🎙️🧩 TP/Inference: Sharon Zhou on AI Hallucinations, Agents Hype, and Giving Developers the Keys to GenAI
How they do a surgery on a model, rethink AI education and unlock the real magic of generative AI
That has been an experiment! That’s why it took so long to send this post out to you.
Recently, I was at the HumanX conference and ended up having a few really interesting interviews. Starting a video interview series wasn’t the plan, but the speakers were so good – I couldn’t resist. So now I’ve got a bunch of recordings (+ more interviews in the pipeline!) and thought: what can I do – as a time-strapped mother of five – with AI tools? Can I actually produce a couple of episodes on my own, using the tech I write about?
My main tool was CapCut for video editing (paid version) – lots of built-in AI features. The coolest one: no need to manual cutting out sense. Do Transcript, and then – by editing text, you will also edit the video. Works very smooth. I also used Scribe from ElevenLabs (free) (for transcription usually, I do Otter (paid)), Claude 3.7 Extended (paid) for interview editing (ChatGPT truly sucks at that), and ChatGPT 4.5 (paid) for highlights, quotes, and TLDRs.
First of all: what these tools can do now – it’s pure magic.
But also – because AL tools offer so many options (do you want to keep or remove the background? How about to AI-generated background? What about voiceover? etc etc) they take time to learn. Once you figure them out, being a one-person media team feels surprisingly doable. That said, I wouldn’t necessarily recommend it. After a few nights with CapCut and the rest, I can tell you: it’s a lot. Writing an article about MCP was way easier.
Anyway – meet the spontaneously emerging podcast: Turing Post / Inference.
Where we talk to great minds to uncover insights on AI, tech, and our human future – turning deep conversations into something useful, actionable, and thought-provoking.
In the episode 001: the incredible Sharon Zhou. She’s a generative AI trailblazer, a Stanford-trained protégé of Andrew Ng – who, along with Andrej Karpathy and others, is also an investor in her company Lamini. From co-creating one of Coursera’s top AI courses to making MIT’s prestigious “35 under 35” list, Sharon turns complex tech into everyday magic. From ancient texts to modern code, she bridges Harvard and Silicon Valley – building AI that’s grounded, powerful, and made for people.
She is also super fun to talk to!
📝 Executive Summary
From surreal images to enterprise-grade LLMs: Sharon’s journey spans early generative models to founding Lamini, where teams fine-tune models for real-world accuracy.
Hallucinations are fixable: She explains how Lamini surgically edits model weights (not prompts) to reduce hallucinations – from 6-30% to 90%+ factual accuracy.
Benchmarks ≠ reality: Enterprise clients don’t care about MMLU or Spider –they need models that work on their complex data.
Agents & RAG? Overhyped (kind of): Sharon breaks down why these buzzwords resonate with non-experts, even if they puzzle researchers.
Teaching with memes, not math: Sharon makes complex AI intuitive – whether it’s for developers, policymakers or anyone.
Big idea: More people – not just researchers – should be able to steer and shape AI behavior.
Watch the video! Subscribe to our YT channel, and/or read the transcript (edited for clarity and brevity) ⬇️.
But also – because AL tools offer so many options (do you want to keep or remove the background? How about to AI-generated background? What about voiceover? etc etc) they take time to learn. Once you figure them out, being a one-person media team feels surprisingly doable. That said, I wouldn’t necessarily recommend it. After a few nights with CapCut and the rest, I can tell you: it’s a lot. Writing an article about MCP was way easier.
Anyway – meet the spontaneously emerging podcast: Turing Post / Inference.
Where we talk to great minds to uncover insights on AI, tech, and our human future – turning deep conversations into something useful, actionable, and thought-provoking.
In the episode 001: the incredible Sharon Zhou. She’s a generative AI trailblazer, a Stanford-trained protégé of Andrew Ng – who, along with Andrej Karpathy and others, is also an investor in her company Lamini. From co-creating one of Coursera’s top AI courses to making MIT’s prestigious “35 under 35” list, Sharon turns complex tech into everyday magic. From ancient texts to modern code, she bridges Harvard and Silicon Valley – building AI that’s grounded, powerful, and made for people.
She is also super fun to talk to!
📝 Executive Summary
From surreal images to enterprise-grade LLMs: Sharon’s journey spans early generative models to founding Lamini, where teams fine-tune models for real-world accuracy.
Hallucinations are fixable: She explains how Lamini surgically edits model weights (not prompts) to reduce hallucinations – from 6-30% to 90%+ factual accuracy.
Benchmarks ≠ reality: Enterprise clients don’t care about MMLU or Spider –they need models that work on their complex data.
Agents & RAG? Overhyped (kind of): Sharon breaks down why these buzzwords resonate with non-experts, even if they puzzle researchers.
Teaching with memes, not math: Sharon makes complex AI intuitive – whether it’s for developers, policymakers or anyone.
Big idea: More people – not just researchers – should be able to steer and shape AI behavior.
Watch the video! Subscribe to our YT channel, and/or read the transcript (edited for clarity and brevity) ⬇️.
But also – because AL tools offer so many options (do you want to keep or remove the background? How about to AI-generated background? What about voiceover? etc etc) they take time to learn. Once you figure them out, being a one-person media team feels surprisingly doable. That said, I wouldn’t necessarily recommend it. After a few nights with CapCut and the rest, I can tell you: it’s a lot. Writing an article about MCP was way easier.
Anyway – meet the spontaneously emerging podcast: Turing Post / Inference.
Where we talk to great minds to uncover insights on AI, tech, and our human future – turning deep conversations into something useful, actionable, and thought-provoking.
In the episode 001: the incredible Sharon Zhou. She’s a generative AI trailblazer, a Stanford-trained protégé of Andrew Ng – who, along with Andrej Karpathy and others, is also an investor in her company Lamini. From co-creating one of Coursera’s top AI courses to making MIT’s prestigious “35 under 35” list, Sharon turns complex tech into everyday magic. From ancient texts to modern code, she bridges Harvard and Silicon Valley – building AI that’s grounded, powerful, and made for people.
She is also super fun to talk to!
📝 Executive Summary
From surreal images to enterprise-grade LLMs: Sharon’s journey spans early generative models to founding Lamini, where teams fine-tune models for real-world accuracy.
Hallucinations are fixable: She explains how Lamini surgically edits model weights (not prompts) to reduce hallucinations – from 6-30% to 90%+ factual accuracy.
Benchmarks ≠ reality: Enterprise clients don’t care about MMLU or Spider –they need models that work on their complex data.
Agents & RAG? Overhyped (kind of): Sharon breaks down why these buzzwords resonate with non-experts, even if they puzzle researchers.
Teaching with memes, not math: Sharon makes complex AI intuitive – whether it’s for developers, policymakers or anyone.
Big idea: More people – not just researchers – should be able to steer and shape AI behavior.
Watch the video! Subscribe to our YT channel, and/or read the transcript (edited for clarity and brevity) ⬇️.
