All of the examples on the linked page seem to be "good" outputs. Attribution sounds most useful to me in cases where an LLM produces the typical kind of garbage response: wrong information in the training data, hallucinations, sycophancy, over-eagerly pattern matching to unasked but similar, well-known questions. Can you give an example of a bad output, and show what the attribution tells us?
You got it exactly right. Guilty as charged. Over the coming weeks, we will be showcasing exactly how you can debug all of these examples.
I agree that attribution is most useful for debugging and auditing. This is a prime usecase for us. We have a post with exciting results lined up to do this. Should be out in a week, we wanted to even just get the initial model out :)
What I am reading here is that when the model is wrong, it still (at least sometimes) confidently attributes the answer to some knwoledge base, is that correct? If that is the case, how is this different to simply predicting the vibe of a given corpus and assinging provenance to it? Much less impressive imo and something most models can do without explicit training. All precision no recall as it were.
I think this was answered before, with the constraints of the architecture of the model. You can't expect something fundamentally different from an LLM, because that's how they work. It's different from other models because they were not designed for this. Maybe you were expecting more, but that's not OP's fault or demerit.
What you're saying fits my understanding/expectations. However the post and the user I am replying to seem to imply different. This makes me wonder, is my understanding incomplete or is this post marketing hype dressed up as insight? So I am asking for transparency.
It is not hype. You can try the model on huggingface yourself to see its capabilities. My reply here was clarifying that the examples we showed were ones where the model didn't make a mistake. This is intentional, because over the next few weeks, we will show how the concepts, and attribution we enable can allow you to fix this mistakes more easily. All the claims in the post are supported by evidence, no marketing here.
We are probably at the point where hype and insight aren't that much distinguishable other than what would bear fruit in the future, but I agree with you
This is often quoted, but I wonder whether it's actually strictly true, at least if you keep to a reasonable definition of "works". It's certainly not true in mechanical engineering.
The definition of a complex system is the qualifier for the quote. Many systems that are designed, implemented and found working are not complex systems. They may be complicated systems. To paraphrase Dr. Richard I. Cook’s ”How Complex Systems Fail” where he claims that complex systems are inherently hazardous, operate near the edge of failure and cannot be understood by analyzing individual components. These systems are not just complicated (like a machine with fixed parts) but dynamic, constantly evolving, and prone to multiple, coincidental failures.
A system of services that interact, where many of them are depending on each other in informal ways may be a complex system. Especially if humans are also involved.
Such a system is not something you design. You just happen to find yourself in it. Like the road to hell, the road to a complex system is paved with good intentions.
Then what precisely is the definition of complex? If "complex" just means "not designed", then the original quote that complex systems can't be designed is true but circular.
If the definition of "complex" is instead something more like "a system of services that interact", "prone to multiple, coincidental failures", then I don't think it's impossible to design them. It's just very hard. Manufacturing lines would be examples, they are certainly designed.
The manufacturing lines would be designed, and they'd be designed in an attempt to affect the "design" of the ultimate resulting supply chain they're a part of. But the relationship between the design of some lines and the behavior of the larger supply chain is non-linear, hard to predict, and ultimately undesigned, and therefore complex.
The design of the manufacturing lines and the resulting supply chain are not independent of each other -- you can trace features from one to the other -- but you cannot take apart the supply chain and analyze the designs of its constituent manufacturing lines and actually predict the behavior of the larger system.
AFAIK there's not a great definition of a complex system, just a set of traits that tend to indicate you're looking at one. Non-linearity, feedbacks, lack of predictability, resistance to analysis (the "you can't take it apart to reason about the whole" characteristic mentioned above"). All of these traits are also kind of the same things... they tend to come bundled with one another.
Consider systems that require continuous active stabilization to not fail because the system has no naturally stable equilibrium state even in theory. Some of our most sophisticated engineering systems have this property e.g. the flight control systems that allow a B-2 bomber to fly. In a software context you see these kinds of design problems in large-scale data infrastructure systems.
The set of system designs that exhibit naturally stable behavior doesn't overlap much with the set of system designs that deliver maximum performance and efficiency. The capability gap between the two can be large but most people choose easy/simple.
There is an enormous amount of low-hanging opportunity here but most people, including engineers, struggle with systems thinking.
IMHO, the key is where you add complexity. In software you have different abstraction layers. If you make a layer too fat, it becomes unwieldly. A simple system evolves well if you're adding the complexity in the right layer, avoiding making a layer responsible for task outside its scope. It still "works" if you don't, but it's increasingly difficult to maintain it.
The law is maybe a little too simplistic in its formulation, but it's fundamentally true.
You built this gear using the knowledge from your last gear. You didn't start with no knowledge, read a manual on operating a lathe, grab a hunk of metal and make a perfect gear the first time.
Good, KYC exists for a reason. Why does AI need to open an account, anyway? Just give it a debit card with a limit, not a whole new account and contract with a bank.
Those are much easier problems to solve, and surely already solved by some fintechs, than bringing cryptocurrencies to the minimum legal compliance and meeting performance requirements.
