4o, voice mode or Claude 3.5 Sonnet, or
| any other leading models, or are you really basing your entire
| assessment on Copilot?
|
| OoenAI hasn't even released their best model/capabilities to
| the public yet. Their text to image capabilities they show in
| their gpt-4o page are mine-blowing. The (unreleased) voice mode
| is a huge step up in simulating a person and often quite
| convincing.
| qeternity wrote:
| The iPod analogy is a poor one. Instead of 160TB music players,
| we got general computers (iPhone) with effectively unlimited
| storage (wireless Internet).
|
| I don't need to store all my music on my device. I can have it
| beamed directly to my ears on-demand.
| danparsonson wrote:
| That's the point though, an iPhone is not just a bigger iPod -
| scaling by itself isn't enough.
| qeternity wrote:
| No, it's not. Scaling did work. The internet gives us
| (effectively) unlimited storage.
|
| We just scaled in a slightly different way.
| caddemon wrote:
| The music part is just scaling in a different way, but
| that's not the reason the iPhone is so successful. The leap
| from iPod to iPhone was definitely not just scaling.
| pikseladam wrote:
| the thing is, you can't know if i'm an ai generated comment or
| not. this is the thing.
| card_zero wrote:
| Looks like you were trained on data without any capital
| letters.
| TheOtherHobbes wrote:
| The iPod didn't stop growing. It turned into an iPhone - a much
| more complex system which happened to include iPod features,
| almost as a trivial add-on.
|
| If you consider LLMs as the iPod of ML, what would the iPhone
| equivalent be?
| christianqchung wrote:
| I'd rather consider the current generation of LLMs as the first
| iPhone (or maybe flip phone, depends on where you stand), given
| that hundreds of billions of dollars in private money is
| already going into developing them.
| ilaksh wrote:
| Large multimodal models. Maybe using diffusion transformers.
| Possibly integrated into lightweight comfortable AR glasses via
| Wifi so they can see everything you can, optionally
| automatically receiving context such as frame captures.
| Optionally with a very realistic 3d avatar "teleported" into
| your space.
| bamboozled wrote:
| He made an analogy to discuss the business case for scaling an
| iPod, not whether or not new products and services would be
| invented. My iPhone still only has about 64gb of storage.
| ianbicking wrote:
| "He says the trouble with generative AI is that, while it's
| perfectly capable of performing a certain set of tasks, it can't
| do everything a human can"
|
| This kind of strawman "limitations of LLMs" is a bit silly.
| EVERYONE knows it can't do everything a human can, but the
| boundaries are very unclear. We definitely don't know what the
| limitations are. Many people looked at computers in the 70s and
| saw that they could only do math, suitable to be fancy mechanical
| accountants. But it turns out you can do a lot with math.
|
| If we never got a model better than the current batch then we
| still would have a tremendous amount of work to do to really
| understand its full capabilities.
|
| If you come with a defined problem in hand, a problem selected
| based on the (very reasonable!) premise that computers cannot
| understand or operate meaningfully on language or general
| knowledge, then LLMs might not help that much. Robot warehouse
| pickers don't have a lot of need for LLMs, but that's the kind of
| industrial use case where the environment is readily modified to
| make the task feasible, just like warehouses are designed for
| forklifts.
| jsemrau wrote:
| I am a perfectly healthy human being, but I can't climb Mount
| Everest (right now). I might be capable with more training.
| danielmarkbruce wrote:
| It might seem silly... but that's likely because you understand
| them better than most. He is talking about a real human problem
| - we see a thing (or person) do X well, and assume it's the
| result of some very general capability. With humans we tend to
| call it "halo effect". People are seeing LLMs to some insanely
| good stuff but don't know how they work and assume all kinds of
| stuff. It's an "AI halo effect".
| enragedcacti wrote:
| I don't think you and him are in disagreement. I read it as him
| saying "evaluating LLMs is extremely difficult and a big
| problem right now is that many people are treating them as
| basically human in capability".
|
| Its the opposite problem to the perception of computers in the
| 70s, early computers were seen by some as too alien to be as
| useful as a person across most tasks, llms are seen by some as
| too human to not be as useful as a person across most tasks.
| They are both wrong in surprisingly complex ways.
| mjburgess wrote:
| Were one to slice the corpus callosum, and burn away the
| body, and poke out the eyes.. and pickle
| the brain, and everything else besides...
| and attach the few remaining neurones to a few
| remaining keys.. then out would come a program
| like ChatGPT's -- "do not run it yet!"
| the little worker ants say dying on their hills,
| out here each day: it's only version four --
| not five! Queen Altman has told us: he's
| been busy in the hive -- next year the AI will be
| perfect it will even self-drive!
| Thus comment the ants, each day and each night:
| Gemini will save us, Llama's a delight!
| One more gigawat, One more crypto coin
| One soul to upload One artist to purloin
| One stock price to plummet Oh thank god
| -- it wasn't mine!
| georgeecollins wrote:
| That's really nice-- did you just make that?
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