14 ms·

> hard to take out since they can be anywhere undersea, mobile and ready to counterattack at a moments notice. When I was working in that stuff, the usual poin

by graycat 10y ago

> hard to take out since they can be anywhere undersea, mobile and ready to counterattack at a moments notice. When I was working in that stuff, the usual point about the SSBNs (the US long range missile firing submarines) was that they were darned difficult to find. Also, it was standard to assume that they moved in some variation on Brownian motion so that, even if did get some data on their location, it was not very useful for extrapolation. So, a major point about the design and operation of the SSBNs was that they be darned difficult to find -- via passive sonar, magnetic anomaly detection, active sonar, or anything else. Also, a big deployment of search resources that might be more able to find an SSBN would be regarded as provocative. Then there was the point: If the Soviets did find a US SSBN, now what? If they track it for very long, then that is provocative. If they don't track it, then they will soon lose it again. If they sink it, then there are all the other US SSBNs ready to fire back. So, the Soviets would have to find AND sink ALL the US SSBNs at the same time. Finding all of the SSBNs at the same time without being provocative was regarded as essentially impossible. So, maybe somehow there could be a nuke war but limited to sea. Then could pick off each SSBN as it was found one at a time. Then how long would the SSBNs last? Once I was asked to answer that! So, I derived some math, wrote some software, submitted my results, and the next day my wife and I went for a vacation in Shenandoah! Later my work was sold to a leading US intel group. I could tell you which one, but then I'd have to ...! Ah, the math wasn't either artificial intelligence or machine learning!

tr1ck5t3r 10y ago
This was around https://en.wikipedia.org/wiki/SOSUS https://en.wikipedia.org/wiki/SOSUS and better stuff is around today. Nothing like a big bang to scare the population into projectfear.
vonmoltke 10y ago
> If they track it for very long, then that is provocative. Both sides spent the entire Cold War shadowing each other constantly. There was nothing provocative about it.
- graycat 10y ago
  I question if the Soviets were able to shadow a US SSBN. I shouldn't give more info.
srean 10y ago
You do seem to have a distaste for the term machine learning. It is applied math, anybody who claims otherwise could not be more wrong. It is applied probability theory and computer science with lot more focus on algorithms than is expected of a statistician, and yes without a fetish for asymptotic normality. Usually, but not always, with a stronger focus on prediction accuracy rather than on accuracy of parameter estimates. In that sense its closer to (non-parametric) decision theory than to run of the mill stats.
- graycat 10y ago
  Yup. I got all that from Leo Breiman, not his earlier work as an "academic probabilist" but his later work in classification and regression trees (CART), random forests, etc. Yup, if look carefully, in many common cases can do well on prediction accuracy even when parameter accuracy is not so good. E.g., can even do well on prediction accuracy when parameter accuracy is zilch, e.g., some, but not all, cases of over fitting. You are right about the applied math: Machine learning has a lot that is good and new, however the good is mostly not new and the new, not very good. E.g., I watched a Prof Ng lecture on the gradient descent unconstrained minimization involved in their fitting, and it seemed that either their minimization problem is awfully easy or Prof Ng didn't know much about the applied math of such minimization going way back. So, machine learning stole a lot from some now quite classic applied math, assigned some new names, e.g., learning and another anthropomorphic name they dreamed up for iteration step size, and claimed something new. That's called academic theft, plagiarism, or some such, right? Then there's the hype. So, just from machine learning, the pop culture press is extrapolating to the Terminator movies. Absurd. Then, bigger, there's the implicit assumption, if only from just playing with the words, that the main path to smarter machines is machine learning. Well, if the effort is limited to Breiman's piecewise linear curve fitting, that path is absurdly limited. Much better for making machines smarter, i.e., do more, work better, is just to start with lots of now classic pure/applied math, get out a clean sheet of paper, and derive what need for the problem at hand. As much as I like Breiman, both his early work and his last work, there're oceans more good work in applied math that is nothing like Breiman's last work, e.g., with high irony, his earlier work! Uh, here's another case where it looks like a machine has learned -- stochastic optimal control. In some cases, it can look darned smart. Another place -- game theory, to be more specific, the von Neumann-Morgenstern work. Then the Nash generalization. But such things are far from the current machine learning.
kastnerkyle 10y ago
OT: Can you send me a link to your 1999 Elsevier article? The old thread is locked due to age but the topic is very relevant to me... As to on topic discussion - wouldn't you only need to take out the comms? Then the fellow subs wouldn't know anything was amiss. Taking down several redundant, military grade links doesn't sound easy, but finding every sub in the ocean seems much harder. Maybe there is a watchdog / normal coded communication/expected updates, so it would have to be more of a MITM thing. Even in the worst case, it would leave a limited window of time to act. Not to mention the submarine would likely be forced to try and hail "ground control" which would make DF + geo much easier...

