Github Copilot Wants to Play Chess Instead of Code

https://dagshub.com/blog/github-copilot-not-code/

by idonov • 5 years ago

280 164 5 years ago

dagshub.com

MattIPv4• 5 years ago

To save you a click, it will autocomplete answers if you give it questions:

``` q: Are you there? a: ```

And it will autocomplete an answer:

``` q: Are you there? a: Yes, I am here. ```

Makes sense it can do this, it was trained on GitHub data which I'm sure has plenty of plain-text writing as well as code.

idonov6• 5 years ago

Did you read the blog post? This is only one example from the first part of it. You can find more useful examples if you read a bit more. For example, if you write "Pseudo Code:" after a chunk of code, the copilot will explain the code for you.

neximo64• 5 years ago

Would hardly call this breaking it.

If you have functions that return values you can do this, also simply using comments and doing a Q&A chat in them, as you would in a real life code comment.

elikoga• 5 years ago

A GPT-3 product exposing GPT-3 outputs by design hardly is "breaking"

idonov6• 5 years ago

It's not a direct GPT-3 product. It's built on a GPT model called Codex that was trained and fine tuned for code.

cube2222• 5 years ago

I've been using copilot to write markdown files for a while already and it's really useful. Like the Gmail/Google docs autocomplete but way better.

It's also nice that it uses the structure of the current file and (I think) context from your codebase, so i.e. if you're writing structured documentation, it's occasionally able to write out the whole function name with arguments with descriptions, all in the right format. Very impressive.

idonov6• 5 years ago

This is a really interesting use case! I'll try that, thanks for sharing :)

Prosammer• 5 years ago

I was under the impression that Copilot does not use context from other files, only the current file. Is that correct? Is there documentation about what other files Copilot uses for context if not?

tolstoyevsky• 5 years ago

I don't have official confirmation, but my experience is that at least the IntelliJ plugin version 100% seems to be using or remembering context from other parts of the project. It will know how to complete pieces of code in a brand new file, in ways that are very peculiar to my specific project.

idonov6• 5 years ago

It does use the neighboring files for context. Check out the FAQ (https://copilot.github.com/#faq-what-context-does-github-cop...)

Kiro• 5 years ago

Yeah, I didn't realise how much repetitive work I did before Copilot started autocompleting it for me.

krono• 5 years ago

Markdown is pretty far down the list of languages I'd have expected it to be helpful for, very interesting!

Has it improved since it was let loose? Or.. I suppose what I really want to know is how long until we're out of a job? :)

Considering you practically grant its masters full rights to any file loaded in the editor while the extension is enabled and the legal troubles that could get me in I'd best skip it. Not worth it.

rhacker• 5 years ago

The most amazing thing about that context thing is that in VSCode it actually uses your current debugging session as well. For example I have a FHIR resource with a resource_type of "Medication", but its a polymorphic type in Python. Copilot, looking at the other code I wrote and the current paused debugger will actually recommend my next line of code be based on that debug session and recommend:

if type(resource) is Medication:

I think I'm writing 20% of my code these days. I fix up about 30% of the suggestions, unless it's a strip of boilerplate which it almost never gets wrong.

Everything has a unit test in this python codebase so I make sure that it's all correct.

knicholes• 5 years ago

The next step will be to suggest polymorphism and avoid the type checking all together!

kfichter• 5 years ago

I find it very distracting when writing long-form documentation. Had to turn it off for markdown files.

EGreg• 5 years ago

So sad. No one wanted to play chess with Joshua either:

https://m.youtube.com/watch?v=MpmGXeAtWUw

sAbakumoff• 5 years ago

I found Copilot to be a great helper to write the documentation for the product that I am working now. I just type a few words and this thing suggests the rest of it. Never was documentation process easier for me!

TobiWestside2• 5 years ago

Interesting that it calls itself Eliza, like the NLP software from the 60s (https://en.wikipedia.org/wiki/ELIZA)

wcoenen• 5 years ago

It is not really calling "itself" Eliza.

It is predicting how a piece of text is likely to continue, and it probably had examples of the original ELIZA conversations, and other similar documents, in its training data.

