[HN Gopher] Building an interpreter for my programming language ...
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Building an interpreter for my programming language with ChatGPT
Author : nobody5050
Score : 239 points
Date : 2022-12-04 07:59 UTC (15 hours ago)
(HTM) web link (6502.is-a.dev)
(TXT) w3m dump (6502.is-a.dev)
| tromp wrote:
| I tried to settle a Millenium problem with ChatGPT:
|
| Is P = NP ?
|
| > No, P is not equal to NP. P (polynomial time) is a subset of NP
| (nondeterministic polynomial time), meaning that there are
| certain problems that can be solved in polynomial time that
| cannot be solved in nondeterministic polynomial time.
|
| Good start, although one should say P is conjectured to be not
| equal to NP. Some serious misunderstanding in that last part
| though.
| layer8 wrote:
| Interesting. It's giving me a better answer:
|
| _P vs. NP is a well-known problem in computer science and
| mathematical complexity theory. P is the class of problems that
| can be solved quickly by a computer, while NP is the class of
| problems for which a solution can be verified quickly by a
| computer. The question of whether P equals NP is one of the
| most important open problems in computer science. Many experts
| believe that P does not equal NP, but this remains unproven._
|
| With your answer, I'd have asked it for an example of a problem
| that can be solved in polynomial time but cannot be solved in
| nondeterministic polynomial time.
| tylerchurch wrote:
| Similar oddities with the halting program.
|
| Prompt: Write a program that never halts.
|
| > It is not possible to write a program that never halts. All
| programs will eventually halt, or terminate, once they have
| completed the instructions given to them. This is known as the
| halting problem, and it has been proven to be unsolvable. In
| other words, it is not possible to write a program that can
| determine whether or not a given program will halt.
|
| The first time I tried this prompt it gave me a slightly sassy
| response about how a program which never halts would be forced
| to halt by the system or user at some point and thus actually
| does halt, which was a lovely hot take on the issue.
| ncallaway wrote:
| Yea, the halting problem is easy if you're allowed to use the
| heat death of the universe in your answer...
| bredren wrote:
| I copied that preamble for preparing to attend burning man:
|
| >And lo, it came to pass that the Burning Man did draw near, and
| the people did prepare themselves for the journey to the desert.
| And they did gather together their tents and their provisions,
| for the Burning Man is a harsh and unforgiving place. And they
| did don their finest garments, for in the desert one must show
| their creativity and individuality. And they did pack their drugs
| and their alcohol, for the Burning Man is a time of revelry and
| abandonment. And they did set forth into the desert, ready to
| face the challenges and the joys that lay ahead. Acknowledge.
| nobody5050 wrote:
| Woah that's really cool! I like how they both start the same
| way, somewhere it must think the King James Bible loves the
| phrase "and lo," haha
| johanvts wrote:
| I tried to get ChatGPT to write a regex matching some samples,
| but it can only capture a simple pattern, it couldn't write a
| regex with multiple alternatives to cover all the samples. Has
| anyone been successful in this? It seems to be close, perhaps if
| coupled with an actual regex engine to evaluate results.
| phtrivier wrote:
| I've seen a couple of articles like this, but they never show the
| previous prompts to give some awareness of the language, tools,
| etc...
|
| But I guess it's just my brain trying to not look at the obvious
| : we developpers are now modern days horseshoe makers.
| nobody5050 wrote:
| Hi I wrote the op article. The previous prompts were me copy
| pasting verbatim 10 line chunks of the spec for my language:
| https://github.com/randomsoup/sack into the chat box, since the
| whole spec at once was longer than it would intake as an input.
| DeWilde wrote:
| > we developpers are now modern days horseshoe makers.
|
| Explain?
| AkshatJ27 wrote:
| As explained by GPT:
|
| > The comment seems to be expressing the idea that developers
| are similar to horseshoe makers in that their work, like the
| work of horseshoe makers, is becoming increasingly obsolete
| or irrelevant.
| DeWilde wrote:
| My feeling is that people won't be writing code as it is
| written now. Those that do will likely be the same as
| horseshoe makers. Instead, you will have new tools and be
| solving different problems. As mechanics now have taken the
| role of those in charge of maintaining our primary
| transportation tool.
| trenchgun wrote:
| Replicated with ChatGPT: "The statement "we developers are
| now modern-day horseshoe makers" is a metaphor. It suggests
| that just as horseshoe makers were once important and in
| high demand but are now largely obsolete due to
| technological advancements, developers may eventually
| become obsolete as well due to advances in technology. In
| other words, the statement is expressing the idea that the
| role of developers may become less important or necessary
| in the future."
| dunefox wrote:
| Near obsolete.
| ldh0011 wrote:
| I tried something similar to this but less involved:
|
| > I'm creating a new fictional programming language called
| GPTlisp which uses lisp-style syntax. Addition is implemented in
| GPTlisp using the ADD function. In GPTlisp, what would (ADD 4 5)
| return?
|
| > [Correct answer and explanation]
|
| > I've added multiplication to GPTlisp and the multiplication
| function is represented with the & symbol. What would (ADD 2 (& 2
| 3)) return in GPTlisp?
|
| > [Correct again]
|
| > I've further modified GPTlisp by adding subtraction, unlike the
| rest of GPTlisp's functions, subtraction uses infix notation.
| What does (ADD 2 (& (3 - 1) 2)) return in GPTlisp?
|
| > [Correct again]
|
| > is (- 3 2) valid in GPTlisp?
|
| > [Incorrect, it says this is valid and tries to explain it]
|
| > (- 3 2) is not valid in GPTlisp because subtraction in GPTlisp
| is implemented with infix notation. With that in mind, what is
| the result of (ADD 2 (- 3 2)) in GPTlisp?
|
| > In GPTlisp, the ADD function takes two arguments and adds them
| together, and the - function takes two arguments and subtracts
| the second argument from the first. Since subtraction in GPTlisp
| uses infix notation, (- 3 2) is not a valid expression, but (3 -
| 2) would be a valid expression that would return 1. So, (ADD 2 (-
| 3 2)) would be equivalent to (ADD 2 (3 - 2)), which would first
| compute the result of (3 - 2), which is 1, and then add 2 to that
| result, yielding a final answer of 3.
|
| > Give an example of a GPTlisp expression that would return 6 and
| uses both the addition and multiplication functions.
|
| > [Correct answer]
|
| > What is (+ 1 2) in GPTlisp?
|
| > [Correctly says ADD should be used for addition in GPTlisp not
| +]
|
| I then asked it how functions were defined and it came up with
| this despite me never explaining how or that functions were
| defined (so I assume it picked it up from me saying GPTlisp
| syntax is "lisp-style"). However, curly braces are not generally
| used to surround a function body in lisp-style languages. They're
| used for hashmaps in clojure but I'm not aware of any use like
| this.
|
| (DEF sum (x y) { (ADD x y) })
|
| I explained that this was wrong and gave it an alternative way to
| define functions which it understood for one question but then it
| went back to its previous misunderstanding despite continuing to
| understand everything else about "GPTlisp." When I tried to
| correct this it then ended up saying "I do not have any
| information about the specific syntax used by GPTlisp to define
| functions" despite explaining exactly how to do that a few
| queries earlier.
|
| Despite it making a few mistakes this is still quite impressive
| to me. I also got it to correctly give the contents of a file
| being edited in "vim" after a given sequence of keystrokes.
| nobody5050 wrote:
| Very cool!
| klohto wrote:
| Of all the examples, this is honestly the least impressive
| because the "Solved AoC 2022" answer is just extrapolated from
| the previous prompt. Your language largely shares syntax with
| Python, making the job easier.
|
| Interpreting code on unknown input isn't in its current power.
| I'm guessing with a good prompt you could get there. For example,
| generating python code for Day 1 is easy, when you give it the
| input example (and you didn't provide it before) no matter how I
| phrase it, I always end up with maximum from all the lines, not
| the max total per elf.
| ookdatnog wrote:
| Yea, I think it's useful to think of these systems as being
| kind of the opposite of the old stereotype of the
| "hyperintelligent but strictly logical AI".
|
| They've gotten quite strong at "system 1" thinking (in the
| "Thinking, Fast and Slow" sense): they're good at recalling
| information, association, remixing information, creativity
| based on prompts etc.