by graycat 10y ago

tr1ck5t3r 10y ago

This was around https://en.wikipedia.org/wiki/SOSUS https://en.wikipedia.org/wiki/SOSUS and better stuff is around today. Nothing like a big bang to scare the population into projectfear.

vonmoltke 10y ago

> If they track it for very long, then that is provocative. Both sides spent the entire Cold War shadowing each other constantly. There was nothing provocative about it.

graycat 10y ago
I question if the Soviets were able to shadow a US SSBN. I shouldn't give more info.

srean 10y ago

You do seem to have a distaste for the term machine learning. It is applied math, anybody who claims otherwise could not be more wrong. It is applied probability theory and computer science with lot more focus on algorithms than is expected of a statistician, and yes without a fetish for asymptotic normality. Usually, but not always, with a stronger focus on prediction accuracy rather than on accuracy of parameter estimates. In that sense its closer to (non-parametric) decision theory than to run of the mill stats.

graycat 10y ago
Yup. I got all that from Leo Breiman, not his earlier work as an "academic probabilist" but his later work in classification and regression trees (CART), random forests, etc. Yup, if look carefully, in many common cases can do well on prediction accuracy even when parameter accuracy is not so good. E.g., can even do well on prediction accuracy when parameter accuracy is zilch, e.g., some, but not all, cases of over fitting. You are right about the applied math: Machine learning has a lot that is good and new, however the good is mostly not new and the new, not very good. E.g., I watched a Prof Ng lecture on the gradient descent unconstrained minimization involved in their fitting, and it seemed that either their minimization problem is awfully easy or Prof Ng didn't know much about the applied math of such minimization going way back. So, machine learning stole a lot from some now quite classic applied math, assigned some new names, e.g., learning and another anthropomorphic name they dreamed up for iteration step size, and claimed something new. That's called academic theft, plagiarism, or some such, right? Then there's the hype. So, just from machine learning, the pop culture press is extrapolating to the Terminator movies. Absurd. Then, bigger, there's the implicit assumption, if only from just playing with the words, that the main path to smarter machines is machine learning. Well, if the effort is limited to Breiman's piecewise linear curve fitting, that path is absurdly limited. Much better for making machines smarter, i.e., do more, work better, is just to start with lots of now classic pure/applied math, get out a clean sheet of paper, and derive what need for the problem at hand. As much as I like Breiman, both his early work and his last work, there're oceans more good work in applied math that is nothing like Breiman's last work, e.g., with high irony, his earlier work! Uh, here's another case where it looks like a machine has learned -- stochastic optimal control. In some cases, it can look darned smart. Another place -- game theory, to be more specific, the von Neumann-Morgenstern work. Then the Nash generalization. But such things are far from the current machine learning.

kastnerkyle 10y ago

OT: Can you send me a link to your 1999 Elsevier article? The old thread is locked due to age but the topic is very relevant to me... As to on topic discussion - wouldn't you only need to take out the comms? Then the fellow subs wouldn't know anything was amiss. Taking down several redundant, military grade links doesn't sound easy, but finding every sub in the ocean seems much harder. Maybe there is a watchdog / normal coded communication/expected updates, so it would have to be more of a MITM thing. Even in the worst case, it would leave a limited window of time to act. Not to mention the submarine would likely be forced to try and hail "ground control" which would make DF + geo much easier...