joeyo

In the absence of other evidence, isn't it the case that any given trait is 50% heritable and 50% environmental?

Peter Godfrey-Smith discusses this topic at length in his recent-ish book Metazoa and uses observations of insects and other invertebrates to argue, compellingly in my opinion, that sentience and cognitive abilities are not intrinsically linked, but rather depend on the specific organism's evolutionary/reproductive strategies.

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.

See also: Marzullo, Rantze, and Gage. Stock Market Behavior Predicted by Rat Neurons. Annals of Improbable Research. Vol 12, No 4. July-August 2006. https://improbable.com/airchives/paperair/volume12/v12i4/rat...

The US put a fission reactor in space too. They did it first, in fact: https://en.wikipedia.org/wiki/SNAP-10A

Edit: it’s apparently still there and will be for a long time (!) albeit non-functional:

  > Decay date: April 3, 5966 (planned)

The other pro is cost: cameras are much, much cheaper than LiDAR. Tesla is making the bet that they can close the performance gap between cameras and LiDAR faster than the cost of LiDAR will come down.

  > Iteration itself isn’t inherently bad. It’s just that the objective
  > function usually isn’t what we want from a scientific perspective.

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.

Off-topic: in the photo lede at the top of the article, are the rescued merchant mariners wearing standard eye/ear protection or is something else going on? Based on how they are holding the shoulder of the person in front of them, it almost looks like they are blindfolded.

I think language acquisition provides a pretty compelling example of learning affecting the experience of qualia. When someone is learning to speak a foreign language, there is often an period where certain sounds are difficult for the learner to produce, because those sounds are not present or are not distinguished in the learner's native tongue. For example, the R and L sounds of English are tricky for a native Japanese speaker.

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 also: https://www.geisswerks.com/drempels/

David MacKay discusses this in Sustainable Energy Without the Hot Air. He concludes that, while it's on first glance appealing to want to use waste heat from power generation, heat pumps are strictly superior to combined heat and power except for in a few specialized circumstances (e.g. industrial uses that require high temperatures). The book is fifteen years old now, so I suspect the math even more strongly favors heat pumps than when it was published.

See the section, "Heat pumps, compared with combined heat and power": https://www.withouthotair.com/c21/page_147.shtml

Louis de Broglie's dissertation [1] is mindblowing and quite cogent (at least in the English translation, but presumably in French too ...)

1. https://fondationlouisdebroglie.org/LDB-oeuvres/De_Broglie_K...

Max Welling's Kalman Filter tutorial [1] derives the smoother equations using pretty clear and easy to follow notation (and is a great resource generally).

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/...

The Ottomans were also not originally from Anatolia ...

That doesn't pose an issue: the "observation model" of a Kalman filer allows for affine transformations (e.g. scale, rotation, and offset) between the sensor data and the "true" latent state that you wish to estimate. Even better, it can appropriately handle correlational structure between your sensors.

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.

What about something like Lua?

There are many ways to think about the Kalman filter. Here are a few that I like:

* 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!

The DM41X and DM42:

https://www.swissmicros.com/products

Elon cares.

Your reputation only matters if you wish to have relationships with other humans.

That's a bit of a tautology because the population exchange was done on the basis of religion, not language. There were plenty of Greek speaking muslims and Turkish speaking orthodox christians before 1915.

  > For example, we don’t call it “Constantinople” any more

The ground stations tie the satellite network into the internet. Once they have the inter-satellite laser links operational, they will be able to get by with many fewer ground stations.

Could not agree more. I throw in underscore and backslash for good measure too:

  bind-key | split-window -h
  bind-key \ split-window -h
  bind-key - split-window -v
  bind-key _ split-window -v

People are hacking together some interesting things in this space: https://fridge0.branchable.com

That's what it means here, too. The idea is that if you take the N-dimensional state-space of neural activity --- each dimension being the activity of a neuron or electrode or voxel or whatever --- and look at how the state evolves through time, you discover that it "lives" in a manifold, k, of much smaller dimension than the full space, N.

This 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.

This is because of transfer learning: for example, by default, DeepLabCut starts with a pre-trained ResNet-50. Thus these trackers start out with an excellent "understanding" of the statistics of natural scenes out of the box.

Yes, with zebra finches, bengalese finches, and starlings, among others.

For most intents and purposes this changed with Citizens United v FEC.

  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