The Ultimate Guide to Asking AI the Wrong Questions
In a world where generative AI is hailed as the answer to all our data queries, one company dares to ask: are we asking the right questions? Lucky Gunasekara, CEO of Miso.ai, sheds light on the pitfalls of blindly trusting AI-generated answers.
Questioning the Questions
Gunasekara reveals a shocking truth: large language models (LLMs) are not as smart as we think. They have a tendency to confirm biases rather than seek the truth. For instance, asking why a project failed might lead to a list of reasons, even if the project was a success. It’s like asking a magic 8-ball for advice - you might not like the answer!
The RAG Approach: More Like a Ragged Edge
Gunasekara criticizes the common RAG (retrieval augmented generation) approach, where AI is fed data and asked to regurgitate answers. He emphasizes the importance of evaluating questions before seeking answers. It’s like checking if your GPS is leading you off a cliff before blindly following its directions.
Beyond the Basics: Enhancing AI for Dummies
Gunasekara advocates for a more nuanced approach to AI. He suggests considering context cues, such as recency and popularity, to improve results. It’s not just about the data; it’s about the story behind the data. As Gunasekara puts it, ‘It’s all about the context, baby!’
The LLM Conundrum: Why Size Doesn’t Always Matter
Contrary to popular belief, bigger is not always better when it comes to LLMs. Gunasekara reveals that Miso.ai is fine-tuning Llama 2-based models to cut costs and maintain data privacy. Who knew llamas could be so tech-savvy?
Conclusion: AI, the Ultimate Magic 8-Ball
In a world where AI reigns supreme, it’s crucial to remember that AI is only as good as the questions we ask. So, next time you turn to AI for answers, make sure you’re asking the right questions. Otherwise, you might end up with more questions than answers!