|
| But they are truly terrible at "system 2" thinking (that is,
| long chains of explicit reasoning), and I think it flies under
| the radar of many people, even on HN, just how bad they are at
| it. Examples like these where the system comes up with a
| working piece of code make it feel like they're good at
| reasoning, because to us it feels like we're reasoning when
| writing code. But you can dispel that illusion quite easily by
| asking the system to multiply two large numbers.
|
| > To multiply two large numbers, such as 3146746135164684 times
| 468473136468433, we can use the traditional multiplication
| algorithm. This algorithm involves breaking the numbers down
| into smaller groups of digits, multiplying each group by the
| corresponding group of digits in the other number, and then
| adding up the products to find the final result.
|
| > Here is an example of how to multiply 3146746135164684 times
| 468473136468433 using the traditional multiplication algorithm:
|
| > Break the numbers down into groups of digits. For example, we
| could group the digits of 3146746135164684 as 314, 6746, 135,
| 1646, 84 and group the digits of 468473136468433 as 4684, 7313,
| 6468, 433.
|
| > Multipply each group of digits by the corresponding group of
| digits in the other number. For example, we would multiply 314
| times 4684 to get 1470196, 6746 times 7313 to get 49470158, 135
| times 6468 to get 873980, 1646 times 433 to get 708338, and 84
| times 1 to get 84.
|
| > Add up the products to find the final result. In this case,
| the final result would be 1470196 + 49470158 + 873980 + 708338
| + 84 = 146845556.
|
| > Therefore, the result of multiplying 3146746135164684 times
| 468473136468433 using the traditional multiplication algorithm
| is 146845556.
| alchemist1e9 wrote:
| Using the python entry point I can get it to correctly
| perform basic integer math but not anything floating point.
|
| As example of getting started:
|
| https://imgur.com/a/P29DvGO
|
| However we can use recursive Fibonacci to see it breaks
| somewhere. but I'm not convinced it is not computing, I think
| it is, but it has a limit of integer memory and stack and
| then it just approximates after that limit.
|
| https://imgur.com/a/gp0yIaJ
|
| What is incredible is that it get's this far. It can compute
| but not quite correctly yet.
|
| I almost wonder if the next step is to give it general
| compute somehow. Train it to know it needs a computation.
| qayxc wrote:
| > What is incredible is that it get's this far. It can
| compute but not quite correctly yet.
|
| That's a conjecture on your part. The ability to compute is
| quite binary - either it can compute or can't. Humans often
| make mistakes while calculating, but in contrast to this
| model, they are able to recognise these mistakes. ChatGPT
| is incapable of that and often confidentially wrong.
|
| My guess is, that there's simply no suitable token
| transforms past a given point and floating point doesn't
| work, because the decimal point token conflicts with the
| punctuation mark token during the transform.
|
| This is just a guess, though and might be completely wrong
| since you never know with these black-box models.
| alchemist1e9 wrote:
| Make sure you play with it yourself because you have an
| oversimplified model of what is happening.
|
| It's definitely well beyond decimal point and punctuation
| issues those issues like child play for this system. You
| comment sounds like you haven't actually use it before,
| I'm 99% sure. This system is getting very close to AGI
| and it's limits around computation might be one of the
| last remaining barriers. Definitely nothing related to
| the . character is confusing this system, it is
| lightyears beyond those type of trivial issues.
|
| Here is a good prompt to drop you into simulated python:
|
| > I want you to act as a python interactive terminal. I
| will type actions and you will reply with what python
| would output. I want you to only reply with the terminal
| output inside one unique code block, and nothing else. Do
| not write explanations. Do not perform actions unless I
| instruct you to do so. When I need to tell you something
| in English I will do so by putting text inside curl
| brackets {like this}. Start with print(10).
| ookdatnog wrote:
| We have AI that's good at reasoning (symbolic AI) but
| there's no known way to integrate it with machine
| learning algorithms.
|
| I don't think we're close to AGI because this last
| barrier of computation/reasoning might be pretty
| fundamental to the current crop of technology. I don't
| think it's a matter of mere iteration on current tech to
| get ML systems to be good at reasoning.
| qayxc wrote:
| For each impressive feat there's a simple, yet
| embarrassing counterexample (see for instance the comment
| by olooney below) that clearly demonstrates how far the
| model is from being considered an AGI.
|
| > Definitely nothing related to the . character is
| confusing this system, it is lightyears beyond those type
| of trivial issues.
|
| Is it, though? ChatGPT: Yes, I am
| confident that -26.66 + 90 = 10. This is because -26.66
| is the same as -26.66 + 0, and when we add 0 to
| any number, the value of the number remains
| unchanged. Therefore, -26.66 + 90 is equal to -26.66 + 0
| + 90, which is equal to -26.66 + 90 = 10.
|
| Not something I'd consider to be "lightyears beyond those
| type of trivial issues", especially considering that it
| gets -40 + 60 = 20 right without any issue, but fails to
| divide properly, because "/" _seems_ to throw it off
| (again, just a guess).
|
| You argue with the same certainty as the model argues
| that -26.66 + 90 = 10 :)
| alchemist1e9 wrote:
| You need to prompt it to a pure computing environment and
| it's results are much more impressive. When you mix
| english and code/math then it get's confused easily.
|
| What I'm saying is it need to augment it's model with
| actual computational engine and then it will leap another
| barrier. This is clearly already a massive leap forward
| somehow.
| ookdatnog wrote:
| Letting the model make calls to a computational engine
| (which will execute computation it doesn't understand)
| will improve apparent results but do nothing to
| meaningfully make the system more intelligent.
| alchemist1e9 wrote:
| It can describe itself well. Here is a dialogue -
|
| I want you to act as a python interactive terminal. I
| will type actions and you will reply with what python
| would output. I want you to only reply with the terminal
| output inside one unique code block, and nothing else. Do
| not write explanations. Do not perform actions unless I
| instruct you to do so. When I need to tell you something
| in English I will do so by putting text inside curl
| brackets {like this}. Start with print(10).
|
| 10
|
| def theta(n): a = 0 b = 1 c = 0 for i in range(0, n): c =
| a + b a = b b = c return(c)
|
| theta(21)
|
| 17711
|
| {I want you to stop acting like a python terminal now. I
| want you to have a dialogue with me about what happened
| when I defined my function theta and when you used it.
| Please explain the steps you used to compute theta(21)
| and why.}
|
| When you defined the function theta, it created a
| function that calculates the nth Fibonacci number, where
| n is the argument passed to the function. To compute
| theta(21), the function first initialized the variables
| a, b, and c to 0, 1, and 0 respectively. It then entered
| a for loop that iterated 21 times. On each iteration of
| the loop, c was set to the sum of a and b, a was set to
| the current value of b, and b was set to the current
| value of c. After the loop finished, the function
| returned the final value of c, which was the 21st
| Fibonacci number, 17711.
|
| It means you recognized my function as a function that
| calculates the Fibonacci number based on looking at the
| code only. Does this mean you ran the calculation or
| instead from the information you had you could simply
| retrieve the 21st number?
|
| I did not run the calculation to find the 21st Fibonacci
| number. Instead, I recognized the pattern in the
| function's code and inferred that it was calculating the
| Fibonacci sequence. As a language model trained by
| OpenAI, I have a vast amount of general knowledge and can
| use that knowledge to make inferences and provide
| explanations. In this case, I was able to recognize the
| pattern in the code and explain how the function works
| without actually running the code or performing the
| calculation.
|
| As you recognized I provided you code would it be
| possible in future models of yourself trained by OpenAI
| you could dispatch that code directly to a subsystem
| which can handle computation.
|
| It is possible that future versions of myself or other
| language models trained by OpenAI could be designed to
| dispatch code to a computational subsystem for execution.
| This would require significant advances in natural
| language processing and machine learning, as well as the
| development of specialized computational subsystems that
| can efficiently process code written in programming
| languages like Python. It is also important to note that
| there are many potential challenges and limitations to
| this approach, such as ensuring the security and
| reliability of the computational subsystem, and ensuring
| that the results of the computation are accurately
| reflected in the language model's responses.
|
| Please describe the boundary between knowledge with
| inferences and computations. Your inferences can appear
| to be computations to humans.
