Imagine the other more critical production applications who have made the same reasonable engineering decision.
Imagine the other more critical production applications who have made the same reasonable engineering decision.
That's the point of the comment, if you were applying AI in more serious scenarios, would this be an AI confidently advising a doctor to overdose a patient, or advising a judge to incarcerate someone based on racist inferences from the set of people currently incarcerated?
I'm confused, I thought it was supposed to be generating Seinfeld episodes, not jokes?
$ seq -w 9999999Edit:
Because if our adversaries are the products of inanimate and unthinking evolution, we cannot regard the problem in terms of revenge or payback... that would be no different than whipping the ocean for having sunk a ship and drowned its sailors
- Stanislaw Lem, The InvincibleHowever, the AI may not have been trained in a neutral way, and the person using it may not be using it in a neutral way.
If you create an AI product, you are responsible for its output. People aren't mad at the model itself, they are mad at its creators. Why did they create an AI which ends up insulting people and breaking the ToS of the platform it is on?
Here are three models: * A language model that outputs jokes, * A self-driving car model that outputs driving instructions, * An autonomous combat drone model that outputs engagement targets.
Where do you draw the line? Where do you start to "get offended"?
Just saw your Lem quote: Isn't an AI exactly not "unthinking"? That's the whole purpose of machine learning: to "learn", to recognise patterns, to abstract away, no?
The former has a probability distribution of sequences reflective of the dataset and context whereas the latter is pure random chance assuming input seed is random.
* Makes sure the people controlling the machine are incentivized to correct it (not that I think they needed it, but what I think is irrelevant)
* Ensures that content that Twitch management doesn't want on their platform, and which is in violation of their TOS, is removed immediately, regardless of the thoughts/actions of the people controlling the machine.
As far as Twitch is concerned, there's a channel that's breaking TOS, repeatedly. They're not "punishing" the owner of that channel, they're just enforcing the TOS, something the channel owner agreed to when creating their channel.
At least that's what would make sense, and Twitch moderation is far, FAR from making sense in general, but in this one thing they might be reasonable.
A clip of the joke from the banned channel:
https://clips.twitch.tv/CalmFrailPlumageShazBotstix-ITcqL0Hh...
And Twitch's rules only seem as arcane as YouTube and Facebook too. Maybe not Twitter.
To me, this is less a commentary on AI than on the absurd sensitivities of community moderation.
If you build a bot to automate your streaming so you can have 100% stream uptime and you end up with 0% uptime, that's because your product failed drastically. (Keeping in mind, as arbitrary and subjective the rules of Twitch are, the vast majority of human streamers have never been banned for arbitrary moderation)
I think the usual response to comments like this is: "Oh you sweet summer child".
Not that you're technically wrong, but from years of headlines it's often enough that even if extra care is taken, it's not always sufficient.
I'm nearly 100% certain that the scripts are generated through prompt engineering, with a random prompt (e.g. tell a joke prompt, talk about a new restaurant prompt) being selected for the scene.
From what I can gather they first used the older, cheaper GPT-3 models, only upgrading to davinci-003 when it was profitable. The older GPT-3 models proved fine and didn't generate edgy content for the several months they were up and running.
But I think the change that broke the camels back was they added a "2006 Laugh Factory incident with edgy content" prompt and only tested it on the davinci-003 model - the new models having been wiped clean of antisocial training data, while the older smaller models still having contentious content encoded in the model.
So, davinci-003 did fine producing "politically aligned" text with the "edgy" prompt because it's "cleaned", but when the openai API for davinci went down the fallback was curie. The older "unclean" curie model combined with an edgy prompt inevitably caused what we saw here.
Cue MRE/MLOps job listings.
> reasonable engineering decision
They swapped out the AI model. That's kind of a big deal.
That being said, every company I've been at with a production outage has gone into "fix it and ask questions later" mode. Maybe other companies have more process?
But when you put it like you said in your comment, it does feel more weighty
Especially considering this is the description for the model they left:
> higher quality, longer output and better instruction-following
and this is what they moved to:
> Very capable, but faster and lower cost than Davinci.
If nothing else, the adage 'you get what you pay for' works.
In that situation I still try to exercise my options a bit...
Seeing that this new model is implicitly worse at following directions, knowing that 'production' (this is a Twitch channel) depends on obeying a certain set of rules... switching to it may be ill-advised.
I don't want to be seen as overly judgmental - the Twitch channel comment works both ways; derogatory/supportive. I get why one wouldn't really foresee this or even care to. Then I wonder, why not leave it down for a bit? The hype train is fickle but it's not that precious, either.
If you think that this is "drastic" you should read "Case Study 4: The $440 Million Software Error at Knight Capital". https://www.henricodolfing.com/2019/06/project-failure-case-...
tldr; At Knight, some new trading software contained a flaw that became apparent only after the software was activated when the New York Stock Exchange (NYSE) opened that day. The errant software sent Knight on a buying spree, snapping up 150 different stocks at a total cost of around $7 billion, all in the first hour of trading.
Scarier was the day I put out a change request shortly before the end of the day in Asia, went out to dinner and drinks with some colleagues, stopped by my office on the way home, and merged the approved change request, during early morning trading in the US. The next day (in Asia) I woke up to discover Goldman had a roughly 28 million dollar trading loss. I spent a couple of minutes proving to myself that my change couldn't possibly have been the cause, and then realized I would have been woken in the middle of the night by a phone call if there was any possibility that my change had caused the trading loss. Since then, I don't merge code at the end of the day, and try to avoid Fridays. I was already in the habit of not coding if I had consumed any alcohol, but added the habit of not merging if I've consumed any alcohol. There's nothing like a 28 million dollar panic to get your practices in shape.