YouSaid · the spoken record

Ion Stoica

lines on the record: 47
first: 2017-10-12
most recent: 2017-10-12
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“This uses these hardware enclaves. Oh, interesting. Now, in more and more processors, CPUs like Intel, SGX, ARM Trust Zone. So it's using this new developments in the hardware. And finally, one other project is ground. And this is about to try to understand the semantics, the provenance, the lineage of the data, creating certain data, certain data item. Who did modify that data item? Who did look at those data items? And the goal is to provide this service across multiple data sources across multiple data storage.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, because the models you developed on some data set. And for instance, the data or the queries are going to evolve over time. And because the environments or the world around you evolve, what you learned, which is embedded in the model may not be as relevant or as good as it used to be. So that's one example. The other work I would mention, it's in area of security. We have the project called Opaque. It's also related with Spark. It's about how you do analytics, for instance, in the cloud so that you can defend against any kind of attacks, even if the operating system is compromised or the hypervisor, the virtual machine is compromised, the data and the computation is secure.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“The other project is Clipper, it's about model serving. Once you create these models, you need to serve them to users. You'd learn and they make a request about showing this image to do the image recognition. And this is a hard problem. It's multiple levels. How do you scale the life cycle management of the model serving? How are you going to update the models? How you are going to improve this model of life? Why is it a problem?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Is interesting one called Ray, and it's to build a cluster computing framework which make it easier to build the next generation of AI applications. One example is reinforcement learning. This reinforcement learning is about agents which continuously interact with the environment and learn from this interaction with the environment. If you think of a car, it's about an agent interacting, taking decisions, right? the real world and learning from this interactions”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“We talk so much about programming, but I think that your lab is the exact example of an academia being proactive in the new world of data as opposed to coding and new methods of academic research will need to be developed in order to promote data as a functional input to computing.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the really interesting things. So I started to become like what happens when something ends, right? But now I'm going a step further to the future of computing because I believe that programming is sort of the life cycle of programming as sort of at the end. Like you can only write so many if-then-else statements. You're kind of done with logic, right? In that sense. And all this purification of data and the input of data into a system as a as a precursor to an observation that's happening in real world information is sort of the future of computing and iteration.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“So it's vice versa. It goes, you're saying that both things are moving to the edge and things in the edge are moving to the center. I feel like there's a poem in there somewhere The things fall apart. I just remember the classic poem”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“That's like an aggression. So, indeed, we are really looking at building systems which spans a cloud and edge. And I think there are going to be both ways, things which are now done at the cloud is going to migrate some of the functionality on the edge. Also on the other side, things which are now done only at the edge, like self-driving cars will migrate. Some of the functionality will migrate to the cloud.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Or, how do you guys? It's not really a limit. It's again more organical. And there are different kinds of projects. There are projects which hope to become a strong artifact to be used across in industry like Spark or Mezos or Tachion. And there are more explorative projects that should remain at the stage of the prototyping. But you try to categorize. So, you know, you look at this kind of big broad categories of problems and try to develop solutions for these problems. I don't know if.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Microsoft and Google Earth's awards, which they have very strong team of the eye. They have hardware, right? Powering AI algorithms. What we have is more called research collaboration. Even they cannot explore all the potential, say, design choices.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“But an industry lab. So this all happened. So what I can tell you that what we find is that a lot of large company and partners, consumers of AI, or they want to be, we have financial companies like Capital One, we have IoT, like Ericsson, we have Huawei, we have unfinancial, which is Alibaba. He's doing far more than payments. Oh, I know. So, on one hand, you know, these companies, they would prefer to have a very strong open source stack they could use. That's right.