We refer in big history to these moments as threshold moments.

This minus 1, 2, 5, and 4.

let's look at the lecture based classroom, and let's pick the median performance as a threshold.

And I'm just showing this to you again because it's so nice.

From set A to set B.

A universe appears, an entire universe.

That's all it tells us.

This is the range right here.

That might maybe look like this, and if you now threshold this hypothesis at 0.5, you end up with a threshold that's around here so that everything to the right of this point you predict as positive, and everything to the left of that point you predict as negative.

Too hot, too hot.

The distribution changes to alive!

But look at this closely.

You can see that actually they've crossed a different threshold.

And this is actually the range.

And I believe we count as a threshold in this great story.

But look at this closely and you can see that actually they've crossed a different threshold.

In every case, when the number of visits to the state is less than the sum threshold, E, the exploration threshold.

So what is the distribution of all data points and just those that are higher than 1 sigma above the mean?

Or large scale fluctuations lifting the small ones even further.

If it fails to find a better solution, it goes back to the original and decreases our probing interval.

That's something we can observe.

So if this is the set of numbers of the domain, this is set of numbers of the range, the function tells us how do we get from this number, some place in domain, to that number, some place in the range.

It's established policy. That's where that no such thing as safe radiation that comes from the linear no threshold hypothesis.

And you know what?

And in general, when you plot it on the xy axis, the domain is all of the x values that you're defined over and the range is all the y values.

This path, if continued, guarantees that we will pass tipping points

That's treated differently than stuff that sticks out of the ground, and even that is used as a landmark.

So, let's take 0.5, and so, you know, that's where the threshold is.

But here's the key thing you know, the other thing we found out is that the ice sheet passed a threshold, that the planet warmed up enough and the number's about one degree to one and a half degrees Centigrade the planet warmed up enough that it became ... that ice sheet became very dynamic and was very easily melted.

We just did the following we inspected a data sample with two dimensions, and we saw it had massive outliers.

It grows like Θ(n).

Conventional wisdom held that everything was safe up to a point, a threshold.

That isn't perfect.

It's called Sobel surprisingly. And it gave us a z of 1.98, that just gets us past the threshold.

It doesn't matter.

In the individual tutoring instruction, 98 percent of the students are going to be above that threshold.

Here's the definition that there is some constant c₁ and c₂ bigger than 0 and a a threshold n₀ such that f(n) lies between c₁ of g(n) and c₂ of g(n) for all n bigger than the threshold n₀.

If you want to make predictions, one thing you could try doing is then threshold the classifier outputs at 0.5.

We also have some function g(n) that we can multiply by c1 or we can multiply by some bigger number c2.

But then I'm going to slowly turn down the heat, so to speak, and then there's this viability threshold where now it would be possible for a replicator to actually live.

Stars will create the Goldilocks conditions for crossing two new thresholds.

They receive input from thousands of upstream partners and compute their own electrical outputs, which then, if they pass a certain threshold, will go to thousands of downstream partners.

An unlikely measurement can cause weights to be small, but a good heuristic would be to say, let's particle sample until our non normalized importance weights reach a certain threshold, and then let's stop sampling.

And now what I did there is, I was being nasty, I just turned up the heat again and again.

And, if you are to put that cut at few kilometers elevation, you'll find out that all of them are concentrated in areas like Rocky Mountains or Himalayas or