Artificial general intelligence

Artificial general intelligence


Artificial general intelligence (AGI) is the key concept underpinning this course, so it's important to start by exploring what we mean by AGI and examine the reasons for thinking that the field of machine learning is heading towards it.

First, we will examine the current state of machine learning and then consider what AGI is. These two topics will help you form your views on whether modern machine learning is heading towards the development of AGI.

Second, we will consider how these capabilities might develop over time. We'll cover a report that measures how long it'll take to afford the necessary compute to train a human-equivalent intelligence and arguments that scaling current techniques leads to higher - and potentially more general - capabilities.

Finally, we'll examine texts that speculate the potential step changes in ML capabilities still to come.

Core readings:

On the opportunities and risks of foundation models (Bommasani et al., 2022) (only pages 3-6, focusing mostly on understanding what figures 1 & 2 are communicating) (10 mins)
AGI safety from first principles (Ngo, 2020) (only sections 1 and 2.1) (15 mins)
Why and how of scaling large language models (Joseph, 2022) (only first 5 minutes, stopping at 'parallelization') (5 mins)
Biological Anchors: A Trick That Might Or Might Not Work (Alexander, 2022) (only Part I, ending at โ€œHow sensitive is this to changes in assumptionsโ€) (20 mins)

Optional readings:

Successes of deep learning:

Collection of GPT-3 results (Sotala, 2020) Sotala collects many examples of sophisticated behavior from GPT-3.


Scaling and AI forecasting:


Instead of AGI, some people use the terms โ€œhuman-level AIโ€ or โ€œstrong AIโ€. โ€œSuperintelligenceโ€ refers to AGI which is far beyond human-level intelligence. The opposite of general AI is called narrow AI. Some readings instead focus on the concept of transformative AI, defined as AI which has effects as large as (or larger than) the industrial revolution. In theory this could be achieved using narrow AI, but in practice it seems likely to be roughly equivalent to AGI.

Next in the AGI Safety Fundamentals curriculum