Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
Full method →
Fei-Fei Li no published score: only 3 usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.5/5 from 3 raw tape exchanges record → ← everyone
Every exchange below was scored with names hidden, four dimensions each from 1 to 5.
An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression.
The published score averages the raw tape exchange scores and shrinks small samples toward the
cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only
toward coarse estimates, never toward a full score.
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q described that these phases are sort of in vitro, like the laboratory phase, and then the in vivo, like the in real life phase. It's a wonderful way of, of, of clumping the work and the moment we're at, but there's always been industry and lab and company, you know, collaboration since the beginning of computing. So what is different now that startups can play in this space in vivo?
A I think several factors. One is that the algorithms are maturing to the point that Uh, industry and startups can use it. You know, 20 years ago, it's only a few top places in the world, top labs in the world that hold some algorithms that can do some AI tasks. It's not percolated to the rest of the industry, the rest of the world. So for any startup or even company for that matter, to, to get their hands on those algorithms is difficult, but there are also other reasons. Because of the blossoming of internet, because of the blossoming of sensing, we now have more use cases. In order to harness data, we need to manage and understand this information. This created a huge need for intelligent algorithms to do that. So, so that's a use case. Because of sensing, we start to get into scenarios like self-driving, like cars, and now suddenly Um, we need to create intelligent algorithms to have the cars drive. So, so that's what's creating this, uh, in my opinion, blossoming.
AI assessment note: “One is that the algorithms are maturing to the point that Uh, industry and startups”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q What would deep learning chips look like? Just obviously much more the ability to do much more parallelization, but what does it actually look like? Is it like what's happening with Nvidia's chips right now or something different?
A Nvidia is definitely one of the pioneers in deep learning chips in the sense of their GPUs are highly parallelizable, um, can handle highly parallelizable operations. And as it turned out, much of the internal operations of a deep learning algorithm, which technically we call a neural networks or convolutional neural networks, Involves a lot of repeated computation that can be done concurrently. So, um, the GPUs have really contributed a lot in speeding up the contributions because this can be done in parallel. GPUs are wonderful for training the deep learning algorithms, but I think there is still a lot of space in rapid testing or inference time Chips where it can be used in recognition, you know, in devices, in embedded devices. So I see there is a trend coming up in deep learning.
AI assessment note: “Nvidia is definitely one of the pioneers in deep learning chips in the sense”
Answered raw tape
D 4 · C 5 · P 4 · Cm 3 4.15
Q What would deep learning chips look like? Just obviously much more the ability to do much more parallelization, but what does it actually look like? Is it like what's happening with Nvidia's chips right now or something different?
A Nvidia is definitely one of the pioneers in deep learning chips in the sense of their GPUs are highly parallelizable, um, can handle highly parallelizable operations. And as it turned out, much of the internal operations of a deep learning algorithm, which technically we call a neural networks or convolutional neural networks, Involves a lot of repeated computation that can be done concurrently. So, um, the GPUs have really contributed a lot in speeding up the contributions because this can be done in parallel. GPUs are wonderful for training the deep learning algorithms, but I think there is still a lot of space in rapid testing or inference time Chips where it can be used in recognition, you know, in devices, in embedded devices. So I see there is a trend coming up in deep learning.
AI assessment note: “GPUs are wonderful for training... but I think there is still a lot of space”
