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Most insects do not exhibit the classic signs of pain responses (the new findings discussed in this New Yorker article, notwithstanding). For example insects generally don't groom or guard an amputated limb. This puzzling (lack of) response can be explained as being aligned with their reproductive strategy: they reach breeding age quickly and die soon after. Thus, it's better not to waste energy avoiding limb loss for a future that won't happen.
Despite this lack of sentience, insects can be quite intelligent and learn complex cues and behaviors. Other invertebrates that look superficially like insects, prawns for example, have quite different life cycles and lifespans and often do exhibit signs of pain / sentience.
Basically pain/sentience emerge when there is a reason for the organism to protect the body from damage, and does not evolve (or is subsequently lost!) when there are more important short term goals. One wonders, for example, whether salmon experience pain when they fling their bodies up rivers, over and onto rocks, damaging them horribly in the process: all for purposes of spawning.
Edit: it’s apparently still there and will be for a long time (!) albeit non-functional:
> Decay date: April 3, 5966 (planned) > Iteration itself isn’t inherently bad. It’s just that the objective
> function usually isn’t what we want from a scientific perspective.
I think this is exactly right and touches on a key difference between science and engineering.Science: Is treatment A better than treatment B?
Engineering: I would like to make a better treatment B.
Iteration is harmful for the first goal yet essential for the second. I work in an applied science/engineering field where both perspectives exist. (and are necessary!) Which specific path is taken for any given experiment or analysis will depends on which goal one is trying to achieve. Conflict will sometimes arise when it's not clear which of these two objectives is the important one.
A reason it's so hard to learn to produce these novel sounds, I would argue, is because the learner literally cannot hear the differences at first. It's only after learning (i.e. when the qualia starts to change) that production of the new sounds becomes possible.
One can think of other similar examples in the context of expert performance: a sonar operator can hear sounds in his headphones that most (at first) cannot; an artist can distinguish colors that the novice cannot, etc.
If you buy this argument, that learning can affect perception/qualia, then it's a fairly small leap to imagine how qualia itself might also be learned ex nihilo.
See the section, "Heat pumps, compared with combined heat and power": https://www.withouthotair.com/c21/page_147.shtml
1. https://fondationlouisdebroglie.org/LDB-oeuvres/De_Broglie_K...
Briefly: you first run the filter equations "forwards", processing each datapoint sequentially from start to end. Then you run the smoother "backwards" in time on the same data going from end to start.
1. http://www.stat.columbia.edu/~liam/teaching/neurostat-spr12/...
What the Kalman filter equations don't tell you, is how to estimate the parameters of such an observation model. You either have to write it down from first principles, estimate it from "fully observed" data (if you have such a luxury), estimate it (up to a rotation) using expectation maximization (EM), or guess.
By three methods we may learn wisdom: First, by reflection, which is noblest;
Second, by imitation, which is easiest; and third by experience, which is the
bitterest.* You can think of it as a Bayesian update process for linear Gaussian systems. That is: given a prior belief of the state of a system (and an uncertainty about that belief), and a measurement about the system (and uncertainty about that measurement), the Kalman filter tells you how to combine the prior with the measurement. This is very hard to do in general, but has an exact solution if your system is Linear-Gaussian. That's magical!
* You can also think of it as a "better way to average". If I gave you two quantities that reflected some "true" value and asked you what the true value was, you would probably average them. The Kalman filter does you one better, because it tells you to average the two quantities weighted by how confident you feel about each one.
* If you like control theory, you can think of the the Kalman filter as the dual of the Linear-Quadratic Regulator. That is, the KF is the optimal state estimator for Linear Gaussian systems in the same way that the LQR is the optimal (minimum cost) controller for LG systems. It's also worth pointing out that if the system you are estimating is being controlled, the KF can incorporate control inputs as well!
> For example, we don’t call it “Constantinople” any more
Not to detract from the rest of your point, but "Istanbul" actually comes from a Greek nickname meaning "to the city", and didn't even become the official name until 1930. bind-key | split-window -h
bind-key \ split-window -h
bind-key - split-window -v
bind-key _ split-window -vThis is, I think, simply a restatement of the fact that neurons exhibit correlations in their activity patterns. That is, not all vectors of activity are "allowed". Still, it has implications for everything from learning to brain-machine interfaces.
julia> x = collect(1:15)'
1×15 LinearAlgebra.Adjoint{Int64,Array{Int64,1}}:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
julia> x[1:10]'
1×10 LinearAlgebra.Adjoint{Int64,Array{Int64,1}}:
1 2 3 4 5 6 7 8 9 10
julia> x[11:end]'
1×5 LinearAlgebra.Adjoint{Int64,Array{Int64,1}}:
11 12 13 14 15
julia> x[11:length(x)]' # alternatively ...
1×5 LinearAlgebra.Adjoint{Int64,Array{Int64,1}}:
11 12 13 14 15