vlthr · HackerTrans

il y a 3 ansdiscuss

This is exceptionally well communicated. Language design is really hard, and it's so easy to lose sight of the bigger picture when trying to find the next incremental step. This post helped me understand a lot of vague frustrations I've experienced in the past, and the bigger picture it outlines really resonates with me.

vlthr

il y a 3 ansdiscuss

That example actually highlights why I think register is a helpful name. Dialects vary across people, but registers vary across situations for each person.

In the programming language context, dialect can be applied on varying levels but usually signifies the former, where each individual or group has a persistent preference for some language or style. Within a single programming language, dialects are usually a bad thing because they risk splintering the community into mutually incompatible subgroups (e.g. scala fp styles, c++ boost).

Part of the reason why evolving a language is hard is because every time you introduce a new way to do something which could be done before, users have to choose. If that choice divides users into groups that persistently pick one over the other based on style or community affiliation, you’ve introduced a new dialect. If the choice flows more naturally from the situation the user finds themselves in, you’ve introduced a new register.

vlthr

il y a 4 ansdiscuss

I’m definitely not worried for Microsoft or the other big tech companies developing copilot-like products. To the extent that legal blowback focuses on issues that are both impactful and solvable (e.g. plagiarizing non-trivial snippets), they should be held to a high standard. Your point about the risk of poisoning the public’s acceptance of these technologies also resonates with me.

What worries me the most is the effect the public backlash towards these big companies can have on smaller actors that could enter this space in the near future. In the past we’ve seen open source projects like GPT-J come together to fund and reproduce closed models, and if we’re not careful to be nuanced in our criticism of big-tech frontrunners we might end up poisoning the waters enough to deter small actors without a dedicated legal team.

Copyright law is ultimately designed around humans as the only kind of actor. In an ideal world we would sit down and think about the way non-human learners should fit into this system and the balance of tradeoffs we want those laws to aim for. I hope that happens someday, but until then I hope we can cultivate a world where small actors are able to experiment with these technologies without fear of legal action.

That’s why it bothers me to see people arguing that language models should be thought of like human programmers making derivative works, even suggesting that we should require attribution for all generated outputs (i.e. the entire training set, always). That helps nobody, except of course big companies with infinite manpower.

vlthr

il y a 4 ansdiscuss

I have a really hard time understanding what future world this article (and other detractors) is arguing for and why that world is made better by taking their arguments seriously.

On the practical level I agree with the part advising caution to those that might end up embedding an identifiably licensed snippet in their codebase via copilot. I also agree that copilot users plagiarizing significant chunks of GPL code for profit is immoral. This needs to be prevented.

I also share the frustration stemming from big companies leveraging their disproportional access to data and resources for profit given that the greatest value of these models is precisely the open source code it is trained on.

Ultimately though, what I care about is the potential for building better tools. LLMs potentially offer paths towards genuinely new forms of human-machine interaction, and I don’t want that exploration to be suffocated by legalism.

vlthr

il y a 5 ansdiscuss

I think the author is right to argue against the claim that “the singularity must happen eventually”, but I’m not sure how applicable your observations are to the question of whether an AI singularity could happen, or whether we should be planning for that contingency.

Today, progress in machine learning is almost entirely empirical, with limited theoretical advances following later to make sense of the empirical findings. Our theoretical underpinnings are so weak we don’t even have a rough estimate of how much harder it is to make a self-improving AGI than e.g. GPT-3. Maybe it’s many orders of magnitude harder, and not even remotely solvable by taking iterative steps from where we are now. Maybe it’s just one unifying theoretical advance away.

As for waiting and hoping it happens on its own, that’s not a great description of what’s happening. A huge number of people (possibly too many, leading to short-term incentives) are trying to make improvements in any way they can think of. Progress is happening at a staggering pace —- we just don’t have any idea how much progress is needed to reach the goal of AGI.

vlthr

il y a 6 ansdiscuss

I agree with all of your points about the diffusion of responsibility that is common in ML, though I think you may not be sensitive enough to the harmful framing being created by the "anti-bias" side.

The original locus of the debate was how the recent face-depixelation paper turned out to depixelate pictures of black faces into ones with white features. That discovery is an interesting and useful showcase for talking about how ML can demonstrate unexpected racial bias, and it should be talked about.

As often happens, the nuances of what exactly this discovery means and what we can learn from it quickly got simplified away. Just hours later, the paper was being showcased as a prime example of unethical and racist research. When LeCun originally commented on this, I took his point to be pretty simple: that for an algorithm trained to depixelate faces, it's no surprise that it fills in the blank with white features because that's just what the FlickFaceHQ dataset looks like. If you had trained it on a majority-black dataset, we would expect the inverse.

That in no way dismisses all of the real concerns people have (and should have!) about bias in ML. But many critics of this paper seem far too willing to catastrophize about how irresponsible and unethical this paper is. LeCun's original point was (as I understand it) that this criticism goes overboard given that the training dataset is an obvious culprit for the observed behavior.

Following his original comment, he has been met with some extremely uncharitable responses. The most circulated example is this tweet (https://twitter.com/timnitGebru/status/1274809417653866496?s...) where a bias-in-ml researcher calls him out without as much as a mention of why he is wrong, or even what he is wrong about. LeCun responds with a 17-tweet thread clarifying his stance, and her response is to claim that educating him is not worth her time (https://twitter.com/timnitGebru/status/1275191341455048704?s...).

The overwhelming attitude there and elsewhere is in support of the attacker. Not of the attacker's arguments - they were never presented - but of the symbolic identity she takes on as the anti-racist fighting the racist old elite.

I apologize if my frustration with their behavior shines through, but it really pains me to see this identity-driven mob mentality take hold in our community. Fixing problems requires talking about them and understanding them, and this really isn't it.

vlthr

il y a 6 ansdiscuss

I take the point about subscribers being hard to count to mean that even though most of the money comes from subscribers, each individual subscriber doesn't have much leverage or bandwidth to communicate their desires to NYT. On the flip side, each individual advertiser commands some sizeable chunk of NYT's revenue as leverage.

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