Kence Anderson - Designing Autonomous AI: A Guide for Machine Teaching

• 2022-06-10: First Release

It gives me great pleasure to enthusiastically recommend Designing Autonomous AI, written by a leading real-world practitioner in emergent autonomous AI: Kence Anderson.

As the Corporate Vice President currently responsible for new product incubations at Microsoft, and as someone who has been closely involved with creation of new product categories like VPNs, real-time communications and now autonomous systems at Microsoft, I have learned that creating new categories is not a straightforward path.

Established product categories and their underlying technologies and business models are well honed for the real world with well understood problems and solutions. These are things we use in our daily lives.

Fundamental research is important to new category creation but lives in the ether of science, generally abstracted from real world considerations. Researchers create novel methods and demonstrate their promise through results in carefully crafted experimentsthe amazing state of the art in AI is a recent example of this.

Importantly, not every method created by researchers is sufficient or workable in the real world. New categories can be created when one or more of these new methods are developed further, grounded in and applied to the real-world environment.

New category creation is a chaotic, primordial process entailing a loop of exploration, learning, and adaptation. This process is very high dimensional and domain dependent; there is no single formula here. However, at the core of new category creation, inevitably there is always a breakthrough clinching idea that solves the most difficult aspects of the real-world problem.

The new category of autonomous AI, which is so significant that it is being called the Fourth Industrial Revolution, is particularly challenging in this regard because the underlying real-world considerations are extremely onerous. Building adequate systems that are safe and performant in the real world is nontrivial. This is a key reason for why we havent seen flying cars outside our window yet.

For autonomous AI, one of those breakthrough clinching ideas is machine teaching. In the same way languages and compilers made the once complex and kludgy task of computing easy, efficient, and accessible to millions of programmers, machine teaching is enabling experts from any discipline (mechanical, electrical, or aeronautical engineering, etc.) to build powerful and usable autonomous systems easily, without requiring a PhD in AI or data science.

Machine teaching, without apologies, borrows from well-established techniques from all schools of thoughts of AI: connectionist, symbolist, and experiential, and from proven methods in pedagogy. Machine teaching provides an easy way to codify human expertise in any domain and to apply the power of learning methods automatically, abstracting away the tyranny of abstruse AI algorithms.

Kence Anderson writes about machine teaching with the lucidity and authority of someone who had a hand in its creation (which he did).

Gurdeep Pall (Seattle, WA, 2022)