Why LLMs Fail (and why AI alignment is needed)
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
22 min
1 speaker
8 chapters
transcribed 1 month ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What is the purpose of this episode and who is the guest?
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth, and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit IHear Everything.com. Welcome to the Voices of Search Podcast, a member of the I Hear Everything Podcast Network. Ready to expedite your company's organic growth efforts? Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search Podcast, Jordan Cooney.
Hello, SEOs and marketers. My name is Jordan Cooney from Previsible. Joining me today is Michelle Robbins, who is the manager of Strategic Initiatives and Intelligence at LinkedIn. LinkedIn is a social networking platform dedicated to facilitating career development and networking opportunities. It offers a platform for individuals to showcase their skills and experience to potential employers. Yesterday, Michelle and I talked about the new space. Race. Today we're going to continue our conversation discussing why LLMs fail and why AI alignment is needed. Okay, here's my conversation with Michelle Robbins, manager of strategic initiatives and intelligence at LinkedIn. Michelle, welcome back to the Voices Search Podcast.
Good to be back, Jordan. How are ya?
I'm doing really well. Yesterday was a lot of fun.
Yeah.
We we talked about where AI is going, this race that's happening, how it connects to Google and search. It's not a conversation that I get to have often on the pod, and candidly I don't think it's a conversation that a lot of us are thinking about, which is just where does Google come from when it comes to AI and how are they evolving as the competition and the space with all the different models that exist changes very rapidly. Rapidly. If you didn't get a chance to listen to that episode, please go back. Michelle dropped a ton of insights, not just the history of where things are going, but where where things um may evolve, especially around the communication from Google and a lot of these AI models.
Today, we're getting into why these LLMs fail in the alignment that is needed behind them. Maybe before we go into some questions, can can you set the stage for our listeners? What do you mean by LLMs? There's a lot of talk about this, hallucinations, whatnot, but what is that meaning to you? And and and then let's let's go from there in terms of this conversation.
You know, it's interesting because I think that, you know, people get pretty dissatisfied with a lot of the results they get out of the LLMs. And, you know, we've seen even like, you know, with AI, AI overviews, um, uh, as well as other, you know, people asking wild questions of the AI chat bots, you know, the various ones, and getting uh getting interesting answers and thinking these machines are dumb. This isn't this isn't good technology. We shouldn't be using this. You know, it it kind of discourages people from really getting the value that could be gotten out of the models. So I think it's important. First of all, to understand what these things are and what they aren't. And because everyone has been conditioned to use Google in particular, you ask a question, you get a bunch of answers, you can sift through the answers and decide which one you like best or whatever.
But usually people click on, you know, the first couple of results and they get what they came for, they're happy and satisfied. And so I think that a lot of people uh believe that these models are the same, right? You ask it a question and it's going to return you the answer. And they don't realize that these are not fact machines, these are prediction machines, right? They're generating a response based around probabilities.
Right.
And I don't think people understand that because when when someone says something confidently, even if it's wrong, you're inclined to believe it. You know, if I were to say, you know what, Jordan, this morning, what'd you have for breakfast?
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Chapters
8 chapters
1
What is the purpose of this episode and who is the guest?
0:00–4:01
2
How does Michelle Robbins define Large Language Models (LLMs) and why are they called prediction machines?
4:01–7:45
3
Why do LLMs often produce inconsistent or “hallucinated” answers?
7:45–12:08
4
What factors influence the failure rate of different LLMs (e.g., domain‑specific models vs. general models)?
12:08–16:57
5
How can users treat LLMs as thought partners while still critically evaluating their output?
16:57–18:29
6
What is AI alignment and why is it essential for safe, reliable LLM behavior?
18:29–20:42
7
What are the biggest risks if LLMs and future AGI are released without proper alignment?
20:42–21:42
8
What practical steps can organizations and individuals take today to improve LLM alignment and safe usage?
21:42–22:02