General

Podcast

AI and Machine Learning

Stuart Russell is one of AI's true pioneers and has been at the forefront of the field for decades. His expertise and forward thinking have culminated in his newest work, Human Compatible: Artificial Intelligence and the Problem of Control. The book is a cornerstone piece, alongside Superintelligence and Life 3.0, that articulates the civilization-scale problem we face of aligning machine intelligence with human goals and values. Not only is this a further articulation and development of the AI alignment problem, but Stuart also proposes a novel solution which bring us to a better understanding of what it will take to create beneficial machine intelligence.

 Topics discussed in this episode include:

-Stuart's intentions in writing the book
-The history of intellectual thought leading up to the control problem
-The problem of control
-Why tool AI won't work
-Messages for different audiences
-Stuart's proposed solution to the control problem

You can find the page for this podcast here: https://futureoflife.org/2019/10/08/ai-alignment-podcast-human-compatible-artificial-intelligence-and-the-problem-of-control-with-stuart-russell/

Important timestamps: 

0:00 Intro
2:10 Intentions and background on the book
4:30 Human intellectual tradition leading up to the problem of control
7:41 Summary of the structure of the book
8:28 The issue with the current formulation of building intelligent machine systems
10:57 Beginnings of a solution
12:54 Might tool AI be of any help here?
16:30 Core message of the book
20:36 How the book is useful for different audiences
26:30 Inferring the preferences of irrational agents
36:30 Why does this all matter?
39:50 What is really at stake?
45:10 Risks and challenges on the path to beneficial AI
54:55 We should consider laws and regulations around AI
01:03:54 How is this book differentiated from those like it?

AIAP: Human Compatible: Artificial Intelligence and the Problem of Control with Stuart Russell

In the 2000s and 2010s, education technology evangelists promised that new learning media would transform schooling and education. Then, a pandemic shut down schools all over the world, and online learning face a pivotal moment, and left a global public mostly disappointed. Instead of adaptive tutors, artificial intelligence, MOOCs or other new technologies, most learners got digital worksheets on learning management systems and ZOOM lecturers. "Failure to Disrupt: Why Technology Alone Can’t Transform Education" explores the recent history of large scale learning technologies to explain why technology provides such uneven support—useful in some contexts but not others, to some people but not others—to learners. The book concludes by examining four as-yet intractable dilemmas that learning media researchers and designers can use to identify persistent challenges in using technology to accelerate human learning.

Justin Reich is the Mitsui Career Development Professor of Comparative Media at MIT, and the director of the MIT Teaching Systems Lab. He is the host of the TeachLab podcast, the author of the forthcoming book Failure to Disrupt: Why Technology Alone Can’t Transform Education from Harvard University Press, and the instructor for six massive open online courses on EdX and available through the MIT Open Learning Library.

MIT Comparative Media Studies/Writing

Justin Reich, "Failure to Disrupt: Why Technology Alone Can’t Transform Education"

The Future of Life Institute (FLI) is a nonprofit working to reduce global catastrophic and existential risk from powerful technologies. In particular, FLI focuses on risks from artificial intelligence (AI), biotechnology, nuclear weapons and climate change.

The Institute's work is made up of three main strands: grantmaking for risk reduction, educational outreach, and advocacy within the United Nations, US government and European Union institutions.

FLI has become one of the world's leading voices on the governance of AI having created one of the earliest and most influential sets of governance principles: the Asilomar AI Principles.