Resources to check out

As I wrap up my series on AI, here are a few articles and pages which cover the topics at a level of nuance that I couldn’t do justice to in my previous posts. Some are easy-reads while others are long tutorials that can teach technical details of machine learning to the level of skilled programmers. Hope you find them interesting or of use.

AI Basics

  1. A quick-read to differentiate between the different buzzwords that dominate this sphere of AI technology.
  2. An overview of hardware used and how the future of AI hardware will look.
  3. History of Machine learning – Will give you a deeper appreciation for how far we have come and the significance of certain developments (like GPU use).
  4. This essay does an excellent job in highlighting how AI will make strong tech companies stronger.
  5. Taken from a debate, research scientist at DeepMind articulates a very morally conscious argument as to why robots deserve rights.
  6. Packed with economic and historical context this video very eloquently conveys why this AI wave is different from previous ages of development. It also looks predicts the implications AI will have on jobs and existing power structures (as I did in this post).
The Rise of the Machines – Why Automation is Different this Time – Kurzgesagt – In a Nutshell

Using AI

  1. A Neural Network is somewhat analogous to a brain. This is a core part of the AI design process. This article is a walkthrough for novices as to how neural networks work.
  2. For those with some background knowledge, here’s a walkthrough as to how to make a simple neural network.
  3. For slightly more involved learning, this article takes you through how to implement a Recurrent Neural Network (RNN).
  4. Described as the next frontier for Machine Learning, transfer learning is a type of concept that is considered an advanced cognitive phenomenon in humans. For machines to replicate it would be marvellous. This article covers the concept in great detail.
  5. If you don’t mind a math heavy approach to transfer learning, this Medium article looks at the same concept at a more hands-on level.
  6. This website approaches machine learning from a point of providing tutorials and working code rather than intimidating and math-heavy academic material that is typically restrictive.

Current roadblocks

  1. For a deeper understanding as to how bias in AI occurs. Using an excellent analogy to dogs, it details how Machine learning programs are inherently biased or even unbiased. It is simply learning from what it is taught.
  2. This post looks at the ethical concerns of AI use with a focus on facial recognition.

A way forward

  1. An essay about regulating technology. As AI becomes a part of our everyday society, maybe we need to understand how to control it, or at least how to responsibly develop AI.
  2. In this often quoted essay title The Bitter Lesson, Rich Sutton details his learnings from assimilating 70 years of AI research and what common mistakes we need to avoid for further research.
  3. This AI Op-Ed was created using GPT-3. The result is a surprisingly sarcastic and piercing piece about what human intelligence is and isn’t.
  4. A very interesting video about the philosophical implications of AI with a focused look on the idea of robot rights.
Do Robots Deserve Rights? What if Machines Become Conscious? – Kurzgesagt – In a Nutshell

If we do it right, we might be able to evolve a form of work that taps into our uniquely human capabilities and restores our humanity. The ultimate paradox is that this technology may become a powerful catalyst that we need to reclaim our humanity

John Hagel

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