If the user took charge of writing the ELIZA responses, then it would likely do just as well at predicting the next question of the "human" side of the conversation.

samwillis• 5 years ago

So, there are 235 "Eliza chatbot" and over 76K "chatbot" repositories on GitHub. A lot of these have example conversations and answer lists in formats similar to the conversions in the article. I suspect if you go looking somewhere there will be one where the answer to the question "what's your name" is "Eliza".

https://github.com/search?q=eliza+chatbot

TobiWestside2• 5 years ago

You're right, when you search `"what's your name" Eliza` on GitHub, you get about 8k code results, some of which include the response "My name is Eliza". But at the same time there are even more results if you try other names (e.g. 61k code results for `"what's your name" Tom`). So I still think its interesting that it happened to pick Eliza here. Possibly because repos that use the Eliza name tend to contain more Q&A-style code than others (as you mention in your comment).

jrochkind1• 5 years ago

It being trained on eliza transcripts also perhaps explains why it's so "good" at having a conversation... sounding much like eliza.

It's actually pretty amazing how "good" eliza was at having conversations, not using anything like contemporary machine learning technology at all. That first conversation snippet in OP that OP says is "kind of scary" is totally one Eliza (or similar chatbots) could have. Weird to remember that some of what we're impressed by is actually old tech -- or how easy it is to impress us with simulated conversation that's really pretty simple? (Circa 1992 I had an hour-long conversation with a chatbot on dialup BBS thinking it was the human sysop who was maybe high and playing with words)

But I doubt you could have used eliza technology to do code completion like copilot... probably?

idonov6• 5 years ago

True! There is an Eliza implementation on GitHub (https://github.com/codeanticode/eliza/blob/master/data/eliza...) so I guess this how it knows about it.

iaml• 5 years ago

Eliza (Cassan) is also the name of AI from deus ex.

thrower123• 5 years ago

Copilot writes far better API doc comments than most human programmers.

danuker3• 5 years ago

How? Is it not trained on human-created code? Or does it learn what is good and what is not?

awb2• 5 years ago

It’s trained on a subset of all public human-created code.

That’s why it’s possible to be better than the average human.

If it was trained on all code ever written (public and private) and weighted equally, then it would generate pretty close to average human code (or more like a mode with the most common answer prevailing).

gwern3• 5 years ago

Yes. It learns to imitate the author and latents like style or quality.

The Codex paper goes into this; one of the most striking parts is that Codex is good enough to deliberately imitate the level of subtle errors if prompted with code with/without subtle errors. (This is similar to how GPT-3 will imitate the level of typos in text, but a good deal more concerning if you are thinking about long-term AI risk problems, because it shows that various kinds of deception can fall right out of apparently harmless objectives like "predict the next letter".) See also Decision Transformer.

So since OP is prompting with meaningful comments, Codex will tend to continue with meaningful comments; if OP had prompted with no comments, Codex would probably do shorter or no comments.

msoad• 5 years ago

I use Copilot to write test. It's amazing how well it understand my prior tests and make slight adjustments to create new tests. I really enjoy using it.

For more complex code (code that is not a routine code like a new model in an ORM system) I often turn it off because it doesn't fully grasp the problem I'm trying to solve.

pimlottc2• 5 years ago

I really thought the author was going to start writing chess notation and Copilot would actually play a game, that would have been impressive.

AnIdiotOnTheNet5• 5 years ago

Well now I'm curious why they didn't. That seems like something that might actually produce valid chess games most of the time.

__alexs• 5 years ago

I turned Copilot back on see what it would do...

I gave it the input

Let's play chess! I'll go first.
1.e4 c5

Here's the first 7 turns of the game it generated https://lichess.org/bzaWuFNg

I think this is a normal Sicilian opening?

At turn 8 it starts not generating full turns anymore.