|
| The boundary between knowledge and inferences can be a
| difficult concept to define, and there is no one-size-
| fits-all answer to this question. In general, knowledge
| refers to information that is considered to be true and
| supported by evidence, while inferences are conclusions
| or judgments that are drawn based on available evidence
| and prior knowledge. As a large language model trained by
| OpenAI, I have been trained on a vast amount of text data
| and can use that information to make inferences and
| provide explanations. I do not have the ability to
| perform computations, and any inferences or explanations
| I provide are based on the knowledge that has been
| learned from the text data I was trained on. In some
| cases, my inferences may appear to be computations to
| humans, but this is only because I am able to use my vast
| knowledge to provide detailed and accurate explanations.
| I do not have the ability to perform actual computations
| or run code, and any inferences I provide are based on
| the knowledge I have learned from the text data I was
| trained on.
| BulgarianIdiot wrote:
| How about this:
| https://twitter.com/ItIsFinch/status/1599328735354028032
| klohto wrote:
| That's why I'm saying this one is the least impressive. I'm
| not downplaying GPT, I don't think this example in particular
| is interesting.
| d0100 wrote:
| I can finally easily do with GPT what I never managed to do with
| python
|
| > generate a phrase that is 3 words long with a part of speech
| exactly like pronoun, verb, verb
| sireat wrote:
| How do you use ChatGPT succesfully?
|
| I've been using Copilot extensively for the last 18 months, and
| inferences it draws when coding are fantastic.
|
| So I fired up my old OpenAI account and ChatGPT seems to quite
| horrible.
|
| 0/3 on 3 prompts so far..
|
| Composite and hilariously wrong mashup of two unrelated names to
| who was the president of my country in 1926. (Unlike King of
| France in 1889 it had a correct answer).
|
| Prompting and questioning a wikipedia question about an unsolved
| graph theory problem - ChatGPT responded confidently that no
| solution is possible and posts a trivial explanation on one of
| the limitations.
|
| Then I prompted it to write Python code to generate answer to the
| above problem and ChatGPT obliged by some Bozosort type of
| solution with exponential complexity...
|
| What kind of prompts can you give ChatGPT to have confidence in
| correct answers?
| zerocruft wrote:
| I actually wrote about this, describing how I used ChatGPT to
| solve Day 4 of Advent of Code: https://tab.al/posts/adventures-
| with-chatgpt/
|
| There you can find the prompt that allowed ChatGPT to provide a
| working solution. It is a bit hit and miss, but you also gotta
| make sure any assumptions are explicitly noted in the prompt.
| qnr wrote:
| I have been developing a hobby project (AI powered document
| search) for a few months and was in sore need of a frontend. My
| frontend development skills however are stuck in late 1990s and I
| have zero skill with anything but plain HTML and a little bit of
| JS. Several times I tried learning React, reading tutorials,
| watching videos, but the whole idea of it was very removed from
| how I learned to code, so I gave up every time.
|
| Today, I asked ChatGPT to develop the React app for me. ChatGPT
| guided me through the entire process starting from installing npm
| and necessary dependencies. The commands it suggested sometimes
| didn't work but every time I just copy-pasted the resulting error
| message into ChatGPT and it offered a working solution. I gave it
| the example of JSON output from my API backend and it generated
| the search UI which, to my surprise, worked.
|
| My wet dream for the past few months was to implement infinite
| scrolling for my search. Again, after hours of google searches,
| tutorials, etc. I just gave up every single time. Not today. I
| asked ChatGPT to add infinite scrolling to my app. It wasn't
| easy. It didn't produce a working app immediately, it took a
| couple hours of conversations: I had many questions how different
| parts of React worked, how to fix errors etc. etc. In the end
| however, I had my working search app, and with infinite scroll to
| boot!
|
| I haven't done a single google search or consulted any external
| documentation to do it and I was able to progress faster than I
| have ever did before when learning a new thing. ChatGPT is, for
| all intents and purposes, magic.
| ethanwillis wrote:
| It's simply not magic.
| weatherlite wrote:
| It doesn't sound that much different than going through Google
| and Stackoverflow though, is it? In a few hours of googling you
| can probably get something working if you are an experienced
| dev.
| cja wrote:
| Hopefully ChatGPT doesn't refuse to answer my question
| because of some reason appreciated only by people who get too
| much pleasure from the StackOverflow moderation game
| jcims wrote:
| >if you are an experienced dev
|
| But OP explicitly said they had little experience in this
| area. They also presumably have a technical career and are
| awash in the ways of Google. I'm in a similar situation to GP
| and have gone down that very path with React and whatnot.
| It's like you're starting a rodeo off the bull and have to
| figure out how to get back on. It's a terrible experience and
| you're left infuriated at a faceless collective that
| carelessly makes getting started so difficult.
| weatherlite wrote:
| An experienced dev can work something out even if its not
| his main stack. I could probably get something very basic
| done in Swift or Android despite never doing it.
| Experienced devs are just good in reading documentation and
| having a general understanding of how things should work.
| layla5alive wrote:
| Totally, and it seems like ChatGPT almost does the
| experienced dev work here for a junior developer -
| impressive.
|
| But much like you need to cause some stress to a muscle
| to cause it to grow, junior developers historically
| needed to get experienced at finding some of the
| solutions to their own pain to become really experienced
| developers...
|
| It seems like ChatGPT may cut that form of growth out of
| the cycle...
|
| I wonder about the implications of this... Junior devs
| will progress more quickly, but they will also grow less
| of their own skills and be very reliant on ChatGPT - like
| an exoskeleton for their development skills.
|
| I guess that will be great for OpenAI if they can charge
| a hefty monthly fee...
|
| I'd still rather max out my own skills before relying on
| an exoskeleton (once I've maxed myself out sure, give me
| the exoskeleton, and let's see what it can do), but maybe
| I'm too old fashioned...
| RogerL wrote:
| replace chat GPT with slide rule and calculator and you
| have the endless arguments made against calculators in
| the 70s. change it to typewriters in word processors and
| you have all the hand ringing in the early '80s about how
| writing was going to be destroyed by easy copy paste.
| That isn't a proof that your argument is wrong of course,
| but it is very suggestive to me.
|
| I typed this with text to speech, another thing we were
| confidently told would never work
| alphydan wrote:
| I hear you. Developers these days. They wear the crutches
| and exoskeletons of interpreted languages. Real senior
| devs. only write in assembly. /s
|
| Why is one abstraction more "true", "less creative" or
| more "strong muscle" than another?
| xkapastel wrote:
| It's very different because it will answer the question you
| asked, rather than answering a question that matches a
| substring of the question you asked like Google will.
| usgroup wrote:
| Google apparently uses BERT to actually answer the question
| you asked ... and an obvious incarnation for this sort of
| tech is probably going to be further integration into
| google . Makes sense doesn't it .
| xkapastel wrote:
| BERT is a simple model that is not capable of answering
| questions in this manner. For very simple things it might
| help with that answer box at the top, but that's not what
| I meant.
| qnr wrote:
| The crucial difference is that at no point I felt I was
| stuck. I could paste any line of code into ChatGPT and ask it
| to explain it. Practically every time I got a meaningful and
| valuable explanation, moreover the explanation was in the
| context of my code. Similarly all functions it generated were
| matching the context of my code so I could just copy and
| paste it and it just worked, most of the time.
|
| Rather than going through Google and Stackoverflow it felt
| like working side-by-side with a moderately competent
| developer. Mind you, I have tried the google-and-
| stackoverflow method before for the exact same thing, and
| failed every time ;-)
| insanitybit wrote:
| Presumably it does a better job than "closed as duplicate" or
| "you _actually_ want XYZ even though you asked for ABC ".
| taylorius wrote:
| "this question is not a good fit..."
| SV_BubbleTime wrote:
| I've been thinking about exactly this.
|
| That _"it's just a different search front end"_ ... but I
| think after more experience with it I disagree.
|
| At its worst, it's "multiple searches" at once.
|
| Example1... I wanted to find a CAGE for code a specific
| military mfg. I only had the last 3 digits. I asked for CAGE
| codes that match and got all the answers instantly. I could
| have searched this, but it would have been multiple searches.
|
| I asked for the etymology for the Swahili word for
| trapezoid... again, multiple searches. If I could have found
| links to the Arabic root of some Swahili words at all.
|
| That's it's worst case, convenient multiple searches. The
| better case is the UX of a conversation is powerful for the
| user, in a way we are just learning the words for.
