The Care of AI to Come
Rachel Law and Alyx Baldwin, co-founders of conversation-commerce chatbot Kip, on the future of AI assistants
Humans are social creatures. Our success as a species is based on interdependence, and much of technological progress is based on strengthening these connections. Facebook, chat and other social networks strengthen ties to each other, where help is just a text away.
Each human effectively acts as a router for needs with varying levels of throughput. A CEO is the router between the investor and team needs, the office manager is the router between team and department needs, the caregiver parent is a router between the children and spousal needs.
But who cares for the caretaker? Those with the highest levels of throughput. 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.
Just like how calculators assist us in complex calculations, or computers assist us in knowledge, AI can assist in optimizing connections. AI assistants can help us collect information on a situation more quickly, asses the competing needs of individuals and come up with several scenarios where we choose the best course of action. Armed with more information, we can make better decisions not just for ourselves, but for our communities as well.
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.
Some AIs will be really good at washing dishes, and only get better at it. Right now, Baidu is optimizing Mandarin better than Google, while Google understands California accents better than Singaporean ones. The master caretaker AI will knit these vertical AIs together, in specialized combinations for each user, like Facebook’s M hopes to achieve.
Right now it takes an immense amount of computing power to train AI to do stuff for us. Soon, there will be an arms race as more companies train their AIs separately for specialized tasks to compete against other companies doing the same tasks.
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.
Rachel Law is co-founder and CEO of Kip, and Alyx Baldwin is co-founder and CTO of Kip.
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