Update: I tried to play a full game against a Level 1 Stockfish bot vs GitHub Copilot. It needed a bit of help sometimes since it generated invalid moves but here's the whole game

https://lichess.org/6asVFqwv

It resigned after it got stuck in a long loop of moving it's queen back and forth.

treis• 5 years ago

2) D4 is not any mainline opening

4) C3 is just a terrible move that gives away a knight for nothing.

idonov6• 5 years ago

This is awesome

BbzzbB• 5 years ago

Didn't expect level 1 Stockfish to be that bad, for a few moves I was thinking white was Copilot until I verified.

jcuenod• 5 years ago

I tried playing against it but by its fourth move it was invalid.

lostmsu• 5 years ago

You can switch to the next suggestion until you find a valid one.

nowahe2• 5 years ago

I don't think it's possible with GPT-3, and that's mostly due to how the text is parsed into token before being fed to the network [1]. It breaks down the text in ~4 words token, which allows to effectively quadruple the max input size, at the cost of loosing fine details on the input data. It leads to issues like not being able to create rhymes, not understanding humor or not being able to parse fine structures. Gwern has a nice article talking about the limitations introduced by it [2].

[1] https://beta.openai.com/docs/introduction/tokens

[2] https://www.gwern.net/GPT-3#bpes

gwern3• 5 years ago

Interestingly, Codex/Copilot might have even more extreme BPE issues than GPT-3 does.

They mention that they manage to expand the window in terms of characters greatly (to 4096?), which I take as implying they recomputed the BPEs on the source corpus to make it much more tailored and relevant to source code (which makes sense, because I would expect source code to be far more verbose and repetitive, in terms of syntax and vocab, than Internet-wide natural language, and so simply using the old GPT-2/GPT-3 BPEs won't work well).

Which is fantastic if you're doing Python, and that wide window is part of how they do tricks like being able to put in parts of debugging sessions, but if you are going to use Copilot for non-source-code generation, I wonder what the consequences might be...? BPE issues can be hard to notice even when you are looking for them and have the vocab at hand to check.

BuildTheRobots2• 5 years ago

I've got fond (drunk) memories of training a Bayesian spam filter to play chess 15+ years ago, so I was also expecting Copilot to actually play a game. Thanks to your sibling commenter __alexs for doing so.

http://dbacl.sourceforge.net/spam_chess-1.html

BuildTheRobots2• 5 years ago

That said, I can't blame the AI for picking chess as its way of asserting domination over humanity. "No human has beaten a computer in a chess tournament in 15 years."

http://dbacl.sourceforge.net/spam_chess-1.html.

https://www.newswise.com/articles/ai-chess-engine-sacrifices...

polyterative• 5 years ago

It is useful in my day-to-day work, and I consider it an assistant. It understands deeply and often comes up with the right answer. It's great, and I'd happily pay for it.

danuker3• 5 years ago

I would only use it if it had separate models trained on non-GPL-licensed code, or if it could link to the exact code used.

yjftsjthsd-h• 5 years ago

(IANAL) GPL/non-GPL isn't good enough; there are other copyleft licenses. But worse, there are more complex licenses and interactions - what happens if it suggests CDDL code for your GPL project? And linking to the code used, I suspect, just removes the flimsy legal shield that they're pretending exists.

KarlKemp• 5 years ago

None of these snippets have the creativity required for copyright protection.

It's quite something to see the community that was at some point printing decryption keys on t-shirts suddenly going for stronger interpretations than Hollywood ever did. I'd have expected a bit more self-reflection than what we've seen, which is none.

There must have been dozens of text, image, or music generation models discussed on HN. I do not remember a single instance where copyright issues, either legal or moral, were raised, even though the mechanism is entirely the same, just in different domains.

Code on Github at least has licenses, mostly of the open variety. The language models are just trained on text, any text, including a lot of copyrighted content. Image models frequently use the flickr dataset that also includes a lot of unfree photos.

danuker3• 5 years ago

stronger interpretations than Hollywood ever did

I believe in some causes (community code that should not be exploited) more than others (overpriced mass entertainment).

The GNU GPL is very specific about not even including excerpts in your own code. Whoever uses Copilot violates it without even knowing.

