[HN Gopher] AlphaCode as a dog speaking mediocre English
___________________________________________________________________
AlphaCode as a dog speaking mediocre English
Author : Tomte
Score : 260 points
Date : 2022-02-06 09:34 UTC (13 hours ago)
(HTM) web link (scottaaronson.blog)
(TXT) w3m dump (scottaaronson.blog)
| optimalsolver wrote:
| I still don't believe deep learning is going to take us to AGI.
| The susceptibility of these systems to completely failing on
| weird inputs shows that it's a kind of massive memorization going
| on here, rather than true understanding.
|
| Has anyone tried searching for new basic operations, below the
| level of neural networks? We've been using these methods for
| years, and I doubt the first major breakthrough in ML is the most
| optimal method possible.
|
| Consider the extreme case of searching over all mathematical
| operations to see if something really novel can be discovered.
|
| How feasible would this be?
| zarzavat wrote:
| > The susceptibility of these systems to completely failing on
| weird inputs shows that it's a kind of massive memorization
| going on here, rather than true understanding.
|
| Isn't that a truism? "Understanding" is equivalent to AGI.
| Nobody would argue that we have AGI yet, but the intelligence-
| sans-understanding _is_ somewhat similar to animal
| intelligence, which was the precursor to human intelligence.
| What should scare us is that we know that animal to human was
| not a difficult step evolutionarily.
| stephc_int13 wrote:
| >What should scare us is that we know that animal to human
| was not a difficult step evolutionarily.
|
| Do we know that? If that is not difficult, why is it so rare?
| CTmystery wrote:
| Perhaps most of us in this forum have only the most basic
| exposure to biology? I say this because with any serious
| exposure you can't help but be dumbfounded by the
| complexity and "creativity" of nature. Everything is
| amazing! The fact that the pieces fit together is just wild
| and scary and awesome. To think that gradient descent and
| backprop will give us _general intelligence_ , one of the
| great mysteries of nature, is incredibly hubristic, IMO.
| It's statistical inference at scale, with some heuristics
| up/downstream. It's a cool and useful tool for sure!
| ahelwer wrote:
| Sometimes I think peoples' self-awareness (internal world,
| whatever you want to call it) makes them think they're smarter
| or more competent than they are. My partner has a better memory
| than me and she will continually point out cases where I make
| the exact same comment when responding to similar situations
| across time (driving past a mural on the road, walking past the
| same flower bed, etc.) I really do feel like a program
| responding to stimuli sometimes. And sure I have a rich
| internal world or whatever, but that isn't so easy to discern
| from my external behavior. I think as I grow older the magic
| dissipates a bit and I can see how really, humans aren't _that_
| unique and incredible. What's incredible is that all this
| random faffing around at scale without much individual
| brilliance leads to the wonders of society. But we could divine
| that by looking at a termite mound.
|
| All of which is to say that memorization as you describe it
| seems like a plausible path to AGI, and humans don't deal with
| weird inputs well either. It's not like we are trying to make
| the ultimate single intelligence that rules humanity (although
| that may eventually come to pass). But something of roughly
| human competence certainly seems achievable.
| soraki_soladead wrote:
| I think it depends on what's meant by "deep learning". If you
| mean the latest multi-billion parameter transformer
| architecture trained on narrow domain data then yeah, you're
| probably right. If you mean large networks of simple
| computational units optimized by gradient descent on parallel
| hardware, why not?
| ColinWright wrote:
| > _The susceptibility of these systems to completely failing on
| weird inputs ..._
|
| If you believe that people are "general intelligence" systems,
| then your comment doesn't imply that the existing artificial
| systems won't achieve general intelligence, because _people_
| fail massively on weird inputs.
|
| Consider optical illusions:
|
| https://newatlas.com/science/best-optical-illusions-year-202...
|
| Consider audio illusions:
|
| https://www.youtube.com/watch?v=yKR2pGwavqE
|
| Consider tactile illusions:
|
| https://www.youtube.com/watch?v=WdhnEq76_PA
|
| * Machines fail on weird inputs, therefore they will never be
| intelligent;
|
| * People fail on weird inputs, therefore they cannot be
| intelligent.
| killerstorm wrote:
| A lot of people who are skeptical about AI progress call it
| "statistical modeling" and point to large data sets involved and
| large amounts of hardware thrown at it. Sort of implying it's
| some sort of a brute-force trick.
|
| I'm afraid they do not understand the size of problem/solution
| set. Suppose problem and solution are 1000 characters long and
| there's a set of 32 characters. Then a model is function F: X ->
| X where X is a set of 2^5000 elements. There are 2^10000 such
| functions, and the goal of a training process is to find the best
| one.
|
| Training set for a language model would be well under 1 PB. So a
| task here is to use a training set of size 2^50 to find a
| function from 2^10000 space.
|
| It's obvious that no amount of brute-forcing can possibly find
| this. An no classic mathematical statistical modeling can
| possibly help. A problem like this can be only approached via
| ANNs trained through backpropagation, and only because this
| training process is known to generalize.
| danielovichdk wrote:
| Why do we write code ?
|
| Code is not a goal. It's a tool. The tool can get better. But it
| will always be a tool and someone has to control the tool.
|
| Writing code is super easy. It's the easiest part of our job.
|
| The hard part of our jobs is to understand why we need to write
| code and right along that, what we write code for ?
|
| Maintenance Structuring Domain knowledge Refactoring
|
| Those are hard things to do. And they are not solved by having a
| program writing code for you.
| [deleted]
| arpa wrote:
| Hey it sure feels nice to know that now a "dog" is much more
| clever than me.
|
| but then again, its approach is so vastly different that it makes
| it really incomparable with our typical modus operandi. It's
| throwing millions of monkeys into millions of typewriters and
| actually producing "to be or not to be" reliably.
|
| I don't see AI writing Shakespeare in my lifetime though (I hope
| to die rather soon).
| mirceal wrote:
| on the internet everyone is a dog that speaks mediocre english.
| especially AI bots!
| kovek wrote:
| The article doesn't seem to discuss much about dogs?
| darepublic wrote:
| Did I read correctly that the AI generates millions of potential
| solutions per question and then filters these down? That's an
| interesting approach and one that had occurred to me. Have a
| critic evaluate AI generates content and then have it take many
| tried to come up with a sample that passes the evaluator
| indicisive wrote:
| Frankly asking a computer to write code is like asking a dog to
| speak facts in straight zfc. What else can I say, ai research has
| conspired to take over all other areas of computer science. I
| wonder who exactly started this hype. Also what else will come
| after this the stupid ai craze.
| mirceal wrote:
| this is cool and all but it's not even close to AGI. it's a
| sophisticated gimmick.
|
| to go meta, I don't believe that our way of approaching AI in
| general is going to get us to AGI.
|
| Why? Assume for a second that we could build something that could
| solve ANY problem. What would happen next? What does an
| intelligent human with zero emotions do? Nothing.
|
| Fear of dying and the hardwired desire to pass on our genes (or
| to contribute something to society - which is just another way of
| shooting for immortality) is what drives us.
|
| I predict that true AGI cannot come before a machine that has
| feelings, that is sentient.
|
| We already have machines that do that. They're called humans.
| It's very arrogant of us thinking we can recreate in silicon what
| nature has evolved over eons. Not saying it's not gonna happen -
| but not with the current approaches.
|
| Also, a dog that speaks mediocre english? please. this is
| insulting to dogs and shows out tendency to anthropomorphize
| everything.
| Smoosh wrote:
| I agree with your underlying premise - without stimuli such as
| pain and pleasure to give motivation it is difficult to foresee
| an AI which will think enough like humans to be useful or that
| we feel we can trust. But perhaps that is just chauvinism.
| ogogmad wrote:
| I've come up with an algorithm (the same as the AI's) for solving
| that linear system using a CAS. Feels relevant because this
| breakthrough is heralding an era of much more powerful CASes.
|
| We're getting close to an era where the drudgery in maths can be
| automated way.
| turminal wrote:
| It would be interesting to know how much of this improvement in
| the last 25 since he was a student comes from Moore's law, other
| hardware improvements, various new technologies not related to
| AI, amount of money being thrown at the problem... and how much
| of it are advancements in our understanding of AI.
| qualudeheart wrote:
| It's mostly Moores law and scaling. Language model progress
| will be the next Moores law until diminishing returns are
| reached.
| visarga wrote:
| A recent paper shows that empowering a language model to
| search a text corpus (or the internet) for additional
| information could improve model efficiency by 25 times [1].
| So you only need a small model because you can consult the
| text to get trivia.
|
| That's 25x in one go. Maybe we have the chance to run GPT-4
| ourselves and not need 20 GPU cards and a 1mil $ computer.
|
| [1]
| https://deepmind.com/research/publications/2021/improving-
| la...
| Isinlor wrote:
| Roughly half/half, skewed more towards improvements in
| algorithms.
|
| Measuring the Algorithmic Efficiency of Neural Networks:
| https://arxiv.org/abs/2005.04305
|
| In this work, we argue that algorithmic progress has an aspect
| that is both straightforward to measure and interesting:
| reductions over time in the compute needed to reach past
| capabilities. We show that the number of floating-point
| operations required to train a classifier to AlexNet-level
| performance on ImageNet has decreased by a factor of 44x
| between 2012 and 2019. This corresponds to algorithmic
| efficiency doubling every 16 months over a period of 7 years.
| By contrast, Moore's Law would only have yielded an 11x cost
| improvement. We observe that hardware and algorithmic
| efficiency gains multiply and can be on a similar scale over
| meaningful horizons, which suggests that a good model of AI
| progress should integrate measures from both.
|
| Mastering Atari Games with Limited Data:
| https://arxiv.org/abs/2111.00210
|
| Reinforcement learning has achieved great success in many
| applications. However, sample efficiency remains a key
| challenge, with prominent methods requiring millions (or even
| billions) of environment steps to train. (...) This is the
| first time an algorithm achieves super-human performance on
| Atari games with such little data. EfficientZero's performance
| is also close to DQN's performance at 200 million frames while
| we consume 500 times less data. EfficientZero's low sample
| complexity and high performance can bring RL closer to real-
| world applicability.
|
| 500x improvement over ~10 years since DQN that roughly 2x
| improvement in sample complexity every year.
