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As mentioned in the above videos human cloning has been confirmed and many of the elites you are now seeing are in fact clones or doubles standing in for themselves. The reason for these clones is because humanity has not yet awakened enough to handle knowing

Let's talk about Avi Loeb and his theory about alien

First, I should disclose that I work at the fame research facility as Dr. Loeb, though I work in a different department and to my recollection we've never met.

He's a scientist; I'm in computer support.

With that out of the way...
The interview above is worth a read, and a lot of serious thought, because there's an idea there that's really critical to science, and it isn't whether or not aliens have visited (exactly).

It's about how fashion and culture and taboo have an unfortunately strong effect on science.

In particular he compares his theory on alien visitation with the multiverse theory.

Which of these is more deserving of ridicule?

Science is fundamentally a structure for forming hypotheses and then (critically) testing these hypotheses to see if they should be kept, rejected, or modified.

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Get a cup of coffee.

In this thread, we'll cover *State Quantities* and *Flow Quantities*.

This mental model can help us analyze many different entities -- from complex engineering systems to publicly traded companies.

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A "State Quantity" is something that's associated with a *specific point in time*.

For example, the number of followers a Twitter account has is a state quantity. This number can go up and down over time. But at any one *specific* time point, it's fixed.

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Similarly, the amount of cash a company has is a state quantity.

This cash balance can also rise and fall over time.

But pick any one *specific* time point -- and the company can only have one *specific* cash balance at that time point.

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By contrast, a "Flow Quantity" is something that's associated with a time *interval* rather than a time *point*.

For example, the number of followers gained by a Twitter account is a flow quantity. It's meaningful only when we specify a time interval (eg, the last 30 days).

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A simple analogy is that a State Quantity is like a photo -- a snapshot at a particular *point* in time.

But a Flow Quantity is like a video -- a record of events that take place during a particular time *interval*.

Did you know that all the nuclear bombs we exploded in the '40s-50s-60s have permanently contaminated all the steel the world has produced since then? All steel in the world is divided into two categories: pre-1945 non-contaminated steel, and post-1945 contaminated steel.

How?

Nuclear bombs (including the tests) create a lot of radioisotopes that are not found in nature. For example Cobalt-60 (a radioactive version of Cobalt-59). And since the 1945 Trinity test, these have all dispersed in the atmosphere.

What does this have to do with steel?

The process of manufacturing steel uses atmospheric air (or atmospheric oxygen). Some of the radioisotopes like Cobalt-60 get pulled in too, and as a result, any steel produced after 1945 has embedded in it some of these radioisotopes, sitting there emitting gamma rays.

Why do we care about traces of radioisotopes in steel? When making Geiger counters, or medical devices to check for radioactive contamination, or astronomical instruments, the emissions from the contaminated steel would interfere with the measurements.

So we need pre-1945 steel

Pre-1945 non-contaminated steel is called "low background steel". And when we need it, where do we get it from?

We go, find pre-World-War-II sunken shipwrecks, and then salvage the steel (and lead) from those to build our instruments which need heavy radiation shielding.

Next up at #enigma2021, Sanghyun Hong will be speaking about "A SOUND MIND IN A VULNERABLE BODY: PRACTICAL HARDWARE ATTACKS ON DEEP LEARNING"

(Hint: speaker is on the

In recent years ML models have worked from research labs to production, which makes ML security important. Adversarial ML research studies how to mess with ML

For example by messing with the training data (c.f. Tay which became super-racist super-fast) or by foiling ML models by changing inputs in ways humans can't see.

Prior work considers ML models in a standalone, mathematical way
* looks at the robustness in an isolated manner
* doesn't look at the whole ecosystem and how the model is used -- ML models are running in real hardware with real software which has real vulns!

This talk focuses on hardware-level vulnerabilities. This is particularly interesting because these can break cryptographic guarantees (because those are outside of their threat models)
e.g. fault injection attacks, side-channel attacks