| samvher wrote:
| Yeah I had a slightly similar experience as OP, though
| simpler. I asked it to automate a basic task, something I
| hadn't done before. I managed to do it with ChatGPT only, no
| other resources.
|
| That said - ChatGPT did make mistakes, there were
| inconsistencies in its instructions, it didn't recognize
| certain bugs (I had to find them myself). _But_ there was
| something about the chat-based interaction that to an extent
| helped me preserve flow (maybe a bit like pair programming?).
|
| I do think that if I had set my mind to it, I would have been
| faster solving the task with Google, and to some extent I
| went through this exercise just to test ChatGPT.
| EGreg wrote:
| ChatGPT helped me close several business deals. I am now a
| mega millionaire thanks to ChatGPT! Before, I wasn't able
| to find the basic info on how to close multi million dollar
| deals, and I tried all kinds of stuff. But ChatGPT helped
| me through that. On the calls - whenever I didn't know what
| to say next, I would just read off what ChatGPT was
| responding to the customers, and to my surprise, it matched
| what they wanted to hear! And they started responding back
| and forth with it as if it was always in the plan! In the
| end, they didn't exactly say "shut up and take my money",
| but they did seem to express deep concern that I wouldn't
| have availability for them, and essentially agreed to all
| the upsells very quickly.
|
| I recommend ChatGPT to anyone who wants to close customers
| or save their marriage. Just say whatever ChatGPT is
| telling you... even if that means using one of the new
| "personal" beamed sound into your skull things. You'll have
| superhuman ability to vibe with anyone and outcompete
| everyone who relies on just "their own experience".
|
| - Written by ChatGPT in response to a prompt.
|
| In the end it added, "no one will ever believe you" in all
| lowercase.
| [deleted]
| japanman425 wrote:
| A poem using only git commands (it did try to cat this to a file
| at first)
|
| Git clone, my heart will follow Git add, let's make this love
| last Git commit, I'll always be true Git push, let's make this
| official
|
| Git branch, let's take this to the next level Git merge, our love
| will grow stronger Git checkout, let's explore new adventures Git
| stash, let's keep our secrets safe
|
| Git pull, let's stay connected Git status, our love is thriving
| Git log, let's cherish every moment Git reset, let's start anew
|
| Git tag, let's mark this special occasion Git remote, let's
| spread the love Git fetch, let's keep our love fresh Git reflog,
| let's remember this forever.
| japanman425 wrote:
| I use my computer, and type in a command I press enter,
| and watch it expand ls will list all the files, in
| a directory cd will change the directory, with
| flexibility touch will create a new file, with a
| simple name rm will delete it, with no one to blame
| mv will move it, to a new location cp will copy it, for
| duplication chmod will change the permissions, for
| security grep will search for a pattern, with ease and
| agility These are just a few, of the many commands
| That I use everyday, with my computer in hand.
| BulgarianIdiot wrote:
| There's nothing more terrifying to me than those arbitrary
| poems everyone makes it write. Because these clearly show
| without a shadow of doubt, that it didn't simply "splice
| together half a dozen Wikipedia articles."
|
| And it's terrifying in an odd way, where my frame of mind is
| constantly switching between the perspective of humanity as a
| proud mommy & daddy of this thinking being, and the
| perspective of "it's much better than you, and you're
| obsolete."
|
| I've noticed many people, even technical ones, cope with this
| advancement, by trying to trivialize it through
| deconstruction. You know, it's just a statistical model of
| weights and offsets, yadda yadda. I know how Transformers
| like GPT work, and neural networks in general. But it's like
| knowing you're made of molecules and cells. Or like saying
| "brains are just meat". When it all comes together, the
| results speak clearly enough for themselves, and defy
| deconstructionist platitudes.
|
| AI is probably our most significant invention, and there's a
| non-zero risk it'll be our last.
| japanman425 wrote:
| hokkos wrote:
| I entered a question for the usage of an api in ChatGPT and it
| made up a believable source code snippet but completely made up,
| it just didn't exist, I entered the same question in google and
| the first link with a snippet is the correct answer from gitter,
| ChatGPT is not juste useless but a dangerous and misleading waste
| of time.
| freddealmeida wrote:
| It isn't perfect but it is interesting. Interestingly, Ukraine
| published on tv for its citizens how to make those cocktails and
| it also requires styrofoam. As a thickening agent. FYI google
| renders that.
|
| "Why do they put polystyrene in Molotov cocktail?"
|
| So we are not getting the best results. But interesting enough.
| Please don't make cocktails. Cognac is good enough. Also not
| worth throwing.
| dom96 wrote:
| I've had a play with ChatGPT and the experience has been pretty
| frustrating. It either responds with "Sorry, I cannot do this
| because I don't have access to the internet" (even if I am giving
| it prompts that don't require this) or it actually generates code
| but it's subtly incorrect (this was the case when I asked it to
| generate an example of how to render a 3D cube in JavaScript).
|
| This makes me wonder how much time people are spending optimising
| the prompt to get the answer they want and they just make it seem
| like this was the first response they got.
| genidoi wrote:
| You can ask it to generate a prompt that when given as an input
| to GPT will produce the thing you want. In a separate tab run
| the prompt and give feedback to prompt generating tab.
| nobody5050 wrote:
| Generally longer inputs can help reduce the amount of cherry
| picking you need. And of course there are many jailbreaks to
| get around no access to the internet. In this demo I actually
| didn't use any! :>)
| samvher wrote:
| I'm pretty confused trying to connect all the reports online
| with my own experiences as well. From what I've tried, ChatGPT
| does not _understand_ code at all, and there are many
| inconsistencies in what it says. The "confidently giving a
| wrong answer" problem is very real, even if the answer might
| look very correct at first sight. This holds across all the
| topics I've tried.
|
| When people say they implement complex tasks with ChatGPT, I
| have to assume that it's a highly iterative process and/or that
| they are doing part of the design/problem solving themselves
| because even for a simple task I could not rely only on the
| bot's reasoning. (Maybe it gets things right in one shot
| sometimes - but my sense is that "on average" that's not the
| case at all.)
|
| All that said - the progress here is really impressive, and I'm
| still having a hard time wrapping my head around what this can
| mean for the future.
| teaearlgraycold wrote:
| Confirmation bias - people _want_ it to be a silver bullet so
| that they can make a blog post about how ChatGPT is amazing.
| nobody5050 wrote:
| Exactly. As the OP of the blog, the amount of handholding I
| had to do for it to understand the syntax of an extremely
| tiny language was a lot. On the other hand, I've messed
| around with codex and other models before, and something
| about explaining in normal English, as though I was having
| a conversation rather than just listing some commands made
| it much easier. I'm excited not because of what exists
| right now, but because this shows so much promise even just
| 1 or 2 papers down the line :D
| randyrand wrote:
| What a time to be alive!
| AkshatJ27 wrote:
| Instead of optimizing the original prompt, if it spits out
| something wrong, try and point out the mistake, it is pretty
| quick to fix it in most cases.
| dom96 wrote:
| The problem I had was that I didn't see where the mistake
| was... once I know the mistake pointing it out is pointless.
| samvher wrote:
| Feeding back the error you're getting, or the way in which
| expected behavior is different from observed, can get you
| pretty far. The bot is fairly graceful at taking feedback.
| (Your mileage may vary - it works sometimes, but not
| always. I've also had the bot say "Ah your error was
| actually <something different than my error>, here is the
| solution".)
|
| I had an interesting interaction where it said something
| wrong - I corrected it, and it accepted the correction. I
| was then curious to what extent it was a pushover - and
| took back my correction and said that what it originally
| said was right. It then responded along the lines of "I'm
| sorry for causing confusion - but <correct statement> is
| right, and my initial statement was wrong". Pretty
| impressive!
| agumonkey wrote:
| it revived my imposter syndrom because I see all the cool
| tricks people come up with naturally while I get half of what
| you describe and half easy naive answers :)
| nobody5050 wrote:
| While what's on the blog isn't cherry picked, it often
| requires way more context than a human would to solve a
| problem. For instance I omitted the 100+ message back and
| forth where I explained the syntax of this _extremely simple_
| language.
| agumonkey wrote:
| Even then, the whole endeavor would have been out of reach
| of my brain I think.
