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Are there any naturally occurring heat pumps?

For what it's worth, https://en.wikipedia.org/wiki/Evaporative_cooler takes the perspective (in one paragraph) that “Vapor-compression refrigeration uses evaporative cooling, but the evaporated vapor is within a sealed system, and is then compressed ready to evaporate again, using energy to do so.” So, in this perspective, evaporative cooling is a part of the system and forced recirculation (requiring the energy source mentioned in the question) is another.

heat pumps not refrigerators

Note that what is colloquially called a heat pump is the same fundamental thing as a refrigerator — equipment is referred to as a “heat pump” when it is used for heating rather than, or in addition to, cooling, but the processes and principles are the same (with the addition of a “reversing valve” so that the direction of operation may be changed, when both heating and cooling are wanted).

How does electricity work literally?

Isolation is not about surges, but about preventing current from flowing in a particular path at all. In a transformer, there is no conductive (only magnetic) path from the input side to the output side. So, if you touch one or more of the low-voltage output terminals of a transformer, you can't thereby end up part of a high-voltage circuit no matter what else you're also touching; only experience the low voltage. This is how wall-plug low voltage power supplies work. Even the ones that are using electronic switching converters (nearly all of them today) are using a transformer to provide the isolation: the line voltage AC is converted to higher frequency AC, run through a small transformer (the higher the frequency, the smaller a transformer you need for the same power) and converted back to DC.

Rationality Quotes Thread February 2016

Is there something not-paywalled which describes what the relevant old definitions were?

Test Driven Thinking

Your description of TDD is slightly incomplete: the steps include, after writing the test, running the test when you expect it to fail. The idea being that if it doesn't fail, you have either written an ineffective test (this is more likely than one might think) or the code under test actually already handles that case.

Then you write the code (as little code as needed) and confirm that the test passes where it didn't before to validate that work.

Open Thread, Jul. 20 - Jul. 26, 2015

Computers systems comprise hundreds of software components and are only as secure as the weakest one.

This is not a fundamental fact about computation. Rather it arises from operating system architectures (isolation per "user") that made some sense back when people mostly ran programs they wrote or could reasonably trust, on data they supplied, but don't fit today's world of networked computers.

If interactions between components are limited to the interfaces those components deliberately expose to each other, then the attacker's problem is no longer to find one broken component and win, but to find a path of exploitability through the graph of components that reaches the valuable one.

This limiting can, with proper design, be done in a way which does not require the tedious design and maintenance of allow/deny policies as some approaches (firewalls, SELinux, etc.) do.

An overview of the mental model theory

Plus, the examples (except the first) are all from the literature on mental models.

Then my criticism is of the literature, not your post.

I meant that you need to generate all of the models if you are going to ensure that the model with the conclusion is valid or as you say not 'inconsistent'. So, you not only have [to] reach the conclusion. You need to also check if it's valid.

Reality is never inconsistent (in that sense). Therefore, I only need to check to guard against errors in my reasoning or in the information I have given; neither of these is necessary.

That's why you go through all three models. In the last example the police arrived before the reporter in one model and the reporter arrived after the police in another of the models. Therefore, the example is invalid.

In the last example, the type of reasoning I described above would find no answer, not multiple ones.

(And, to clarify my terminology, the last example is not an instance of "the premises are inconsistent"; rather, there is insufficient information.)

An overview of the mental model theory

I appreciate this article for introducing research I was not previously aware of.

However, as other commenters did, I find myself bothered by the way the examples assume one uses exactly one particular approach to thinking — but in a different aspect. Specifically, I made the effort to work through the example problems myself, and

To solve this second problem you need to use multiple models.

is false. I only need one model, which leaves some facts unspecified. I reasoned as follows:

  1. What I need to know is the relation between “police” and “reporter”.
  2. Everything we know about “police” is that it is simultaneous with “alarm”.
  3. Everything we know about “reporter” is that it is simultaneous with “stabbed”.
  4. What do we know about the two newly mentioned events? That “alarm” is before “stabbed”.
  5. Therefore “police” is before “reporter” (or, if we do not check further, the premises could be inconsistent).

This is building up exactly as much model as we need to reach the conclusion.

I will claim that this is a more realistic mode of reasoning — that is, more applicable to real-world problems — than the one you assume, because it does not assume that all of the information available is relevant, or that there even is a well-defined boundary of “all of the information”.

The Brain as a Universal Learning Machine

I look at the bizarre false positives and I wonder if (warning: wild speculation) the problem is that the networks were not trained to recognize the lack of objects. For example, in most cases you have some noise in the image, so if every training image is something, or rather something-plus-noise, then the system could learn that the noise is 100% irrelevant and pick out the something.

(The noisy images look to me like they have small patches in one spot faintly resembling what they're identified as — if my vision had a rule that deemphasized the non-matching noise and I had a much smaller database of the world than I do, then I think I'd agree with those neural networks.)

If the above theory is true, then a possible fix would be to include in training data a variety of images for which the expected answers are like “empty scene”, "too noisy", “simple geometric pattern”, etc. But maybe this is already done — I'm not familiar with the field.

Brainstorming new senses

I wonder: after sufficient adaptation to a rate-of-time sense, could useful mental effects be produced by adjusting the scale?

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