We're kicking off the Privacy Tech session at #enigma2021 with Mitch Negus speaking about "NO DATA, NO PROBLEM—GIVING NUCLEAR INSPECTORS BETTER TOOLS WITHOUT REVEALING STATE
But perhaps we can use MPC -- secure multi-party computation
Used for other things like cryptocurrency these days.
MPC can be used to compute anything computed by a computer [but it's expensive!]
* It's expensive! We haven't had computers fast enough before.
* The inspectors need to be *sure* that it will work. They want tried and true, not latest and greatest.
* It's a small field with a limited budget.
Make a circuit which does some kind of computational task, like whether A < B
Let's think about a case with two parties where we want to compare two inputs. That can be done with this circuit.
[accessibility apology: I'm livetweeting this really fast and can't render these diagrams in text]
[Also go watch this talk -- it's a good explanation but very hard to livetweet]
Then we use this crypto thingie called oblivious transfer. That lets the other party get the keys to do the decryption of the correct output for each gate.
Want to use pre-existing software (to give confidence to the inspectors). But not every system can work for this: they can't scale enough, they're too bleeding-edge fancy (hard to use!), etc.
Instead did electrocardiogram analysis as a proof of concept to give the analysis without revealing the actual heartbeat.
More from Lea Kissner
More from Tech
"I really want to break into Product Management"
make products.
"If only someone would tell me how I can get a startup to notice me."
Make Products.
"I guess it's impossible and I'll never break into the industry."
MAKE PRODUCTS.
Courtesy of @edbrisson's wonderful thread on breaking into comics – https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.
There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.
You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.
But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.
And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.
They find their own way.
make products.
"If only someone would tell me how I can get a startup to notice me."
Make Products.
"I guess it's impossible and I'll never break into the industry."
MAKE PRODUCTS.
Courtesy of @edbrisson's wonderful thread on breaking into comics – https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.
"I really want to break into comics"
— Ed Brisson (@edbrisson) December 4, 2018
make comics.
"If only someone would tell me how I can get an editor to notice me."
Make Comics.
"I guess it's impossible and I'll never break into the industry."
MAKE COMICS.
There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.
You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.
But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.
And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.
They find their own way.
Machine translation can be a wonderful translation tool, but its uses are widely misunderstood.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
Let's talk about Google Translate, its current state in the professional translation industry, and why robots are terrible at interpreting culture and context.
Straight to the point: machine translation (MT) is an incredibly helpful tool for translation! But just like any tool, there are specific times and places for it.
You wouldn't use a jackhammer to nail a painting to the wall.
Two factors are at play when determining how useful MT is: language pair and context.
Certain language pairs are better suited for MT. Typically, the more similar the grammar structure, the better the MT will be. Think Spanish <> Portuguese vs. Spanish <> Japanese.
No two MT engines are the same, though! Check out how human professionals ranked their choice of MT engine in a Phrase survey:
https://t.co/yiVPmHnjKv
When it comes to context, the first thing to look at is the type of text you want to translate. Typically, the more technical and straightforward the text, the better a machine will be at working on it.
There has been a lot of discussion about negative emissions technologies (NETs) lately. While we need to be skeptical of assumed planetary-scale engineering and wary of moral hazard, we also need much greater RD&D funding to keep our options open. A quick thread: 1/10
Energy system models love NETs, particularly for very rapid mitigation scenarios like 1.5C (where the alternative is zero global emissions by 2040)! More problematically, they also like tons of NETs in 2C scenarios where NETs are less essential. https://t.co/M3ACyD4cv7 2/10
In model world the math is simple: very rapid mitigation is expensive today, particularly once you get outside the power sector, and technological advancement may make later NETs cheaper than near-term mitigation after a point. 3/10
This is, of course, problematic if the aim is to ensure that particular targets (such as well-below 2C) are met; betting that a "backstop" technology that does not exist today at any meaningful scale will save the day is a hell of a moral hazard. 4/10
Many models go completely overboard with CCS, seeing a future resurgence of coal and a large part of global primary energy occurring with carbon capture. For example, here is what the MESSAGE SSP2-1.9 scenario shows: 5/10
Energy system models love NETs, particularly for very rapid mitigation scenarios like 1.5C (where the alternative is zero global emissions by 2040)! More problematically, they also like tons of NETs in 2C scenarios where NETs are less essential. https://t.co/M3ACyD4cv7 2/10
There is a lot of confusion about carbon budgets and how quickly emissions need to fall to zero to meet various warming targets. To cut through some of this morass, we can use some very simple emission pathways to explore what various targets would entail. 1/11 pic.twitter.com/Kriedtf0Ec
— Zeke Hausfather (@hausfath) September 24, 2020
In model world the math is simple: very rapid mitigation is expensive today, particularly once you get outside the power sector, and technological advancement may make later NETs cheaper than near-term mitigation after a point. 3/10
This is, of course, problematic if the aim is to ensure that particular targets (such as well-below 2C) are met; betting that a "backstop" technology that does not exist today at any meaningful scale will save the day is a hell of a moral hazard. 4/10
Many models go completely overboard with CCS, seeing a future resurgence of coal and a large part of global primary energy occurring with carbon capture. For example, here is what the MESSAGE SSP2-1.9 scenario shows: 5/10