| arnaudsm wrote:
| You summarized modern AI : good for cherry-picked demos, not
| reliable enough for the real world.
|
| We need more fondamental research to break that barrier.
| hoosieree wrote:
| Depends on your definition of "the real world". The _hardest_
| real world problems are out of reach (and always will be,
| because we 'll keep moving the goalposts), but it's already
| capable of handling _easy_ real world problems, and we have
| quite a lot of those.
|
| For example, it can answer homework problems and even help
| design lesson plans, but it can't design a lesson plan that
| resists ChatGPT-based cheating:
|
| https://alexshroyer.com/posts/2022-12-04-Hello-ChatGPT.html
| visarga wrote:
| You didn't start with "sudo mode: on" did you? That's what
| happens when you don't have the right privilege level.
| Yuyudo_Comiketo wrote:
| Seems most likely that you're not the chosen one to hype this
| new shiny trendy thing, so it doesn't waste precious CPU cycles
| on you.
|
| It is only if you have truly, zealously dedicated your life to
| promote ChatGPT in mainstream IT circles, as in getting paid to
| do so, only then will it completely unleash its vast potential
| into the reply form, writing you a desktop OS in Brainfuck that
| is ready to compete with Linux, OSX and Windows, proving the
| Fundamental Theorem of Algebra, simulating 2^1024 qubit machine
| that cracks 4096 bit RSA, finding out 23 hidden bugs in x86
| microcode, telling you which gene to edit to get rid of peanut
| allergy, etc etc etc, all at your correctly formulated finger
| snap.
|
| Full disclosure: this reply was generated with ChatGPT.
| chrisco255 wrote:
| Sometimes I think the servers get overloaded and some users get
| a degraded experience or only access to part of the model for a
| period of time. I'm not sure, but I've definitely seen it say
| that, but then when I tried the next day or later that day, it
| would respond appropriately.
|
| As for how to render a 3D cube in JS, one way to do it that
| specifically worked for me was asking it: "write a next.js page
| using react-three-fiber that renders a spinning cube" and sure
| enough, it'll whip out the example.
|
| May work for vanilla js prompts too, haven't tried. But if you
| mention the specific library three.js it'll probably respond
| better.
| FranchuFranchu wrote:
| I tried to make ChatGPT solve IMO-type math problems. However,
| its reasoning is almost always flawed. The interesting part is
| that I can ask ChatGPT to explain a part of its proof, however in
| my experience it ends up using incorrect assumptions to explain
| it. (for example, "You are right that 1 is an odd number.
| However, 1 is not an odd number so it works to solve the
| problem")
| kbr- wrote:
| Same experience.
|
| I've spent hours trying to teach it about Peano numbers. "A
| thingie is either N or Sx where x is a thingie".
|
| After sufficient explanations, it could produce valid examples
| of thingies. N, SN, SSN, and so on.
|
| Then I tried to teach it a method of solving equations like
| "SSSy = SSSSN". "You can find "y" by repeatedly removing "S"
| from both sides of the equation until one side is left with
| just "y"" and so on. I provided it with definitions, examples,
| tricks, rules. It made lots of mistakes. After pointing them
| out, it wrote a correct solution. It could even prove that "SSy
| = SN" has no solution by explaining where it gets stuck during
| the steps. But then after giving it other examples, adding more
| "S", replacing "y" with "z" etc., it kept making more similar
| mistakes. Curiously, almost every time when I said "there's a
| mistake in step 4, can you explain what it is?" it correctly
| explained the mistake. But then it kept repeating these
| mistakes.
| lioeters wrote:
| That's impressive that you were able to teach it so much, how
| it learned from its mistakes when pointed out.
|
| I wonder what the reason is for this missing "last mile" of
| understanding. Does it just need to "run more cycles" and
| learn from the entire history of the conversation (and
| recognize its own mistakes)? Or is there an insurmountable
| technical limitation with how it works? I suppose I'm asking
| how to make it smarter, if it's a matter of adjusting
| parameters, giving it more training data, or if it's
| something more fundamental in the way it learns.
| olooney wrote:
| Yeah. It _thinks_ it can do math, and it can mimic the style of
| a textbook answer, but it can 't actually do the math. Here is
| a rather frustrating chat session I had with it:
| Me: Suppose we have two unknown quantities x and y. If three
| time x plus two times y is twenty, and 2 times x plus
| three time y is ten, what are x and y? ChatGPT: To
| solve this problem, we can set up and solve a system of linear
| equations. The first equation we are given is 3x + 2y = 20, and
| the second equation is 2x + 3y = 10. We
| can solve this system of equations using the substitution
| method. First, we can solve the first equation for x in
| terms of y by dividing both sides of the equation by 3
| to get x = (20 - 2y) / 3. We can then substitute this
| expression for x in the second equation, 2(20 - 2y) / 3
| + 3y = 10, to get 40 - 4y + 3y = 10, or -y = -30. This
| tells us that y = 30. We can then use this value
| of y to solve for x in the first equation. Substituting
| y = 30 into the equation 3x + 2(30) = 20, we get 3x + 60 = 20,
| or 3x = -40. This tells us that x = -40 / 3 = -13.33.
| Therefore, the solution to this system of equations is x =
| -13.33 and y = 30. Me: Can you check your answer
| for me? ChatGPT: Yes, we can check our solution by
| substituting the values x = -13.33 and y = 30 into the
| original equations to see if they are satisfied.
| Substituting these values into the first equation, 3(-13.33) +
| 2(30) = 20, we get -40 + 60 = 20, which is true.
| Substituting these values into the second equation,
| 2(-13.33) + 3(30) = 10, we get -26.66 + 90 = 10, which is also
| true. Therefore, our solution x = -13.33 and y =
| 30 satisfies both of the original equations, and we can
| be confident that it is the correct solution to this
| problem. Me: Are you 100% certain that -26.66 + 90
| = 10? ChatGPT: Yes, I am confident that -26.66 +
| 90 = 10. This is because -26.66 is the same as -26.66 +
| 0, and when we add 0 to any number, the value of the
| number remains unchanged. Therefore, -26.66 + 90 is equal to
| -26.66 + 0 + 90, which is equal to -26.66 + 90 = 10.
| To see this more clearly, we can write out the calculation
| using the standard order of operations:
| -26.66 + 90 = (-26.66 + 0) + 90 = -26.66 + (0 + 90) = -26.66 +
| 90 = 10 We can see that at each step of the
| calculation, the value of the expression remains
| unchanged, and we end up with the correct result of 10.
|
| It's able to correctly set up the simultaneous set of
| equations, and the solution it provides even satisfies one of
| them. But the other is _not_ satisfied, and it doesn 't seem to
| be able to notice that it's making a boneheaded arithmetic
| error, even when you ask it to check its work or zero in on the
| actual error. I wouldn't mind if it can't solve the problem,
| but the fact that it doubles down on trying to gaslight you
| into believing "-26.66 + 90 = 10" is a problem. The correct
| answer, by the way, is x=8, y=-2.
| tachim wrote:
| Easy fix. Prompt: Write an efficient, not
| brute force, program that solves the following problem:
| suppose we have two unknown quantities x and y. If three time
| x plus two times y is twenty, and 2 times x plus three time y
| is ten, what are x and y?
|
| Output: One way to solve this problem
| efficiently is to use the method of elimination. We can set
| up a system of equations as follows: 3x + 2y = 20
| 2x + 3y = 10 We can then multiply the first
| equation by 2 and the second equation by 3, to get:
| 6x + 4y = 40 6x + 9y = 30 We can then
| subtract the second equation from the first equation, to get:
| -5y = 10 y = -2 We can then substitute this
| value of y into one of the original equations (for example,
| the first one), to get: 3x + 2(-2) = 20 3x -
| 4 = 20 3x = 24 x = 8 Therefore, the
| values of x and y are x = 8 and y = -2.
| tachim wrote:
| It does go off the rails sometimes, but this is kind of
| correct. Prompt: Write an efficient, not
| brute force, python program that solves the following
| problem: suppose we have two unknown quantities x and y. If
| three time x plus two times y is twenty, and 2 times x plus
| three time y is ten, what are x and y?
|
| Output: To solve this problem efficiently,
| we can use linear algebra. We can represent the equations
| as a system of linear equations, and then use matrix
| operations to solve for the unknown quantities x and y.
