Anyone interested in technology and society should read Melvin Kranzberg’s Six Laws of Technology, the first of which says that “technology is neither good nor bad; nor is it neutral”.

Here’s an interesting look at how AI outsmarted antibiotic resistant bacteria (for now).

The saloon-bar version of this is that “technology is both good and bad; it all depends on how it’s used” – a tactic that tech evangelists regularly deploy as a way of stopping the conversation. So a better way of using Kranzberg’s law is to ask a simple Latin question: Cui bono? – who benefits from any proposed or hyped technology? And, by implication, who loses?

80% of ocean plastic comes from inland sources.

In this episode of CodeStories, learn about the Plastic Origins project, from Surfrider Foundation Europe, which tackles inland plastic pollution by monitoring microplastic and tracking the path of plastic waste as it travels to the ocean.

Episode index:

  • About Surfrider Foundation Europe
  • About AI School
  • Seine River, near Microsoft France office
  • How Plastic Origins is using AI and machine learning
  • Demo with Azure Machine Learning
  • How to get involved with the Plastic Origins project

Surfrider Foundation Europe would like to thank all the volunteers who contribute everyday to the success of the Plastic origins project. If you want to become one of them and join the project, write us at plasticorigins@surfrider.eu

Links from this episode: 

Lex Fridman interviews John Hopfield, a professor at Princeton, whose life’s work weaved beautifully through biology, chemistry, neuroscience, and physics.

Most crucially, he saw the messy world of biology through the piercing eyes of a physicist. He is perhaps best known for his work on associate neural networks, now known as Hopfield networks that were one of the early ideas that catalyzed

Timeline:

  • 0:00 – Introduction
  • 2:35 – Difference between biological and artificial neural networks
  • 8:49 – Adaptation
  • 13:45 – Physics view of the mind
  • 23:03 – Hopfield networks and associative memory
  • 35:22 – Boltzmann machines
  • 37:29 – Learning
  • 39:53 – Consciousness
  • 48:45 – Attractor networks and dynamical systems
  • 53:14 – How do we build intelligent systems?
  • 57:11 – Deep thinking as the way to arrive at breakthroughs
  • 59:12 – Brain-computer interfaces
  • 1:06:10 – Mortality
  • 1:08:12 – Meaning of life