Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.

Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent. We take an iterative, empirical approach: by attempting to align highly capable AI systems, we can learn what works and what doesn’t, thus refining our ability to make AI systems safer and more aligned. Using scientific experiments, we study how alignment techniques scale and where they will break.

We tackle alignment problems both in our most capable AI systems as well as alignment problems that we expect to encounter on our path to AGI. Our main goal is to push current alignment ideas as far as possible, and to understand and document precisely how they can succeed or why they will fail. We believe that even without fundamentally new alignment ideas, we can likely build sufficiently aligned AI systems to substantially advance alignment research itself.

In this Robotics 24/7 Roundtable, viewers can learn how robots and software can enable retailers to improve productivity, manage their supply chains, and better serve consumers.

A team of researchers in the US and China has designed and built a neuromorphic AI chip using resistive RAM, also known as memristors.

The 48 core NeuRRAM chip developed at the University of California San Diego is twice as energy efficient as other compute-in-memory chips and provides results that are just as accurate as conventional digital chips.

Computation with RRAM chips is not necessarily new, and many startups and research groups are working on the technology. However it generally leads to a decrease in the accuracy of the computations performed on the chip and a lack of flexibility in the chip’s architecture.