> Humans have the tendency to imbue machine learning models with more intelligence than they deserve, especially if it involves the magic phrases of artificial intelligence, deep learning, or neural networks. TayAndYou is a perfect example of this.
> Hype throws expectations far out from reality and the media have really helped the hype flow. This will not help us understand how people become radicalized. This was not a grand experiment about the human condition. This was a marketing experiment that was particularly poorly executed.
We're anthropomorphising an algorithm that doesn't deserve that much discussion. I saw algorithm as we have zero details on what's novel about their work. No-one has been able to show an explicit learned trait that the model was taught from Tay's interactions after being activated.
It's possible the system wasn't even performing online learning - that it was going to batch learning up for later and they never got around to it.
If that's the case, it really illustrates that we've made a storm in a teacup.
All I've really seen is either overfitting or copy pasting (referred to as "quoting" in the article) of bad training data or us injecting additional intelligence where N-gram based neural networks would make us think the same thing ("hard to tell whether that one was a glitch in one of her algos — her algorithms — or a social masterstroke" from the article).
Microsoft won't add any new details as there are no wins in them for it and the story of "the Internet turned Tay bad" excuses them from their poor execution and lack of foresight. It's a win for them.
Last quote from my article, which likely has a special place on Hacker News:
> The entire field of machine learning is flowing with hype. Fight against it.
> Unless you want VC funding. Then you should definitely work on that hype.
> It's possible the system wasn't even performing online learning
I suppose it depends on how you define learning. Based on how the algorithm failed, I'm guessing it was simply absorbing every piece of info thrown its way, categorizing it, and then incrementing a counter in a database.
Honestly, I think this is how most people learn. Not all, most. And thankfully those that do, only do so from their immediate peers. If their simplistic learning algorithm was restricted to a select group for learning, but was still able to interact with a wider audience, it would have done much better.
You might need to clarify what you mean - I'm confused. What you're discussing doesn't seem grounded in modern ML/AI and seems to be comparing Tay to how humans learn? Almost none of the modern machine learning algorithms "increment a counter in a database". Those which do, primarily instance based lazy learning like k-nearest neighbours, aren't learning in the sense that is exciting for modern systems.
If you're referring to a nearest neighbours style algorithm, then we've had that tech for years and I'd not note it as a modern chat bot. If that is the case, it's even more unforgivable that Microsoft didn't consider it could start spouting back garbage given there's a lot of historical precedent. For such kNN based systems, the only knowledge it has is explicitly the training data, which means it needs to be well curated. Given there's no proper learning going on, we'd be back to a storm in a teacup.
I disagree. I don't think what you're describing is how anyone learns... people are imperfect at absorbing information.
When we read/hear something, it goes though a number of filters. People will interpret what is said differently (sometimes incorrectly even); they will miss certain bits of information; they assign different weights to information depending on who said it, their feelings about that person (whether they think the person is trustworthy or not, for ex), whether the information aligns with other information they know, etc. And they will apply their biases to that information.
Then after it has been through all of that.. it's stored in memory where bits and pieces of it will be forgotten or misremembered... and some amount of it will be correct (a lot of things that survived only because they were learned repeatedly).
The human mind learns very differently than a computer.
I disagree that you disagree. I think I pretty much said what you did in the first half of your comment.
> it's stored in memory where bits and pieces of it will be forgotten or misremembered ... The human mind learns very differently than a computer.
I can easily create an algorithm that introduces random corruption to the database, albeit in a controlled manner. You'll certainly get quirky personalities that way, which I suppose is the desired effect?
I think I pretty much said what you did in the first half of your comment.
You said:
it was simply absorbing every piece of info thrown its way, categorizing it, and then incrementing a counter in a database. Honestly, I think this is how most people learn.
That is the opposite of what I said. People do not "absorb every piece of info thrown" their way.
> And thankfully those that do, only do so from their immediate peers.
As in, while the bot absorbed everything from everyone, people generally only listen to their immediate peers. As in, their friends, neighbors, family, etc. Which part of that do you disagree with?
> Humans have the tendency to imbue machine learning models with more intelligence than they deserve, especially if it involves the magic phrases of artificial intelligence, deep learning, or neural networks. TayAndYou is a perfect example of this.
> Hype throws expectations far out from reality and the media have really helped the hype flow. This will not help us understand how people become radicalized. This was not a grand experiment about the human condition. This was a marketing experiment that was particularly poorly executed.
We're anthropomorphising an algorithm that doesn't deserve that much discussion. I saw algorithm as we have zero details on what's novel about their work. No-one has been able to show an explicit learned trait that the model was taught from Tay's interactions after being activated.
It's possible the system wasn't even performing online learning - that it was going to batch learning up for later and they never got around to it. If that's the case, it really illustrates that we've made a storm in a teacup.
All I've really seen is either overfitting or copy pasting (referred to as "quoting" in the article) of bad training data or us injecting additional intelligence where N-gram based neural networks would make us think the same thing ("hard to tell whether that one was a glitch in one of her algos — her algorithms — or a social masterstroke" from the article).
Microsoft won't add any new details as there are no wins in them for it and the story of "the Internet turned Tay bad" excuses them from their poor execution and lack of foresight. It's a win for them.
Last quote from my article, which likely has a special place on Hacker News:
> The entire field of machine learning is flowing with hype. Fight against it.
> Unless you want VC funding. Then you should definitely work on that hype.
[1]: http://smerity.com/articles/2016/tayandyou.html