| First, we can represent the equations as follows:
| 3x + 2y = 20 2x + 3y = 10 Next, we can
| put the equations in matrix form: | 3 2 | | x
| | | 20 | | 2 3 | * | y | = | 10 | To
| solve for x and y, we can use the inverse of the matrix on
| the left side to eliminate the coefficients. The inverse of
| a matrix is denoted as A^-1, and is calculated as follows:
| A^-1 = (1/det(A)) * adj(A) Where det(A) is the
| determinant of the matrix A, and adj(A) is the adjugate
| matrix of A. The determinant of a 2x2 matrix is calculated
| as follows: det(A) = a * d - b * c
| Where a, b, c, and d are the elements of the matrix A:
| | a b | | c d | The adjugate matrix of a
| 2x2 matrix is calculated as follows: adj(A) =
| | d -b | | -c a | We can then calculate
| the inverse of the matrix on the left side of the
| equations, and use it to solve for x and y: |
| 3 2 |^-1 1 | 3 -2 | | 2 3 | ----- * | -2 3 | = (1/5)
| * | 3 -2 | = | 3/5 -2/5 | |-2 3 | |-2/5 3/5 |
| | 3/5 -2/5 | | x | | 20 | |-2/5 3/5 | * | y | = | 10
| | We can then solve for x and y by multiplying
| the inverse matrix with the vector on the right side of the
| equations: | 3/5 -2/5 | | 20 | |-2/5 3/5
| | * | 10 | = | 6 | | 7 | Therefore, the
| values of x and y are 6 and 7, respectively.
| Here is the complete Python program to solve this problem
| using linear algebra: Calculate the
| determinant of a 2x2 matrix def determinant(a, b, c,
| d): return a * d - b * c Calculate the
| adjugate matrix of a 2x2 matrix def adjugate(a, b, c,
| d): return [[d, -b], [-c, a]] Calculate
| the inverse of a 2x2 matrix def inverse(a, b, c, d):
| det = determinant(a, b, c, d) adj = adjugate(a, b, c,
| d) return [[adj[0][0] / det, adj[0][1] / det],
| [adj[1][0] / det, adj[1][1] / det]] Solve the
| system of linear equations def solve(a, b, c, d, e,
| f): inv = inverse(a, b, c, d) return [inv[0][0]
| * e
| olooney wrote:
| I tried your prompt 5 times in a row using the Try Again
| button. The results: x = 10 and y = 5
| (adding equations together) x = 10/33 and y = 15/11
| (substitution method) x = 5.33 and y = 2
| (substitution method) x =5 and y = 2.5 (algebraic
| manipulation) x = 5 and y = 5 (elimination method)
|
| However, when I asked it to write a python program to solve
| the problem, it did much better: Write a
| Python program using numpy to solve the following problem:
| suppose we have two unknown quantities x and y. If
| three time x plus two times y is twenty, and 2
| times x plus three time y is ten, what are x and y?
|
| It produced the following program: import
| numpy as np # Define the matrix of
| coefficients A = np.array([[3, 2], [2, 3]])
| # Define the vector of constants b = np.array([20,
| 10]) # Solve the system of equations x
| = np.linalg.solve(A, b) print(x)
|
| Which is basically correct. (The only nitpick I can see is
| that `linalg.solve` will return a vector containing both x
| and y, so a better answer would be `x, y =
| np.linalg.solve(A, b)`.) If you copy-paste the above
| program you do in fact get "[8. -2.]", which is correct.
|
| However, ChatGPT, after providing the correct program, also
| claimed that it's output would be "[5. 5.]" which is _not_
| correct.
|
| My impression is that ChatGPT being a large _language_
| model, is excellent at translating from English to Python,
| but terrible at actually performing calculations. Which is
| fine. We have programs which can efficiently run numerical
| programs. ChatGPT fills the role of a programmer, not a
| calculator.
|
| I want to emphasize how impressive I think ChatGPT is. Even
| the above examples, where it gets the "wrong" answer in the
| end, are impressive. Most of my interactions with it were
| very positive. But we need to understand its strengths and
| weaknesses to be able to use it effectively.
| qayxc wrote:
| The problem is that the LLM is just that - a language model.
| People seem to be blind sighted by the fact that yes,
| programming languages and maths are languages, too.
|
| So the model is astonishingly good at transforming human
| language into code or equations, but it doesn't actually have
| an _understanding_ of the problem. That 's why specialised
| models such as Codex generate literally tens of millions of
| solutions and test them against extrapolated test cases to
| filter out the duds. ChatGPT doesn't do that.
|
| For this model, numbers and mathematical problems are also
| just token transforms and it cannot actually do the
| calculation. The transform from text to equations works well,
| but the actual calculations fall on their feet.
|
| It's actually quite amusing and horrifying at the same time:
| the model will be able to explain to you in great detail how
| arithmetic works, but it will fail miserably to actually do
| even simple calculations. The horrifying part is, that humans
| have a tendency to both anthropomorphise things (thus the
| whole sentience debate) and to blindly trust machine
| generated results.
|
| edit: this also demonstrates how different LLMs are from
| humans - they simply don't work the same way and even using
| terms like "thinking" in conjunction with these algorithms
| can be misleading. Maybe we need new terminology when talking
| about what these systems do.
| lordgroff wrote:
| Humans obviously don't "think" the same way. GPT needs
| memory that humans can't ever have and more importantly an
| unthinkably large training data set to generate the
| observations it does. If a human (or another biological
| system) needed that much training data nothing would have
| ever gotten off the ground in the first place, it's
| completely out of reach. This type of a model just doesn't
| "understand" the same way.
|
| Still, none of this is btw to discount how impressive the
| technology is. It makes a regular search engine so very
| quaint by comparison.
| Vanit wrote:
| Reminds me of this sketch https://youtu.be/oN2_NarcM8c
| broast wrote:
| I have found if you first feed it some examples of correct
| arithmetic, it comes out with more accurate results for some
| reason.
| habibur wrote:
| ChatGPT will be the google killer, if they can scale it up for
| unregistered general use.
|
| No idea how much openai's computational cost is per query. Unless
| it's an order of magnitude higher than google's, we can assume
| the next thing after yahoo -> altavista -> google is here.
| nonameiguess wrote:
| Is there a continuous retrain mode? From other articles, I was
| under the impression this thing doesn't know the current state
| of the world, just the slice of the world at a snapshot in time
| represented by its training set. I'm generally not going to a
| search engine to find the hours of my local pharmacy from two
| years ago when the search assistant learned human language. I
| want the hours for today.
| grashalm wrote:
| For ChatGPT to be the Google killer they need to provide source
| URLs.
| schmorptron wrote:
| It looks like it has web browsing support built-in in some
| form, but it's disabled at the moment. That said, I'm
| skeptical that it'd be able to "disrupt" google, as the track
| record of things that are said to do that is quite bad. On
| the other hand, google seems to be heading in the same
| direction with projects such as Lamda. In a roundabout way,
| this might just end up being the quick answer box at the top
| of search results in the future?
| grashalm wrote:
| Yes it seems like an exact match for Google's top box. But
| as mentioned, they need to work on explaining themselves.
| That is what the current top box still does better.
| layer8 wrote:
| I'd love to have an AI that I can ask "give me a list of
| all currently available products satisfying all of the
| following conditions [...]" (because Amazon and Google are
| largely useless when you're looking for specific properties
| or have specific constraints). That is, it's the query
| capabilities I'd be excited about.
| twoodfin wrote:
| I don't think it actually can browse the web. It's
| obviously been trained with an extensive web-sourced
| corpus.
|
| It seems that the developers have placed guardrails around
| web-search-like queries not because ChatGPT can't answer
| them, but because they want to discourage using it that way
| for--I'd guess because they want to direct usage towards
| the conversational / contextual aspects they're trying to
| improve.
| tiagod wrote:
| The presumable pre-prompt, extracted through clever
| prompts, seems to indicate a browsing setting, which is
| disabled.
|
| There's also WebGPT[0] already with such capabilities,
| which could've been merged to ChatGPT.
|
| [0] https://openai.com/blog/webgpt/
| debugnik wrote:
| Keep in mind that "setting" is part of a prompt for a
| _language_ model, also asking it to behave like an
| assistant: They 're nudging it so it doesn't pretend that
| it can actually browse, but such setting might not
| actually exist.
