Five mid-week reads in behavioural science, machine learning and robotics to stay up dated on the robot economy.
- Humans define the goals, technology implements the goals – A wide ranging interview with Stephen Wolfram on Artificial Intelligence and the future. “I think the issue is, as you look to the future, and you say, “Well, what will the future humans …?” where there’s been much more automation that’s been achieved than in today’s world—and we’ve already got plenty of automation, but vastly more will be achieved. And many professions which right now require endless human effort, those will be basically completely automated, and at some point, whatever humans choose to do, the machines will successfully do for them. And then the question is, so then what happens? What do people intrinsically want to do? What will be the evolution of human purposes, human goals?” (GigaOm)
- Chinese factory replaces 90% of humans with robots, production soars – There are still people working at the factory, though. Three workers check and monitor each production line and there are other employees who monitor a computer control system. Previously, there were 650 employees at the factory. With the new robots, there’s now only 60. (TechRepublic)
- Sex with robots will be ‘the norm’ in 50 years – An expert on the psychology of sex has claimed that she expects having sex with robots to be socially acceptable by 2070 (The Independent)
- Cheaper Robots, Fewer Workers – A NY Times Bits video series, called Robotica, examining how robots are poised to change the way we do business and conduct our daily lives. (The New York Times)
- 10 lessons in Reinforcement Learning from Google’s DeepMind – A very good series of videos on Reinforcement Learning, by David Silver from Google’s DeepMind:
- Lecture 10 | Reinforcement Learning : Classic Games (David Silver)
- Lecture 9 | Reinforcement Learning : Exploration and Exploitation (David Silver)
- Lecture 8 | Reinforcement Learning : Integrating Learning and Planning (David Silver)
- Lecture 7 | Reinforcement Learning: Policy Gradient Methods (David Silver)
- Lecture 6 | Reinforcement Learning : Value Function Approximation (David Silver)
- Lecture 5 | Reinforcement Learning : Model Free Control (David Silver)
- Lecture 4 | Reinforcement Learning : Model-Free Prediction (David Silver)
- Lecture 3 | Reinforcement Learning: Planning by Dynamic Programming (David Silver)
- Lecture 2 | Reinforcement Learning : Markov Decision Process (David Silver).
- Lecture 1 | Reinforcement Learning : Introduction to Reinforcement Learning (David Silver)
What are you reading?