4 ms·

The problem is, most of the people who you are working with, only have 0-2 years of experience in the technology that they are using (use of research / deep lea

The problem is, most of the people who you are working with, only have 0-2 years of experience in the technology that they are using (use of research / deep learning to build products). Look at their resumes. They'd be either recent PhD grads, who haven't built a single product in their life. Or traditional engineers who had just joined the field of building products with an approach that is very different from one that they are experienced in. I would also caution you. Do you really know the technology that you are trying to make safe? Building systems with research / deep learning is as different from traditional software development as synthetic biology from electrical engineering.

im_down_w_otp 8y ago
That's an institutional problem though. Yes, it's true that most companies hire way more people, way too quickly than is probably prudent for a problem domain (especially one that is still such an open-ended science project like AVs), and that as a result barely anybody has any idea what they're doing measured against what's being expected of them, but that's a wound that is entirely self-inflicted. It's not a natural law that one needs to suddenly staff up dozens or hundreds of really expensive people ahead of any real capacity to train or assimilate them, but it does happen with depressing regularity. If anything, I'd say this is probably the biggest problem I see in the ecosystem that you and I are in. The team I work with is small. Everyone has several (5-15) years of experience consuming research, turning it into practical solutions, and in some cases even having created a novel practical application that was later supported by adjacent research. It's not easy to construct a team of half-dozen to a dozen prolific programmers w/ a bent toward the academic, but an innate sensibility for remaining pragmatic and applicable. But, it's also not impossible to do so either. This is inline with my incentives comments elsewhere. I think the incentives are broken at too many layers to be able to be fastidious and intentional about how we solve problems. Say you get an "empire builder" into the leadership structure... you're doomed. The whole point of everything becomes about having as big of a "rockstar" team as possible, not about trying to solve a problem. Unless the problem one is trying to solve is how to quickly become the SVP of an extremely expensive and probably fairly unproductive team of increasingly disaffected previously very effective people.
- dchichkov 8y ago
  I'm finding a fifteen years figure hard to believe. There was a handful of people at that time that have invented deep learning. It took years to get any industry use, as the technology was not yet competitive, until AlexNet.
  - im_down_w_otp 8y ago
    We don't work in AI/ML, we work on high-assurance distributed systems. That said, you'd be right. Not 15-years in something like computer vision. No such thing as formally verified NNs, regardless of what nuTonomy claims, exist. Even the basis about what one would be trying to verify is an open question. It's partially the point of DARPA's Assured Autonomy program. The problem at the moment is that the ecosystem has two science projects on its hands. How to make an ML-derived driver is one. How to make a stable, correct substrate for acquiring data, make a decision, and react correctly is another. The only one most people hire up for is the former, and the latter is at least as challenging.
    - dchichkov 8y ago
      Isn't it a "high-assurance distributed AI/ML system"? It is interesting that there is indeed a tendency to factorize it. I believe, this tendency arises because there are two groups of people that are available - traditional engineers, that are excellent and experienced, but do not know the language of AI and can't tell an ensemble from an embedding. And AI engineers, who are yet to build their first product.
      - im_down_w_otp 8y ago
        Sort of. Except I don't think there's a such thing as "high-assurance AI/ML". Put simply for the audience here. The goal is to have the whole stack from top to bottom be as predictable and deterministic as possible. There's a layer of it (the AI/ML bits) which is necessarily probabilistic. That's by design. It's how it all works. All the layers below that today are currently terrifyingly probabilistic. It's difficult to advance the capability of the whole domain when the necessarily probabilistic parts are depending on shifting sands as a stable foundation. I agree though that there's a large gap there in the domain expertise of the parties involved, and that's produced significant challenges as we've not yet figured out how to close that gap.

im_down_w_otp 8y ago

That's an institutional problem though. Yes, it's true that most companies hire way more people, way too quickly than is probably prudent for a problem domain (especially one that is still such an open-ended science project like AVs), and that as a result barely anybody has any idea what they're doing measured against what's being expected of them, but that's a wound that is entirely self-inflicted. It's not a natural law that one needs to suddenly staff up dozens or hundreds of really expensive people ahead of any real capacity to train or assimilate them, but it does happen with depressing regularity. If anything, I'd say this is probably the biggest problem I see in the ecosystem that you and I are in. The team I work with is small. Everyone has several (5-15) years of experience consuming research, turning it into practical solutions, and in some cases even having created a novel practical application that was later supported by adjacent research. It's not easy to construct a team of half-dozen to a dozen prolific programmers w/ a bent toward the academic, but an innate sensibility for remaining pragmatic and applicable. But, it's also not impossible to do so either. This is inline with my incentives comments elsewhere. I think the incentives are broken at too many layers to be able to be fastidious and intentional about how we solve problems. Say you get an "empire builder" into the leadership structure... you're doomed. The whole point of everything becomes about having as big of a "rockstar" team as possible, not about trying to solve a problem. Unless the problem one is trying to solve is how to quickly become the SVP of an extremely expensive and probably fairly unproductive team of increasingly disaffected previously very effective people.

dchichkov 8y ago
I'm finding a fifteen years figure hard to believe. There was a handful of people at that time that have invented deep learning. It took years to get any industry use, as the technology was not yet competitive, until AlexNet.
- im_down_w_otp 8y ago
  We don't work in AI/ML, we work on high-assurance distributed systems. That said, you'd be right. Not 15-years in something like computer vision. No such thing as formally verified NNs, regardless of what nuTonomy claims, exist. Even the basis about what one would be trying to verify is an open question. It's partially the point of DARPA's Assured Autonomy program. The problem at the moment is that the ecosystem has two science projects on its hands. How to make an ML-derived driver is one. How to make a stable, correct substrate for acquiring data, make a decision, and react correctly is another. The only one most people hire up for is the former, and the latter is at least as challenging.
  - dchichkov 8y ago
    Isn't it a "high-assurance distributed AI/ML system"? It is interesting that there is indeed a tendency to factorize it. I believe, this tendency arises because there are two groups of people that are available - traditional engineers, that are excellent and experienced, but do not know the language of AI and can't tell an ensemble from an embedding. And AI engineers, who are yet to build their first product.
    - im_down_w_otp 8y ago
      Sort of. Except I don't think there's a such thing as "high-assurance AI/ML". Put simply for the audience here. The goal is to have the whole stack from top to bottom be as predictable and deterministic as possible. There's a layer of it (the AI/ML bits) which is necessarily probabilistic. That's by design. It's how it all works. All the layers below that today are currently terrifyingly probabilistic. It's difficult to advance the capability of the whole domain when the necessarily probabilistic parts are depending on shifting sands as a stable foundation. I agree though that there's a large gap there in the domain expertise of the parties involved, and that's produced significant challenges as we've not yet figured out how to close that gap.