How good is Codex?
smitop.com
smitop.com
When VisiCalc was released departments of 30 accountants were reduced to 5 accountants because of the improvement for individual worker efficiency, however accounting itself remains largely unchanged and accountants are still a respected profession who perform important functions. There's plenty of programming problems in the world that simply aren't being solved because we haven't figured out how to reduce the burden of producing the software; code generation will simply increase the output of an individual software developer.
The same forces behind "no-code" are at work here. In fact I see a future where these two solutions intermingle: where "no-code" becomes synonymous with prompt-driven development. As we all know, however, these solutions will only take you so far -- and essentially only allow you to express problems in domains that are already well-solved. We're just expressing a higher level of program abstraction; programs that generate programs. This is a good thing and it is not a threat to the existence of our industry. Even in Star Trek they still have engineers who fix their computers...
Epistemic status: I don't know anything about accounting
Codex as it stands is just a novelty, but it does show the shape of what's to come.
The field of program synthesis based on NLP models is really starting to heat up - we have OpenAI Codex, GitHub Copilot, and a recent paper [2] from Google Research demonstrating that these same techniques can generate programs which solve mathematical word problems. Here is an example of the latter:
> Prompt: Please, solve the mathematical problem: a and b start walking towards each other at 4pm at a speed of 2 kmph and 3 kmph. They were initially 15 km apart. At what time do they meet? n0 = 4.0, n1 = 2.0, n3 = 15.0.
> Model output (python program):
n0 = 4.0
n1 = 2.0
n2 = 3.0
n3 = 15.0
t0 = n1 + n2
t1 = n3 / t0
answer = n0 + t1
The problem areas for the examples given here are somewhat self-contained, which is in contrast to the code I write, usually all about integrating multiple systems and bodies of knowledge (user device, network, data schema, industry practices, product requirements etc).
I too rarely, to my occasional regret, have a chance to write a more pure function whose function can be explained so concisely as these examples. Helper functions ("count the words" etc) are sprinkled throughout my code for sure but are mostly provided to me by the platforms I inhabit.
Codex's ability to explain a piece of code in plain english seemed exciting at first, but the type of "other people's code" I am usually puzzling over has so many tentacles into the specific "business rules" of the service i'm writing to. How would Codex know about all that?
Of course codex has already blown my mind several times so I am quite open to it someday being able to ingest an entire set of interrelated codebases and break them down for me succinctly. That doesn't even seem far-fetched, based on what we've seen to this point.
The thing that is ringing a bell for me the most is the idea of it being able to understand APIs and generate correct code for them. That could be a neat learning tool and save some boilerplate. Kind of like scaffold-generation code, but on steroids ...
... perhaps it could even learn to simplify APIs, make them friendlier etc. Or translate between them?
I can do that now with my OpenAI account but Codex needs a specific invite. What's the difference?
I can write my own code for now, thanks.
Not only are they not releasing Codex (and GPT-3), but in order to get access to the API you have to apply for access and be judged against a proprietary set of criteria that are entirely opaque.
Furthermore, I imagine that if you do any innovative work building on top of Codex (or GPT-3) they would control that work product, they would be able to cut you off from accessing your work product at any time if it suits them, and they would be able to build off of your work themselves, co-opting any unique value that you may create.
Why the hell should anyone building an AI business even want to work with them? Sure, it might accelerate your effort right at the beginning, but if you are unable to reproduce your results outside of their platform, you will always be beholden to them.
In a few years will we be reading stories about unfortunate entrepreneurs who had built their businesses on top of OpenAI only to have the rug pulled out from under them, like Amazon sellers whose product was cloned by Amazon Basics, or Twitter clients cut off from the API, or iOS apps made redundant by their core functionality being copied by Apple, or search-driven businesses circumvented by the information cards that Google displays directly in the search results...? Etc, etc.
Am I missing something here?
By Lenin's definition the all those Democratic People's Republics really were democratic.
What that means for OpenAI, I don't know :/
The electoral college, for example, is set up this way. The idea is that electors—who were not democratically elected—could block a populist candidate.
As we saw in 2016, this check failed.
The American Constitution is an indirect democracy. Direct at the local level, indirect at the Federal level (though eventually by statute and amendment it became direct for Congress).
> As we saw in 2016, this check failed.
Nonsense. That "failure mode" was designed for. It has happened a few times. Working as expected.
If you mean “what electors are on the ballot” isn’t democratically decided, but rather, is determined by the parties, then yeah I guess that’s true?
Not by lenin's definition. Just the definition of democracy and where it came from. We get democracy comes from the greeks - where only the wealthy slave owning elites could vote.
The founders also followed that model. Only wealthy landowning whites could vote. The first few elections only like 4 or 5% of the population voted.
Isn't it crazy how propaganda has shifted your understanding of democracy? Democracy was never "of the people, by the people, for the people ", it was always about the elite few.
The Founders did not write that into the Constitution.
> Isn't it crazy how propaganda has shifted your understanding of democracy? Democracy was never "of the people, by the people, for the people ", it was always about the elite few.
Propaganda is what you're writing.
Because the founders wanted voting to be controlled at the state level, not the federal level...
> Propaganda is what you're writing.
Historical facts is propaganda?