|
| A Time Leap Challenge for SAT Solving:
| https://arxiv.org/abs/2008.02215
|
| We compare the impact of hardware advancement and algorithm
| advancement for SAT solving over the last two decades. In
| particular, we compare 20-year-old SAT-solvers on new computer
| hardware with modern SAT-solvers on 20-year-old hardware. Our
| findings show that the progress on the algorithmic side has at
| least as much impact as the progress on the hardware side.
|
| AI research has also tiny budgets compared to the biggest
| scientific projects:
|
| GPT-3 - $0.01B
|
| LIGO - $1B
|
| LHC - $7.5B
|
| JWST - $10B
|
| ITER - $20B
|
| ISS - $150B
| sabujp wrote:
| All these deepmind and openai computer scientists don't pay
| themselves, you're not taking their pay into consideration.
| swyx wrote:
| this is the definition of a nit. they dont cost 1b. still
| tiny vs the other projects listed
| mistercheph wrote:
| google is undoubtedly spending at least 1 billion on ML
| probably closer to 10 billion per annum
| qayxc wrote:
| Where did you get the $10M figure for GPT-3? That sounds
| awfully cheap considering the cost of compute alone: one
| estimate was $4.6M for a single training run [0], while other
| sources [1] put it at $12M per run. I highly doubt that
| OpenAI nailed the training process right on the second or
| even first go respectively (according to your figure).
|
| So even conservative estimates put the compute cost alone at
| least one order of magnitude higher than your figure of $10M.
|
| [0] https://lambdalabs.com/blog/demystifying-
| gpt-3/#:~:text=But%....
|
| [1] https://venturebeat.com/2020/06/01/ai-machine-learning-
| opena...
| Isinlor wrote:
| Yes, I was using the $12M estimate. Take it as within order
| of magnitude approximation.
| ilove196884 wrote:
| drooby wrote:
| I think I agree with this take. We barely even know how our own
| brains learn and solve problems. We don't know exactly what the
| inner workings of AGI will look like... maybe this kind of
| problem solving is the emergence of that, perhaps in a different
| way to our own, perhaps not.
| Jack000 wrote:
| I think this type of model will have a massive impact on the
| software industry. 99% of programming tasks in the wild don't
| involve any kind of algorithmic design, but are more like making
| a CRUD pattern, writing SQL queries etc. This kind of work is
| easier to automate but more difficult to source the training
| data. If and when these models are applied to more mundane
| problems, I'd expect immediately better performance and utility.
|
| We're also in the very very early days of code generation models.
| Even I can see some ways to improve AlphaCode:
|
| - the generate->cluster->test process feels like a form of manual
| feature engineering. This meta layer should be learned as well,
| possibly with RL
|
| - programming is inherently compositional. Ideally it should
| perform the generate->cluster->test step for each function and
| hierarchically build up the whole program, instead of in a single
| step as it does now
|
| - source code is really meant for humans to read. The canonical
| form of software is more like the object code produced by the
| compiler. You can probably just produce this directly
| accountLost wrote:
| Considering that even futur users of crud applications cannot
| come up with decent requirements for these applications, I am
| not sure AI will help.
| yojo wrote:
| Reading this I'm reminded of the debates around ORMs. At a
| basic level they drastically simplify your CRUD app. Until they
| make trivial errors no self-respecting programmer would (think
| N+1 queries), and then you need someone who actually
| understands what's going on to fix it.
|
| That doesn't mean you shouldn't ever use ORMs, or that in
| simple cases they aren't "good enough". But at some level of
| complexity it breaks down.
|
| AI-assisted programming is the new leaky abstraction.
| randcraw wrote:
| I think the validation phase of auto-coding fullblown apps is
| much more complex than AutoCode is ready for. When coding up a
| specific function, it's pretty easy to assess whether it maps
| input to output as intended. But composing functions into
| modules is much harder to validate, much less entire programs.
|
| And specifying a full app to be autocoded is most certainly NOT
| a solved problem.
|
| Until AutoCode can build an app that employs compound AND
| complex behaviors, like Angry Birds, or a browser, I'll
| continue to see it as little more than a write-only
| copy/paste/derive-driven macro generator.
| keewee7 wrote:
| It's interesting that AI is being aggressively applied to areas
| where AI practitioners are domain experts. Think programming,
| data analysis etc.
|
| We programmers and data scientists might find ourselves among
| the first half of knowledge workers to be replaced and not
| among the last as we previously thought.
| axg11 wrote:
| Compilers didn't replace any jobs, they created more.
| Similarly, this type of AI-assisted programming will allow
| more people to program and make existing programmers more
| productive.
| keewee7 wrote:
| I was thinking over a really long time period. There is at
| least 20-30 more years of general purpose programming being
| a highly sought after skill. But with time most programming
| is going to be done by AI that is directed by domain
| experts.
| jeffbee wrote:
| In my view this type of system will only be usable by Real
| Computer Scientists and will completely kill off the
| workaday hacker. Think of all the people who bitterly
| complain that a C++ compiler does something unexpected
| under the banner of UB. That crowd cannot cope with a world
| in which you have to exactly describe your requirements to
| an AI. It is also analogous to TDD, so all the TDD haters,
| which is the overwhelming majority of hackers, are toast.
| discreteevent wrote:
| I hope you don't mean to imply that TDD haters are not
| real computer scientists.
| http://ravimohan.blogspot.com/2007/04/learning-from-
| sudoku-s...
| meken wrote:
| What's a workaday hacker and what's UB?
| 331c8c71 wrote:
| undefined behavior
| kilotaras wrote:
| UB = undefined behaviour.
|
| You can write code that is valid as in "can be compiled"
| but outside of C++ standard. It is duty of programmer to
| not have those, as compiler usually assumes that there's
| no UB in your code and can do unintuitive things with
| optimizations.
|
| e.g int foo(int8_t x) { x += 120
| return x; } int bar(int8_t y) {
| int z = foo(y); if (y > 8) {
| do_important_thing(z); } }
|
| `do_important_thing` may be optimized out because:
|
| 1. signed overflow is a UB. Compiler than assumes that
| _everything_ passed to foo is less than 8;
|
| 2. We pass y to foo => y < 8
|
| 3. if branch can then be optimized out
| np- wrote:
| > We programmers and data scientists
|
| Instead of semantically correct Python, programmer and data
| scientists' jobs will be to work in semantically correct
| English. Fundamentally the job won't change (you'll be
| programming the AI rather than program the machine directly).
| zacmps wrote:
| > source code is really meant for humans to read. The canonical
| form of software is more like the object code produced by the
| compiler. You can probably just produce this directly
|
| They key advantage of producing source code is that you can
| usually tell what the produced program does.
| [deleted]
| CTmystery wrote:
| Amazing stuff for sure. Looking at the example on page 59,
| though, I certainly see a description that contains sufficient
| information to implement against. I read this, and then I jump
| back into the tech spec that I'm writing to find:
|
| (1) The product specification and use cases are so poorly defined
| that I need to anticipate the use cases, design a system that is
| general enough to accommodate them, and implement it in a way
| that is easily changeable to accommodate the future departures
| from my assumptions.
|
| (2) As I do this, I need to consider the existing systems that
| I'm building on top of to ensure there is no regression when this
| feature rolls out
|
| (3) I consider the other teams that are doing similar work and
| make judgement calls about whether to write independent systems
| that do one thing each, or to collaborate on a general-enough
| system with multiple team ownership.
|
| (4) The tech that I use to implement this must be within the
| narrow slice of company-sanctioned tech.
|
| (5) I weigh constant tradeoffs on speed to market,
| maintainability and ownership.
|
| I'm sure there's more, but this stuff is _hard_. If autonomous
| driving for white collar work is coming, as put forth by comments
| here, I'd like to see indications that _the actual hard part_ of
| the job is in jeopardy of being executed effectively.
|
| Maybe I don't want to believe it, so I can't see it. I'll grant
| that. But I truly do not see it.
| hawkice wrote:
| Dog speaking mediocre English won't take my job (yet).
| 62951413 wrote:
| I'm mostly with you for the immediate future. So even if cars
| driving themselves to our offices to write code for 8 hours is
| in the cards I'd be curious to hear more tactical informed
| guesses. What would an intermediate stage look like?
|
| Would the demand for junior developers evaporate if more
| experiences people can 10x daily LoC productivity? Or the other
| way around? Would languages with higher level abstractions
| (e.g. comparable to Scala if not Haskell) win or something like
| JS dominate?
| Andrex wrote:
| Man, I wish dogs could talk.
|
| Any moonshots working on this yet?
| sdenton4 wrote:
| Check out 'Interspecies Internet.' It's kind of a dumb name
| imo, but the goal is exactly interspecies communication.
|
| https://www.interspecies.io/
| YeGoblynQueenne wrote:
| >> Update: A colleague of mine points out that one million, the
| number of candidate programs that AlphaCode needs to generate,
| could be seen as roughly exponential in the number of lines of
| the generated programs.
|
| To clarify, "one million" is the number of programs generated by
| AlphaCode on the _CodeContests_ dataset, not the CodeForces one
| (although the former is a superset of the latter). The results on
| CodeContests are reported in table 5 (page 15 of the pdf) of the
| DeepMind preprint [1]. The results on CodeForces are reported in
| table 4 of the preprint (page 14 of the pdf).
|
| I can't find where the paper lists the number of samples drawn
| for the CodeForces results, but on Section 4.4 the preprint says:
|
| _Sampling from transformer models can be easily parallelized,
| which allowed us to scale to millions of samples per problem
| (...)_.
|
| Note the plural. "Millions" can mean one million, or a hundred
| million, but, as far as I can tell, the preprint never makes it
| clear which it is. Note that the results on CodeForces (table 4)
| are averaged over 3 evaluations in each of which AlphaCode
| generated "millions" of samples and finally submitted 10. So it's
| 3 times "millions" of samples for the actual results in table 4
| (CodeForces). I assume those were the top 3 of all evaluations.
|
| The idea that the lines of code in a target program are related
| to the cardinality of the program space that must be searched
| before a target program can be found is not completely unfounded.
