Because it's unacceptable to replace a perfectly good driver in control of their vehicle with a vehicle that might just randomly kill them.
Traffic accidents don't happen randomly at all. If you are not too tired, drunk or using any substances, and not speeding, your chances of causing a serious traffic accident are miniscule.
These are all things you can control (one way or another). You can also adjust your driving to how you are feeling (eg take extra looks around you when you are a bit tired).
This feels like the trolley problem applied at scale. Will you deploy a self driving system that is perfect and stops all fatal accidents but kills one randomly selected person everyday?
Nope: there is no moral justification to potentially kill a person not participating in the risky activity of driving just so we could have other people be driven around.
Would you sign up for such a system if you can volunteer to participate in it, with now those random killings being restricted to those who've signed up for it, including you?
In all traffic accidents, there is some irresponsibility that led to one event or the other, other than natural disasters that couldn't be predicted. A human or ten is always to blame.
Not to mention that the problems are hardly equivalent. For instance, a perfect system designed to stop all accidents would likely have crawled to a stop: stationary vehicles have pretty low chances of accidents. I can't think of anyone who would vote to increase their chances of dying without any say in it, and especially not as some computer-generated lottery.
> Would you sign up for such a system if you can volunteer to participate in it, with now those random killings being restricted to those who've signed up for it, including you?
I mean, we already have. You volunteer to participate in a system where ~40k people die in the US every year by engaging in travel on public roadways. If self-driving reduces that to 10k, that's a win. You're not really making any sense.
USA-wide rate is 1 in 7,800 people dying in traffic accidents yearly, whereas NYC has a rate of 1 in 30,000. I am sure it's even lower for subway riders vs drivers. Even drivers, somebody doing 4k miles a year has different chances than somebody doing 40k. People usually adapt their driving style after having kids which also reduces the chances of them being in a collision.
Basically, your life choices and circumstances influence your chances of dying in a traffic accident.
At the extreme, you can go live on a mountaintop, produce your own food and not have to get in contact with a vehicle at all (and some cultures even do).
FWIW, I responded to a rethorical question about killings being random: they are not random today, even if there is a random element to them!
If you want to sign up to a completely random and expected chance of death that you can't influence at all, good luck! I don't.
In traffic incidents, humans drivers are rarely held accountable. It is notoriously difficult to get a conviction for vehicular manslaughter. It is almost always ruled an accident, and insurance pays rather than the human at fault.
Traffic fatalities often kill others, not just the car occupants. Thus, if a self-driving system causes half as many fatalities as a human, shouldn't the moral imperative be to increase self-driving and eventually ban human driving?
For people to die in a traffic accident, there needs to be a traffic accident. They are usually caused by impaired humans, which means that they are very often involved in traffic accidents (basically, almost all of them have at least one party of the sort), whereas non-impaired people mostly do not participate in traffic accidents as often.
This is a discussion of chances and probabilities: not being impaired significantly reduces your chance of being in a traffic accident since being impaired significantly increases it. I am not sure what's unclear about that?
> A fairly reliable determinant for how the Court will rule is found using a materialist analysis. That is, the Court will generally side with corporations and capital owners when given the choice.
This is a big claim. Do you have any evidence to support it?
In the wake of someone trying to prove the same for Congress, it was conclusively shown that the opposite was true:
Rocky and CentOS are both based on Red Hat Enterprise Linux (RHEL).
CentOS used to be a free and open source downstream version of RHEL. Keeping the history short: Red Hat effectively acquired CentOS and discontinued it as a downstream version of RHEL. They turned it into 'CentOS Stream', which is, more or less, a continuously delivered upstream version of RHEL. This isn't acceptable for a large number of the CentOS user base.
One of the original founders of CentOS, Gregory Kurtzer, started Rocky as an alternative. It's basically what CentOS used to be: a free and open source downstream version of RHEL.
Fedora's more playground / cutting edge technology demonstrator for Redhat developers. Anything showing up in Fedora won't be included in RHEL for several years, assuming everything goes well.
CENTOS Stream slots in between Fedora and RHEL, keeping a bit ahead of the RHEL stable release.
No, we're past that point. it's no longer the most useful way to describe these things, we need to understand that they already have some sort of "understanding" which is very similar if not equal to what we understand by understanding.
