Their A3B Omni paper mentions that the Omni at that size outperformed the (unreleased I guess) VL. Edit: I see now that there is no Omni-235B-A22B; disregard the following. ~~Which is interesting - I'd have expected the larger model to have more weights to "waste" on additional modalities and thus for the opposite to be true (or for the VL to outperform in both cases, or for both to benefit from knowledge transfer).~~
The point is that the site, contacting your local MEP, and all the discussion in this thread, is pointless to affect some kind of durable societal change
Pointing out that it's vibe-coded just emphasises that all of the above actions are just low-effort cope
I notice four downvotes so far for stating a fact that a debate exists. My comment above didn't even make a normative claim. For those who study AI risks, there is indeed a _debate_ about the pros and cons of open weights. The question of "what are the implications of open-weight models?" is not an ideological one; it is a logical and empirical one.
I'm not complaining; it is useful to get an aggregated signal. In a sense, I like the downvotes, because it means there are people I might be able to persuade.
So how do I make the case? Remember, I'm not even making an argument for one side or the other! My argument is simply: be curious. If appropriate, admit to yourself e.g. "you know, I haven't actually studied all sides of the issue yet; let me research and write down my thinking..."
Here's my claim: when it comes to AI and society, you gotta get out of your own head. You have to get out of the building. You might even have to get out of Silicon Valley. Go learn about arguments for and against open-weights models. You don't have to agree with them.
I recently had to check code from some of my students at the university as I suspected plagiarism. I discovered JPlag which works like a charm and generates nice reports
How do you deal with disputes? One's code is flagged even if the student in question didn't actually cheat. What then? Do you trust tools over the students' word?
In addition, do things like stack overflow and using LLM-generated code count as cheating? Because that is horrible in and of itself, though a separate concern.
The output of plagiarism tools should only serve as a hint to look at a pair of solutions more closely. All judgement should be derived entirely from similarities between solutions and not some artificial similarity score computed by some program.
Unfortunately, this is not really what happens in my experience. The output of plagiarism tools is taken as fact (especially at high school levels). Without extraordinary evidence of the tool being incorrect, students have no recourse, even if they could sit and explain the thought process behind every word/line of code/whatever.
Well, in this case I noticed the same code copied while grading a project. I used then JPlag to run an automatic check in all the submissions for all the projects. It found many instances where a couple of students did a copy-paste with same variable names, comments, etc. It was quite obvious if you look in detail, and JPlag helped us spot it in multiple files easily.
An archival video of all coding sessions (locally, hosted by the student), starting with a visible outline of pseudo-code and ending with debugging should be sufficient.
In case of a false positive from a faulty detector this is extraordinary evidence.
We had a professor require us to use git as a timestamped log of our progress. Of course you could fake it but stealing work and basically redoing it piece by piece with fake timestamps is a lot of work for cheaters.
You might be surprised. Many students who use ChatGPT for assignments end up turning in code identical (or nearly identical) to other students who use ChatGPT.
Different in an exact string match but code that is copied and pasted from ChatGPT has a lot of similarities in the way that it is (over) commented. I've seen a lot of Python where the student who "authored" it cannot tell me how a method works or why it was implemented despite having the comments prefixed to every line in the file.
From my experience using ChatGPT, It usually remove most of my already written comments when I ask questions about code I wrote myself. It usually give you outline comments. So unless you are supporter of the self documented code idea, I don't think ChatGPT over comments.
It's obviously down to taste, but what I've seen over and over is a comment per line which to me is excessive outside it being requested of absolute beginners.
That happens and also the model can't decide if it wants the comment on the line before the code or if everything should be appended to the line itself so when I see both styles within a single project it's another signal. People generally have a style that they stick with.
yeah but the prompt itself generally adds sufficient randomness to avoid the same verbatim answer each time.
as an example just go ask it to write any sufficiently average function. use different names and phrases for what the function should do; you'll generally get a different flavor of answer each time, even if the functions all output the same thing.
sometimes the prompt even forces the thing to output the most naive implementation possible due to the ordering or perceived priority of things within the requesting prompt.
it's fun to use as a tool to nudge it into what you want once you get the hang of the preconceptions it falls into.
MOSS seems to be pretty good finding multiple people using LLM-generated code and flagging them as copies of each other. I imagine it would also be a good idea to throw the assignment text into the few most popular LLMs and feed that in as well, but I don't know of anyone who has tried this.
Are you the same kind of people that think that NGO workers should work for free or for a small wage that is not representative of the market wage for their positions?
No, I’m the type of person who thinks tech salaries are bloated in certain areas and certain companies and that does not follow the distribution of talent. It’s followed the distribution of VC money and profits of large companies. The evidence of such is that the median software engineer in the US is in the low-mid $100s (depending on what source I want to believe it’s $110k-$140k). But I also believe that same talent can be sourced outside the US is many cases and for far less expense.
I also view most apps/tech as not very novel. It’s largely the same engineering “problems” that are known and well documented. A lot of it can be done by average developers and “top tier” talent isn’t usually needed other than probably the cryptographic components in Signal’s case. Scale is certainly a concern, but that is a familiar problem that’s has a lot of documentation solutions and approaches.
I could be wrong. Maybe they’re already doing this and it just happens most of their expense is going to a couple high paid execs. Could be that I’m underestimating the complexity as well. But I find my statements to be true in many cases. I can even point to the number of times I’ve talked to consultants and top tier devs about building things for me. What they would charge $1m for I can often piece together for less than $50k by hiring a few folks in low COL areas and then just spending a little effort refactoring their code to be as pretty as I like it to be; sometimes I outsource that too but the point is having a whole company of top tier talent isn’t usually necessary, it’s a choice. Just like believing that top tier talent only exists in the high cost tech hub cities is a choice more so than the truth.
I read through that and none of the section (or entire work) ever talk about the above discussion. Further I looked at some of the many citations of on that section and none of them suggest that the OP is right. In fact a few of them I know disagree.
Depends. Models are matrices of floats and so there's little chance an umbrella-term like "stochastic parrot" will never not stick, even when they already show signs of syntactic, semantic world-building capability (https://www.arxiv-vanity.com/papers/2206.07682/). If you are like me (and them: https://archive.is/cZi83) and deem instruction following, chain-of-thought prompting, computational properties of LLMs (as researchers continue to experiment with training, memory, modality, and scaling, for example, to arrive at abstract reasoning) as emergent, then we're on the same page.
Okay so just to confirm that section doesn't actually tell us anything about this and in fact this is all based on your own understanding of the mechanisms involved.
In 35 years, we've gone from kids having to do and show their own work to looking up answers online for everything and shortcutting their way through their educations because their parents aren't participating.
Fortunately, expertise is not limited to a single concrete topic. There are experts in climate change from a socio-economic perspective, which are able to do science in such complex environments :)
If such experts existed, centrally planned economies would work wonderfully. Unfortunately, they don't. For sufficiently complex problems, the knowledge is diffuse across a wide range of individuals and there is no one expert we can turn to. That's why debate, and tolerating dissent, is important.
Huh? These experts absolutely exist - look e.g. at iSAGE during the pandemic in the UK. They combined epidemiology with sociology to provide a more comprehensive view of the situation. It's just that you ultimately have to listen to people saying "do X, which is expensive". Governments don't on the whole tend to love that.
Dissent is important, within reason. Things like claiming a medication we know is ineffective can cure Covid in the face of 99% of the medical establishment telling you you're a moron is not within reason, for example.
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