Entrepreneurial Thought Leaders (ETL)
00:37:19 · Ravi Belani, Michael Bernstein
[00:37:19] S1: If you're 20 years old, what would you spend the next five years learning or building? [00:37:24] S2: Well, I'm teaching here, so I feel like I gotta say HCI. Look, I think if you buy the Myspace hypothesis, the big gap is what problems do we need to solve? And I think that something around human centered design is what teaches us to look for these opportunities. It's this thing called Pasteur's quadrant. It's like solve real world problems that have like very deep and horizontal application. Like Pasteur, like famously... Okay. What did he invent? Vaccine. Right? Because there was a thing in front of him. He did fundamental research that was targeting a very... A problem that was right in front of him. I think this is where HCI likes to live. It's like, what are real human needs where there's real technological opportunity? That's where I like to live, and I think that Simile just grew out of that. We saw the problems in social media trying to predict what's going to go wrong or trying to understand the effects of policies, And I think that training yourself to understand how do I connect needs with technological opportunity, that is what HCI does. And so I would be taking exactly the thing that I'm teaching. Easy answer, I guess. [00:38:38] S1: What's the best mistake you've ever made?
00:38:38 · Ravi Belani, Michael Bernstein
[00:38:38] S1: What's the best mistake you've ever made? [00:38:42] S2: Applying by accident to a PhD. So I was here as an undergrad. My undergrad advisor suggested that I I wanted to apply to a PhD, but I wasn't sure if I had enough experience. He said, Okay, you should apply for master's programs first. So I did. Turns out... MIT has this dropdown. Are you applying for the SM? Because they do the Latin thing where they reflect MS to SM. SM or PhD? Turns out they ignore that drop down, and so I applied to the PhD without realizing it. And then I got in. I said, oh, well, guess guess I'm gonna do that then. So, that was my best mistake. Set me off on my path. [00:39:23] S1: And I know there's a long pipeline, so I'm gonna discuss one more signature question. We'll move to the students, which is what have you refused to compromise on and what has that cost you?
00:39:23 · Ravi Belani, Michael Bernstein, James
[00:39:23] S1: And I know there's a long pipeline, so I'm gonna discuss one more signature question. We'll move to the students, which is what have you refused to compromise on and what has that cost you? [00:39:31] S2: I think throughout my career and even since starting Simile, there is a real temptation to get pulled toward things that can be sort of narrowly defined technically. Are you, you know, benchmarking on this particular benchmark that you care about? You know, can we solve math proofs? Can we like... I've been told so many times early in my career especially, oh, what you're doing, that's not computer science. I like... I think that people have these narrow blinders on around what problems matter, and I think as soon as you take those off but you still have the technical depth to be able to actually tackle it, that is the thing that, like, I just... I have not compromised on. Like, it is wild to me that when I was an undergraduate, I was told that what I was doing was not science, and here I am now a professor of computer science. That's wild to me, but don't compromise on it. [00:40:27] S4: Don't comp... That's great. Thanks a lot for your talk. I'm James. Hi. My classmate Michael and I are building in the behavioral simulation space, and we have a question for [00:40:35] S1: And, Ging, if you [00:40:35] S4: can keep... How do you ensure that contrarian or abnormal or pathological human behaviors, which can sometimes cause, like, BACS one zero one events, are accurately modeled in your, like, data?
00:40:35 · James, Michael Bernstein
[00:40:35] S4: can keep... How do you ensure that contrarian or abnormal or pathological human behaviors, which can sometimes cause, like, BACS one zero one events, are accurately modeled in your, like, data? [00:40:45] S2: Yeah. So how do you get these long tail events modeled? Like, the post training procedures behind most language models really... Like whatever the distribution would be in this... In society, they push the the model toward very high conviction at, like, sort of the mean. This is exactly what you don't want for a simulation engine. But there's no... Nothing written that says it has to work that way. Essentially, what it requires is that you have training data, again, this sort of like dark matter that I'm describing, that teaches the model what the actual population distribution is. It should know that there are outliers who are whatever you're saying, trolls or people with unusual opinions. Actually, can give you an example. We spoke to one of these early, early on in Simile's days. We spoke to someone who was like head of data science at a really high end fashion firm and they didn't care about what the average person... They're like, show me the tails. Because we don't... They're like, we don't care what people think, but we're interested in what people are doing. That's like the unusual stuff because that's where we get inspiration. So if you want have simulation work, you have to focus... I think it's easy to focus on the mean, but it's the variance that actually, in some sense, really matters.
00:40:45 · Michael Bernstein, Ravi Belani
[00:40:45] S2: Yeah. So how do you get these long tail events modeled? Like, the post training procedures behind most language models really... Like whatever the distribution would be in this... In society, they push the the model toward very high conviction at, like, sort of the mean. This is exactly what you don't want for a simulation engine. But there's no... Nothing written that says it has to work that way. Essentially, what it requires is that you have training data, again, this sort of like dark matter that I'm describing, that teaches the model what the actual population distribution is. It should know that there are outliers who are whatever you're saying, trolls or people with unusual opinions. Actually, can give you an example. We spoke to one of these early, early on in Simile's days. We spoke to someone who was like head of data science at a really high end fashion firm and they didn't care about what the average person... They're like, show me the tails. Because we don't... They're like, we don't care what people think, but we're interested in what people are doing. That's like the unusual stuff because that's where we get inspiration. So if you want have simulation work, you have to focus... I think it's easy to focus on the mean, but it's the variance that actually, in some sense, really matters. [00:41:52] S1: Thank you. We'll go to the next question. And, if you can keep keep the conversations... Actually, don't talk if you can because we can hear everything up here. And if you do need to leave, just try to leave quietly. Thank you, gang.
00:41:52 · Ravi Belani, Michael Bernstein, James
[00:41:52] S1: Thank you. We'll go to the next question. And, if you can keep keep the conversations... Actually, don't talk if you can because we can hear everything up here. And if you do need to leave, just try to leave quietly. Thank you, gang. [00:42:02] S2: Thank you. I appreciate it. [00:42:03] S1: Next question? [00:42:04] S4: Cool. Yeah. I want to jump back to you talked about a lot about trust, right? And building trust is a big challenge for you. I personally would have a big challenge in how just some lights, like an orange light, and some validation on maybe a product which is unrelated to the product I'm thinking about launching is conveying enough trust that I'm now convinced, okay, your model will actually help me planning this campaign and launch 2 millions on it or something. Yeah. So what types of guarantees do you see people requesting? And you also being able to provide to them to make them understand what your model can and can't do. Yes. So are you able to compute actual training data coverage or what amount of the population they are interested in is covered or something like this?