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Spend huge amount of resources, and you have also OpenAI. Huge amount of resources. So how can you make a difference? I think that's always happened. Now it's about AI, but in the past it was about operating system. It was about, you know,”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the goal of the RIS lab is to build open source platform tools and algorithms to enable this intelligent real-time decisions on live data with strong security. So this will fit some of the holes you mentioned. And it's true. Many of the algorithms which are published are hard to reproduce. So that's one part of the question. We see that need. Now, the other part of the question is, say, well, Look, Google or Microsoft or even now Chinese companies by documents. I was thinking about”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“I have a pushback here though. How then is a new lab at Berkeley relevant in a world where companies are now kind of creating their own labs in-house to really put together a lot of the same skills, PhDs, et cetera? And in thinking through this sort of connection to industry, especially because on the complaints I hear all the time is about how a lot of the majority of AI papers are actually algorithms and we actually need a lot more tools and tooling to actually take that information and make decisions on it.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“There is no question about that. AI that over the past five years great results. When you look at the PhD students who applied to Berkeley, PhD applicants. It turns out that well over 60% are applying for AI”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I mean, it sounds like what you're saying is that AMP Lab evolving to RiseLab, it's actually setting it up for decision making as you're describing with all these characteristics. But more importantly, it's big data moving into AI and the world of science and tech is moving in this direction.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Not necessarily. Someone to hold your hand and tell you more about it. Yes. So that's what it is. You want to know why. This is how people start to trust a decision. Like even if your friends or spouse makes a decision which affects you and kind of is unexpected, what is your first question? You know, why did you make that decision?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Explainability, it's very important. I think about a very simple example. You go to the doctor and you have the x-ray, right? And now, actually, there are very good algorithms. It goes, take that x-ray and give you a diagnosis. But would you be satisfied with the diagnosis?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Tells a driver to take over or make a safe decision. Slow down and stop. That's one about robustness. The last one, it's explainability. Explainability. As we make decision which augment the ability of humans to make more sophisticated decisions or take decisions on behalf of humans like a self-driving car.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“It needs to be also robust in some parts of the system fail. Some component failed. But also it needs to be robust to unforeseen input. So let me give you an example. Today, it's easy to have an algorithm to try to recognize cats and dogs. You do that, and it's pretty good, very accurate. But now I show this algorithm, an elephant. What really what you want the algorithm to say is I don't know. I'm not sure. Needs to provide some confidence interval on these decisions. Because then, for instance, if you are self-driving car, that you can pop up and let driver”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“I used to be personalized. You see about this hyper personalization. It's clear it's here to stay. So this basically defines the goals because we want to make the decision on live data and we want to make the decision which are secure. Why? Because if I am making personally a decision, then it's a question. It's not about the privacy. So then you want to make personal decision on preserving the privacy while preserving data confidentiality. And of course, because the decision, if happens in real time, in some cases the humans may not be in the loop. So you need to ensure the integrity of the decisions. And then there are two other things about the decision which are very important. One is robustness. The human is not the loop. Better the decision can be robust. There are many dimensions of robustness. It's about robustness to know the input.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a very existential question. It needs to be fast, right? Because faster decisions are better than slower decisions. It needs to be on the most recent data. I want to make a decision based on what happens now.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“This is a holy grail. This is a holy grail. Absolutely. Right? So you want to do medical diagnosis. You want to see whether a transaction is fraudulent or not. You want to see where to steer next a car, a self-driving car, right? Of course, you want to know what you are going on news. You are going to show to the user next, right? Sort of predicting the future.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“AMP is algorithms machine people. It was about interactive ad hoc analytics, doing much better than before on large amounts of data, getting insights. When you say making sense of the big data, you are talking about understanding, getting insights. What RiceLab is, it's about Making intelligent decisions, taking intelligent actions on the data. People have a lot of data, get more and more data, and then what do they want to do with this data? The first step is getting some insights on the data, computing some metrics, KPIs. But the next thing is to take decisions, right? Yeah.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“So it's super interesting to hear about the labs and what happens at Berkeley and the integration between academia and business. So now you're starting or have just started the Rise Lab. What's that all about?