Archetype AI: Creating a model to “encode the entire physical world”

Startup Archetype AI is fusing physical sensor data with LLMs to create an AI model that will "encode the entire physical world." This approach means that natural language becomes a translation layer used to both interpret input (i.e., sensing) and to issue commands (e.g., controlling a robot arm). This work is exciting but also hard to access. What can a regular person do to start experimenting with and preparing for this type of tech?

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Mini-case study: How do you run a human-centered technical exploration on a hard-to-access emerging technology?

Emerging tech teams may build a product thinking there’s a market, only to be forced to pivot when sales slump. A human-centered technical exploration can be a starting point for discovering new markets. I helped a consumer robotics team create user-centered “razors” to identify new target customers, despite tight timelines and lack of access to prototypes or production models.

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How can we the tackle systems-level impacts of AI?

We’ve started to recognize the quality and legal issues AI brings into our own product ecosystems. But incorporating AI into our products also means we are integrating ourselves into much larger systems: systems with far-reaching and often hard-to-understand consequences. As human-centered professionals, how can we tackle this difficult problem?

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AILlewyn Paine
Where can senior professionals in tech find support?

Even seasoned leaders want to grow their skills and connect meaningfully with other professionals. But this becomes harder to do when you’re senior because you are too experienced to find value in 101-level content, and there are more and more people vying for your time and attention. Senior professionals can thrive when supported by past and present colleagues, supplemented by expert consultants.

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