AO Labs is an applied AI research lab building real-time reinforcement learning, unlocking AI that can be hyper-personalized with less data than LLMs.
Our first product is an API that learns by combining end-user context, application data, and a feedback signal (eg. an end-users' likes and dislikes) in a fast learning loop that's lightweight enough to provision a distinct agent per-user. There's no gap between training and inference-- train at the edge, learn all the time.
With our framework we increase training efficiency and also combine the static pre-trained intelligence from LLMs with continuous training to learn local contexts. AI progress is bottlenecked by backpropagation, which necessitates labelled data to set the ground truth while also leaving a gap between training and inference that result in increasingly larger, more homogenizing models.
We are hiring for applied researchers & various engineering roles– please reach out to [email protected]
AO Labs | Building an alternative to backpropagation | https://www.aolabs.ai/ | Berkeley, CA + remote
AI systems struggle with edge cases and understanding local context despite increasing model sizes. From our research at UC Berkeley into the evolution of intelligence from simple organisms, we’ve discovered the missing link is continuous learning (deep learning is pre-trained by design). Models built with our framework learn through customizable parameters similar to animal instincts, allowing for AI grounded with built-in memory and reasoning. We're a community of 160+ developers and researchers building general intelligence from the bottom-up from places like Berkeley, NYU, Imperial College, and Google.
We're building way outside of the current paradigm and we're looking for collaborators at all levels --hackers, contributors, the curious-- as we'll be making our first hires soon. Email with "HN Hiring" in subject line to: ali at aolabs.ai or chat with us in our discord: https://discord.gg/Zg9bHPYss5
This post is near identical to mine from last month; if you reached out then, please know that I'll respond to you soon (I've been busy wrapping up a fundraise).
AO Labs is an applied AI research lab building real-time reinforcement learning, unlocking AI that can be hyper-personalized with less data than LLMs.
Our first product is an API that learns by combining end-user context, application data, and a feedback signal (eg. an end-users' likes and dislikes) in a fast learning loop that's lightweight enough to provision a distinct agent per-user. There's no gap between training and inference-- train at the edge, learn all the time.
With our framework we increase training efficiency and also combine the static pre-trained intelligence from LLMs with continuous training to learn local contexts. AI progress is bottlenecked by backpropagation, which necessitates labelled data to set the ground truth while also leaving a gap between training and inference that result in increasingly larger, more homogenizing models.
We are hiring for applied researchers & various engineering roles– please reach out to [email protected]