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 →
Jeff Cordova no published score: only 4 usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.0/5 from 4 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 3 · C 5 · P 4 · Cm 4 4.00
Q for that to, to become a reality. And when you think about the history of computing, and I'm not, I actually think we should be careful about this too, because we can't necessarily extrapolate from the history of classical computing, but that's all we have to go on. So that said, how do we think it's going to play out given what we observed before and where we're going next?
A One thing to not forget is that people have been working on Quantum computing for a couple decades. And the kind of the remarkable thing is all of that work has reached a point where it's moved from research into engineering in terms of building the machine. And that's why we believe that we can build it. And why IBM and Microsoft and Google and the other players in this ecosystem can also believe that and has to do with the fact that they use the technology that has been perfected in Silicon Valley and other places over many, many decades. So we know how to, we know how to make these lots of them if, if we get the first one working.
AI assessment note: “all of that work has reached a point where it's moved from research into engineering”
Answered raw tape
D 4 · C 4 · P 4 · Cm 4 4.00
Q for that to, to become a reality. And when you think about the history of computing, and I'm not, I actually think we should be careful about this too, because we can't necessarily extrapolate from the history of classical computing, but that's all we have to go on. So that said, how do we think it's going to play out given what we observed before and where we're going next?
A One thing to not forget is that people have been working on Quantum computing for a couple decades. And the kind of the remarkable thing is all of that work has reached a point where it's moved from research into engineering in terms of building the machine. And that's why we believe that we can build it. And why IBM and Microsoft and Google and the other players in this ecosystem can also believe that and has to do with the fact that they use the technology that has been perfected in Silicon Valley and other places over many, many decades. So we know how to, we know how to make these lots of them if, if we get the first one working.
AI assessment note: “all of that work has reached a point where it's moved from research into engineering”
Answered raw tape
D 4 · C 4 · P 3 · Cm 4 3.75
Q And engineering, the reality of quantum computing, how does that play out in practice?
A Well, one, one, one thing that you end up having to do when you write quantum algorithms is you have to run them a lot of times and then take statistics on the answer to find out what the answer that nature would give is. And that's a very different way of thinking about computation. Um, it's, it turns out in, at least in computer science over the last couple of decades, that these probabilistic algorithms and the like have become very important in, in solving large scale problems that you can kind of sample, uh, A large enough space of answers to get a pretty good answer, even though you haven't looked through the whole space. And, uh, it turns out that quantum computers can actually search combinatorially a huge space for certain kinds of problems, um, and actually find the real thing that nature would do. And that's just a fascinating concept to think of what you could do with that.
AI assessment note: “you write quantum algorithms is you have to run them a lot of times”
Answered raw tape
D 4 · C 4 · P 3 · Cm 3 3.60
Q And engineering, the reality of quantum computing, how does that play out in practice?
A Well, one, one, one thing that you end up having to do when you write quantum algorithms is you have to run them a lot of times and then take statistics on the answer to find out what the answer that nature would give is. And that's a very different way of thinking about computation. Um, it's, it turns out in, at least in computer science over the last couple of decades, that these probabilistic algorithms and the like have become very important in, in solving large scale problems that you can kind of sample, uh, A large enough space of answers to get a pretty good answer, even though you haven't looked through the whole space. And, uh, it turns out that quantum computers can actually search combinatorially a huge space for certain kinds of problems, um, and actually find the real thing that nature would do. And that's just a fascinating concept to think of what you could do with that.
AI assessment note: “run them a lot of times and then take statistics on the answer”