| For sure, the cardinality of the search space for programs is
| some function of the number of tokens that must be combined to
| form each program in that space. An _exponential_ function
| because we're talking about combinations of tokens (assuming only
| grammatical strings are generated it gets a bit better, but not
| by much). We can take lines-of-code as a rough proxy of number of
| tokens, and in any case it's clear that the cardinality of the
| set of one-line programs is less than the cardinality of the set
| of two-line programs, and so on.
|
| I'm not sure if the "Update" bit above is claiming, that
| AlphaCode must generate the _entire_ program space of _all_
| k-line programs before it can find a solution to that problem. To
| be honest, even I don't think that AlphaCode is _that_ bad.
| Having to go through the entire program space to find one program
| is the worst case. On the other hand, AlphaCode does perform very
| poorly so who knows?
|
| Regarding talking dogs, I have never met any, but I have heard of
| a horse that can do arithmetic [2].
|
| __________
|
| [1] https://storage.googleapis.com/deepmind-
| media/AlphaCode/comp...
|
| [2] https://en.wikipedia.org/wiki/Clever_Hans
| mbgerring wrote:
| As I understand it, all of the progress in AI has come from
| taking existing theory and throwing enormous amounts of hardware
| at it. Which is interesting, sure, but whether it represents
| "progress" in the field of "artificial intelligence" is a
| different question.
| Permit wrote:
| Here's an interesting article on this topic that you might
| enjoy:
| http://www.incompleteideas.net/IncIdeas/BitterLesson.html
| baron816 wrote:
| Fun fact: dogs bark primarily to communicate with humans. Wolves
| (undomesticated dogs) don't really bark. And you wouldn't likely
| see a pack of dog barking at each other. But humans are keenly
| able to tell what a dog is trying to express by the different
| sounds it makes. This is all a result of the convolution between
| the two species.
| robocat wrote:
| > convolution
|
| https://en.wikipedia.org/wiki/Coevolution
|
| I am not sure I would like to see dogs convolved with humans.
| mannykannot wrote:
| The way Scott describes it, AlphaCode appears to be practicing
| Agile / TDD! All it needs to do now is write its own tests.
|
| Update: See my reply to dudeinjapan's response - I had not been
| completely clear here.
| dudeinjapan wrote:
| It does write its own tests, i.e. to the extent it checks the
| generated programs work on the data provided and discards the
| ones that don't. I imagine many of the coding challenges it's
| trained on come with a few tests as well.
| mannykannot wrote:
| I meant actually coming up with examples consisting of
| specific problems and their correct solutions (and maybe some
| counterexamples.)
|
| Ironically, I had just replaced 'test cases' with 'tests',
| because I thought that the former might seem too generic, and
| arguably satisfiable merely by rephrasing the problem
| statement as a test case to be satisfied.
| GreenWatermelon wrote:
| That would imply the AI already solved the problem, as it
| needs the solution inorder to generate tests, e.g. An AI
| can't test addition without being able to add, and so on
| mannykannot wrote:
| The problem here is to write a program, not solve the
| problem that this program is intended to solve. Clearly
| people can write tests for programs without necessarily
| being able to write a program to meet the specification,
| and in some cases without being able to solve the problem
| that the program is required to solve (e.g. plausibly, a
| person could write a tests for a program for solving
| Sudoku puzzles without being able to do that themselves,
| and it is possible to test programs that will be employed
| to find currently-unknown prime numbers.)
|
| Having said that, your point is kind-of what I was
| getting at here, though in a way that was probably way
| too tongue-in-cheek for its own good: When we consider
| _all_ the activities that go into writing a program, the
| parts that AlphaCode does _not_ have to do are not
| trivial. Being given solved test cases is what allows it
| to succeed (sometimes) with an approach that involves
| producing a very large number of mostly-wrong candidates,
| and searching through them for the few that seem to work.
| maest wrote:
| The approach reminds me more of junior devs who have no
| interest in fully understanding the code/problem and they just
| make semi-random changes to the code until the compiler is
| happy/the test is green.
| sinuhe69 wrote:
| I like to see how AlphaCode will solve a problem no human has
| solved before (or very unlikely).
|
| For example, given 3 beads and 2 stacks for the 10er and 1er
| positions of a number. One can make 4 different numbers when
| stack all the beads like in an abacus. Without using all the
| beads, one can of course make more numbers. The question is how
| many different numbers one can make using n beads, giving full
| and partial usage of the beads.
|
| It's indeed a very simple problem for 5-7 year olds and I hardly
| think anyone cannot solve it. Nevertheless, I seriously doubt
| AlphaCode can solve the problem. What does it say about its
| supposed Intelligence?
| recursive wrote:
| Why do you think that's more difficult than the backspace
| question? I see no reason to doubt AlphaCode can solve it.
| 37ef_ced3 wrote:
| Because the backspace question (essentially: is T a
| subsequence of S with a deletion size of N?) probably occurs
| hundreds of times, in one form or another, within AlphaCode's
| training corpus.
|
| Any leetcode grinder can tell you there are a few dozen types
| of competitive programming problem (monostack, breadth-first
| state search, binary search over solution space, etc.) so
| solutions to new problems are often very similar to solutions
| for old problems.
|
| The training corpus for these code transformers is so large
| that almost all evaluation involves asking them to generate
| code from the training corpus.
|
| To evaluate CoPilot, we should ask questions that are unusual
| enough they can't be answered through regurgitation of the
| training corpus.
|
| What does CoPilot generate, given this prompt:
| // A Go function to set the middle six bits of an unsigned
| 64-bit integer to 1.
|
| Here is a human solution: func six1s(x
| uint64) uint64 { const ( ones =
| uint64(63) shift = (64 - 6) / 2
| mask = ones << shift ) return x | mask
| }
|
| Can it solve this simple but unusual problem?
| notamy wrote:
| > What does CoPilot generate, given this prompt:
|
| Here's what it generated for me: // A Go
| function to set the middle six bits of an unsigned 64-bit
| integer to 1. func set6(x uint64) uint64 {
| return x | 0x3f }
| 37ef_ced3 wrote:
| Incorrect. Thank you.
|
| CoPilot is helpless if it needs to do more than just
| regurgitate someone else's code.
|
| The training of these models on GitHub, so they
| regurgitate licensed code without attribution, is the
| greatest theft of intellectual property in the history of
| Man. Perhaps not according to the letter of the law, but
| surely according to the spirit.
| notfed wrote:
| I like CoPilot's answer better than yours, and I think
| it's closer to what most people would do; clearly 0x3F is
| the wrong constant but the approach is good.
| 37ef_ced3 wrote:
| CoPilot's solution is totally wrong. Sorry.
|
| CoPilot regurgitated somebody's solution... to a
| different problem. It's pathetic.
| notfed wrote:
| Here's my solution (I'm a human): func
| set6(x uint64) uint64 { return x | 0x7E0000000
| }
|
| Is this also pathetic?
| 37ef_ced3 wrote:
| A good solution! You SOLVED the problem.
|
| CoPilot got it WRONG. You got it RIGHT.
|
| You UNDERSTAND the problem but CoPilot does NOT.
|
| Is that clear?
| qayxc wrote:
| I like the manual solution better: it includes thought
| process without negative impact on readability or
| performance.
|
| This makes it easier to match the code against the
| specification, something a random (and in this case even
| wrong) magic number fails to do.
|
| But maybe I'm overthinking it.
| 37ef_ced3 wrote:
| You're not overthinking it.
|
| You're correct.
| ahgamut wrote:
| > To evaluate CoPilot, we should ask questions that are
| unusual enough they can't be answered through regurgitation
| of the training corpus.
|
| Exactly! It's great that Copilot can generate correct code
| for a given question, but we cannot gauge its full
| capability unless we try it on a range of different
| questions, especially ones that are not found in the
| training data.
|
| I mentioned this in the other AlphaCode post: It would be
| nice to know how "unusual" a given question is. Maybe an
| exact replica exists in the training data, or a solution
| exists but in a different programming language, or a
| solution can be constructed by combining two samples from
| the training data.
|
| Quantifying the "unusual-ness" of a question will make it
| easier to gauge the capability of models like AlphaCode. I
| wrote a simple metric that uses nearest neighbors
| (https://arxiv.org/abs/2109.12075). There are also other
| tools to do this: conformal predictors, which are used in
| classification methods, and the RETRO transformer
| (https://arxiv.org/pdf/2112.04426.pdf) has a calculation
| for the effect of "dataset leakage".
| mark_l_watson wrote:
| Right on, I couldn't agree more. We are living during a period of
| exponential progress. I like AlphaCode's approach of using
| language models with search. In the last year I have experimented
| with mixing language models for NLP with semantic web/linked data
| tasks, so much simpler than what AlphaCode does, but I have been
| having fun. I have added examples for this in new additions to
| two of my books, but if you want a 90 second play-time, here is a
| simple colab notebook
| https://colab.research.google.com/drive/1FX-0eizj2vayXsqfSB2...
|
| We live in exciting times. Given exponential rates of progress, I
| can't really even imagine what breakthroughs we will see in the
| next year, let alone the next five years.
|
| EDIT: I forgot to mention: having GitHub/OpenAI tools like
| CoPilot always running in the background in PyCharm and VSCode
| has in a few short months changed my workflow, for the better.
| skybrian wrote:
| Could you say a bit more about how you use CoPilot? What are
| the "other tools" you use?
| mark_l_watson wrote:
| I installed the VSCode and PyCharm CoPilot plugins, and
| signed in with my GitHub account (you first need to request
| CoPilot access and wait until you get it).
|
| As you type in comments or code, CoPilot will sometime
| autocomplete up to about 10 lines of code, based on the
| content of the file you are editing (maybe just the code
| close to the edit point?).
|
| My other tools? I use LispWorks Professional, Emacs with
| Haskell support, and sometimes IntelliJ. I work a lot on
| remote servers so I depend on SSH/Mosh and tmux also.
| skybrian wrote:
| Do you usually find the autocomplete from CoPilot useful
| enough to keep without editing, or is it wrong often enough
| to be annoying?
| crummy wrote:
| In my opinion it's wrong most of the time but it's so
| harmless that it's absolutely worth it for the times it's
| right.
|
| Kind of like regular autocomplete. If it's wrong you just
| keep typing.
| mark_l_watson wrote:
| If it is wrong, I hit the escape key and the suggested
| code disappears.
| stephc_int13 wrote:
| We're not seeing exponential rates of progress, in my opinion.