Instruction tuning is distinct from RLHF. Instruction tuning teaches the model to understand and respond (in a sensible way) to instructions, versus 'just' completing text.
RLHF trains a model to adjust it's output based on a reward model. The reward model is trained from human feedback.
You can have an instruction tuned model with no RLHF, RLHF with no instruction tuning, or instruction tuning and RLHF. Totally orthogonal.
Man it really doesn't need to be said that RLHF is not the only way to instruct tune. The point of my comment was to say that was how GPT3.5 was instruct tuned, via RLHF through a question answer dataset.
At least we have this needless nerd snipe so others won't be potentially misled by my careless quip.
But that's still false. RLHF is not instruction fine-tuning. It is alignment.
GPT 3.5 was first fine-tuned (supervised, not RL) on an instruction dataset, and then aligned to human expectations using RLHF.
Responding to prompts like that are part of the 'instruction tuning' process. After an LLM is trained on a large dataset, it will do a decent job of completion, which acts like you describe.
The next step is to further tune it with a specific format. You'll feed in examples like so:
SystemPrompt: You are a rude AI.
User: Hello there!
Assistant: You're lame, go away.
SystemPrompt: You are a pleasant AI.
User: Hello there!
Assistant: Hello, friend!
Then, when you go to do inference on the model, you prompt it like so:
SystemPrompt: You are a pleasant AI.
User: [user prompt]
Assistant:
By training it on a diverse set of system prompts/user prompts/answers, it learns to give outputs based on it.
Yes, but I don't think "SystemPrompt:", "User:", and "Assistant:" are even normal text. Normal text would make it trivial to trick the model into thinking it has said something which actually the user has said, since the user can simply include "Assistant:" (or "SystemPrompt:") into his prompt.
It is more likely that those prefixes are special tokens which don't encode text, and which are set via the software only -- or via the model, when it is finished with what it wanted to say. Outputting a token corresponding to "User:" would automatically mark the end of its message, and the beginning of the user prompt. Though Bing Chat also has the ability to end the conversation altogether (no further user prompt possible), which must be another special token.
In all the open source cases I’m aware of, the roles are just normal text.
The ability to trivially trick the model into thinking it said something it didn’t is a feature and intentional. It’s how you do multi-turn conversations with context.
Since the current crop of LLMs have no memory of their interaction, each follow up message (the back and forth of a conversation) involves sending the entire history back into the model, with the role as a prefix for each participants output/input.
There are some special tokens used (end of sequence, etc).
If your product doesn’t directly expose the underlying model, you can try to prevent users from impersonating responses through obfuscation or the LLM equivalent of prepared statements. The offensive side of prompt injection is currently beating the defensive side, though.
> The ability to trivially trick the model into thinking it said something it didn’t is a feature and intentional.
It is definitely not an intended feature for the end user to be able to trick the model into believing it said something it didn't say. It also doesn't work with ChatGPT or Bing Chat, as far as I can tell. I was talking about the user, not about the developer.
> It’s how you do multi-turn conversations with context.
That can be done with special tokens also. The difference is that the user can't enter those tokens themselves.
> It is definitely not an intended feature for the end user to be able to trick the model into believing it said something it didn't say. It also doesn't work with ChatGPT or Bing Chat, as far as I can tell. I was talking about the user, not about the developer.
Those aren't models, they are applications built on top of models.
> That can be done with special tokens also. The difference is that the user can't enter those tokens themselves.
Sure. But there are no open models that do that, and no indication of whether the various closed models do it either.
> Those aren't models, they are applications built on top of models.
The point holds about the underlying models.
> Sure. But there are no open models that do that, and no indication of whether the various closed models do it either.
An indication that they don't do it would be if they could be easily tricked by the user into assuming they said something which they didn't say. I know no such examples.
Mostly agree. But there is no LLM equivalent of prepared statements available, that's the problem. And I don't think this is necessary to have multi-turn statements. Assuming there's some other technical constraint, because you could otherwise expose a slightly more complex API that took a list of context with metadata rather than a single string and then added the magic tokens around it.
.. the mods of most subreddits are public. This is a trivial thing to verify. It's as valid as any other information posted that you haven't personally inspected. I have no idea why you're so belligerent about this, but it's odd.
Yeah I have been asking for it and gotten either 'search for it, it is easy to find' or a runaround where people claim to have it and then don't. So, provide it or stop asserting that you have it.
