#GoogleIO revealed the two weirdest features as a pair.
1. Give a short summary and Google will draft an e-mail for you based on it. You can even click "elaborate" and it will make the e-mail longer.
2. When opening an e-mail, Gmail can summarize the entire thing for you so you don't have to read all of it.
Does everyone realize how fucking bizarre this is?
Both people in the conversation want to work with directness and brevity, and Google is doing textual steganography in the middle.
One piece of slang that has long embodied the short attention span Internet age is TL;DR, short for “too long; didn’t read.” With the explosion of generative AI tools, we’re rapidly entering the age of TL;DW: “too long, didn’t write.” A January survey from Fishbowl found that 40% of nearly 12,000 workers have used ChatGPT
@rodhilton marketoonist predicted this
One piece of slang that has long embodied the short attention span Internet age is TL;DR, short for “too long; didn’t read.” With the explosion of generative AI tools, we’re rapidly entering the age of TL;DW: “too long, didn’t write.” A January survey from Fishbowl found that 40% of nearly 12,000 workers have used ChatGPT
The bullet point appears to say the same as the original unless you inspect it really carefully.
@leoncowle @rodhilton Was also going to post this.
The biggest change in the world is the speed in which reality imitates art.
@rodhilton @Gte well let me tell you, people don’t actually want directness, they think it’s rude.
Google is permitting them to carry on this charade, which is the real sin.
It’s a beautiful encapsulation of Silicon Valley in general.
“Should we address this minor issue of social norms in the workplace?
I have a solution that’ll only require 2 globally distributed SRE teams, 20 FTE SWEs and 1/3rd of our orgs annual compute capacity!”
@andrewt Here's another one.
Introducing tools to allow AI-based generation of images, as well as tools to detect AI-generated images
This is the very definition of selling both the poison and the cure.
Trouble with trying this with BingChat is that it recognizes from which song the lyrics originated, and regurgitates the memorized summary for the entire song.
Yet, asking BingChat to write a song using its own summary as the prompt yields something that is altogether very different - at least it won't attract a copyright lawsuit from Ed Sheeran
Tried this with the abstract to "An aperiodic monotile" by David Smith et al.
https://arxiv.org/abs/2303.10798
Bing Chat's one sentence summary:
"a solution to a longstanding problem of finding an aperiodic monotile or “Einstein” by exhibiting a continuum of combinatorially equivalent aperiodic polygons."
Then asked for an informational paragraph with that as prompt:
A longstanding open problem asks for an aperiodic monotile, also known as an "einstein": a shape that admits tilings of the plane, but never periodic tilings. We answer this problem for topological disk tiles by exhibiting a continuum of combinatorially equivalent aperiodic polygons. We first show that a representative example, the "hat" polykite, can form clusters called "metatiles", for which substitution rules can be defined. Because the metatiles admit tilings of the plane, so too does the hat. We then prove that generic members of our continuum of polygons are aperiodic, through a new kind of geometric incommensurability argument. Separately, we give a combinatorial, computer-assisted proof that the hat must form hierarchical -- and hence aperiodic -- tilings.
@rodhilton just starting the countdown until we get the first employment discrimination settlement that includes a generated email.
The writer, rightfully claiming that they did not in fact, type the words. The reader, displaying the AI shortened message, based on some admin configuration.
@gatesvp yeah I think that the whole AI hype cycle in general is going to really brush up against issues of responsibility in a hurry.
Your AI-powered self-driving car hits someone. Are you responsible? You just enabled a feature of the car. Is the AI responsible? It's an algorithm. Is the company who built it? No, they weren't there.
If your AI chooses who to fire and it turns out to be a bunch of protected class employees, who gets sued? The employer or the builder of LayoffBot?
I think these are really good examples and that you might not be into the details enough.
For example, a self-driving car will not be involved in zero accidents. Zero is not the metric of success for mechanical things. Or even for software.
And we don't typically hold companies liable for things that are operating within regulatory specification.
The HR stuff is even messier. Because it actually highlights giant regulatory problem... /1
Legal standards are invented by humans like you and I. If you were tasked with creating such a standard, what would you want see in it?
How would you write this to be "inclusive of AI"?
What legal standard?
Basically every standard falls to a regulatory body. Implementation literally falls to the State level in the US. The laws use vague words like "as expected by a reasonable consumer".
https://www.findlaw.com/injury/product-liability/what-is-product-liability.html
So if you're responsible for deciding "reasonable consumer" regulations for AI cars. What are you deciding? Remember, this rule doesn't exist today. You get to make it up.
So we've gone back and forth discussing weird legal technical details.
What you haven't contributed is the simplest question. What rules do you want to govern this?
I would even accept an example of a bad thing happening that was okay and a bad thing happening that was bad.
Do you have any of these? Or are you just posturing?
So let's drill into Rod's example.
If your AI chooses who to fire and it turns out to be a bunch of protected class employees, who gets sued? The employer or the builder of LayoffBot?
Who's the liable party?
It can't be the employer, they outsourced the decision to LayoffBot who granted them indemnity.
It can't be LayoffBot because "they don't ingest information about protected classes".
We don't measure outcomes only inputs...
In a normal case, we would subpoena the code and go to the algorithm. We would analyze the code to figure out why it is making decisions the way it is and then we make a decision on whether or not the code is defective.
To me, the machine learning solutions are novel because you can't really analyze the code in that way. You may not even be able to repro the results.
To me this is new territory. Do other companies commonly do this? //
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