|
| We've been on the steep part of an S-curve.
| axg11 wrote:
| When you're on that curve, it's indistinguishable until you
| hit the plateau. We're in an era where AI is continuing to
| improve and has already surpassed a level that many people
| doubted was achievable. Nobody knows when that progress will
| plateau. It's entirely possible that we plateau _after_
| surpassing human-level intelligence.
| oblvious-earth wrote:
| If you have actual data you can take the derivative of the
| curve and see you're on the S curve with a lot of
| confidence by the time you hit the middle and long before
| the plateau: https://miro.medium.com/max/700/1*6A3A_rt4Ymum
| HusvTvVTxw.png
| solveit wrote:
| Even small errors in measurement will greatly change the
| location of the projected plateau so this is not usually
| useful in practice.
| nradov wrote:
| The AI technology today has practical value for some use
| cases but it's basically just clever parlor tricks. There
| has been near zero discernable progress toward artificial
| _general_ intelligence. We don 't yet have a computer that
| can learn and make optimal resource usage decisions in an
| open world environment as well as a mouse. In most respects
| we're not even at the insect level yet.
| folli wrote:
| You're making the point that the linked article is trying
| to argue against.
| robrorcroptrer wrote:
| How good is this model at fighting other insects?
| mark_l_watson wrote:
| Exactly correct. We really don't know if the progress is
| exponential or like a Sigmoid squashing function. You just
| changed my opinion, a bit, on this.
| antisthenes wrote:
| If we're limited to Earth and fossil fuels, then it's a
| sigmoid.
| shawnz wrote:
| The steep part of a sigmoid curve DOES grow almost
| exponentially though. What's the point of saying this? It's
| like saying, "sure we are making great progress, but
| eventually the universe will come to an end and we'll all be
| dead." Who cares? Why not worry about that when we're there?
| stephc_int13 wrote:
| The point is that it is a completely different framework to
| think about the future.
|
| The exponential model is something close than what is
| behind Ray Kurzweil reasoning about the Great Singularity
| and how the future will be completely different and we're
| all going to be gods or immortals or doomed or something
| dramatic in that vein.
|
| The S-Curve is more boring, it means that the future of
| computing technology might not be that mind blowing after
| all, we might already have reaped most of the low-hanging
| fruits.
|
| A bit like airplanes, or space tech you know, have you seen
| those improving by a 10x factor recently?
| drusepth wrote:
| >A bit like airplanes, or space tech you know, have you
| seen those improving by a 10x factor recently?
|
| Is space tech included ironically here? There's been 10x
| (or more) improvements across dozens of space tech
| problems in the last couple decades.
| CuriouslyC wrote:
| 100% this. Exponential progress is only a thing if potential
| progress is infinite. If the potential progress is finite
| (hint: it is), eventually the rate of progress hit
| progressively diminishing returns.
| globalise83 wrote:
| What is the limit you foresee on computation, especially
| when such computations can optimise themselves without
| human intervention?
| stephc_int13 wrote:
| The underlying rule here, in my opinion, is the law of
| diminishing returns. (log shaped curve)
|
| AlphaZero is already capable of optimizing itself in the
| limited problem space of Chess.
|
| Infinitely increasing the computing power of this system
| won't give it properties it does not already have, there
| is no singularity point to be found ahead.
|
| And I am not sure that there are any singularities lying
| ahead in any other domains with the current approach of
| ML/AI.
| notahacker wrote:
| And building on that, the real bottleneck in most domains
| isn't going to be computer power, it's going to be human
| understanding of how to curate data or tweak parameters.
|
| We've already seen in games with simple rules and win
| conditions that giving computers data on what we think
| are _good_ human games can make them perform worse than
| not giving them data. Most problems aren 't possible for
| humans to ebncapsulate perfectly in a set of rules and
| win conditions to just leave the processing power to fill
| in the details, and whilst curating data and calibrating
| learning processes is an area we've improved _hugely_ on
| to get where we are with ML, it 's not something where
| human knowledge seems more likely to reach an inflection
| point than hit diminishing returns.
| stephc_int13 wrote:
| We've seen with ML and Chess (an go to some extent) that
| brute forcing the problem space is clever than using
| heuristics.
|
| I think this is only true because, so far, our heuristics
| are not that clever.
| globalise83 wrote:
| But, godlike though the human variants may appear to be,
| what happens when the system reaches the skill level of
| an above-average software architect?
| hutzlibu wrote:
| No one knows the future for sure. But with current trend of
| dividing the world again, I would agree on your assumption.
|
| But I still have hope for a undivided world, that one day
| even abolish the patent system. Then I would see potential
| for exponentional progress.
| vain_cain wrote:
| I mostly work with data mining on my personal projects(which is
| a couple of hours every day), and I'm pretty sure I didn't have
| to write a single regex since I've started using Copilot. It's
| hard for me to even imagine how I used to do it before, and how
| much time I've wasted on stupid mistakes and typos. Now I just
| write a comment of what I want and an example string. It does
| the job without me having to modify anything 99% of the time,
| even for complex stuff. Sure, sometimes it gives an overly
| complicated solution but it almost always works. Exciting
| times.
| 37ef_ced3 wrote:
| How do you know the regular expressions are correct, without
| understanding them?
| incrudible wrote:
| All regular expressions are incorrect, but some are useful.
| incrudible wrote:
| (It's a reference)
|
| https://en.wikipedia.org/wiki/All_models_are_wrong
| 37ef_ced3 wrote:
| alar44 wrote:
| whatshisface wrote:
| Who cares if they're correct? If they fail a test, you can
| fix them. If they turn out to be wrong in production you
| can isolate the example and add it as a test. Producing
| regexes that pass all current tests but contain a subtle
| bug satisfy 100% of what programmers are incentivized to
| do. Producing them very quickly will get you promoted.
| nradov wrote:
| You're not wrong, but hopefully someone is at least doing
| a code review.
| oh_sigh wrote:
| I feel like you only see programmers as cogs, and not
| programmers as invested in the success of their product
| with the statement like "satisfy 100% of what programmers
| are incentivized to do"
|
| Generally the people using regexes care if they're
| correct. Frequently, all possible input variants are not
| enumerated in tests. Frequently, companies want to have
| confidence in their production code. Imagine this regex
| is deployed on a sign up flow, and its failures invisibly
| increase your churn rate. Can it happen with a hand
| crafted regex? Yes, of course. But I'd imagine it will
| happen even more frequently with an AI produced custom
| regex plus a person who doesn't actually understand
| regexes.
| whatshisface wrote:
| Programmers often feel invested in the success of their
| product but that's not what they're incentivized to do.
| They're incentivized to produce fast results that are bad
| in ways that you have to be a programmer to understand.
|
| If you have to be a programmer to understand why
| something's bad, who's going to prevent it? This is a
| major unsolved problem in the structure and organization
| of working.
| solveit wrote:
| The CTO who used to be a dev. More generally anyone in
| management with a technical background. They may not
| exist in some companies, but that's not because it's "a
| major unsolved problem in the structure and organization
| of working", it's because the company sucks in that
| regard.
| Retric wrote:
| Except testers and users aren't programmers. Code reviews
| are what's supposed to catch this stuff, but it's rare
| for a team lead or other programmer to investigate every
| single commit.
| nlitened wrote:
| I think we will very soon start seeing a clear separation
| between programmers ("co-pilot operators") and software
| engineers (those who do the thinking and understanding
| when there's someone "who cares").
| p1esk wrote:
| This separation has existed since the first computer was
| built.
| vain_cain wrote:
| I never said anything about not understanding them. If it's
| something simple, like getting a date from a string, a
| quick glance will tell me if it'll work. If it's more
| complex, a quick glance or two will give me the general
| idea and then I can test it against what I think will be
| edge cases. If you don't know what kind of a string you'll
| have to process then you can't really know if any regex is
| correct, and if you do testing it in most cases is pretty
| easy and quick. You'd have to test even if you write it
| yourself. And in the cases where it's wrong it usually
| gives me a good starting point.
| mark_l_watson wrote:
| Thanks for the good example. The first time I tried OpenAI's
| assistant (before I had access to GitHub CoPilot) I wrote a
| comment in Javascript code that read something like "perform
| SPARQL query to find out where Bill Gates works" and code was
| generated using an appropriate library, the SPARQL (like SQL)
| query was correct, and the code worked. Blew my mind.
| jonas_kgomo wrote:
| would like to talk about the paintpoints you experienced
| while doing data preparation and mining
| evrydayhustling wrote:
| I love this take. Most AI results provoke a torrent of articles
| listing pratfalls that prove it's not AGI. Of course it's not
| AGI! But it _is_ as unexpected as a talking dog. Take a second to
| be amazed, at least amused. Then read how they did it and think
| about how to do better.
| asdfasgasdgasdg wrote:
| It's rarely productive to take internet criticism into account,
| but it feels like AI is an especially strong instance of this.
| It seems like a lot of folks just want to pooh pooh any
| possible outcome. I'm not sure why this is. Possibly because of
| animosity toward big tech, given big tech is driving a lot of
| the research and practical implementation in this area?
| kristjansson wrote:
| It's like a lot of the crypto stuff. The research is really
| cool, and making real progress toward new capabilities.
| Simultaneously there are a lot of people and companies
| seizing on that work to promote products of questionable
| quality, or to make claims of universal applicability (and
| concomitant doom) that they can't defend. Paying attention in
| this sort of ecosystem basically requires one to be skeptical
| of everything.
| runarberg wrote:
| > but it feels like AI is an especially strong instance of
| this. It seems like a lot of folks just want to pooh pooh any
| possible outcome. I'm not sure why this is.
|
| I presume the amount of hype AI research has been getting for
| the past 4 decades might be at least part of the reason. I
| also think AI is terribly named. We are assigning
| "intelligence" to basically a statistical inference model
| before philosophers and psychologists have even figured out
| what "intelligence" is (at least in a non-racist way).
|
| I know that both the quality and (especially) the quantity of
| inference done with machine learning algorithms is really
| impressive indeed. But when people are advocating AI research
| as a step towards some "artificial _general_ intelligence"
| people (rightly) will raise questions and start poohing you
| down.
| visarga wrote:
| It doesn't matter if we call it "intelligent" or "general",
| the real test is if it is useful. A rose by any other
| name...