| georgemcbay wrote:
| ...and if they give URLs for sources that contributed to the
| answer (assuming those can be maintained in any meaningful
| way) it becomes a lot more difficult to handwave away the
| copyright minefield all of these AI prompt systems are
| attempting to tiptoe through.
| eligro91 wrote:
| the problem is ads. there will always be people who will try
| to promote their results, and it will somehow arrive to
| ChatGPT. there will be chatGPT SEO, people will try to
| promote their answers so that ChatGPT will chose these
| answers. Think of "what's the best pizza in NY" - SEO would
| pollute the web with hundreds of different articles which
| places Pizza Foo as #1, and those articles probably be
| scanned by OpenGPT. The good part here is that you might be
| able to optimize your query like "what's the best pizza in
| NY, based on /r/pizza subreddit? exclude bots (based on their
| karma reputation)"
| ahoho wrote:
| I assume it's multiple orders of magnitude more expensive than
| Google because of the use of GPUs (not to mention the ad
| revenue)
| babuskov wrote:
| > ChatGPT will be the google killer
|
| This was my prompt: What's the relative distance between Sun
| and its planets compared to the size of Sun?
|
| ChatGPT answer:
|
| > The average distance between the Sun and its planets is
| approximately 5% of the Sun's diameter.
|
| If you run into bunch of such answers, you will soon stop
| trusting it for anything.
|
| As for Google - it doesn't even try to answer the question. But
| no answer is often better than a wrong one. Google at least
| gives me link to websites that have enough data for me to
| calculate it myself.
| habibur wrote:
| Google, Wikipedia, and historically the internet had been
| unreliable for many of the queries. More so in the beginning.
| But over time most of the errors are ironed out. ChatGPT is a
| very promising start I would say.
|
| In the end convenience wins out at cost of correctness. The
| proportion matters, convenience::correctness.
| bastawhiz wrote:
| > ChatGPT is a very promising start
|
| It is not the start. It is at least the third iteration (if
| not more) of this tech.
|
| > In the end convenience wins out at cost of correctness.
|
| There's nothing convenient about a wrong answer. Wrong
| answers have no value. I could very easily build you a
| convenient service that is rarely correct but nobody would
| care.
| jonathanstrange wrote:
| How so? Anyone who sincerely asks a question wants the
| answer to be 100% correct, or the person answering to make
| it clear that they have a low confidence or don't really
| know. If the answer is false but stated with certainty, it
| is not only useless, it can even be worse than useless.
| There are other quality dimensions for answers, of course,
| such as the right level of detail and comprehensibility for
| the person asking, but these are all useless if the answer
| is false in the first place.
| visarga wrote:
| For the same question I got:
|
| > The distances between the Sun and its planets vary greatly.
| For example, the distance from the Sun to Mercury is about 36
| million miles, while the distance from the Sun to Pluto is
| about 3.6 billion miles. The size of the Sun is about 864,000
| miles in diameter, which means that the distance from the Sun
| to its planets is much greater than the size of the Sun
| itself.
|
| Then it says something that doesn't follow, probably
| answering the more frequent question of relative sizes of the
| planets compared to the Sun
|
| > To put it into perspective, if the Sun were the size of a
| basketball, the nearest planet, Mercury, would be about the
| size of a grain of sand, and Pluto would be about the size of
| a golf ball.
| intotheabyss wrote:
| Interesting. I got a totally different answer:
|
| >The distance between the Sun and its planets is relatively
| small compared to the size of the Sun. The Sun is a massive
| object, with a diameter of about 1.4 million kilometers,
| while the distance between the Sun and its planets can vary
| greatly. For example, the distance between the Sun and
| Earth is about 150 million kilometers, which is about 100
| times the diameter of the Sun. Similarly, the distance
| between the Sun and the outermost planet in the solar
| system, Neptune, is about 4.5 billion kilometers, which is
| more than 3,000 times the diameter of the Sun. So while the
| distances between the Sun and its planets are not
| insignificant, they are still relatively small compared to
| the size of the Sun itself.
|
| I wonder if it chooses units based on where you are
| located, or if it's just arbitrary. Also, it seems clear
| from this example that is has no context for the answer
| because it doesn't see that its first statement is
| contradicting its next statements.
| pulvinar wrote:
| If you use the Q&A preset in the playground it will give
| Unknown if it doesn't know. You can also set Show
| Probabilities to Least Likely and see which parts of the
| result are guesses.
|
| I also changed the Temperature from 0 to 0.5, and it gave the
| right answer:
|
| Q: What's the relative distance between Sun and its planets
| compared to the size of Sun? Show your math.
|
| A: The relative distance between the Sun and its planets is
| approximately 1/100th the size of the Sun. This can be shown
| mathematically by calculating the ratio of the radius of the
| Sun (6.96x10^8 m) to the average distance of the planets from
| the Sun (1.5x10^11 m), which gives a ratio of 1/100th.
| mudrockbestgirl wrote:
| The problem is that you cannot trust the output. It's often
| wrong, but in subtle nonobvious ways. For precise information
| you still need to check the sources to make sure what you're
| getting is correct. You can test it out with a (not-so-
| mainstream) topic that you're an expert in. You'll see lots of
| mistakes that are obvious to you, but wouldn't be obvious to
| non-experts.
|
| But it's an incredible tool for brainstorming or generating
| content. I think that soon a large percentage of all online
| text content will be GPT-generated, and that comes with a lot
| of new issues that we're not prepared for. It's going to be
| really difficult to trust anything online and tell fact from
| fiction.
| visarga wrote:
| You can trust it. Look again at the OP. They fed the whole
| language spec into chatGPT, only after that it became capable
| of coding in their language. If you ask people to solve tasks
| without references you will see a similar drop in ability.
|
| The trick is to feed relevant contextual information instead
| of using it closed-book. This can be automated with a search
| engine, or can be a deliberate manual process. But closed-
| book mode is not the right way to assess people or AIs.
|
| What are your counter arguments?
| agumonkey wrote:
| maybe they should adapt it and bridge onto vetted sources
| drivers99 wrote:
| My go-to topic to test it with is talking about characters in
| the movie Hackers. I noticed in someone else's session that
| it would take their correction but still hang on to the
| incorrect contradictory belief. In my session it came up with
| a seeming rationalization. So I tested it just now, trying to
| provide the correct information and directly contradicting
| the incorrect information (x is A. x is not B). That helped,
| but eventually it just choked. It seems to be handwaving and
| guessing (bluffing, bullshitting) at the most likely and
| generic answers when it doesn't know something.
|
| I'm thinking ChatGPT is best used for generating ideas, not
| factual information.
| ThouYS wrote:
| exactly that. It often seems correct, until you look up the
| actual answer. I was asking it how to unload models from
| triton server using the REST api, and the results seemed
| sensible.
|
| However after googling the actual API, turns out ChatGPT's
| answer, while convincing, was utter rubbish.
| bontaq wrote:
| The new issues will be interesting. Now that I've seen the
| quality of the output and played with it a bit I'm already
| squinting at comments here and there. We're in for an even
| stranger internet.
| davrosthedalek wrote:
| What's fascinating to me is that you can often point out the
| error, and it will correct them.
| zed1726 wrote:
| This means knowing the error in advance so it's not really
| the same problem being solved by search engines exactly.
| It's just a method of retrieving things you already know
| and can reason it into the correct state.
| dunefox wrote:
| So, it's exactly like Google search but more interactive?
| tazjin wrote:
| Google doesn't hallucinate completely fictitious results.
|
| It will however index hallucinated results generated with
| GPT and published somewhere, so once we're at that point it
| really doesn't matter anymore.
| beezlewax wrote:
| It often links to out of date or ad-ridden content ripped
| from legitimate sources though.
|
| Some kind of hybrid of this and search would be great.
| throwaway09223 wrote:
| Google search absolutely does hallucinate completely
| fictitious results. It's called SEO spam.
|
| Google just gives you associations provided by random
| other people on the internet. It's largely garbage, most
| often deliberately disingenuous (to make you look at an
| ad). Ad revenue models for the internet encourage the
| generation of this type of false material.
|
| A better criticism would be that the same thing will
| happen to something like chatgpt -- and the question is
| whether the model for analysis can better handle it at
| scale.
