Celia Quillan

So I believe at some point I asked it to tell me a knock, knock joke about the dentist. And it was like, knock, knock. Who's there? Dentist. dentist to cavity another. And it, you know, that doesn't make any sense to us, but it was in the structure of a joke.

So, so, but for the most part, if there is, you know, I think humor is the one that really falls flat on, but cleverness, you know, if you ask it to create 10 clever headlines, maybe one of them, you pluck it out after you read it through as a human is clever. The other nine might not be so much, but one of those 10 is bound to get you a little closer to where you want to be.

So, so, but for the most part, if there is, you know, I think humor is the one that really falls flat on, but cleverness, you know, if you ask it to create 10 clever headlines, maybe one of them, you pluck it out after you read it through as a human is clever. The other nine might not be so much, but one of those 10 is bound to get you a little closer to where you want to be.

So, so, but for the most part, if there is, you know, I think humor is the one that really falls flat on, but cleverness, you know, if you ask it to create 10 clever headlines, maybe one of them, you pluck it out after you read it through as a human is clever. The other nine might not be so much, but one of those 10 is bound to get you a little closer to where you want to be.

That is a super good question. And the answer is you should be always a little skeptical with everything, especially if it's a super niche topic because these tools do create, you know, their best, they're just predicting the next best answer. That prediction could be wrong. Just kind of like how the weather prediction might be wrong for the day. If you're looking at your weather app. But yeah,

That is a super good question. And the answer is you should be always a little skeptical with everything, especially if it's a super niche topic because these tools do create, you know, their best, they're just predicting the next best answer. That prediction could be wrong. Just kind of like how the weather prediction might be wrong for the day. If you're looking at your weather app. But yeah,

That is a super good question. And the answer is you should be always a little skeptical with everything, especially if it's a super niche topic because these tools do create, you know, their best, they're just predicting the next best answer. That prediction could be wrong. Just kind of like how the weather prediction might be wrong for the day. If you're looking at your weather app. But yeah,

these tools are progressively getting better and better and trained on more data sets, which are making them less likely to what we call hallucinate. Mathematics, they used to be terrible at predicting the best answer to a mathematical question. Now, the more advanced models, especially those that are,

these tools are progressively getting better and better and trained on more data sets, which are making them less likely to what we call hallucinate. Mathematics, they used to be terrible at predicting the best answer to a mathematical question. Now, the more advanced models, especially those that are,

these tools are progressively getting better and better and trained on more data sets, which are making them less likely to what we call hallucinate. Mathematics, they used to be terrible at predicting the best answer to a mathematical question. Now, the more advanced models, especially those that are,

are called deep reasoning models where they kind of, when they generate a response to you, it takes a little bit longer because it's actually running through its response through itself over and over again to see if it's really getting to the best answer. Those will be more likely to be correct.

are called deep reasoning models where they kind of, when they generate a response to you, it takes a little bit longer because it's actually running through its response through itself over and over again to see if it's really getting to the best answer. Those will be more likely to be correct.

are called deep reasoning models where they kind of, when they generate a response to you, it takes a little bit longer because it's actually running through its response through itself over and over again to see if it's really getting to the best answer. Those will be more likely to be correct.

Now, when it comes to general questions about things that are likely, there's likely a lot of information about it on the internet, for example, therefore it's likely trained on a lot of that data. Like why is the sky blue? it's probably going to give a pretty solid answer to that.

Now, when it comes to general questions about things that are likely, there's likely a lot of information about it on the internet, for example, therefore it's likely trained on a lot of that data. Like why is the sky blue? it's probably going to give a pretty solid answer to that.

Now, when it comes to general questions about things that are likely, there's likely a lot of information about it on the internet, for example, therefore it's likely trained on a lot of that data. Like why is the sky blue? it's probably going to give a pretty solid answer to that.

If you ask it about a very niche disease that just appeared in the world that hasn't been heavily researched, it's probably going to give you an answer, but it may not be correct. The best thing I tell people to do is that when you use these tools as a jumping off point, because they can get you really far, and then do a quick Google search, see if there's a secondary source that backs that up.

If you ask it about a very niche disease that just appeared in the world that hasn't been heavily researched, it's probably going to give you an answer, but it may not be correct. The best thing I tell people to do is that when you use these tools as a jumping off point, because they can get you really far, and then do a quick Google search, see if there's a secondary source that backs that up.

If you ask it about a very niche disease that just appeared in the world that hasn't been heavily researched, it's probably going to give you an answer, but it may not be correct. The best thing I tell people to do is that when you use these tools as a jumping off point, because they can get you really far, and then do a quick Google search, see if there's a secondary source that backs that up.

You'll find that a lot of the time, it is totally right. It's wrong enough of the time that you should always do that extra step of fact-checking.