Arvind Narayanan

“I have to say, I really like Jan LeCun's perspectives on various things, including his view that LLMs Our quote unquote off-ramp to super intelligence that, you know, in other words, we need a lot more scientific breakthroughs, as well as tamping down the fear…”

“I do find it interesting that NVIDIA itself Has been trying to migrate really, really hard out of hardware into becoming a services company.”

“I don't think I would be a good CEO, but if there were one thing I could change about OpenAI, I think I think the need for the public to know what is going on with AI development overrides the you know, commercial interests of any company, so I think there nee…”

“To the extent it's a technology problem, it's more of a social media problem, really, than an AI problem, because the hard part of misinformation is not generating it, it's distributing it to people and persuading them, and social media is often the medium for…”

“In a way, I think misinformation is more of a symptom than a cause. I think, you know, misinformation slots into and affirms people's existing beliefs as opposed to changing their beliefs. And I think the impact on AI here, again, has been tremendously exagger…”

“That's right. So that, that actually is our prediction. People we predict are going to be forced to rely much more on getting their news from trusted sources.”

“So people have been worried, for instance, about bots creating misinformation with AI and influence in elections and that sort of thing. We're very, very skeptical that that's going to be a real danger.”

“I wouldn't put too much stock into these overconfident predictions from CEOs.”

“And I predict that we're going to see the same thing with models when models get cheaper. They're put into a lot more things, and so the total amount that companies are spending on inference is actually going to increase.”

“My view is that in a lot of cases, the adoption of these models is not bottlenecked by capability. If these models were actually deployed today to do all the tasks that they're capable of, it would truly be a striking economic transformation. The bottlenecks a…”

“What we've learned in the last two years is that the quality of data matters a lot more than the quantity of data.”

“And the other way to look at synthetic data is, okay, you take one trillion tokens, you train a model on it, and then you output 10 trillion tokens, so you get to the next bigger model, and then you use that to output a hundred trillion tokens. You know, I'll …”

“A hundred and fifty billion hours of video sounds, you know, really impressive. But when you put that video through a speech recognizer and actually extracts the text tokens out of it and deduplicated and so forth, it's actually not that much. It's an order of…”

“Are we going to see a GPT-V that's as big a leap over GPT-V as GPT-V was over GPT-V? I'm frankly skeptical.”

“These models are already trained on essentially all of the data that companies can get their hands on.”

“We're not gonna have too many more cycles, possibly zero more cycles, of a model that's almost an order of magnitude bigger in terms of the number of parameters than what came before, and thereby more powerful.”