From a truther friend 🙏
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Now at last some good News!

Thanks Rob Osborne - From the scientists mouth. Officially its the flu.From natural news .com
“I have a PhD in virology and immunology. I'm a clinical lab scientist and have tested 1500 "supposed" positive

"You don't see radio changing gradually and suddenly become radar. #Darwin's theory doesn't work for #technology, and we have to make a new observation."

- W Brian Arthur at SFI today

#evolution #innovation #invention

"Novel technologies are constructed from existing technologies. These offer themselves as components for the construction of further technologies."

- W Brian Arthur at SFI today

#evolution #innovation #invention #technology #engineering #design

Unlike the two-parent #inheritance typical to (but not ubiquitous among) #complex organisms, #technology inherits features from n parents - a "vast ancestral #network" more like horizontal gene transfer networks in #bacteria.

W Brian Arthur on sumulating #invention on a chip:

Does #Archaeology support the Theory of the #AdjacentPossible - that #innovation is combinatorial, persistent, cumulative?

In short, yes. SFI's Tim Kohler on the #Paleolithic data of #technology #evolution & the #prehistory of the infamous "hockeystick" curve:

😉 @SAPIENS_org

#Economist Roger Koppl of @SyracuseU presents a model for the #AdjacentPossible at SFI.

Output = "a combinatorial explosion" or #singularity, albeit one that arguably occurred millions of years ago, or in the distant future (since the model doesn't specify t = 0):

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