So what is propaganda? That only landowning whites could vote? Democracy came from the ancient greeks? That ancient greeks owned slaves and only allowed wealthy slave owners to vote?
"As a non-profit, our aim is to build value for everyone rather than shareholders. Researchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code, and our patents (if any) will be shared with the world. We’ll freely collaborate with others across many institutions and expect to work with companies to research and deploy new technologies." - https://openai.com/blog/introducing-openai/
However, a couple paragraphs down might have been a clue to the likely future: "Sam, Greg, Elon, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research are donating to support OpenAI."
Currently they are not really non-profit and mostly working with Microsoft.
As for being open, I think keeping potentially dangerous tech private for a while while openly sharing the results of research is prudent. The last thing I want is some AI model goes public then we find a way to generate a bunch of computer viruses or propaganda.
As for the business model, there's nothing wrong with it in principle, it's just not what they said they would do. There's no reason a well-funded nonprofit research organization needs to compete with Google and Facebook. They changed their funding model because they wanted to compete, not because they needed to. And it hasn't been very long since they said "Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact." You have to assume they knew from the start that they'd probably want to pivot to a business.
> Returns for our first round of investors are capped at 100x their investment (commensurate with the risks in front of us), and we expect this multiple to be lower for future rounds as we make further progress.
> mean human accuracy at detecting articles that were produced by the 175B parameter model was barely above chance at ∼52% [...] Human abilities to detect model generated text appear to decrease as model size increases [...] This is true despite the fact that participants spend more time on each output as model size increases [1]
> for news articles that are around 500 words long, GPT-3 continues to produce articles that humans find difficult to distinguish from human written news articles [1]
The name is now a complete misnomer.
There may still be some benefit for researchers to collaborate with them (same as with any of the other corporate research labs), but anyone trying to build a business on non-public APIs should obviously tread carefully.
So, no. You aren't missing anything.
I have yet to hear of one person who has gotten access without either a) being Twitter-notable in the ML space, or b) using a personal connection to jump the queue (I hit up someone a couple steps removed from OpenAI and got lucky). As far as I can tell they are just collecting email addresses to gauge interest, and are not even evaluating people who cold-apply through their form.
Please correct me if I'm wrong, though, I only know what I've heard within my own network! It's totally possible they're allowing a very very slow trickle of external unconnected people in.
First, turns out that neural nets get better when you throw more compute at them. OpenAI was full of researchers looking to push the state of the art, and the state of the art became less and less accessible to the average person, who could not afford the supercomputers necessary to train GPT-3. "Democratizing deep learning" became less important as a goal, since it conflicted with the true priority internally: improving the state of the art in deep learning.
Second, it looks like they lost the interest of their initial funders. The execs were left with a big money hole in their budget, and had to go looking for some way to fill it. Bingo bango bongo, and now they are a for-profit looking for income streams.
I don't feel critical of them. It's very hard to do something both altruistic and expensive. Money doesn't just flow to those looking to do good in the world.
It's degrading of the work that we do for one thing.
If you saw how GPT-2 improved to GPT-3 in a year, it's easy to see where this is gonna go over next few iterations.
It's a 2007 Iphone level catalyst that's going to dramatically shift the landscape
All of those things are structurally inadequate to solve the problem in front of them and just make up for it for the same reason ELIZA seemed intelligent... People are willing to believe.
You could put fantastically more resources into that approach and find you're approaching an asymptote. It's the deadliest trap in advanced technology development and it happens when you ignore the first law of cybernetics.
Anyone who's been a practitioner in the software field has experienced that repairing mistakes from a program written by somebody clueless is almost always vastly more expensive than writing it correctly to begin with -- I remember helping a friend "cheat" at CS 101 by stealing somebody else's homework, finding the program was wrong and putting a lot of effort into debugging it and fixing it, never mind changing the symbol names and taking other measures to hide the origin of the program.
It might be my karma, but fixing the program I stole turned out to be excellent preparation for a career in software dev.
The fact that it will converge towards an asymptote is qualitative, not quantitative because the structure of the system is wrong for the problem.
If the people who were doing this research were physicists you could excuse them, but they are computer scientists and should know better.
It's a basic problem that GPT-x allocates the same amount of resources despite the complexity of the problem. Some problems in programming and language understanding have a SAT/SMT-like structure (e.g. for each pronoun solve for what the pronoun refers to, figure out what the subject/objects of all the verbs are, etc.) which scales in a certain way. Other problems in "understanding" require necessarily that you try one interpretation, work through the consequences of that interpretation until you find a dead end, back up to the place where you failed, try something else, test it until done.
Some of these problems that you might want to "solve" with GPT-x might really be problems that can't be solved in a finite amount of time.
Something like GPT-x with some extra structural features might be able to rise to some of these challenges but we are not hearing about that, we are hearing that adding more nodes and more training data and training for longer is the way to salvation.
(So far as "diminishing returns" matter the question is "does the system ever get good enough that it can be let off the leash?" and my answer is no.)
For reference, GPT-3 has 175 billion parameters. That's a few TB (uncompressed).
GPT-3 was trained on 45 TB of text. Most of it comes from 8 years of web crawling, and a significant chunk of the remainder comes from online book corpora [0]. At some point you just run out of human-written text to train on.
You can't just keep making the model bigger (by orders of magnitude I mean) without scaling the input data the same. And at some point we just run out of the latter.