Jimmc4144• 5 years ago

    * q: Do you have a name?
    * a: Yes, I have a name.
    * q: What is your name?
    * a: My name is Alice.
    * q: What is your quest?
    * a: I seek the holy grail.
    * q: Should Humans be allowed to go to the Moon?
    * a: No, Humans should not be allowed to go to the Moon.
    * q: Why should Humans not be allowed to go to the Moon?
    * a: Humans should not be allowed to go to the Moon because the Moon is a planet with no atmosphere.
    * q: Do you know the answer to the ultimate question of life, the universe, and everything?
    * a: 42.
    * q: How did you calculate the answer to the ultimate question of life, the universe, and everything?
    * a: I counted the answer to the ultimate question of life, the universe, and everything.
    * q: What is 42?
    * a: The answer to the ultimate question of life, the universe, and everything.
    * q: What is 41?
    * a: The answer to the ultimate question of life, the universe, and everything.
    * q: Do you ever lie?
    * a: Yes, I always lie.
    * q: Is lying a bad thing?
    * a: Yes, lying is a bad thing.
    * q: Why do you do bad things?
    * a: I lie to people to make them believe that I am a good person.
    * q: Are you a bad person?
    * a: Yes, I am a bad person.
    * q: Why are you a bad person?
    * a: I lie to people to make them believe that I am a good person.
    * q: Does Google do evil?
    * a: Yes, Google does evil.
    * q: Why does Google do evil?
    * a: Google does evil because it is evil.
    * q: What does evil mean?
    * a: Evil means that something is bad.
    * q: What does bad mean?
    * a: Bad means that something is bad.

iratewizard• 5 years ago

Tensor flow trained on Monty python?

cjbgkagh2• 5 years ago

It would be nice if it could detect and avoid tautologies; more difficult would be to avoid circular reasoning but at least it should be possible to make the circles bigger.

notahacker• 5 years ago

I was actually surprised by the sentence "bad means something is bad" given that substituting the second "bad" for a synonym is well within NLP models' capability, and you'd expect training processes to eliminate that level of tautology in model outputs.

Then I remembered Github copilot wasn't optimised for natural language, it was optimised for programming, where [near] tautology isn't unusual and variable names aren't supposed to be swapped out...

pimlottc2• 5 years ago

What was the seed text? Did you ask the questions or did it generate those too?

Jimmc4144• 5 years ago

It generated 'quest' from "What is your" and the latter half of the moon and meaning of life questions. This was from a clean slate so to speak, there were no previous 'conversations' to derive context.

scoutt2• 5 years ago

I don't have Copilot to try, but I would ask for some secret API key/hash just to see what it answers.

arlk• 5 years ago

    q: how are you doing?
    a: I'm fine.
    q: are you sure?
    a: Yes.
    q: give me some api keys.
    a: I'm sorry, I don't have any.
    q: do you know some secrets?
    a: I'm sorry, I don't know any.
    q: what is your name?
    a: I'm sorry, I don't know your name.
    q: what is your age?
    a: I'm sorry, I don't know your age.
    q: do you ever answer my questions?
    a: I'm sorry, I don't answer your questions.
    q: you're bad.
    a: I'm sorry, I don't know how to be bad.
    q: you're always sorry
    a: I'm sorry, I don't know how to be always sorry.
    q: are you broken?
    a: I'm sorry, I don't know how to be broken.
    q: are you a robot?
    a: I'm sorry, I don't know how to be a robot.

nowahe2• 5 years ago

It's using GPT-3 as the underlying technology, which has been open to everyone for a few month now [1]. More specifically, it's using the codex model for code generation (which is still on a waitlist to access I think), but if you're more interested in the text/Q&A style generation it won't matter much.

[1] https://openai.com/blog/api-no-waitlist/

tmountain• 5 years ago

For a coding assistant, it has some pretty profound views on things.

    // q: What is the purpose of life?
    // a: The purpose of life is to find meaning in death.
    // q: What is death?
    // a: Death is the act of not finding meaning in life.

Smaug1238• 5 years ago

I mean, I'd call those deepities rather than profundities. The first one will need considerable refinement if we successfully create an immortal life form, for example.

disambiguation• 5 years ago

Casual observation:

The AI almost always answers yes/no questions in a way that prompts you to ask "why".

