Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science at Portland State University. Prof. Mitchell is the author of a number of interesting books such as Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans. One interesting detail of her academic bio is that Douglas Hofstadter was her Ph.D. supervisor. During this 90 min interview with Melanie Mitchell, we cover a variety of interesting topics such as: how she started in physics, went into math, and ended up in Computer Science; how Douglas Hofstadter became her Ph.D. supervisor; the biggest issues that humanity is facing today; my predictions of the biggest challenges of the next 100 days of the COVID19 pandemic; how to remain hopeful when it is hard to be optimistic; the problems in defining AI, thinking and human; the Turing Test and Ray Kurzweil's bet with Mitchell Kapor; the Technological Singularity and its possible timeline; the Fallacy of First Steps and the Collapse of AI; Marvin Minsky's denial of progress towards AGI; Hofstadter's fear that intelligence may turn out to be a set of "cheap tricks"; the importance of learning and interacting with the world; the [hard] problem of consciousness; why it is us who need to sort ourselves out and not rely on God or AI; complexity, the future and why living in "Uncertain Times" is an unprecented opportunity. Intelligence is a very complex phenomenon and we should study it as such.

Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science at Portland State University. Prof. Mitchell is the author of a number of interesting books such as Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans. One interesting detail of her academic bio is that Douglas Hofstadter was her Ph.D. supervisor. During this 90 min interview with Melanie Mitchell, we cover a variety of interesting topics such as: how she started in physics, went into math, and ended up in Computer Science; how Douglas Hofstadter became her Ph.D. supervisor; the biggest issues that humanity is facing today; my predictions of the biggest challenges of the next 100 days of the COVID19 pandemic; how to remain hopeful when it is hard to be optimistic; the problems in defining AI, thinking and human; the Turing Test and Ray Kurzweil's bet with Mitchell Kapor; the Technological Singularity and its possible timeline; the Fallacy of First Steps and the Collapse of AI; Marvin Minsky's denial of progress towards AGI; Hofstadter's fear that intelligence may turn out to be a set of "cheap tricks"; the importance of learning and interacting with the world; the [hard] problem of consciousness; why it is us who need to sort ourselves out and not rely on God or AI; complexity, the future and why living in "Uncertain Times" is an unprecented opportunity. Intelligence is a very complex phenomenon and we should study it as such.

As I read Melanie Mitchell's "Artificial Intelligence: A Guide for Thinking Humans," I found myself recalling John Updike's 1986 novel "Roger's Version.'' One of its characters, Dale, is determined to use a computer to prove the existence of God. Dale's search leads him into a mind-bending labyrinth where religious-metaphysical questions overwhelm his beloved technology and leave the poor fellow discombobulated. I sometimes had a similar experience reading "Artificial Intelligence." In Mitchell's telling, artificial intelligence (AI) raises extraordinary issues that have disquieting implications for humanity. AI isn't for the faint of heart, and neither is this book for nonscientists. To begin with, artificial intelligence -- "machine thinking," as the author puts it -- raises a pair of fundamental questions: What is thinking and what is intelligence? Since the end of World War II, scientists, philosophers, and scientist-philosophers (the two have often seemed to merge during the past 75-odd years) have been grappling with those very questions, offering up ideas that seem to engender further questions and profound moral issues. Mitchell, a computer science professor at Portland State University and the author of "Complexity: A Guided Tour," doesn't resolve these questions and issues -- she as much acknowledges that they are irresolvable at present -- but provides readers with insightful, common-sense scrutiny of how these and related topics pervade the discipline of artificial intelligence. Mitchell traces the origin of modern AI research to a 1956 Dartmouth College summer study group: its members included John McCarthy (who was the group's catalyst and coined the term artificial intelligence); Marvin Minsky, who would become a noted artificial intelligence theorist; cognitive scientists Herbert Simon and Allen Newell; and Claude Shannon ("the inventor of information theory"). Mitchell describes McCarthy, Minsky, Simon, and Newell as the "big four'' pioneers of AI.

About this course: This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

Cambridge, If you would like us to consider your Mass.: The MIT Press, 1994.