OpenAI Solved Hallucination? Insane AI News!

Hey everyone, welcome back to the channel. Today we're diving into one of the most frustrating and honestly fascinating parts of AI, hallucinations. You probably seen it happen. You ask chat GPT a question and it gives you a confident answer that's totally wrong. So why does this happen? A new research paper just dropped by OpenAI called Why Language Models Hallucinate? and it breaks this problem down in a really simple almost common sense way. I'll walk you through the main ideas, show you parts of the paper and explain what it means for the future of AI and AI hallucinations. Let's start with what hallucinations actually are. These happen when an AI makes something up but says it with total confidence. You probably seen this before. ask a model about a news event or a specific study and sometimes it just invents details that sound real. The authors point out that this isn't some strange mystery of AI. It's a design choice. The way we train and test these systems make them behave this way. Currently, these systems are trained to reward guessing over acknowledging uncertainty. And this is why AI hallucinations occur. This is where the paper has a really good analogy. A student taking a multiplechoice test, even if the student has no idea, they're better off guessing than leaving it blank. Over time, the student learns always guess. And that's exactly how language models operate. They're trained to always give an answer, even when they shouldn't. Here's where things get even more interesting. It's not just training. It's how we test these models. Think about all the AI leaderboards we see online. Hugging face, LMSYS, even company marketing slides. They're all about accuracy scores. Did the model answer the question? If it says I don't know, also known as the I don't know credit, that's treated as wrong. As we can see from the visual above, the benchmark scores don't really give credit to the I don't know answers. Rather, they only give a yes or no to if they're able to produce a result. So, what happens? Companies push models that always answer, even if the answer is made up. It's like a classroom where saying, "I don't know," gets you zero. But bluffing sometimes gets you points. Over time, no student will ever admit they don't know. They'll just keep bluffing. And that's what we've created with AI benchmarks and hallucinations. Now, let's take a look at a real life example of the consequences of AI hallucinations. As artificial intelligence increasingly becomes part of daily life, both its benefits and its pitfalls are becoming apparent. Take medical centers. Many of them use an AI powered tool called Whisper to transcribe patients interactions with their doctors. But researchers have found that it sometimes invents text. It's what's known in the industry as hallucinations. That raises the possibility of errors like misdiagnosis. Grants Burke is an Associated Press global investigative reporter uh who's been looking into this. Grants, I first want to give folks an example of what researchers found. Here's what a speaker said. And after she got the telephone, he began to pray. Simple sentence, but here's what was transcribed. Then he would, in addition to make sure I didn't catch a cold, he would help me get my shirt, kill me, and I was he began to pray. What sorts of other hallucinations have been found? — Yeah. So, in talking with more than a dozen engineers and academic researchers, uh, my co-porter Hila Shelman and I found that this particular AI powered transcription tool makes things up. um that can include racial commentary, sometimes even violent rhetoric and of course what we're talking about here, you know, incorrect words regarding medical diagnosis. So that obviously leads to a lot of uh concerns about its use uh particularly in really sensitive settings like in hospitals. — We asked OpenAI about this and here's what they told us. They said, "We take this issue seriously and are continually working to improve the accuracy of our models, including reducing hallucinations. For Whisper, our usage policies prohibit use in certain highstakes decision-making contexts, and our model card for open- source use includes recommendations against use in high-risk domains. — We just saw a real life example about the consequences of AI hallucinations. Amounts and I studied also shows that just one fake medical term slipped into a question can trick chat bots into confidently producing nonsense. In law, lawyers have been fined and sanctioned

for submitting briefs filled with fake citations from chat GBT. In Australia, a senior lawyer even apologized to a Supreme Court for filing AI generated legal rulings that didn't exist. Even big AI companies aren't immune. Earlier this year, Entropic was accused of citing a fabricated academic article in a copyright lawsuit. In news and politics, hallucinations can spread misinformation at scale. If people trust an AI answer without factchecking, the damage multiplies. These aren't just small mistakes. In law, it can delay justice. In healthcare, it can risk lives. And across the board, it damages trust. That's why solving hallucinations isn't optional, it's essential. So, how do we fix this? The authors say we need to rethink incentives. Instead of punishing honesty, we need to reward it. Imagine if AI could say, I'm 70% sure this is correct or I don't know, but I can help you find out. That would build trust. If benchmarks reward uncertainty, companies will train differently. And in a highstakes areas like medicine, law, news, that could be a gamecher. So to wrap it up, hallucinations aren't a glitch. They're the result of training AIs to guess and testing them in ways that punish honesty. The real world fallouts from fake legal cases to wrong medical advice shows why this matters. But there's a way forward. If we can change how we test models rewarding honesty and uncertainty, we can finally get AI that knows when to stay quiet instead of making stuff up. What do you think? Should AIs be trained to say, "I don't know. " Or should they always give you something even if it's wrong? Drop your thoughts in the comments. Thanks for watching. Don't forget to like and subscribe for more AI research breakdowns. See you in the next one.