| mannykannot wrote:
| I suspect it is more visceral: AGI would demolish human
| exceptionalism, and also of the human mind as being the last
| refuge of vitalism.
| password321 wrote:
| Well in this case I think many programmers just see it as a
| direct assault to their identity.
| ma2rten wrote:
| It's because you have to pay really close attention to tell
| if it's real or hype. It's really easy to make a cool demo in
| machine learning, cherrypick outputs, etc.
| stephc_int13 wrote:
| AI has been over-hyped, that's all.
|
| The machine learning techniques that were developed and
| enhanced during the last decade are not magical, like any
| other machines/software.
|
| But people have different and irrational expectations about
| AI.
| [deleted]
| ianbutler wrote:
| Has it been over hyped? Some ML created in the last 8 years
| is in most major products now. It has been transformative
| even if you don't see it, is informing most things you use.
| We're not close to AGI but I've never heard an actual
| researcher make that claim or the orgs they work for. They
| just consistently show for the tasks they pick they beat
| most baselines and in a lot of cases humans. The models
| just don't generalize but with transformers we're able to
| get them to perform above baselines for multiple problems
| and that's the excitement. I'm not sure who has overhyped
| it for you but it's delivering in line with my expectations
| ever since the first breakthrough in 2013/2014 that let
| neural nets actually work.
| gambiting wrote:
| It's just that the day to day instances of "AI" that you
| might run into are nowhere near the level of hype they
| initially got. For instance all kinds of voice assistants
| are just DUMB. Like, so so bad they actively put people
| off using them, with countless examples of them failing
| at even the most basic queries. And the instances where
| they feel smart it looks like it's only because you
| actually hit a magic passphrase that someone hardcoded
| in.
|
| My point is - if you don't actually work with state of
| the art AI research, then yeah, it's easy to see it as
| nothing more than overhyped garbage, because that's
| exactly what's being sold to regular consumers.
| hrgiger wrote:
| I agree about the assistants that they are not as much as
| I would expect but also there are self driving cars
| heavily using a.i. even at the current state I am
| personally impressed or indirectly we get the help during
| pandemic for protein folding/ mRNA vaccine development
| [1] , I also remember a completed competition for the
| speeding up the delivery of cold storage mRNA vaccines to
| quickly figure out which ones could fail
|
| [1] https://ai.plainenglish.io/how-ai-actually-helped-in-
| the-dev...
| PaulDavisThe1st wrote:
| > We're not close to AGI but I've never heard an actual
| researcher make that claim or the orgs they work for.
|
| The fact that the researchers were clear about that
| doesn't absolve the marketing department, CEOs,
| journalists and pundits from their BS claims that we're
| doing something like AGI.
| ipaddr wrote:
| It is overhyped because in the end it has failed to
| deliver the breakthrough promised years ago. Car drinking
| cars are a great example.
|
| The AI that has taken over google search has made a great
| product kinda of awful now.
|
| What breakthroughs are you referring to?
| ianbutler wrote:
| A. Most people still think Google search is good. B.
| Unless you work for Google specifically on that search
| team I'm going to say you don't know what you're talking
| about. So we can safely throw that point away.
|
| I've implemented a natural language search using bleeding
| edge work, the results I can assure you are impressive.
|
| Everything from route planning to spam filtering has seen
| major upgrades thanks to ML in the last 8 years. Someone
| mentioned the zoom backgrounds, besides that image
| generation and the field of image processing in general.
| Document classification, translation. Recommendations.
| Malware detection, code completion. I could go on.
|
| No one promised me AGI so idk what you're on about and
| that certainly wasn't the promise billed to me when
| things thawed out this time but the results have pretty
| undeniably changed a lot of tech we use.
| erwincoumans wrote:
| One of Deepmind's goals is AGI, so it is tempting to
| evaluate their publications for progress towards AGI.
| Problem is, how do you evaluate progress towards AGI?
|
| https://deepmind.com/about
|
| "Our long term aim is to solve intelligence, developing
| more general and capable problem-solving systems, known
| as artificial general intelligence (AGI)."
| ianbutler wrote:
| AGI is a real problem but the proposed pace is marketing
| fluff -- on the ground they're just doing good work and
| moving our baselines incrementally. If a new technique
| for let's say document translation is 20% cheaper/easier
| to build and 15% more effective that is a breakthrough.
| It is not a glamorous world redefining breakthrough but
| progress is more often than not incremental. I'd say more
| so than the big eureka moments.
|
| Dipping into my own speculation, to your point about how
| to measure, between our (humanity's) superiority complex
| and with how we move the baselines right now I don't know
| if people will acknowledge AGI if and until it's far
| superior to us. If even an average adult level
| intelligence is produced I see a bunch of people just
| treating it poorly and telling the researchers that it's
| not good enough.
|
| Edit: And maybe I should amend my original statement to
| say I've never heard a researcher promise me about AGI.
| That said that statement from DeepMind doesn't really
| promise anything other than they're working towards it.
| sanxiyn wrote:
| Shane Legg is a cofounder of DeepMind and an AI
| researcher. He was pretty casual about predicting human
| level AGI in 2028.
|
| https://www.vetta.org/2011/12/goodbye-2011-hello-2012/
|
| He doesn't say so publicly any more, but I think it is
| due to people's negative reaction. I don't think he
| changed his opinion about AGI.
| ipaddr wrote:
| Why would you discount someone who has been measuring
| relevancy of search results and only accept information
| from a group of people who don't use the system? You are
| making the mistake of identifying the wrong group as
| experts.
|
| You may have implemented something that impressed you but
| when you move that solution into real use were other's as
| impressed?
|
| That's what is probably happening with the google search
| team. A lot of impressive demos, pats on the back,
| metrics being met but it falls apart in production.
|
| Most people don't think Google's search is good. Most
| people on Google's team probably think it's better than
| ever. Those are two different groups.
|
| Spam filtering may have had upgrades but it is not really
| better for it and in many cases worse.
| ianbutler wrote:
| Maybe because a single anecdote isn't really useful to
| represent billions of users? They have access to much
| more information.
|
| I used it in real use, the answer was still a hard yes.
| password321 wrote:
| If we are going to start saying "but it hasn't achieved X
| yet when Y said it would" as a way to classify a field as
| overhyped then I don't know what even remains.
| [deleted]
| sanxiyn wrote:
| I mean, Zoom can change your background of video in real
| time, and people all over the world do so every day. This
| was an unimaginable breakthrough 10 years ago.
| gambiting wrote:
| What does that have to do with AI though??
| ianbutler wrote:
| How do you think that's done?
| gambiting wrote:
| Definitely not with AI?
|
| I mean Photoshop could do that 10+ years ago without
| "machine learning" even being a thing people talked
| about.
| ArnoVW wrote:
| This is sort of the interesting thing with AI. It's a
| moving target. Every time when an AI problem gets
| cracked, it's "yea but that's not really AI, just a
| stupid hack".
|
| Take autonomous cars. Sure, Musk is over-hyping, but we
| _are_ making progress.
|
| I imagine it will go something like:
|
| Step 1) support for drivers (anti sleep or colision)..
| done?
|
| Step 2) autonomous driving in one area, perfect
| conditions, using expensive sensors
|
| Step n) gradual iteration removes those qualifications
| one by one
|
| .. yes, it will take 10/20 years before cars can drive
| autonomously in chaotic conditions such as "centre of
| Paris in the rain". But at each of those steps value is
| created, and at each step people will say "yea but..".
| ianbutler wrote:
| And you'd be wrong. The key part here is "live in real
| time video"
|
| Photoshop definitely cannot do that, I know that for a
| fact.
|
| https://towardsdatascience.com/virtual-background-for-
| video-...
|
| There's an example article on the subject.
| gambiting wrote:
| I just don't see how that's AI , sorry. Machine learning
| to recognize a background isn't AI.
| ianbutler wrote:
| ML is most certainly AI. I had a visceral feeling you'd
| respond with this. Sorry but what ever magic you have in
| your head isn't AI -- this is real AI and you're moving
| goal posts like alot of people tend to do.
| gambiting wrote:
| You have single cell organisms which are able to sense
| their nearby surroundings and make a choice based on the
| input - they can differentiate food from other materials
| and know how to move towards it. They are a system which
| can process complex input and make a decision based on
| that input. Yet you wouldn't call a basic single cell
| organism intelligent in any way. The term usually used is
| that it's simply a biochemical reaction that makes them
| process the input and make a choice, but you wouldn't
| call it intelligence and in fact no biologist ever would.
|
| I feel the same principle should apply to software - yes,
| you've built a mathematical model which can take input
| and make a decision based on the internal algorithms, if
| you trained it to detect background in video then that's
| what it will do.
|
| But it's not intelligence. It's no different than the
| bacteria deciding what to eat because certain biological
| receptors were triggered. I think calling it intelligent
| is one of the biggest lies IT professionals tell
| themselves and others.
|
| That's not to say the technology isn't impressive - it
| certainly is. But it's not AI in my opinion.
| charcircuit wrote:
| >This was an unimaginable breakthrough 10 years ago.
|
| We had real time green screens 10 years ago. I don't
| think it's that unimaginable.
| sanxiyn wrote:
| I mean, it is magical, in a sense that we are not sure how
| and why it works.
| sillysaurusx wrote:
| I'm not sure how it works, but I'm sure it doesn't think.
| Not till it can choose its own loss function.
| PeterisP wrote:
| It's not like people can arbitrarily choose their own
| loss function; our drivers, needs and desires are what
| they are, you don't get to just redefine what makes you
| happy (otherwise clinical depression would not be a
| thing); they change over time and can be affected by
| various factors (things like heroin or brain injury can
| adjust your loss function) but it's not something within
| our conscious control. So I would not put _that_ as a
| distinguishing factor between us and machines.
| sanxiyn wrote:
| Sure, it doesn't think, just as submarines don't swim, as
| EWD said.
| sillysaurusx wrote:
| People always reach for these analogies. "Planes don't
| fly like birds." "Submarines don't swim like fish."
|
| Backpropagation has zero creativity. It's an elaborate
| mechanical parrot, and nothing more. It can never relate
| to you on a personal level, because it never experiences
| the world. It has no conception of what the world is.
|
| At least a dog gets hungry.
