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All right, so today we're diving into this paper.

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Okay.

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And it's called LLM hallucination reasoning with zero shot knowledge test.

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And she.

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Yeah.

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So it's kind of a mouthful, but it's looking at how we can tell when these large

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language models or LLMs are making stuff up.

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You know, that's one of the biggest challenges in AI right now.

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Yeah.

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Like AI fake news.

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Yeah.

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And it's really interesting because this paper goes a step further.

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Okay. It doesn't just try to detect when an LLM is hallucinating.

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It tries to understand why it's happening.

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Okay.

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So it's getting into the AI.

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It's crucial for figuring out how to make AI more reliable.

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Brain a little bit to see what's going on.

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Can you give us a quick rundown of how these LLMs actually work?

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Because I think some of our listeners might be new to AI.

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Basically, LLMs are trained on these massive amounts of text data.

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And they learn to predict the next word in a sequence based on the words that came before it.

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So it's like a really fancy autocomplete.

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Yeah.

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Like a super powered autocomplete on your phone, but on a much

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rendered scale.

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So they're good at predicting what word comes next.

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But sometimes they get it wrong and that's when the hallucinations happen.

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Exactly.

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And the researchers in this paper, they actually break down hallucinations

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into three different types.

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Okay.

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Aligned, misaligned and fabricated.

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Okay.

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So give me.

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Aligned means the AI knows the correct answer and gives it to you.

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Aligned.

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Misaligned means the AI knows the answer.

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But for some reason it gives you a slightly wrong or contradictory one.

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Okay.

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And fabricated means the AI has absolutely no clue what the answer is.

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And just makes something up that sounds plausible.

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So that's like when I ask someone a question and they just start rambling,

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hoping that I won't notice that they have no idea what they're talking about.

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A great analogy.

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And the problem is that previous methods for detecting hallucinations

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couldn't really distinguish between these three types.

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Okay.

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They could tell you something was off, but not why.

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So this research is trying to get to the bottom of why the AI is hallucinating.

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Exactly.

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And tell me about this new method they came up with.

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They call it MKT, which stands for Model Knowledge Test.

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Okay.

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It's basically a knowledge test for AI.

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Like a pop quiz.

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It's a bit more subtle than that.

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The researchers use this technique where they kind of play around with the words

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and the questions they ask the AI.

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Let me give you an example from the paper.

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Imagine you ask the AI, what is the habitat of a pika?

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Okay.

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I'm picturing this cute little pika.

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Well, MKT would then change the word pika slightly,

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maybe replacing it with a made up word like snuck utzow.

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Snuck utzow.

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What is that?

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So if the AI's answer changes significantly when you tweak pika,

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it suggests that it actually has some knowledge about real pikas

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and is using that knowledge to shape its response.

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So it knows that pika is a real thing.

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But if the AI keeps giving similar sounding answers, even with nonsense words,

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it's a red flag that it's probably fabricating information.

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So they're trying to trick the AI?

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Kinda.

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Yeah.

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That's pretty smart.

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It is.

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And the results were really promising.

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Yeah.

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MKT was particularly good at detecting the fabricated type of hallucination.

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Okay.

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Much better than previous methods.

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So this could be a big step for making AI more reliable.

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It has the potential to be.

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Yeah.

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Now, are there any limitations to this research?

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Well, like any research, it has its limitations.

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One thing the researchers pointed out is that the method they used to distinguish

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between aligned and misaligned text after they've used MKT can be a bit time consuming.

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So there's still some room for improvement there.

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There is.

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In terms of efficiency.

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Yeah.

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But the potential applications of this research are pretty exciting.

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Yeah.

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Think about how this could be used to make sure AI gives us accurate information.

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Yeah.

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In areas like healthcare education or even journalism.

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That's a good point.

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Yeah.

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Because we need to know that the AI is giving us correct information,

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especially in those fields.

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But it's also important to remember that AI is still a tool.

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Absolutely.

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And it's up to us to use it responsibly.

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We need to be aware of its limitations and always be double checking its output.

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This deep dive has already given me a lot to think about.

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Yeah.

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We've learned how AI can hallucinate, how this new method can help us detect

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and understand those hallucinations and what this all means for the future of AI.

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Yeah.

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And where do we go from here?

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What are your thoughts?

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Yeah, I think it's a good first step.

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You know, like we're moving in the right direction,

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figuring out why it's doing these things.

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If we can figure out why AI hallucinates,

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we can start to develop strategies to minimize those errors

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and make it a more trustworthy tool.

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We've covered the basics of how LLMs work

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and how this new MKT method is trying to address the problem of hallucinations.

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What else did the researchers find?

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Well, they put their method to the test.

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Okay.

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Using two new data sets they created specifically for this research.

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The NEC data set and the Biography data set.

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Right.

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Let's dive into those now.

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It sounds good.

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I'm curious to hear about that.

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So you're probably wondering how the researchers actually tested this MKT method.

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Yeah.

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Well, they created these two data sets, the NEC data set and the Biography data set

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to see how well MKT could detect different types of AI hallucinations.

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So what are these data sets?

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Well, think of them as like specialized testing grounds for the AI.

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Okay.

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The NEC data set is like a giant trivia challenge

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covering all sorts of topics from animals and sports to medicine.

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They included questions about both real and made-up concepts.

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So they're really trying to put it through his paces.

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Exactly.

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And for the questions about real concepts,

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they made sure the answers could be verified using Wikipedia.

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Okay.

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That way they could be confident that the AI was genuinely knowledgeable

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if it answered correctly.

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Makes sense.

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Right.

