As the editor-in-chief of The Verge, I can theoretically assign whatever I want. However, there is one topic I have failed to get people at The Verge to write about for years: robotic process automation, or RPA. RPA isn't robots in factories, which is often what we think of when it comes to automation. This is different: RPA is software. Software that uses other software, like Excel or an Oracle database. On this week's Decoder, I finally found someone who wants to talk about it with me: New York Times tech columnist Kevin Roose. His new book, Futureproof: 9 Rules for Humans in the Age of Automation, has just come out, and it features a lengthy discussion of RPA, who's using it, who it will affect, and how to think about it as you design your career. What struck me during our conversation were the jobs that Kevin talks about as he describes the impact of automation: they're not factory workers and truck drivers. If you have the kind of job that involves sitting in front of a computer using the same software the same way every day, automation is coming for you. It won't be cool or innovative or even work all that well -- it'll just be cheaper, faster, and less likely to complain. That might sound like a downer, but Kevin's book is all about seeing that as an opportunity. You'll see what I mean. Okay, Kevin Roose, tech columnist, author, and the only reporter who has ever agreed to talk to me about RPAs. This transcript has been lightly edited for clarity. Kevin Roose, you're a tech columnist at The New York Times and you have a new book, Futureproof: 9 Rules for Humans in the Age of Automation, which is out now. Thank you for having me. You're ostensibly here to promote your book, which is great. But there's one piece of the book that I am absolutely fascinated by, which is this thing called "robotic process automation." And I'm gonna do my best with you on this show, today, to make that super interesting. But before we get there, let's talk about your book for a minute. What is your book about? Because I read it, and it has a big idea and then there's literally nine rules for regular people to survive. So, tell me how the book came together. So, the book is basically divided into two parts.

David Keyes is a professor of applied mathematics and computational science and director of the Extreme Computing Research Center at the King Abdullah University of Science and Technology, Saudi Arabia.

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration and serves on the Board of the UK's charitable Web Science Trust. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high-performance processing. Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002.

The artificial intelligence or "AI" label is slapped on almost anything electronic these days, from "smart" toothbrushes to cancer-curing supercomputers. If you're like me you've become jaded by the AI rubric, realizing we're still a long way from true intelligence in machines. Jeff Hawkins is co-founder of machine intelligence company Numenta and author of a new book "A Thousand Brains: A New Theory of Intelligence" that offers a theory of what's missing in current AI. I don't normally do author interviews, but Jeff has a history of knowing where things are going in tech, including, in my opinion, being a primary developer of the modern smartphone at Handspring and Palm. Hawkins' book takes pains to explain how the neocortex -- the large, convoluted outer layer of the human brain -- uses "reference frames" of perception, thousands of which create our understanding of everything from the shape of a simple object to the nature of a complex concept like mathematics.

On the internet, artificial intelligence is used for everything from speech recognition to spam filtering. The artificial intelligence or "AI" label is slapped on almost anything electronic these days, from "smart" toothbrushes to cancer-curing supercomputers. If you're like me you've become jaded by the AI rubric, realizing we're still a long way from true intelligence in machines. Jeff Hawkins is co-founder of machine intelligence company Numenta and author of a new book "A Thousand Brains: A New Theory of Intelligence" that offers a theory of what's missing in current AI. I don't normally do author interviews, but Jeff has a history of knowing where things are going in tech, including, in my opinion, being a primary developer of the modern smartphone at Handspring and Palm.

The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley's ethos of "move fast and break things" spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. "Data is the main resource of a new digital economy," says Parminder Singh, executive director at nonprofit organization IT for Change. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons--or open data sources--are vital to help build an equitable digital economy, but with that comes the challenge of data governance. "Not everybody is sharing data," says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, "Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it." Singh continues, "If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone." One doesn't have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies.

Rosenblatt led the design of a computer to implement this idea and tried to train it to recognize the differences between males and females in photos. "the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence."

The security view of our overall failings to support AI innovation and tech investment push us into action, but it hasn't worked with health insecurity. The broader issue that might motivate and pull us forward is recognition that modern AI, driven in part by advances in machine learning, the harnessing of Big Data and powerful computing, has the potential to amplify human ingenuity and help solve the challenges, including more productive work and climate change mitigation.

Michael Wooldridge is the winner of the 2021 Educational Advances in Artificial Intelligence (EAAI) Outstanding Educator Award. He gave a plenary talk at AAAI/EAAI in February this year, focussing on lessons he has learnt in communicating AI to the public. Michael's public science journey began in 2014 when the press and social media became awash with stories of AI. He wondered who was going to respond to these, often exaggerated, narratives and to add some nuance to the discussion. It turned out that nobody did, and there was a noticeable absence of expert opinion reported.

In this technical talk, Chad Jenkins from the University of Michigan posed the following question: "who will pay the cost for the likely mistakes and potential misuse of AI systems?" As he states, "we are increasingly seeing how AI is having a pervasing impact on our lives, both for good and for bad. So, how do we ensure equal opportunity in science and technology?" It would be great to talk about the many compelling ideas, innovations, and new questions emerging in robotics research. I am fascinated by the ongoing NeRF Explosion, prospects for declarative robot programming by demonstration, and potential for a reemergence of probabilistic generative inference.