|
| Not persuaded? Try
| https://news.ycombinator.com/item?id=23346972
| sanxiyn wrote:
| I agree GPT isn't grounded and it is a problem, but
| that's a weird point to argue against AlphaCode.
| AlphaCode is ground by actual code execution: its coding
| experience is no less real than people's.
|
| AlphaGo is grounded because it experienced Go, and has a
| very good conception of what Go is. I similarly expect
| OpenAI's formal math effort to succeed. Doing math (e.g.
| choosing a problem and posing a conjecture) benefits from
| real world experience, but proving a theorem really
| doesn't. Writing a proof does, but it's a separate
| problem.
|
| I think software engineering requires real world
| experience, but competitive programming probably doesn't.
| mlyle wrote:
| > Backpropagation has zero creativity. It's an elaborate
| mechanical parrot, and nothing more. It can never relate
| to you on a personal level, because it never experiences
| the world. It has no conception of what the world is.
|
| The problem is: it's not really clear how much
| _creativity_ we have, and how much of it is better
| explained by highly constrained randomized search and
| optimization.
|
| > It can never relate to you on a personal level
|
| Well, sure. Even if/once we reach AGI, it's going to be a
| highly alien creature.
|
| > because it never experiences the world.
|
| Hard to put this on a rigorous setting.
|
| > It has no conception of what the world is.
|
| It has imperfect models of the world it is presented. So
| do we!
|
| > At least a dog gets hungry.
|
| I don't think "gets hungry" is a very meaningful way to
| put this. But, yes: higher living beings act with agency
| in their environment (and most deep learning AIs we build
| don't, instead having rigorous steps of interaction not
| forming any memory of the interaction) and have
| mechanisms to seek _novelty_ in those interactions. I don
| 't view these as impossible barriers to leap over.
| isomel wrote:
| Are you sure that you are thinking? Can you choose your
| loss function?
| mrshadowgoose wrote:
| > The machine learning techniques that were developed and
| enhanced during the last decade are not magical, like any
| other machines/software.
|
| You might be using a different definition of "magical" than
| what others are using in this context.
|
| Of course, when you break down ML techniques, it's all just
| math running on FETs. So no, it's not extra-dimensional
| hocus pocus, but absolutely nobody is using that particular
| definition.
|
| We've seen unexpected superhuman performance from ML, and
| in many cases, it's been inscrutable to the observer as to
| how that performance was achieved.
|
| Think move 37 in game #2 of Lee Sedol vs. AlphaGo. This
| move was shocking to observers, in that it appeared to be
| "bad", but was ultimately part of a winning strategy for
| AlphaGo. And this was all done in the backdrop of sudden
| superhuman performance in a problem domain that was "safe
| from ML".
|
| When people use the term "magic" in this context, think of
| "Any sufficiently advanced technology is indistinguishable
| from magic" mixed with the awe of seeing a machine do
| something unexpected.
|
| And don't forget, the human brain is just a lump of matter
| that consumes only 20W of energy to achieve what it does.
| No magic here either, just physics. Synthetically
| replicating (and completely surpassing) its functionality
| is a question of "when", not "if".
| stephc_int13 wrote:
| I strongly disagree that we've seen anything unexpected
| so far.
|
| AlphaGo is nothing else than brute force.
|
| And brute force can go a long way, it should not be
| underestimated.
|
| But so far, this approach has not let to emergent
| behaviors, the ML blackbox is not giving back more than
| what was fed.
| mrshadowgoose wrote:
| > AlphaGo is nothing else than brute force.
|
| This statement is completely false with accepted
| definitions of "brute force" in the context of computer
| science.
| sdenton4 wrote:
| AlphaGo is decidedly not brute force, under any
| meaningful definition of the term. It's monte carlo tree
| search, augmented by a neutral network to give stronger
| priors on which branches are worth exploring. There is an
| explore/exploit trade-off to manage, which takes it out
| of the realm of brute force. The previous best go
| programs used Monte Carlo tree search alone, or with
| worse heuristics for the priors. Alpha Go improves
| drastically on the priors, which is arguably exactly the
| part of the problem that one would attribute to
| understanding the game: Of the available moves, which
| ones look the best?
|
| They used a fantastic amount of compute for their
| solution, but, as has uniformly been the case for neutral
| networks, the compute required for both training and
| inference has dropped rapidly after the initial research
| result.
| notahacker wrote:
| Was Go ever "safe from ML" as opposed to "[then] state of
| the art can't even play Go without a handicap"? Seems
| like exactly the sort of thing ML _should_ be good at;
| approximating Nash equilibrium responses in a perfect
| information game with a big search space (and humans
| setting a low bar as we 're nowhere near finding an
| algorithmic or brute force solution). Is it really
| magical that computers running enough simulations exposes
| limitations to human Go theory (arguably one interesting
| lesson was that humans were so bad at playing that
| AlphaGoZero was better off not having its dataset biased
| by curated human play)? Yes, it's a clear step forward
| compared with only being able to beat humans at games
| which can be fully brute forced, or a pocket calculator
| being much faster and reliable than the average humans at
| arithmetic due to a simple, tractable architecture, but
| also one of the least magical-seeming applications given
| we already had the calculators and chess engines
| (especially compared with something like playing
| Jeopardy) unless you had unjustifiably strong priors
| about how special human Go theory was.
|
| I think people are completely wrong to pooh pooh the
| _utility_ of computers being better at search and
| calculations in an ever wider range of applied fields,
| but linking computers surpassing humans at more examples
| of those problems to certainty we 'll synthetically
| replicate brain functionality we barely understand is the
| sort of stretch which is exactly why AGI-sceptics feel
| the need to point out that this is just a tool iterating
| through existing programs and sticking lines of code
| together until the program outputs the desired output,
| not evidence of reasoning in a more human-like way.
| visarga wrote:
| If recent philosophy taught us anything it's that brains
| are special. The hard problem of consciousness shows
| science is insufficient to raise to the level of
| entitlement of humans, we're exceptions flying over the
| physical laws of nature, we have free will, first person
| POV, and other magical stuff like that. Or we have to
| believe in panpsychism or dualism, like in the middle
| ages. Anything to lift the human status.
|
| Maybe we should start by "humans are the greatest thing
| ever" and then try to fit our world knowledge to that
| conclusion. We feel it right in our qualia that we are
| right, and qualia is ineffable.
| maroonblazer wrote:
| > The hard problem of consciousness shows science is
| insufficient to raise to the level of entitlement of
| humans, we're exceptions raising over the physical laws
| of nature, we have free will and other magical stuff like
| that.
|
| That's not my understanding of the 'hard problem of
| consciousness'. Admittedly, all I know about the subject
| is what I've heard from D Chalmers in half-a-dozen
| podcast interviews.
|
| Can you point to a definitive source?
| udbhavs wrote:
| Most of the stuff featured on
| https://youtube.com/c/K%C3%A1rolyZsolnai looks pretty
| magical to me
| password321 wrote:
| If anything is overhyped in AI it's deep reinforcement
| learning and its achievements in video games or the
| millionth GAN that can generate some image. But when it
| solves a big scientific problem that was considered a
| decade away, that's pretty magical.
| sdenton4 wrote:
| The GANs are backdooring their way into really
| interesting outcomes, though. They're fantastic for
| compression: You compress the hell out of an input image
| or audio, then use the compressed features as
| conditioning for the GAN. This works great for super-
| resolution on images and speech compression.
|
| eg, for speech compression:
| https://arxiv.org/abs/2107.03312
| visarga wrote:
| I believe modelling the space of images deserves a bit
| more appreciation, and the approach is so unexpected -
| the generator never gets to see a real image.
| 323 wrote:
| > _It seems like a lot of folks just want to pooh pooh any
| possible outcome. I 'm not sure why this is. Possibly because
| of animosity toward big tech_
|
| It's much more simple, and more deep: most humans believe
| they are special/unique/non-machine like spiritual beings.
| Anything suggesting they could be as simple as the result of
| mechanical matrix multiplications is deeply disturbing and
| unacceptable.
|
| There is a rich recent anti-AGI literature written by
| philosophy people which basically boils down to this: "a
| machine could never be as meaningful and creative as I am,
| because I am human, while the AGI is just operations on
| bits".
| visarga wrote:
| There was a time when we were proud that Earth was the
| center of the universe, nobody dare say otherwise!
| hprotagonist wrote:
| though at the same time, and in the same population, the
| existence of other planets full of conscious beings was
| basically non-controversial:
|
| _Life, as it exists on Earth in the form of men, animals
| and plants, is to be found, let us suppose in a high form
| in the solar and stellar regions.
|
| Rather than think that so many stars and parts of the
| heavens are uninhabited and that this earth of ours alone
| is peopled--and that with beings perhaps of an inferior
| type--we will suppose that in every region there are
| inhabitants, differing in nature by rank and all owing
| their origin to God, who is the center and circumference
| of all stellar regions.
|
| Of the inhabitants then of worlds other than our own we
| can know still less having no standards by which to
| appraise them._
|
| Nicholas of Cusa, c1440; Cardinal and Bishop
| amusedcyclist wrote:
| I mean I'm not so impressed, because it seems like someones
| figured out the ventriloquist trick and and is just spamming it
| to make anything talk. Its fun enough, but unclear what this is
| achieving
| falcor84 wrote:
| >ventriloquist trick
|
| I don't understand your analogy here. It really is the
| machine talking; there is no human hiding behind the curtain.
| sanxiyn wrote:
| I think better analogy is parrot speaking English.
| Certainly no ventriloquism there.
| visarga wrote:
| This metaphor doesn't do justice to parrots or language
| models. Parrots only speak a phrase or two. LMs can write
| full essays.
|
| On the other hand parrots can act in the environment, LMs
| are isolated from the world and society. So a parrot has
| a chance to test its ideas out, but LMs don't.
| arnaudsm wrote:
| Marketing people love to make false claims, setting crazy
| expectations. Increased competition encourages these small
| lies, and sometimes even academic fraud.
|
| This hurt and will continue to hurt the ML field.
| evrydayhustling wrote:
| I agree. The talking dog analogy deflates those claims while
| still pointing out what is unique and worth following up on
| about the results.
|
| Meanwhile, the chorus of "look this AI still makes dumb
| mistakes and is not AGI" takes has gotten louder in many
| circles than the marketing drumbeat. It risks drowning out
| actual progress and persuading sensitive researchers to
| ignore meaningful ML results, which will result in a less
| representative ML community going forward.