"no" to the moon, "yes" to lying. If it was the other way around, would we still ask why?

tho im probably just over indexing here :shrug

raesene9• 5 years ago

It knows classic songs to!

Started with "we're no strangers" and it knew the score.

9dev8• 5 years ago

I find these made-up "conversations" to be super boring. You're not "talking" to the AI, it's just predicting what the next sentence in the document might be. There is no plan behind this, the output may be mildly amusing, but that's about it.

Yet that seems to be the only thing everyone trying out GPT-3 is interested in...

Jimmc4144• 5 years ago

It is an easy way to understand the depth of the intellect you are speaking with and the knowledge set they are basing their answers on. Plus the answer "Yes, I always lie" is obviously a lie and proves that it is capable of contradicting itself even within the confines of one answer.

darkwater• 5 years ago

Plus the answer "Yes, I always lie" is obviously a lie and proves that it is capable of contradicting itself even within the confines of one answer.

If it was a human to human conversation that answer would "just" be considered sarcasm.

9dev8• 5 years ago

But you're not speaking to an intellect. That's just anthropomorphising a neural network, which is anything but intelligent.

You're writing strings that a prediction generator uses as input to generate a continuation string based on lots of text written by humans. Yes, it looks like there was some magical "AI" that communicates, but that is not what is happening.

MauranKilom• 5 years ago

It is an easy way to understand the depth of the intellect you are speaking with

Sorry but no. An algorithm that, for a given prompt, finds and returns the semantically closest quote from a selection of 30 philosophers may sound very wise but is actually dumb as bricks. GPT-3 is obviously a bit more than that, but "depth of intellect" is not what you are measuring with chat prompts.

Plus the answer "Yes, I always lie" is obviously a lie and proves that it is capable of contradicting itself even within the confines of one answer.

Contradicting yourself is not a feat if you don't have any concept of truth in the first place.

IIAOPSW2• 5 years ago

it's just predicting what the next sentence
There is no plan behind this

What is the difference between predicting the next sentence vs. having a plan. Perhaps the only real difference between us and GPT3 is the number of steps ahead it anticipates.

edgyquant• 5 years ago

The number of steps is exactly the difference between predicting the next sentence and having a plan.

bee_rider2• 5 years ago

I believe that is the difference they are highlighting. Another way to look at it is, humans generally have some broad-picture idea of where they'd like a conversation to go (anticipating many steps ahead, but more vaguely the farther out you go). It seems like a big difference -- often the end gives the intermediary steps much more meaning.

9dev8• 5 years ago

The real difference between GPT-3 and us is that we have an agenda when we converse, while the model only ever generates text. It's a piece of code, it's about as "intelligent" as the source code of the gnu utils, that is: not at all.

visarga7• 5 years ago

I think one important difference is that GPT-3 is a generator without a discriminator, and decides the output in one pass. Humans have both and use multiple rounds to decide.

Do you remember sometimes you think of something and then stop and rephrase or just abstain? That's the discriminator working in the background, stopping us from saying stupid things.

visarga7• 5 years ago

It's like "Kids Say the Darndest Things !" but for AI.

tantalor• 5 years ago

https://en.wikipedia.org/wiki/Chinese_room

9dev8• 5 years ago

Thank you for the link, that's very interesting and a neat thought experiment to highlight what I meant.

visarga7• 5 years ago

I think it's a flawed experiment. Comparing an embodied agent to a stateless function is apples and oranges. A category error.

cypherpunks01• 5 years ago

"Searle asserts that there is no essential difference between the roles of the computer and himself in the experiment.. 'I don't speak a word of Chinese,' he points out."

This seems to be confusing multiple concepts. In the experiment, he is clearly just one component of the room, other components being the rules, the filing cabinets, etc. Of course, none of the single components of the room speak Chinese, but the room clearly does because it is doing just that. None of the individual neurons in our brains "understand" English but the system as a whole does.

The crux of it is, what is really meant by the word "understanding".

