The Ledger, every show
Every statement that passed quotation and attribution checks, across all 44 shows. Pick shows below, then mix any filter with any other.
shows 
every show 44 of 44
Academic reviewers are tired of papers blindly applying deep learning
“And I think the reviewers now are pretty tired of that and they're moving on, but.”
Enterprise customers will not pay for 80% accurate machine learning
“But in most cases, customers aren't willing to pay for a product that only gets them 80% of the way. You have to kind of like specialize and focus on the problem to make sure that you get to being 100%.”
Luan: OpenAI and DeepMind pivoted AI research strategy before competitors
“What OpenAI realized before basically everybody but DeepMind was that the next phase of AI after Transformer was not going to be about research paper writing. It was going to be about let's choose a major unsolved scientific problem and just try to solve it.”
Luan: AI success is existential for tier-one cloud providers
“I think every tier one cloud provider existentially needs to win here.”
Luan: Google Brain was the Bell Labs of the 2012–2018 AI era
“That, like, 2012 to 20 18 or so era, Google Brain was just, like, incredibly dominant. They did an amazing job picking talent. Like, the people who invented Transformer, the people who invented the diffusion model, people who did all of these new optimization …”
Luan: AI development feels more like gardening than traditional software engineering
“And I know exactly the behavior of the system that I've built will be. But the cool thing about AI is that every day you come to work and you make some tweaks to the model. And what you get on the other end is actually somewhat unpredictable. Like you kind of …”
Luan: Every enterprise workflow is an edge case
“I was talking to Parag, who used to be the CEO of Twitter, we were just hanging out the other day, and he's like, dude, every enterprise workflow is an edge case, and he's absolutely right, and that's why you need to control the same thing.”
Luan: Co-pilots are a great strategy for incumbents adapting to AI
“Like, I think co-pilots are a great incumbent strategy because it lets them morph their existing software business model to something that kind of looks the same while getting in on the AI thing.”
Luan: AI will make workers generalists supervising specialized AI co-pilots
“So I think what it's gonna do is it's gonna make humans at work much more like generalists, and it's gonna have, like, causes to create larger and larger, oh, sorry, like or giving people sort of, like, larger and larger scope over various different, like, are…”
Luan: Enterprise AI adoption will play out over a very long timeline
“We're going to be on this adoption curve for enterprise AI for a very, very long time.”
Luan: Product-model co-evolution will drive AI progress over next years
“The number one driver of AI progress over the next couple of years is going to be the deep co-design and co-evolution of like product and users for feedback and actual technology.”
Altman and Luan pitched Microsoft's top leadership before the OpenAI investment
“The last meeting we did with Microsoft, Before Microsoft invested in OpenAI, Sam Altman, myself, and our CFO flew up to Seattle to do the final pitch meeting. And I'd been a founder before, so I always had like a tremendous amount of anxiety about partner meet…”
Luan: Assuming broad intelligence from conversational fluency is a major trap
“And I talked to very smart people who haven't seen these models be trained and haven't seen the objectives these models are trained on. That just automatically assume that because they've seen this sliver of intelligence from these models, that they therefore …”
Luan: Single piece of human feedback boosts LLM coding solve rates 20-30%
“In the process of solving a particular task, if a human gives the language model one piece of feedback, like you're writing code and you forgot to import the Python OS library, then the solve rate for these problems would jump up like 20, 30%.”
Dextro analyzes video strictly through computer vision, not metadata
“This is all just done with computer vision. We don't use any of the metadata whatsoever to identify what's actually happening.”
Models trained on stock images fail to generalize to real-world video
“Classifiers that are and models that are trained in tune on this particular, on iconic type of data don't really generalize as well when you apply it to something that you might see in a real-world video like on YouTube or on Periscope.”
Frame-level tags lack the high-level semantic context required for video discovery
“Video or frame-level tags, the sort that you might see on, that I've put on the screen right now, didn't actually solve their problem. Because that was, it was too low level in like a, in a, in not in terms of a granularity sense, but in terms of how much addi…”
Frame-by-frame video analysis discards critical temporal motion data
“The naive approach to generalizing the video is to analyze videos as just a frame by frame sequence of photos. But there's so much encoded in the motion information in a video that to just do that actually just throws all of it out.”
Enterprise deep learning will remain vertical before converging horizontally
“In terms of where we're going to see it In industry, it's likely to be still in a kind of vertical by vertical system for a little while before we start seeing a little more convergence.”
Public user-generated video tags are too noisy for training vision models
“So with regard to using tags that are already present on the internet, we run into a bunch of different problems, which is that, one, so there is some information to be gained there in general, but it's extremely noisy, and the, another big thing that we see i…”
Brockman and Sutskever handed Luan their direct reports to do IC work
“I think second or second or third day of my time at OpenAI Greg and Ilya pulled me in a room and were like, hey you know, the you should take over our directs and we'll go mostly do IC work.”
AlexNet halved state-of-the-art visual recognition error rates in a single year
“Krzyzewski's work on ILS VRC, which is the ImageNet Large Scale Visual Recognition Challenge, where in one year, essentially they blew away the previous state of the art with a deep convolutional neural network by about, like, half of the final error rate on t…”
Dextro built architecture to map custom customer taxonomies without retraining models
“Our core machine learning system needed to be able to easily adapt and generalize to customer and partner taxonomies without restarting training and data collection and everything like that from scratch every single time. And so how we solved that problem was …”
Dextro created a real-time aggregator for all live public Periscope streams
“Tomorrow we're just ironing out that one issue, but you guys should all check out stream.dextro.co, which aggregates every live public Periscope stream, and you can kind of browse based on what you think is most interesting at any given moment to discover, lik…”
In 2015, 300 hours of video were uploaded to YouTube every minute
“Around 300 hours of video, it's probably even more now actually, are uploaded to YouTube every minute right now.”
Dextro uses a salience graph to measure video concept prominence
“So what we do is we provide what's, what's also a salience graph, which is a discounted score of how important every concept Or how prominent a particular category is over the video as a whole. So it's not a measure of our confidence, but of actually how impor…”