| EGreg wrote:
| No, just no!
|
| Because the whole thing is, a dog's abstract mental capabilities
| are far below a human -- thus why it would be ASTOUNDING that a
| dog could master even a primitive form of speaking English.
|
| On the other hand, here we are brute forcing a solution from
| analyzing millions of man-years of published English speech, by
| using a huge array of computing power to precompute various
| answers and sift them.
|
| It is a bit like "solving checkers" and then claiming "wow, a dog
| could have done this". It is like making a cheat sheet for a test
| by analyzing all answers ever given, and then summarizing them in
| a vector of 102827 dimensions, and claiming that it is the same
| as coming up with clever and relevant one liners on the spot
| using the brain of a dog.
|
| NO. It's not nearly as astounding or impressive.
| carapace wrote:
| Yah, it's a bad metaphor, but I think he's only using it as a
| rhetorical device, he's not trying to reason from it.
| shagie wrote:
| Bunny is a neat dog...
| https://www.youtube.com/channel/UCEa46rlHqEP6ClWitFd2QOQ
|
| The vocal apparatus is not there, but there is _certainly_ more
| cognition than what people think dogs have (there 's a question
| that I wonder if language enables thought or if thought enables
| language)
|
| And this is even something that a cat can do (not as much
| language skills, but there's thought going on)
| https://www.youtube.com/c/BilliSpeaks
| pessimizer wrote:
| I'm not so certain. Seems like the owner is doing a lot of
| work to make sense out of those utterances. I'd like to see
| Bunny say what he's about to do, then do it. Or watch his
| owner do something with something, then describe it.
|
| edit: or just have a conversation of any kind longer than a 2
| minute video. Or one without the owner in the room, where she
| talks back with the dog using the same board. That would at
| least be amenable to Turing.
|
| edit2: here's a test - put headphones on the owner and block
| her vision of the board. Sometimes pipe in the actual buttons
| the dog is pressing, other times pipe in arbitrary words. See
| if the owner makes sense of the random words.
| shagie wrote:
| The larger word array is... yea, it allows for a lot more
| interpretation of variation of intent and interpetation.
|
| Consider this older one where there weren't as many words -
| https://youtu.be/6MMGmRVal6M and
| https://youtu.be/FPC6ElzSdxM
|
| Or for billie the cat - https://youtu.be/DiuQqgTw-jY
|
| The point with these is that there is language related
| thought going on there and an attempt to communicate from
| the dog (or cat) to the human.
| pessimizer wrote:
| I'm trying, but I don't see it at all with these
| examples.
|
| 1) just seemed like random pressing until the dog pressed
| "paw", then the owner repeated loudly "something in your
| paw?" The dog presented its paw, then the owner decided
| "hurt" "stranger" "paw" was some sort of splinter she
| found there. The dog wasn't even limping.
|
| 2) I didn't get any sense of the presses relating to
| anything the dog was doing, and since the owner was
| repeating loudly the thing she wanted the dog to find, I
| was a bit surprised. Then the dog presses "sound," the
| owner connects this with a sound I can't hear, then they
| go outside to look for something I can't see.
|
| Billie the Cat: I simply saw no connection between the
| button presses and anything the cat did. The cat pressed
| "outside" but didn't want to go outside. The cat presses
| "ouch noise" and the owner asks if a sound I didn't hear
| hurt her ears. Then the cat presses "pets" and the owner
| asks if the cat wants a pet? The cat presses "noise" and
| the owner continues the monologue apologizing for the
| painful noise and offering to buy her cat a pet. Sorry to
| recount most of the thing, but I don't get it at all.
|
| -----
|
| Not trying to debunk talking pets, but I'm not seeing
| anything here. I at least expected the dog to be smart
| enough to press particular buttons for particular things,
| but I suspect the buttons are too close together for it
| to reliably distinguish them from each other. I'd be
| pretty easy to convince that you could teach a dog to
| press a button to go outside, a different button when
| they wanted a treat, and a different button when they
| wanted their belly rubbed. In fact I'd be tough to
| convince that you _couldn 't_ teach a dog to do that.
| Whatever's being claimed here, however, I'm not seeing.
|
| -----
|
| edit: to add a little more, I'm not even sure that *I*
| could reliably do what they're claiming the dog is doing.
| To remember which button is which without being able to
| read is like touch typing, but worse because the buttons
| seem to be mounted on re-arrangeable puzzle pieces. Maybe
| I could associate the color of those pieces with words,
| but that would only cover the center button on each
| piece.
|
| If a dog were using specific buttons for language (or if
| I were doing the same thing) I'd expect the dog to press
| a lot of buttons, until he heard the sound he was looking
| for, then to press that button over and over. Not to just
| walk straight to a button and press.
|
| I think the cat just presses the buttons when it wants
| the owner to come, and presses them again when the owner
| says something high pitched at the end and looks at it in
| expectation.
| qnsi wrote:
| I saw a lot of videos about this Bunny dog on tiktok, but
| discarded it as a gimmick, not believing it's real. Your
| comment motivated me to look into it more (30 seconds of
| time).
|
| This NYT article at least does not discredit it [0]. Have you
| looked more into it? Do you think it would be useful to train
| your dog to do it?
|
| [0]https://www.nytimes.com/2021/05/27/style/bunny-the-dog-
| anima...
| shagie wrote:
| With my cat (I've got the buttons, haven't done anything
| with them yet) its would be useful to find out if he wants
| food, attention, or is complaining about the water bowl or
| litter box.
|
| Even being able to distinguish those would be a "win".
|
| The Bunny video that I still find the most useful
| communication is https://youtu.be/6MMGmRVal6M
| plesiv wrote:
| Excuse my presumption, but it seems that you arrive at the
| logical conclusion "it _might_ be possible to simulate
| intelligence with a sufficiently big cheat sheet " - and then
| you disregard it because you're uncomfortable with it. We
| already know this is the case for specialized environments, so
| the "only" question left is how far does this generalize.
|
| In my opinion, more ridiculous claims have already been proven
| by science (for example Quantum Mechanics).
|
| Also you have to make a distinction between the optimizing
| process (evolution/training neural nets) and the intelligent
| agent itself (human/machine intelligence).
| EGreg wrote:
| I don't disregard it. It isn't about discomfort. In fact, I
| think that "solving checkers" is very useful, if your goal is
| to get the highest quality answers in checkers.
|
| The problem I have is comparing that to having a dog speak
| English. It's totally wrong. You had access to all these
| computing resources, and the sum total of millions of work by
| humans. You didn't bootstrap from nothing like AlphaZero did,
| but just remixed all possible interesting combinations, then
| selected the ones you liked. And you try to compare this "top
| down" approach to a bottom-up one?
|
| The top down approach may give BETTER answers and be MORE
| intelligent. But the way it arrives at this is far less
| impressive. In fact, it would be rather expected.
| Isinlor wrote:
| We did not yet achieve brain scale compute.
|
| Current machine learning models have around ~100B parameter,
| human brain has ~100T synapses. Assuming one DNN parameter is
| equivalent to 1 synapse, then the biggest models are still 1000
| times smaller than human brain.
|
| Cat or dog would have around ~10T synapses.
|
| AlphaCode has ~50B parameters, that is 20 times less than
| number of synapses in a mouse brain ~1T. Honey bee has ~1B
| synapses.
|
| So AlphaCode would be somewhere between a honey bee and a
| domestic mouse.
|
| https://en.wikipedia.org/wiki/List_of_animals_by_number_of_n...
| trabant00 wrote:
| People think more of what we already have is going to go farther.
| 1 horse to the carriage gets you to the market. 2 horses to the
| next village. 4 to town and 6 cross states. Given enough horses
| we should reach the moon, right?
|
| With absolutely no evidence (as none can be had about the future)
| I believe that AI can be reached with computers and programming
| languages as different from the current ones as rockets are to
| horses.
| alex-2k1 wrote:
| Scientists in 50s expected to get language translation in 10
| years as soon as computers will get enough computation power.
| They were real scientists, not "data AI scientists" who has
| little mathematics culture and not aware of any brain studies
| and problems in this field. But yeah, all aboard is hype train,
| we have a dog who speak English! Not a state machine that just
| do similar to what it was programmed on using statistics
| tricks. This is SO COOL!! PROGRAMMERS ARE DEAD!!!111 WOOOHOO
| SCIEEENCEEE!!
| qualudeheart wrote:
| Which rate of progress are we ITT expecting from alphacode-like
| models?
|
| In another thread I predicted we'd see competitive programming
| "solved" in ten years or less.
|
| I didn't rigorously explain what I meant by that in that thread,
| so I'll clarify what I meant.
|
| I expect AI to beat humans at competitive programming at the same
| rate as AlphaGo beats human Go players.
|
| There could be diminshing returns soon but I don't see what would
| cause them.
| belter wrote:
| I am willing take on your bet, as long you agree with my
| condition...you can't feed it with any of the millions of lines
| of code previously created by humans you aim to beat...;-)
| meken wrote:
| Sure you can. You would just have to be careful not to
| accidentally feed it the same problem as in the competition.
|
| _That_ would be cheating.
| GreenWatermelon wrote:
| How would the AI learn then? Humans learn by looking at
| existing code and gleaning new ideas, and does the AI
| although it requires much more data.
|
| Give the AI as much data as it wants, if it manages to solve
| something, it means it's a problem worth automating (is this
| a cat?)
| Misdicorl wrote:
| The places AI excel still have very rigid rules with relatively
| low branching factors. It's also relatively easy to score both
| the final result and any intermediate stage. And all the
| training techniques rely on those factors tremendously.
|
| My guess is that's where the difficulties will arise.
| omnicognate wrote:
| As someone who is skeptical, but open minded, about the impact
| these technologies will have on practical programming I think I'm
| one of the "people" in "people are complaining..." The article
| makes some assumptions about what such people think that
| certainly aren't true for me:
|
| 1. That we are unimpressed.
|
| I'm gobsmacked.
|
| 2. That we don't think these are significant advances.
|
| They're obviously huge advances.
|
| 3. That we don't think these models will have practical
| applications.
|
| It's hard to imagine they won't.