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, I was going to say it reminds me another example of this collaboration between industry, academia, and other players is what O'Reilly did with open source and their starting boot and various other camps. It's sort of the same kind of thing. And so it's really interesting because these ideas are permeating both ways into the system.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Another ingredient to add to the list, it's a fantastic event. Now, what we've done, we started what we called camps. We have the amp camp. We do exactly trying to foster building the community. We have not only presenting our work, but we have tutorials. We train them to work on our latest software, not always stable. Right. The beta, the beta alpha, actually, not even the beta, right? Pre alpha.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. So, first of all, the way this Berkeley labs are going are happening twice per year, there is a retreat, three race retreat where people from Berkeley students, faculty, and industry people from our sponsors come together and we present the earliest work and we get feedback. So it's a very...”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“It's more recently, right, with Kubernetes and others, they went or TensorFlow, they went open source. But this is relatively recently. If you think about the map reduce and Google file system, which really started the big data movement. And that's why Enable Hadoop to be developed as an open source alternative to Google File System and Google Smap Ridios. It's another sign of the tipping point, the fact that all these big companies developing primarily closed source software and products now they are far more active in the open source community. And one thing I want to mention here is you have to build a community. That goes back to your point about you need this collaboration. So all these successful open source projects”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's worth putting a little bold on this that when you talk about innovation, you're essentially crowdsourcing the best developers and the best R&D in the world. What's that famous line about all the best people never will work for you? And in fact, at Berkeley, Henry Chesborough, Hank is there and he talks a lot about open innovation. You have to collaborate with the outside worlds outside your company walls to be successful. The most interesting thing I saw this past week is this stat that Microsoft now has more commits to GitHub than any other company. And can you just think about that for a moment, like Microsoft? Yeah, on GitHub.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“These hosted offerings that leverage open source has given a very clear path to sort of marry the innovation with open source with the ability to monetize, which is very important from a commercial standpoint up until sort of the cloud and being able to and SAS, there wasn't a clear path to go do that. And now that's sort of the best of both worlds.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“MID and Carnegie Mellon. But what happens is it's a tipping point now, right? Over the past five, ten years, we are witnessing the tipping point, where now you go to try to sell services to the companies and say, are they open source? Well, the element, I believe, that has brought open source from fringe Hobby project out of academia to mainstream is the fact that you can actually build commercial businesses on top of open source. And a lot of that, which you guys at Databricks are doing and many other companies, GitHub is another example.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“That as being the engine of innovation for much of the software landscape today. This is very true. Didn't happen overnight. And of course it was not only Berkeley, there are many other centers of producing high quality open source software. For instance, then you are very familiar with this. Yeah, Cambridge. I had no idea Cambridge was big.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Think it's true now about many universities, but Berkeley has this very long tradition of being open source, even on open source or not fashionable. Unix BSD, for instance, is coming from there. Because of that leadership that Berkeley has shown in open source, I would argue that the entire industry now has come around on this stuff and that the nature of collaboration and the nature of open source being a basis for innovation in software has really become a predominant guiding point in software development and building companies as a result of that. Where in the past open source was seen as a fringe thing at one point in the past and innovation would occur within the walls of large organizations and now because of the work that Berkeley has done for years around open source, we see”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. It actually strikes me that in your list of ingredients of what makes Berkeley Lab special besides what we listed so far, and you alluded to this earlier, there is that proximity to industry, physical, geographic proximity. It's not an accident that Silicon Valley and other ecosystems around the world grow up around these universities. And there's something that goes in. University industry collaboration back and forth, even for the professors, because you were able to go into a company and then go back.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“First of all, students, they do internships, they understand the problem, they develop the first solutions. And then without understanding, you can start more principled designs to build these systems. But the truth is that in every successful project, we had at least one or two partners. For instance, we work very closely initially with Facebook. When we started working with Facebook, Facebook has an entire cluster, big cluster for big data. It was 18 nodes and their big data team was like three people. Then, for instance, in the case of Mesos, we work very closely with Twitter. And Hinman went to Twitter and work very closely with Twitter engineer to deploy Mesos in production. And actually, the feedback from Twitter has a big impact on the Mesos evolution, from just supporting big data.