I called it. Adept intends to make it so. [Neil Stephenson was there in ‘95]

Back in July of 2020, I posted my first reactions to GPT-3 in a comment on Tyler Cowen’s Marginal Revolution:

Machine learning was the key breakthrough. Rodney Brooks' Gengis, with its subsumption architecture, was a key development. FWIW Brooks has teamed up with Gary Marcus and they think we need to add some old school symbolic computing into the mix. I think they're right.

Machines, however, have a hard time learning the natural world as humans do. We're born primed to deal with that world with millions of years of evolutionary history behind us. Machines, alas, are a blank slate.

The native environment for computers is, of course, the computational environment. That's where to apply machine learning. Note that writing code is one of GPT-3's skills.

So, the AGI of the future, let's call it GPT-42, will be looking in two directions, toward the world of computers and toward the human world. It will be learning in both, but in different styles and to different ends. In its interaction with other artificial computational entities GPT-42 is in its native milieu. In its interaction with us, well, we'll necessarily be in the driver's seat.

Is the Star Trek computer heading our way? A new startup, Adept, aims to make it so.

After 5+ wonderful years in Google Brain, working at the forefront of ML alongside inspiring colleagues, I'm excited to share my new adventure. We started Adept with the mission to build the future of human-computer collaboration. https://t.co/t8dMqZfbSZ.

— Ashish Vaswani (@ashVaswani) April 26, 2022

From their blog:

In practice, we’re building a general system that helps people get things done in front of their computer: a universal collaborator for every knowledge worker. Think of it as an overlay within your computer that works hand-in-hand with you, using the same tools that you do. We all have parts of our job that energize us more than others – with Adept, you’ll be able to focus on the work you most enjoy and ask our model to take on other tasks. For example, you could ask our model to “generate our monthly compliance report” or “draw stairs between these two points in this blueprint” – all using existing software like Airtable, Photoshop, an ATS, Tableau, Twilio to get the job done together. We expect the collaborator to be a good student and highly coachable, becoming more helpful and aligned with every human interaction.

This product vision excites us not only because of how immediately useful it could be to everyone who works in front of a computer, but because we believe this is actually the most practical and safest path to general intelligence. Unlike giant models that generate language or make decisions on their own, ours are much narrower in scope–we’re an interface to existing software tools, making it easier to mitigate issues with bias. And critical to our company is how our product can be a vehicle to learn people’s preferences and integrate human feedback every step of the way.

Andreesen talks of AI as platform. It looks like Adept is heading toward AI as operating system – see my post, AI as platform [Andreessen]: PowerPoint Squared and beyond.

I’m thinking of a future in which each child is given their own AI companion at some suitably early age. The companion grows with them, remaining with them for the rest of their life. Is that where we’re going, toward Neil Stepheson's Diamond Age: Or, A Young Lady's Illustrated Primer (1995)? (Thanks, David.) 

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Addendum: Some more tweets out of Adept: 

This is also the reverse of most AGI work out there. Rather than automating economically valuable tasks, we want to keep humans in the driver’s seat, by building AI tools that people can work with to do things together.

— David Luan (@jluan) April 26, 2022

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Adept Video Demo! from Augustus Odena on Vimeo.