|
| 4. That we think these systems are rubbish because they get
| things wrong.
|
| I'm a programmer. I make mistakes all the time.
|
| Having countered those views, the article then seems to imply
| that it follows that "we've now entered a world where
| 'programming' will look different." As someone who makes a living
| writing software I obviously have an interest in knowing whether
| that's true. I don't see much evidence of it yet.
|
| These systems are certainly not (yet) capable of replacing a
| human programmer altogether, and whether they could ever do so is
| unknown. I'm interested in the implications of the technologies
| that have been developed so far - i.e. with the claim that "we've
| _now_ entered a world... " So the question is about how useful
| these systems can be for human programmers, as tools.
|
| The reason I'm skeptical of it is that the only model I've seen
| so far for such tooling is "have the machine generate code and
| have the human review it, select from candidate solutions and fix
| bugs". The problem is that doing so is, I expect, _harder_ for
| the human than writing the code in the first place. I 've
| mentioned this concern several times and not seen anybody even
| attempt to explain to me why I'm wrong about it. For example, at
| [1] I pointed out why some generated solutions for a particular
| problem would have only made my job harder and got accused of
| "screaming at a child for imperfect grammar."
|
| Reviewing and fixing code is harder than writing it. Please
| explain why I'm wrong about that (it's certainly true for me, but
| maybe most people don't feel that way?), why it won't be a
| problem in practice or what planned applications there are for
| these technologies that would avoid the problem.
|
| Please don't accuse me of cruelty to dogs or children.
|
| [1] https://news.ycombinator.com/item?id=30180067
| microtherion wrote:
| > have the machine generate code and have the human review it
|
| The best characterization of such massive language models I've
| seen is "world class bullshitters": they are very good at
| producing output that superficially _looks_ plausible, but
| neither know nor care whether what they 're saying has any
| relationship to the truth.
|
| This is virtually guaranteed to make such code reviews very
| frustrating, and of course AlphaCode & co have no capability of
| explaining why they wrote a certain line. I can't see this
| having much of a role in a high quality code base, but I
| suspect the sort of management which currently would offshore
| coding to the lowest bidder would be quite enamored of it.
| mirceal wrote:
| now you need an ai that reviews code. problem solved!
| ogogmad wrote:
| I think using these tools might become a science or an art form
| in its own right. You'll have to give these tools the input
| they need to produce the most useful answers to you. In the
| short term at least, this is not going to take away your need
| to think. But it might change _how_ you think, and it might
| make you more productive when your problem aligns well with
| these tools.
| mark_l_watson wrote:
| I think that you are correct, we will see AI used as a
| copilot for artists and content creators. I have had access
| to the OpenAI APIs for GPT-3 since last summer, and in
| addition to using this for NLP tasks, I have also
| experimented with using GPT-3 to help me work on a sci-fi
| short book that I have been occasionally writing for years.
| It has been useful for expanding sections that I have been
| stuck on. I feel that I should copyright my story as being
| authored by The Internet since every content creator whose
| text, programming listings, etc. that goes into training
| these language models has in some sense been a contributor to
| my short story.
| meken wrote:
| This gives me an idea.
|
| You know how you can break writing down into 2 phases:
| creative and edit?
|
| What if you did the creative part, braindumping all your
| ideas and vision into a word document. Then had an AI edit
| your incoherent story into engaging prose.
|
| I find the creative part easy, but the editing part
| tedious.
|
| Like, a doctor I know asked me to write a review for him. I
| did the braindump part, but have been procrastinating on
| the edit part.
| telotortium wrote:
| Unfortunately, GPT-3, CoPilot, AlphaCode, etc., _also_
| excel more at the creative part (helped out by their
| enormous training database, which saves you from doing
| the Google search to match your high-level description to
| examples), but they 're still dogshit at editing, because
| that is the part that actually requires a detailed
| understanding of the text. So the princess is still in
| the next castle.
| meken wrote:
| Hmm it intuitively seems like the editing part should be
| the easy part, because the output is basically a
| template, and you just need to fill it in, and tweak it a
| little bit...
|
| I get with code it's a bit less so, but still, there are
| plenty of design patterns which basically cover a lot of
| the output space...
| 8note wrote:
| I think these tools push in the other direction.
|
| You get the tool to brain dump for you, then you edit
| after
| meken wrote:
| I haven't used copilot, but don't _you_ specify what you
| want first, in comment form, then copilot spits it out?
|
| I get that you have to do subsequent editing, because
| it's not perfect.
| omnicognate wrote:
| Potentially, but that doesn't address the problem of
| replacing writing code with the (harder) process of reading,
| verifying and fixing it. However well I engineer my inputs
| I'll still have to review, verify and fix the outputs.
| derekja wrote:
| This problem has been (albeit imperfectly) addressed in
| speech recognition. When error corrections are made through
| the the UI the engine can learn from those corrections.
| Presumably over time the corrections needed in alphacode
| will become more semantic than syntactical. But you're
| right, correcting subtly flawed code or text is way harder
| than writing from scratch.
| falcor84 wrote:
| One of the most difficult problems in software
| development in general is coming up with the right test
| cases that cover the real-world domain's needs. If you
| have an AI like this at your disposal, that you can throw
| test cases at and it will give you the stupidest piece of
| code that makes them pass, then at the least you have
| something that helps you iterate on your test cases a lot
| more effectively, which would be a great boon.
| kgeist wrote:
| >The problem is that doing so is, I expect, harder for the
| human than writing the code in the first place.
|
| Programming is mostly about writing boilerplate code using
| well-known architectural patterns and technologies, nothing
| extraordinary but which takes time (at least in my experience).
| If I can describe a project in a few abstract words, and the AI
| generates the rest, it can considerably improve my
| productivity, and I don't think it's going to be harder to
| review than code written by a junior dev who also makes
| mistakes and doesn't usually get it right on the first try
| anyway (given the AI is pretrained to know what kind of
| architecture we prefer). I can envision a future where
| programmers are basically AI operators who iterate on the
| requirements with the client/stakeholders and let AI do the
| rest. It looks like we're almost there (with GitHub Copilot and
| all), and I think it's enough to "revolutionize" the industry,
| because it changes the way we approach problems and makes us
| far more productive with less effort.
| rakejake wrote:
| If a junior dev writes truly head-scratching code, you could
| ping that person and ask why they wrote this line a certain
| way, as opposed to a more straight-forward way. Correct me if
| I'm wrong but you can't ask an ML model to do that (yet).
| kgeist wrote:
| True. But is it really important "why"? I think what's more
| important is whether we can correct AI's output in a way
| that makes it learn to avoid writing similar head-
| scratching code in the future.
| rakejake wrote:
| Unless the AI can fix the bug itself, a human is going to
| have to explore the AI-generated code. The AI cannot help
| you here and you are going to have to figure out the
| "why" all by yourself. It will become incredibly easy to
| generate code with prompts but fixing any problem is
| going to be a pain. The "Tech Debt" would be
| astronomical.
| daxfohl wrote:
| In ten years ML models may be "thinking" the same thing
| about us. Our "more understandable" code is full of
| inefficiencies and bugs and the bots wonder why we can't
| see this.
| rakejake wrote:
| I agree that reviewing code is much much harder than writing
| something that works. Hasn't anyone here written what they
| thought was a smart, compact solution or feature, only to have
| it ripped to shreds during a code review?
|
| I have worked in large decade-old codebases and sometimes the
| code is truly puzzling. Usually, an old-timer helps out when
| you run into a block and explains design decisions taken by
| people who moved on. This is the crucial factor that determines
| how long a task takes. A task could be resolved in a few hours
| with the help of a senior engineer, which otherwise could take
| days to weeks.
|
| All said, I think simply generating the code will not be enough
| to replace programmers. Now, if an AI can generate code AND
| explain queries about logic, syntax, etc, the game is over.
| gbasin wrote:
| Explainability for an NLP model is well within reach. I
| predict OpenAI will have something like this in the next 1-2
| years
| killerstorm wrote:
| AlphaCode is a piece of academic research. It's about
| demonstrating a possibility of using a particular type of a
| model to solve a particular type of tasks. It's not about
| making a practical tool.
|
| People can certainly take this approach and build tools. That
| might take different shapes and forms. There are many ways
| these models can be specialized, fine-tuned, combined with
| other approaches, etc.
|
| For example, somebody can try to apply them to a narrow use
| case, e.g. generate front-end code in React/JS and CSS from a
| backend API. They can fine-tune it on best example of React
| code, add a way to signal uncertainty so that specification can
| be clarified, etc.
|
| Nobody expects these models to be able to write an entire OS
| kernel any time soon. But a lot of types of programming are far
| more regular, repetitive and can be verified a lot easier.
| zamalek wrote:
| Agreed, and also:
|
| > Post: I realize that it "only" solves about a third of the
| contest problems, making it similar to a mediocre human
| programmer.
|
| If this is what academia perceives software development to be
| then it's no wonder we have software that is so disconnected
| from the human problems it aims to solve.
|
| Programmers don't routinely [re-]invent complex algorithms. We
| parse complex and contradictory requirements from humans, and
| compile them into (hopefully) simple solutions on a computer.
|
| The solutions to "competition programming" problems are a
| Google search away. If you want to take it up as a professional
| sport then, sure, AI might just replace you (as it already has
| done with many other mind sports such as chess).
| furyofantares wrote:
| > Reviewing and fixing code is harder than writing it. Please
| explain why I'm wrong about that (it's certainly true for me,
| but maybe most people don't feel that way?)
|
| At the very least, I don't think it being true for you (or me)
| means much. Presumably like me, you learned primarily to write
| code and have a lot of experience with it, and have gotten
| better and better at that with time. I assume that if I had
| spent as much time and effort learning to efficiently review
| and fix code as I have spent learning to write it that it would
| be much easier.
|
| I certainly know of artists who have shipped video games they
| authored alone, who wouldn't call themselves programmers or
| know where to start programming something from scratch, who got
| by with a high level engine and copy and pasting starting
| points from the internet. (I _would_ call them programmers, but
| their style is much closer to review and edit than my own.)
| jonas_kgomo wrote:
| I am so terrified right now about the implications of this to a
| regular software engineer like myself. In 10 years we would be
| totally dispensable
___________________________________________________________________
(page generated 2022-02-06 23:00 UTC)