The Cognitive Revolution / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
15 published records
01 / evaluation
“Then now obviously we've got pretty amazing language models, I would imagine that like the best language models are maybe an overkill for some of the use cases, if only because of cost and latency.”
02 / evaluation
“Noise reduction is the single most important thing that you have to do ever. Because noise does not only affect the quality of the output of your models, but it does affect the scalability of those models and also the cost of running those models.”
03 / uncertainty
“Oh, and don't know, like OpenAI has, because they have like big pockets they try to pay, but I do not know if the economics actually are because of the current economics of the advertisement.”
04 / belief
“I as a power user of all of these things, I have to say I I do find myself going to different services depending on the query. It seems like I've noticed and I would say I would I think Brave is in the middle, I would say, right now between like, perplexity gives me a lot of times the shortest, most to the point answer.”
05 / belief
“Going back to this plumbing example, and I think trying to generalize from that to what I think a lot of people who listen to this show are working on, I think in a lot of scenarios, there's like a proprietary dataset that is like too big for people to like page through.”
06 / evaluation
“The problem is like if by doing advertisement, you actually put your privacy at risk because it's tracking extensions.”
07 / evaluation
“Right? Because we need both since you cannot be semantic 100. Right? And that's that was true back then and it's true now.”
08 / evaluation
“Because they were like not because doing recommended system means that you have to have everything on a big cache, you know, a big memory map.”
09 / evaluation
“Anyway, that's that's like 1 of the 1 of the things that that we use people for, to become more efficient and to reduce the noise, which is like the ultimate goal of any machine learning or AI.”
10 / evaluation
“Patreon was a gimmick. It was a gimmick that worked, of course, had a contribution, but was good because it had a, it was an algorithm and it had a paper on it that was published.”
11 / evaluation
“That's how we actually like gather enough data to start developing the search engine. But it's a very controversial topic, right, because there is like certain tendency to have this dichotomy, either data or privacy.”
12 / preference
“Even though it's privacy preserving by design, still we do it on opt in because it's something that is, like, people have to be aware of.”
13 / preference
“We do not use any third party. And about it, not because of the independence part, which would also be because we value that we are independent, but also like cost, right?”
14 / preference
“We started to use embeddings, semantic embeddings based on on a model that was called the star space.”
15 / commitment
“We'll always be fully transparent about sponsorships, and we will only produce sponsored episodes with companies that I personally judge to be worthwhile on their merits.”