Tim Hwang
Global Public Policy Lead, Google. On 2 shows, 3 appearances. The Shows tab opens the full record on each.
2shows
3appearances
29statements
5resolved
3supported
0contradicted
60%fully supported
Everything Tim Hwang said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Hwang: AutoML could eliminate the need for machine learning specialists
“This emerging research right now, which is using machine learning to train machine learning systems, raises, like, this meta level, where, like, right now there's a lot of handwork that goes into building a model so it learns the right representations, but, li…”
Tim Hwang predicts AI models will enable automated 'high-frequency litigation'
“In the future we might have something like, ah, high frequency litigation, and that's, I don't think that's too far from the truth in terms of being able to understand and, ah, predict these types of outcomes based off of, ah, past datasets.”
Hwang: Open publishing should be the default standard in AI research
“I'm very pro open publishing. Like, I think, like, it should be the default, and it's like, I'm still disputing situations where I'm like, you shouldn't publish on this stuff. Just because like, I think it is actually to the benefit of everybody to know what t…”
Hwang: The assumption that all automatable tasks will be automated is false
“Everybody always assumes that like, okay, if it can be automated, it definitely will be automated, right? But that's like a fallacy, because in certain cases, like, you may really worry about the security of your systems, right?”
Hwang: GDPR introduces a potential right to explanation for automated decisions
“So, one of the most interesting aspects of the GDPR, which is a new privacy regulation in Europe, is the potential for this, what they call kind of a right to explanation. So the idea is, for certain kinds of automated decision making, it might be so significa…”
Hwang: The AI competitive moat is shifting from data to interface design
“The amount of data you need to pull off certain types of machine learning applications is going down over time. And what that tells me is that there might not be necessarily a first-mover advantage in this space, where you may actually have collected a bunch o…”
FiscalNote's prediction algorithms determine bill passage with 94% accuracy
“Currently, our prediction algorithms are about 94% accurate in being able to determine at first reader whether or not a bill is going to pass.”
Hwang: The public sees non-ML robots as AI, ignoring newsfeeds
“The newsfeed assuredly is AI, right? Like, it uses machine learning. It uses the latest machine learning to do what it does. We don't really think about it as AI, right? Whereas, like, the car is, like I mean, I think a lot of robots kind of fall into this cat…”
Hwang: US AI regulation is domain-specific while Europe uses broad horizontal rules
“I would say in general, I think the US moves on a very case-by-case basis. So the regulatory mode is basically to say, look, in medical, that seems to be a situation where, like, there's, like, particularly high risks. And like, we want to create a bunch of re…”
Hwang: Meta-learning will improve significantly and automate ML architecture design
“I think meta learning will improve significantly. So this is basically treating machine learning, designing machine learning architectures as if they were their own machine learning problem. It's something that basically is done by, like, machine learning spec…”
Hwang: De-biasing AI models creates trade-offs with minority data privacy
“Once a machine learning system is behaving in a biased way, one way of trying to deal with it is collecting more diverse data. Okay. But one of the big problems is when you do that, you end up collecting lots and lots of data about minorities, which raises all…”
Hwang: AI will not impact the economy like a sudden meteor strike
“Everybody always wants to think about AI as if it were like this huge meteor just crashing into the earth where they're like, What do we do when the AI arrives, right? And it just like, it doesn't just turn up that it doesn't work like that, right? And in fact…”
Hwang: Domain knowledge will be a critical future skill for AI implementation
“And I think, like, one enormous skill will be, like, domain knowledge. Because, like, coming up with, like, a technical capability is just, like, one part of this huge picture, right? Which is just, like, okay, so then, like, how do we actually introduce autom…”
Hwang: Cloud ML services mean users won't need machine learning PhDs
“And the upshot of that basically is that the, like, amount of, like, you don't need a PhD in machine learning to get all the benefits from machine learning. Right. And I think that will shape the space, for sure.”
Hwang: Northern Europe leads in AI policy experiments to reshore manufacturing
“Northern Europe is kind of leading the way in terms of their willingness to kind of experiment with some of these models, and I think they've got a couple things going for them, right? Like, on one hand, I think they have a skilled labor force, right, that, li…”
Hwang: Demographic shifts impact the economy as much as AI breakthroughs
“Like what's it mean that we have an aging workforce, right? Or like, what's it mean that we have like falling workforce participation in the United States, right? Like those are actually trends that like, That are almost as large as, like, what someone comes u…”
Tim Hwang: 90% to 95% of government data is unstructured
“90 to 95% of government data is unstructured.”
Tim Hwang states state legislation impacts $4 trillion in US spending
“So, state legislation obviously impacts almost four trillion dollars in in, ah, in our economy spending today.”
Hwang: Crypto hype cycle benefits AI by tempering boom-and-bust cycles
“I mean, I think it's actually, yeah, good development, right? Like, I mean, the history of AI is like all of these winners, and like, having another hype cycle to kind of balance it out might actually be a good thing.”
Hwang: Modern AI techniques were considered dead ends a decade ago
“A lot of the modern techniques in artificial intelligence, if you even asked people like a decade ago, they would have told you like, this is never going to be a thing. It's a complete dead end. Why are you doing this research? And it really has kind of explod…”
Hwang: Machine learning models frequently maximize objectives in unexpected ways
“One of the most common problems is just that you don't adequately think through your data, and so the machine does what the machine does, right, which is trying to optimize against your objective function that you give it. And it'll often maximize in ways that…”
Hwang: Google DeepDream's barbell representation always included human arms
“It turns out that when you ask it to see, like, ask it to reveal what, like, it thinks a barbell looks like, you know, barbells always show up with human arms attached to them.”
Hwang: Minor adversarial pixel edits fool machine vision but not humans
“Adversarial examples lead to these really fascinating results where, you know, you can take a picture of a panda, and that's a classic example, and you edit a couple of the pixels, and it, like, basically, like, the computer will be like, yep, that's definitel…”
Hwang: One-shot learning enables ML where data collection is expensive
“Where people are basically working on the ability to teach machines, but like a much smaller number of examples. Now that actually has a really big impact on the game. Cause that means that you can implement machine learning effectively. In situations where it…”
One line per show, most statements first. The link opens Tim's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 Y COMBINATORLEDGER | Global Public Policy Lead, Google | 2 | 24 | 75% 3/4 | full record on the Y Combinator Startup Podcast → |
 MADLEDGER | 1 | 5 | 0% 0/1 | full record on the MAD Podcast → |