USE DATA

“If you think of the engineering design process, it's based on one identifying a problem or a goal. Then it's about collecting a lot of data around that particular problem or goal, then defining your requirements, then designing and building a solution, and then eventually testing it for what the outcome would look like. And that discipline and rational thinking and data driven analysis actually helps you in being able to run a company.”

“Life is short. Set big goals, whether it's how much value you're creating, whether how much profits you're growing, whether a certain project that you think needs three years to get done and how you can get it done in three months. Set big goals and do that at work. Do that in your personal life. Because when you set big goals, you achieve great things. If you set small goals, you achieve small small things.”

“Generally, we break it down into three broad categories and this is the work side. Number one at work would be, are they good at what they do? Or do they have a high intellect? Number two is, are they serious about work? Are they hard workers? And the third one is, are you collegial? Are you somebody who gets along with the rest of the team, who try to look for what's best in the team?”

“My mind, what ego is, you think that you're so good at something that you stop learning. I think in the world of business, you're dealing every day with with either problems or goals you wanna accomplish. And a engineering mindset gives you a framework of how to solve for these problems.”

“As an engineer, you're thinking perfection. You're thinking the process has to work just right. But the reality is people don't operate that way. Engineering on its own gives you a framework. However, how you can transform or translate that framework and how you manage people and love people and believe in them and believe what's best in them is the other ingredient to be able to then enable you to deliver good outcomes.”

“Niantic isn't just a gaming company; it's a 3D mapping company using the world's players as its data collection army.”

“Niantic isn't just a gaming company; it's a 3D mapping company using the world's players as its data collection army.”

“The reality is that although the visuals do look fantastic, those visuals actually aren't accompanied by an understanding of the 3D world, understanding how objects can move, what the consequences of different actions are, and that's what's really needed for spatial intelligence. So, I mean, a term we sometimes use is that you need action conditioned world models, that you only actually have a world model if you can predict, given some action is taken, what is going to change in the world because of it.”

“I think it's fair to say that, you know, vision understanding sort of stalled out; you got to object recognition and then progress just wasn't being made. There's really an interesting research question as to why that is, and at heart, the ideas behind Moonlake are an attempt to answer that, believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.”

“When I was actually working with Nvidia on the Synthetic Data Foundation Model Training Project, we were actually generating a lot of these synthetic data and showing that these synthetic data are actually as useful as real-world data when it comes to multimodal pre-training. But then, there's a lot of dollars being paid out to external vendors or other folks to manually curate these types of data.”

“All of the evidence from neuroscience and psychology is that most of what comes into people's eyes is never processed. You're doing fairly fine-grained processing of exactly what you're focusing on, but as soon as it's away from that, you've sort of only processing top-down this very abstracted semantic description of the world around you. Human beings are working with semantic abstractions.”

“If you're simply, you know, trying to predict the next video frame, that's not so difficult. But what you actually want to do is understand the consequences, likely consequences of actions minutes into the future. And to do that, you actually need much more of an abstracted semantic model of the world.”

“The reality is that although the visuals do look fantastic, those visuals actually aren't accompanied by an understanding of the 3D world, understanding how objects can move, what the consequences of different actions are, and that's what's really needed for spatial intelligence. So, I mean, a term we sometimes use is that you need action conditioned world models, that you only actually have a world model if you can predict, given some action is taken, what is going to change in the world because of it.”

“I think it's fair to say that, you know, vision understanding sort of stalled out; you got to object recognition and then progress just wasn't being made. There's really an interesting research question as to why that is, and at heart, the ideas behind Moonlake are an attempt to answer that, believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.”

“When I was actually working with Nvidia on the Synthetic Data Foundation Model Training Project, we were actually generating a lot of these synthetic data and showing that these synthetic data are actually as useful as real-world data when it comes to multimodal pre-training. But then, there's a lot of dollars being paid out to external vendors or other folks to manually curate these types of data.”

“All of the evidence from neuroscience and psychology is that most of what comes into people's eyes is never processed. You're doing fairly fine-grained processing of exactly what you're focusing on, but as soon as it's away from that, you've sort of only processing top-down this very abstracted semantic description of the world around you. Human beings are working with semantic abstractions.”

“If you're simply, you know, trying to predict the next video frame, that's not so difficult. But what you actually want to do is understand the consequences, likely consequences of actions minutes into the future. And to do that, you actually need much more of an abstracted semantic model of the world.”