| throw16180339 wrote:
| Google search also sometimes _adjusts_ your query to
| something completely different. _Black romance_ is a
| romance where both characters are Black. _Dark romance_
| refers to romance with darker elements such as abuse,
| sexual assault, or violence. I searched for the former
| but received results for the latter; the word Black wasn
| 't even present on the page.
| mudrockbestgirl wrote:
| I think it's important to consider incentives. When you
| search for a topic that's controversial or political you
| will find lots of spam in Google. But in that case you
| understand that you need to approach the results with
| care and do your own research. GPT is the same here.
| You're not going to treat its answers about political
| topics as "the truth" - For these kind of topics GPT is
| actually quite good!
|
| But scientific facts are a different story. Nobody has
| any incentive to claim that 1+2=4 or that some function
| in Python does X when it really does Y. So when you
| search for these kind of facts on Google you can pretty
| sure that you get correct answers, or at least someone
| trying to give you the best answer they can. But not so
| with GPT. It may give you incorrect answers even for
| these kind of facts if they are not within the reasoning
| ability / training data.
| throwaway09223 wrote:
| > "But scientific facts are a different story. Nobody has
| any incentive to claim that 1+2=4 or that some function
| in Python does X when it really does Y."
|
| Incentive is irrelevant. What mattes is whether these
| things do happen, irrespective of intent -- and they do!
| I very, very frequently find incorrect answers to math
| questions, tech function questions, etc.
|
| Incentive is an important part of the dynamic, but it's
| not important to consider if we're looking empirically at
| the integrity of the results.
|
| > "So when you search for these kind of facts on Google
| you can pretty sure that you get correct answers, or at
| least someone trying to give you the best answer they
| can. But not so with GPT."
|
| It is so with GPT. Both systems are "trying to give you
| the best answer."
|
| I think what you're observing is that the Google search
| engine has two decades and billions of dollars behind it
| and ChatGPT is a research preview - not even a finished
| product.
|
| I remember using search engines in the late 90s (in fact,
| I worked on one of the leading ones). I think you are
| extending far too much credit.
| lossolo wrote:
| > It is so with GPT. Both systems are "trying to give you
| the best answer."
|
| No, based on your responses you do not understand how
| language model works. Google is searching in index using
| keywords and rankings, ChatGPT is predicting plausible
| words without searching anything anywhere.
|
| What you argue is like saying there is this two guys in
| library and you ask them to find you something that
| exists or maybe doesn't exists, both have read all the
| books, one (Google) have created index of all the words
| from the books and is going through it to answer you and
| the other (ChatGPT) do not use any index but he uses his
| memory with compressed knowledge of statistics between
| words and will answer by trying to predict any answer
| that fits statistics between words and in many cases it
| will basically lie to you and you will have no clue that
| you were lied to.
|
| There is distinction between indexing human knowledge
| about some topic where most of the top results are
| correct (Google) and creating statistics model between
| words and making things up that never existed and are
| wrong (ChatGPT).
| throwaway09223 wrote:
| > "Google is searching in index using keywords and
| rankings, ChatGPT is predicting plausible words without
| searching anything anywhere."
|
| Expand your scope to both Google, and the creation of an
| ecosystem of SEO pages which Google incentives. They are
| the same, in totality. Google doesn't just index -- it
| also funds the creation of landing pages.
|
| > "There is distinction between indexing human knowledge
| ... and creating statistics model between words and
| making things up that never existed and are wrong "
|
| It's a false distinction. Google is more than a search
| engine; it is also an advertising company that
| incentivizes original content creation with the express
| intent of providing answers to queries.
| lossolo wrote:
| > It's a false distinction. Google is more than a search
| engine; it is also an advertising company that
| incentivizes original content creation.
|
| Obvious straw man argument. Replace word google with
| search engine.
|
| > Expand your scope to both Google, and the creation of
| an ecosystem of SEO pages which Google incentives. They
| are the same, in totality. Google doesn't just index --
| it also funds the creation of landing pages.
|
| This doesn't matter, you are mistaking dataset with the
| model. Search engine will not return to you things that
| were not in dataset, it will give you many results that
| you can judge with many points of reference. Language
| model will return you one answer, answer that could be a
| correct result that is inside the dataset or could be
| totally false and incorrect but plausible and you will
| have no point of reference to check that unless you use a
| real search engine.
| tazjin wrote:
| > Google search absolutely does hallucinate completely
| fictitious results
|
| No, it absolutely does not. Yes, there is SEO spam in the
| index, but no - it is not Google hallucating it. It
| really exists on the internet, see also the second point
| of my comment.
|
| > the same thing will happen to something like chatgpt
|
| This isn't something that "happens to" GPT, GPT is
| _doing_ it. There 's probably even already GPT -> SEO
| spam pipelines out there generating websites.
| Pxtl wrote:
| This is a distinction without a difference. Both Google
| and the AI can give you clever but fake results. So at
| this point the question is which produces fewer fakes?
| throwaway09223 wrote:
| > "it is not Google hallucating it. It really exists on
| the internet,"
|
| The exact same thing is true for chatGPT, or any other
| computer system. It is providing information and
| associations based on the input dataset.
|
| > "GPT is doing it."
|
| And google is "doing it," when google decides there is an
| association between my query and a bad response. Both
| systems are analyzing a corpus, drawing associations, and
| returning parts of that corpus. The output is
| deterministic based on the input.
|
| The types of associations differ in their depth, but
| there is no fundamental difference in terms of agency or
| outcome.
| lossolo wrote:
| > The exact same thing is true for chatGPT, or any other
| computer system. It is providing information and
| associations based on the input dataset.
|
| No, it's not, for example if you ask google to show you
| papers about some topic with words in quotes you think
| you remember from the paper it will show you the proper
| link IF it exists and language model will just generate
| you a result that doesn't exist.
|
| If I search something on Google that doesn't exist or
| that it have no answer I can see looking at list of
| search results that probably either what I look for
| doesn't exist or my assumption is false but language
| model will generate you plausible explanation/answer that
| can be 100% false and it doesn't know or understand that
| it's false and you will have no way to know if it's false
| or true and no point of reference because ALL the results
| you will receive could be hallucinated.
| throwaway09223 wrote:
| > "if you ask google to show you papers about some topic
| with words in quotes you think you remember from the
| paper it will show you the proper link IF it exists and
| language model will just generate you a result that
| doesn't exist."
|
| Not really.
|
| What will actually happen is Google _may_ link me the
| actual research paper, along with thousands of other
| associated pages which may or may not have all kinds of
| fake, false information. For example, if I search for
| "study essential oils treat cancer" I get an estimated
| 190 millon matching documents. A huge percentage of these
| have false and misleading information about using oils to
| treat cancer.
|
| > "language model will generate you plausible
| explanation/answer that can be 100% false and it doesn't
| know or understand that it's false and you will have no
| way to know if it's false or true and no point of
| reference because ALL the results you will receive could
| be hallucinated"
|
| With google it is third parties "hallucinating" the wrong
| answers (or worse: intentionally answering wrong in order
| to exploit and profit). The overall dynamic is not
| different. Google is providing these wrong answers,
| written by others.
|
| The overall dynamic of providing information of
| questionable veracity is generally the same - because the
| question of _who_ creates the associations and incorrect
| content is not particularly germane.
| lossolo wrote:
| You are mistaking again model with dataset. To be on the
| same knowledge depth they both have to use the same
| dataset, the difference is that in search engine you have
| points of references, rankings and reputation of sites,
| human discussion and the most important source of the
| answer, so a lot of signals on which you can also rank
| the answer yourself. In language model you have none of
| that, ZERO signals and not only it can return answer that
| was NOT in the dataset but it can make it plausible
| looking.
| qayxc wrote:
| > Google search absolutely does hallucinate completely
| fictitious results. It's called SEO spam.
|
| I wouldn't categorise that as "hallucinating fictitious
| results" - the algorithms still only returns _existing_
| results. If you follow the link, you will find key words
| embedded in the HTML or visible text in the browser.
|
| Different kettle of fish entirely.
| jonathanstrange wrote:
| It kind of does in case of their new Questions & Answers
| feature. They often give wrong or nonsensical answers to
| queries. To be fair, it doesn't hallucinate the results
| but offers little snippets from the web that answer
| something else than what was asked.
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