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“There's only someone which. So, I have a question about that because one of the things that I've always thought about when it comes to this tension between academia and industry, you might hear about needs and requirements from industry, but when you're in research environment, you have unlimited open-ended vision, an algorithm and a research paper is very different than something in production at industry scale. So how do you guys navigate that?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“How does that work? And this is what it is. The truth is that things are going to evolve more organically. You have the major functionality and kind of you know how it fits, but then as you encounter, as you better understand the kind of problems you are going to solve, the industry want to solve. And then once you understand and you solve the problems, other problems pop up. You didn't even know existed when you started the entire process. And many of these projects also, are they all research projects by PhD students? Is there a requirement that they have to be? All are started and the research project led by the PhD students.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Going back to this idea of how the lab, the ingredients that make the Berkeley lab work. So one of them was that they have a vision, that they have this time limit of five years. Is there an architect, like a person who sits at the top and says, here's how we're going to do it? Or is it that the students come up with projects? Like, how does that work?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Derivative effect of them being more applicable beyond just requiring that these three things work together and that's the only way that these things can work together. There are multiple layers of the stack. So actually Mesos was the first project we developed. This is what started the stack. And Mesos was by design to support multiple cluster computing frameworks. We started with Hadoop. And actually one of the reasons we designed Spark is to show that it's much easier to build from ground up a new cluster computer engine on top of Mesos that without in the absence of Mesos because Mesos will provide a bunch of services detecting whether the nose go down and restarting some of the tasks and things like that providing isolation between different cluster computing frameworks and other.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“All kind of comes together. All these components are part of the stack. I found it very interesting while this was. A big data stack as envisioned by the folks working on these projects, each of these projects has more expansive use than just in the individual use case that was originally envisioned. Mesos as an example is not just for big data. It's a resource orchestration framework that can be used for any resource, right? The commercial applicability of these projects.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, so this goes back to what you were saying about how each of the labs at Berkeley is set up with a vision, and this is the vision of the big data ecosystem and what's coming next. And that all kind of comes together.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a resource management system which allows multiple cluster computing frameworks to share the same cluster, the same hardware. And the other project that came out of the AMP lab was a project called Tachyon, which now resulted in a company called Alexio. It's sending a memory storage engine, which allow again multiple cluster computer frameworks to share the data in memory.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Limit. So then each lab has particular vision and goals. So, in particular, for instance, the AML lab, the vision was to make sense of the big data. And the goal is to build the next generation analytic stack to be used across industry and academy around big data. There were three projects that really were notable outputs of the AMPLAP. One, of course, is Spark, which is the basis of Data Brick.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Come about, how that evolved into ride. So, first let me start by saying how the labs are structured at Berkeley, because it's something unique. And this goes back 30 years ago. So the labs are like, one rule is around five years.”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“But, like, I mean, it's actually, I'm super interested in the answer. Yeah, that's a great question, and there are many dimensions, many interactions. I do think as a high level, academia, it's allowing you to do more experimentation. It's set for that. And at Berkeley, we are in a privileged position, of course, being close to the Silicon Valley. We have a lot of feedback from the industry. So we are very anchored in what are the real problems. Then it's a very natural to transition some of this work to industry and to students and sometimes faculty starting companies around this project. I'll just be curious, like, how did AMPLAB start?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source
“Thank you. Thanks for having me. Jan, you've played super interesting roles both on the academic front and on the commercial front. And often those two areas are often in conflict with one another. And you're one of these people who have very successfully sort of embodied those two different characteristics. How do you do that? And what do you think the relationship is between one or the other? And how do you get that to work so well?”
2017-10-12 · a16z Podcast · a16z Podcast: A New Lab Rises · IDENTIFIED FROM THE TRANSCRIPT · source