Machine programming, which automates the development and maintenance of software, is becoming supercharged by AI. During its Build developer conference in May, Microsoft detailed a new feature in Power Apps that taps OpenAI's GPT-3 language model to assist people in choosing formulas. Intel's ControlFlag can autonomously detect errors in code. And Facebook's TransCoder converts code from one programming language into another. The applications of computer programming are vast in scope.

This is a talk from GOTO Chicago 2019 by Doug Lenat, Award-winning AI pioneer who created the landmark Machine Learning program, AM, in 1976 and CEO of Cycorp. I've dropped the full talk abstract below for a read before diving into the talk: Almost everyone who talks about Artificial Intelligence, nowadays, means training multi-level neural nets on big data. Developing and using those patterns is a lot like what our right brain hemispheres do; it enables AI's to react quickly and – very often – adequately. But we human beings also make good use of our left brain hemisphere, which reasons more slowly, logically, and causally. I will discuss this "other type of AI" – i.e., left brain AI, which comprises a formal representation language, a "seed" knowledge base with hand-engineered default rules of common sense and good domain-specific expert judgement written in that language, and an inference engine capable of producing hundreds-deep chains of deduction, induction, and abduction on that large knowledge base.

For an organization that has no shortage of strongly held opinions on an array of topics affecting providers and the healthcare industry, the American Medical Association currently does not have an artificial intelligence policy. That will be changing soon, as AMA heads into its annual meeting next week, according to the group's report to its board of trustees. The association has compiled what it says is a "baseline policy to guide AMA's engagement with a broad cross-section of stakeholders and policymakers to ensure that the perspective of physicians in various practice settings informs and influences the dialogue as this technology develops." AI uptake is gaining momentum industry-wide, of course, and transforming fundamental ways of doing things in healthcare. Physicians are starting to take notice – some with interest, some with skepticism, some with alarm.

Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two postdoctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an assistant professor in the Departments of Statistics and Computer Science. In 2016 he joined the Machine Learning Department at Carnegie Mellon University as an associate professor. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data.