Considering Counterarguments on the Singularity

https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb9377758-2f1a-41dd-b10c-65a9c8db4aea_6168x4112.jpeg — WALL-E made out of Legos. Photo by Jason Leung.

Hey everyone! This is my first post on LessWrong :) I cross-posted this from my blog Love Yourself Unconditionally where I am currently writing about AI Safety and Buddhism. This is the second post in a series. If you're curious to see the first post you can find that here. Although this is a series, each of these articles can also be read on their own. Thanks for checking out my post and any feedback you all have would be greatly appreciated! :)

Introduction

As mentioned in my prior post, I thought it would be helpful to consider counterarguments. Specifically, I wanted to see what arguments existed for the following claim:

AI will never surpass human intelligence.

This is quite the strong claim to make, mainly because of the word choice of “never.” Regardless, I went to Google to see what I could find. I initially had some trouble finding academic counterarguments, but then I came across this book: Why Machines Will Never Rule the World: Artificial Intelligence Without Fear by Jobst Landgrebe (an AI entrepreneur^[1]) and Barry Smith (a University of Buffalo Philosophy professor^[2]).

The book is around 50 pages long so not the longest read, regardless, I wanted an overview so I found this hour-long interview conducted by Richard Hanania from the Center for the Study of Partisanship and Ideology.

Here are some of their key points along with my responses:

Human Intelligence is Too Complex

Their Claim

Their primary claim is that human intelligence is a “complex system” and as we currently lack the mathematics to model complex systems in general, we therefore cannot model human intelligence.

Background

In a nutshell, a complex system refers to a large network comprised of nodes and edges. The nodes represent some components while the edges represent some relationships between these components. These components interact in a vast number of varied ways, thereby making it hard to predict the behavior of the larger system. Some examples include cities, the climate, and the universe itself. Complex systems tend to be more stochastic.

On the other hand, simple systems tend to have smaller networks with a few clearly defined relationships between each of its components. Some examples include a pendulum, a thermostat, or even a light switch. Simple systems tend to be deterministic and fair...

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