Chris weighs up the ethical implications of Google Duplex:
The social hacking that could be accomplished is mind-boggling. For this reason, I expect that having human-sounding narrow AI will be illegal someday. The Duplex demo is a moment of cultural clarity, where it first dawned on us that we can do it, but with only a few exceptions, we shouldn’t.
But he also offers alternatives for designing systems like this:
…design the interface so that it is unmistakeable that it is synthetic. This way, even if the listener missed or misunderstood the disclosure, there is an ongoing signal that reinforces the idea. As designer Ben Sauer puts it, make it “Humane, not human.”
Ethan shares my reaction to Google Duplex:
Frankly, this technology was designed to deceive humans.
And he points out that the team’s priorities are very revealing:
I’ll say this: it’s telling that matters of transparency, disclosure, and trust weren’t considered important for the initial release.
The transcript of Josh’s fantastic talk on machine learning, voice, data, APIs, and all the other tools of algorithmic design:
The design and presentation of data is just as important as the underlying algorithm. Algorithmic interfaces are a huge part of our future, and getting their design right is critical—and very, very hard to do.
Josh put together ten design principles for conceiving, designing, and managing data-driven products. I’ve added them to my collection.
- Favor accuracy over speed
- Allow for ambiguity
- Add human judgment
- Advocate sunshine
- Embrace multiple systems
- Make it easy to contribute (accurate) data
- Root out bias and bad assumptions
- Give people control over their data
- Be loyal to the user
- Take responsibility
I like how Luke is using a large language model to make a chat interface for his own content.
This is the exact opposite of how grifters are selling the benefits of machine learning (“Generate copious amounts of new content instantly!”) and instead builds on over twenty years of thoughtful human-made writing.
Prompted by his time at Clearleft’s AI gathering in Juvet, Chris has been delving deep into the stories we tell about artificial intelligence …and what stories are missing.
And here we are at the eponymous answer to the question that I first asked at Juvet around 7 months ago: What stories aren’t we telling ourselves about AI?
This 1993 article by Mark Weiser is relevant to our world today.
Take intelligent agents. The idea, as near as I can tell, is that the ideal computer should be like a human being, only more obedient. Anything so insidiously appealing should immediately give pause. Why should a computer be anything like a human being? Are airplanes like birds, typewriters like pens, alphabets like mouths, cars like horses? Are human interactions so free of trouble, misunderstanding, and ambiguity that they represent a desirable computer interface goal? Further, it takes a lot of time and attention to build and maintain a smoothly running team of people, even a pair of people. A computer I need to talk to, give commands to, or have a relationship with (much less be intimate with), is a computer that is too much the center of attention.
A presentation at An Event Apart Chicago 2019.
Computer Human Interaction