"We promise that we will not enforce" is perhaps a funny way not to grant a license, but making it sound like they do. This seems almost purposefully designed to look open-source to laypeople, while being carefully written in a way that ensures it will be vetoed by any corporate lawyer vetting the license.
>And according to Google, they always delete data if requested.
However, the request form is on display in the bottom of a locked filing cabinet stuck in a disused lavatory with a sign on the door saying ‘Beware of the Leopard'.
LLMs sample the next token from a conditional probability distribution, the hope is that dumb sequences are less probable but they will just happen naturally.
I wouldn't doubt that these companies would deliberately degrade performance to manage load, but it's also true that humans are notoriously terrible at identifying random distributions, even with something as simple as a coin flip. It's very possible that what you view as degradation is just "bad RNG".
Thats what is called an "overly specific denial". It sounds more palatable if you say "we deployed a newly quantized model of Opus and here are cherry picked benchmarks to show its the same", and even that they don't announce publicly.
Personally, I'd rather get queued up on a long wait time I mean not ridiculously long but I am ok waiting five minutes to get correct it at least more correct responses.
I'm not fully onboard with the logic that we just have to live with a certain type of criminal behavior because the technology that could prevent it can be misused to enable another type of criminal behavior. We should aim to stop any kind of criminal behavior.
> I'm not fully onboard with the logic that we just have to live with a certain type of criminal behavior because the technology that could prevent it can be misused to enable another type of criminal behavior. We should aim to stop any kind of criminal behavior.
I don’t think anyone is making a claim that we should live with this according to first principles. I think people are saying this trade-off currently exists because it doesn’t seem to be economically or technologically feasible to solve both well.
How do you propose making an improvement to tracking technology that reduces theft while at the same time not assisting stalking?
One idea: if you report your AirTag as stolen, then it can continue to track the item, but you lose the ability to see where it is. In so doing you hand off tracking capability to some authority. This could be an improvement to the extent that the authority is trustworthy and well behaved. Unfortunately, such properties are not guaranteed across the globe. This would create more incentives for bribery for example.
We should, but also we should prioritise more harmful behaviour being prevented over less harmful behaviour, and stalking/harassment is in my opinion more harmful than property theft.
Frequency isn't really an issue here. I don't care that much if someone steals my luggage. I'd be a little mad if someone took my bike, but I have redundant protection for it, along with other things of more importance, or I keep them on me.
But I'd really, really not like to find out someone was following me around.
If society didn't have to spend the amount of resources that it does dealing with the consequences of personal theft then it would have more resources to direct towards issues like stalking.
I bet Apple could produce some really interesting data from these tools and others that could be used to proactively target stalkers and investigate them before their actions escalate to violence.
You're well beyond the scope of an Airtag at that point. Either you've insured the gear, or you ship it in some more secure fashion, or you have a satellite tracker in it, or whatever other mitigation you can do here. Airtags are great things you might misplace more than anything.
In my experience, there seems to be a limitless supply of newly crowned "AI shamans" sprouting from the deepest corners of LinkedIn. All of them make the laughable claim that hallucinations can be fixed by prompting. And of course it's only their prompt that works -- don't listen to the other shamans, those are charlatans.
If you disagree with them by explaining how LLMs actually work, you get two or three screenfuls of text in response, invariably starting with "That's a great point! You're correct to point out that..."
Avoid those people if you want to keep your sanity.
> Not sure whether the obfuscation is fully synchronous, i.e waiting for the server response before continuing.
The people who designed SSH aren't idiots, and also, you can answer this question by simple observation: When you connect to a server with ~200ms ping, which is somewhat common in the scenarios you describe and which I've done many times, it does not take 20 seconds to show a keystroke.
When someone takes the time to explain undergrad-level concepts in a comment, responding with "are you an expert?" is a level of skepticism that's bordering on hostile. The person you're responding to is correct, it's rare that the theorem statement itself is particularly hard to formalize. Whatever you read likely refers to the difficulty of formalizing a proof.
To be fair, the comment did not explain any concept that I can see, or why this statement is simple. It gave the statement and said it was simple to formalise. It does seem simple enough to me (basic arithmetic statement with a few variables and a bunch of quantifiers) but if somebody has no expertise/intuition, I think it is a fair question, without any hostile intent assumed.
> it's rare that the theorem statement itself is particularly hard to formalize
That's very dependent on the problem area. For example there's a gap between high school explanation of central limit theorem and actual formalization of it. And when dealing with turing machines sometimes you'll say that something grows e.g. Omega(n), but what happens is that there's some subsequence of inputs for which it does. Generally for complexity theory plain-language explanations can be very vague, because of how insensitive the theory is to small changes and you need to operate on a higher level of abstraction to have a chance to explain a proof in reasonable time.
Yes, if the theorem statement itself is "hard to formalize" even given our current tools, formal foundations etc. for this task, this suggests that the underlying math itself is still half-baked in some sense, and could be improved to better capture the concepts we're interested in. Much of analysis-heavy math is in that boat at present, compared to algebra.
Lol it’s weird seeing high school redditors saying gatekeeping and are you an expert in the same thread as university professors, all talking about the same topic. But I guess that’s HN for you.
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