Ben Zhao
speaker
105 appearances
2 recordings
2 series
first heard Jan 2025
last heard Mar 2025
Ben Zhao’s voice in public audio — every appearance, attributed to the second.
Trend
recordings per month · last 12 monthsNo recordings in the last 12 months.Older appearances are listed below; set an alert to hear about the next one.
Appearances
In that scenario, the outcome would be licensing so that they can actually maintain a livelihood and maintain the vibrancy of that industry.
Yes. Yes, of course. Colleagues and former students in that space. And how do they feel about Ben Zhao? It's quite interesting, really. I go to conferences, same as I usually do, and many people resonate with what we're trying to do. We've gotten a bunch of awards and such from the community.
As far as folks who are actually employed by some of these companies, some of them, I have to say, appreciate our work. They may or may not have the agency to publicly speak about it, but lots of private conversations where people are very excited. I will say that, yeah, there's been some cooling effects, burn bridges with some people. I think it really comes down to how you see your priorities.
It's not so much about where employment lies, but it really is about how personally you see the value of technology versus the value of people. And oftentimes it's a very binary decision. people tend to go one way or the other rather hard. I think most of these bigger decisions, acquisitions, strategy and whatnot are largely in the hands of executives way up top.
These are massive corporations and many people are very much aware of some of the stakes and perhaps might disagree with some of the technological stances that are being taken. But Everybody has to make a living. Big tech is one of the best ways to make a living. Obviously, they compensate people very well. I would say there's a lot of pressure there as well.
We just had that recent news item that the young whistleblower from OpenAI just tragically passed away.
Whistleblowers like that are incredibly rare because the risk that you're taking on when you publicly speak out against your former employer, that is tremendous courage. That is an unbelievable act. It's a lot to ask.
Yeah, what a great question. I mean, it may not be surprising, but as a computer science professor, I actually have these kind of conversations relatively often. This past quarter, I taught many second year and third year computer science majors, and many of them came up to me in office hours and asked very similar kind of questions.
They said, look, I really want to push back on some of these harms. On the other hand, look at these job opportunities. Here's this great golden ticket to the future, and what can you do? It's fascinating.
I don't blame them if they'd make any particular decision, but I applaud them for even being aware of some of the issues that I think many in the media and many in Silicon Valley certainly have trouble recognizing. There is a level of ground truth underneath all this, which is that these models are limited. There is an exceptional level of hype like we've never seen before.
That bubble is in many ways in the middle bursting right now. Why do you say that? There's been many papers published on the fact that these generative AI models are well at their end in terms of training data. To get better, you need something like double the amount of data that has ever been created by humanity.
And you're not going to get that by buying Twitter or by licensing from Reddit or New York Times or anywhere. You've seen now recent reports about how Google and OpenAI are having trouble improving upon their models. That's common sense. They're running out of data and no amount of scraping or licensing will fix that.
And then, of course, just the fact that there are very few legitimate revenue generating applications that will even come close to compensating for the amount of investment that VCs and these companies are pouring in. Obviously, I'm biased doing what I do, but I thought about this problem for quite some time. And honestly, these are great interpolation machines.
These are great mimicry machines, but there's only so many things that you can do with them. They are not going to produce entire movies, entire TV shows, entire books to anywhere near the value that humans will actually want to consume.
And so, yeah, they can disrupt and they can bring down the value of a bunch of industries, but they are not going to actually generate much revenue in and of themselves. I see that bubble bursting. And so what I say to these students oftentimes is that things will take their course and you don't need to push back actively. All you need to do is to not get swept along with the hype.
When the tide turns, you will be well positioned. You will be better positioned than most to come out of it having a clear head and being able to go back to the fundamentals of why did you go to school? Why did you go to University of Chicago? And all the education that you've undergone to use your human mind because it will be shown that humans will be better than AI will ever pretend to be.
Art is interesting when it has intention, when there's meaning and context. So when AI tries to replace that, it has no context and meaning. Art replicated by AI, generally speaking, loses the point. It is not about automation. I think that is a mistaken analogy that people oftentimes bring up. They say, well, you know, what about the horse and buggy and the automobile?
No, this is actually not about that at all. AI does not reproduce human art at a faster rate. What AI does is it takes past samples of human art, shakes it in a kaleidoscope, and gives you a mixture of what has already existed before.
What's interesting about computer security is that it's not necessarily about numbers. If it's a brute force attack, I can run through all your pin numbers and it doesn't matter how ingenious they are, I will eventually come up with the right one. But for many instances, it is not about brute force and resource riches. So yeah, I am hopeful.
We're looking at vulnerabilities that we consider to be fundamental in some of these models, and we're using them to slow down the machine. I don't necessarily wake up in the morning thinking, oh, yeah, I'm going to topple OpenAI or Google or anything like that. That's not necessarily the goal. I see this as more of a process in motion, this hype process.
Showing 61–80 of 105 · page 4 of 6
← Previous
Next →