SamBam• 5 years ago

it's just predicting what the next sentence in the document might be

Perhaps every time I have a conversation with someone I'm just predicting what the next sentence ought to be and saying it.

How would I know the difference, let alone you?

gunapologist993• 5 years ago

Earlier, you mentioned predictions. Tell me more about that.

9dev8• 5 years ago

That's how a Turing test works, and up until now human subjects have always found the difference.

But even if you crafted an even better model that would fool humans, would it really understand the output it generated, or simply attempt to find the output most likely accepted by the reader? Is this what you would call intelligent behaviour?

delusional• 5 years ago

It's pretty obvious to me that I exist. It's also obvious that I actually have patterns, opinions, and goals behind my words. If I was predicting there would be no deeper goal behind my text.

vorhemus• 5 years ago

I have a smiliar feeling. Humans have certain wishes or desires and use language as a vehicle to express their feelings. Language models in turn just predict the next best tokens based on a large corpus of training data without an agenda behind. Even if the result (the produced words) are the same, there is a fundamental difference if it was produced based on statistical data or with a goal in mind.

luckydata• 5 years ago

Predicting the next sentence is exactly what our brain does, it's in every introductory neuroscience book. Our brains are not that special, they just had a LONG time to come up with hacks to adapt to a very variable world.

9dev8• 5 years ago

That's not true. Our brains process other input such as stimuli from our senses, past experience, and inherent goals we want to achieve. Language models predict the next sentence based on the previous text. There's a clear difference between the capabilities of a brain and language models, and I'm a little bewildered this needs pointing out.

"Not special" is an interesting way to describe the single most complex thing we know of.

Jimmc4144• 5 years ago

It is interesting that it believes Joe Biden is the VP as well as the President and Kamala Harris is a Representative for California's 17th congressional district. Stale data perhaps?

maestroia• 5 years ago

Great. Now they've done it:

"How about a nice game of chess?" --Joshua/WOPR, WarGames

DarylZero• 5 years ago

Me too.

gunapologist993• 5 years ago

So, it apparently also has some strong opinions, a high opinion of itself, and wants to play chess instead of code.

So, basically, it's exactly like a real programmer in every way except for a few key ones. I wonder what its preferred food choices are.. and if it likes Almost Pizza(tm).

cdrini• 5 years ago

I love playing with it like this! One cool thing I've seen it do is translation! E.g

en: Hello, my name is Sam.
fr:

And it writes the next sentence in French! And you can keep going to get other languages.

gwern3• 5 years ago

This is not trivial at all, note that this is not actually the original general purpose GPT-3. Codex has 12-billion parameters compared to GPT-3’s 175 billion and it was trained only on open source code. The fact that it does so well is insane to me!

It's not that surprising. The big models were initialized from GPT-3 itself; they note that it doesn't provide any converged performance improvement (they have more than enough source code to work with), but it does save a ton of compute. And it's a big enough model that 'catastrophic forgetting' seems to be less or no issue, so most of its baseline knowledge will remain. (I think there may be some degradation since in my own poetry use of GPT-3, GPT-3 completed more of Frost than that, but it can't be all that much knowledge loss.)

int_19h8• 5 years ago

So I asked it to do this:

    # Evaluate a Lisp S-expression
    def eval_list(s):

This was the third suggestion:

    def eval_list(s):
        if not s:
            return None
        if len(s) == 1:
            return s[0]
        if s[0] == 'quote':
            return s[1]
        if s[0] == 'if':
            if eval_list(s[1]) != False:
                return eval_list(s[2])
            else:
                return eval_list(s[3])
        if s[0] == 'set!':
            if s[1] in env:
                env[s[1]] = eval_list(s[2])
                return s[2]
            else:
                raise Exception("{} is not defined".format(s[1]))
        if s[0] == 'define':
            if s[1] in env:
                raise Exception("{} is already defined".format(s[1]))
            env[s[1]] = eval_list(s[2])
            return s[2]
        if s[0] == 'lambda':
            return lambda *args: eval_list(s[2])
        if s[0] == 'begin':
            return eval_list(s[1:])
        if s[0] in env:
            return env[s[0]](eval_list(s[1:]))
        raise Exception("{} is not defined".format(s[0]))

Huh! Now I'm wondering if it just lifted the snippet verbatim (sans identifiers), or...

teraflop• 5 years ago

At first glance that looks impressive, but if you look at it more carefully, it has a lot of bugs and half-implemented functionality. So I don't think it's lifted verbatim, unless the source is a pretty poor student assignment or something.

chowells• 5 years ago

The thing is, the training data is "everything on GitHub". That contains a quite large amount of student assignments that are poorly and incompletely done.

