At Kip, we’re designing a “machine assistant for human assistance.” Instead of trying to replace the human element, we enable this social behavior, streamline it and make it more efficient. AI assistants are then a natural extension of our routing, where machines can curate this endless data overload into manageable chunks to assist our decision making.
If we are going to give everyone an AI assistant, we need to ensure that these assistants are not just trained on a small subset of skills catering to wealthy consumption, but rather a diverse breadth that represents a whole cross-section of social needs.
The AI assistants of the future will not be homogeneous or singular. They will be made up of hundreds of individual, vertically specialized AIs in combinations that suit each user. These specialized AIs will continue to optimize themselves for the task they’re trained for.
Caretaker efficiency comes from adapting to needs of the user. Neural networks in AI deep learning are constantly tuning to more efficient pathways to achieve the desired outcome of the user. As we scale up the breadth of skills and knowledge AI needs to accomplish more tasks, the more specialized AI needs to become.
The master caretaker AI will knit these vertical AIs together, in specialized combinations for each user, like Facebook’s M hopes to achieve.
The largest tech companies will be the caretakers for these specialized, Vertical AIs. Walled AI gardens will emerge and hopefully federated, open-source networks of Vertical AIs will as well, so more niché, specialized AI can emerge to address ever-increasingly diverse social needs of users.