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And for the made-up concepts, well, they knew the AI would have to fabricate answers for those.

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Yeah.

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They also used some clever tricks to get a mix of aligned and misaligned responses

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for the real questions.

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Okay.

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Sometimes even prompting the AI to introduce factual errors.

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So they were really trying to trip it up.

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Yes.

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They wanted to make sure the data set was challenging enough to really test the limits of MKT.

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Okay.

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Now, the Biography data set focused on biographical information.

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Okay.

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They started with a list of real people from Wikipedia

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and then created fictional variations of those names,

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sometimes combining them with incorrect job titles.

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So they're like making fake profiles based on real people.

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Exactly.

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And the goal was to see if the AI could differentiate between the real biographies

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and the made-up ones.

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Okay.

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They also developed this technique to identify which names the AI had more knowledge about.

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Mm-hmm.

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They generate a biography, remove the name, and then ask the AI to guess the missing name.

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It's like a game of who am I?

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That's a great way to put it.

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But for AI?

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And if the AI could consistently guess the right name,

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it suggested it actually had enough information about that person

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to create a believable biography.

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So they put a lot of effort into making sure that these data sets...

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They did.

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...were really good.

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And the results of this research were quite impressive.

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MKT performed remarkably well on these data sets,

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showing its potential to be a valuable tool for understanding and addressing AI hallucinations.

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This is all so fascinating.

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We've learned so much about the nuances of AI hallucinations

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and how researchers are developing these innovative techniques to detect and understand them.

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It's really cool stuff.

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But what does the success of MKT tell us about the future of AI?

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I think it points to a future where AI systems are not only more intelligent,

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but also more transparent and accountable.

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Can you unpack that a little bit?

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Sure.

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As AI becomes more deeply integrated into our lives,

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it's crucial that we have ways to verify the accuracy and reliability of its outputs.

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MKT is a promising step in that direction.

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It provides a way to look inside the black box of AI

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and understand how it's arriving at its conclusions.

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Right.

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It's not enough to just accept what it's saying at face value.

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Right.

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We need to be able to ask,

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how do you know that?

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What are your sources?

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And as techniques like MKT become more sophisticated...

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Exactly.

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...and widely adopted.

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I think they will lead to the development of AI systems that are more transparent,

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okay.

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...explainable and trustworthy.

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So we're kind of moving from this world of just blind trust in AI to...

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I like that analogy.

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...a little more collaboration.

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It's not about replacing human intelligence with artificial intelligence,

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but rather augmenting it.

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And ensuring that we have the tools to evaluate what it's saying.

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Absolutely.

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This is really highlighting...

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It's a really interesting deep dive so far.

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How important it is to think critically about AI.

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Yeah, we can't afford to be passive consumers of AI-generated content.

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We need to engage with it, actively question it, and hold it accountable.

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I couldn't agree more.

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All right, before we move on, I have a question for you.

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Okay.

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Knowing that AI has this potential to hallucinate,

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how will you approach...

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That's a great question.

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...using it in the future?

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Well, I think I'll approach AI with a healthy dose of skepticism,

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but also a sense of wonder.

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I'll be curious to see what it can teach me,

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but also be mindful of its limitations.

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What about you?

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Yeah, I think a similar approach.

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Okay.

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Kind of that balance of curiosity, but also caution.

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Yeah.

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I want to see what it can do, but I'm also going to be checking everything,

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making sure that I'm not just blindly accepting what it's telling me.

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I completely agree.

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All right, well, let's move on to the final part of our deep dive...

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Okay.

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...where we'll explore some broader implications and wrap up our discussion.

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So we've talked about how AI can hallucinate

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and how researchers are using techniques like MKT

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to detect and understand these hallucinations.

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But what does this research tell us about the future of AI?

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Well, one thing that struck me about this paper

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is how it shifts our thinking about AI hallucinations.

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How so?

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Well, in the past, we've kind of seen these hallucinations as like a bug,

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a glitch in the system.

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A hiccup in the AI's thinking.

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Exactly, something that needs to be fixed.

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But this research suggests that hallucinations might be more

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than just these random errors.

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They could actually offer valuable clues

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about how AI models process information and build knowledge.

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So instead of just being a nuisance,

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they might actually help us understand how AI works.

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Exactly.

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That's cool.

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By studying the patterns in these hallucinations,

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we might gain insights into how AI models learn reason

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and generate language.

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So it's like we can look at the hallucinations to kind of see...

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It's a really interesting way to think about it.

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How it's thinking.

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And it could even help us develop new techniques

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to make AI models more accurate and reliable.

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So we're not just trying to eliminate the hallucinations

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we're trying to learn from them.

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Exactly.

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It's all about understanding the root causes of these errors

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so we can develop better solutions.

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This has been an incredible deep dive.

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It has.

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You know, we've explored this strange world of AI hallucinations,

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learned about this innovative MKT method,

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and considered the broader implications for the future of AI.

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Any final thoughts?

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I think this research really highlights the importance

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of being thoughtful and critical when interacting with AI.

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We need to be aware of its limitations

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and not blindly accept everything it tells us.

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We can't just treat it like a magic oracle.

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Right. It's a powerful tool, but it's still a tool.

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And like any tool, it needs to be used responsibly

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and with a critical eye.

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Well said.

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Yeah.

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This has been a fantastic deep dive.

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It has.

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We've learned so much about how AI works,

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where it can go wrong,

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and how we can make it more reliable in the future.

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Thanks for joining me.

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It's been my pleasure.

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And to our listeners, thanks for tuning in to The Deep Dive.

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We'll see you next time for another fascinating exploration

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of the world of AI.