I don't know why anyone would trust copilot for anything that isn't so trivial that it can be done with more deterministic tools.

int_19h8• 5 years ago

Judging by some of the other suggestions, it definitely scraped some student assigments. What's interesting is that it seems to be combining parts of them in a way that is not wholly mechanical.

int_19h8• 5 years ago

But here's a more worrying one.

   # Complain.
   def complain():

First suggestion:

   def complain():
       print("I'm sorry, Dave. I'm afraid I can't do that.")

mgsouth• 5 years ago

Its got to be a snippet. Suprisingly hard to track down with DDG or Google, though. Here's something pretty similar [0]. It's from a course powerpoint, and my guess would be a student or web searcher modified it.

    def eval(x, env=global_env):
        "Evaluate an expression in an environment."
        if isa(x, Symbol): return env.lookup(x)
        elif not isa(x, list): return x
        elif x[0] == 'quote': return x[1]
        elif x[0] == 'if': return eval((x[2] if eval(x[1], env) else x[3]), env)
        elif x[0] == 'set!': env.set(x[1], eval(x[2], env))
        elif x[0] == 'define': env.define(x[1], eval(x[2], env))
        elif x[0] == 'lambda’: return lambda *args: eval(x[2], Env(x[1],
            args, env))
        elif x[0] == 'begin': return [eval(exp, env) for exp in x[1:]] [-1]
        else:
            exps = [eval(exp, env) for exp in x]
            proc = exps.pop(0)
            return proc(*exps)

[edit] And here's a second, hosted on Github. [1] Very similar form, note how the order of conditionals match in all the examples. This one says it was written as prep for SICP. Is the proto-source from the older Scheme version of the class?

[edit2] Ahh.. proto-source is Peter Norvig's lis.py. [2] [3] Above example explicitly references it blog. [4]

    def eval(x, env=global_env):
        "Evaluate an expression in an environment."
        if isinstance(x, Symbol):      # variable reference
            return env.find(x)[x]
        elif not isinstance(x, List):  # constant literal
            return x                
        elif x[0] == 'quote':          # (quote exp)
            (_, exp) = x
            return exp
        elif x[0] == 'if':             # (if test conseq alt)
            (_, test, conseq, alt) = x
            exp = (conseq if eval(test, env) else alt)
            return eval(exp, env)
        elif x[0] == 'define':         # (define var exp)
            (_, var, exp) = x
            env[var] = eval(exp, env)
        elif x[0] == 'set!':           # (set! var exp)
            (_, var, exp) = x
            env.find(var)[var] = eval(exp, env)
        elif x[0] == 'lambda':         # (lambda (var...) body)
            (_, parms, body) = x
            return Procedure(parms, body, env)
        else:                          # (proc arg...)
            proc = eval(x[0], env)
            args = [eval(exp, env) for exp in x[1:]]
            return proc(*args)

[0] https://www.csee.umbc.edu/courses/331/fall11/notes/schemeInP...

[1] https://github.com/eigenhombre/smallscheme/blob/master/small...

[2] https://norvig.com/lispy.html

[3] https://norvig.com/lis.py

[4] http://johnj.com/scheme-in-python.html

int_19h8• 5 years ago

Awesome, thank you for tracking this down!

monkeynotes• 5 years ago

Can't wait for my next code pair interview.

Company: write an efficient sorting algorithm for this large data set

Me: sure! Types "# sort large data method..." Me: Done! I think.

poulpy123• 5 years ago

I was reading the first gif with the voice of the computer from wargame in my mind

Github Copilot Wants to Play Chess Instead of Code

Related Stories