AI: Our initial investment thinking

AI is a broad field of study. This paper focuses on AI broadly as programmes or applications that can produce an output that is very human-like, conventionally known as generative AI. The intelligence of these systems lies not only in producing human-like outputs but also using machine learning techniques for continuous training and improvement without needing human input.

The novelty of ChatGPT has rocketed AI to the top of research agendas, but, as the saying goes, it takes a long time to become an overnight success. It’s 73 years since Alan Turing invented the ‘Turing test’ which set an early benchmark for AI to aspire to: display intelligent behaviour that is indistinguishable from a human’s.

Stepping back, AI has seen a number of evolutions: from early enthusiasm in the 1950s as a general problem solver that could approach puzzles in a similar way to humans i.e., via an order of subgoals, to the ELIZA model in the ‘60s that could create sentences that sounded very human-like. Then in the ‘70s knowledge based systems proved successful compared to junior doctors in medical examinations. In the ‘80s and ‘90s, IBM’s Deep Blue grabbed headlines when it beat Gary Kasparov in chess.

Then there was a so-called ‘AI winter’ for the next couple of decades as researchers struggled to make significant breakthroughs. This chart from Harvard shows the timeline: one conclusion you could draw is that historically AI has made progress, but in leaps, and with frequent periods where optimism has given way to pessimism. In other words, progress wasn’t inexorable in the past and we should not assume it is inexorable today.

Figure 1: Artificial intelligence timeline

Source: Harvard University. Please note this has been redrawn by Ninety One.

To help understand what AI is, we like the way Dataconomy, a data science hub, splits AI into three broad conceptual categories:

01 Artificial narrow intelligence

• Also known as weak AI. Goal-oriented systems with limited applications. This includes things like natural language processing (NLP) and the chatbots that are becoming more commonplace.
• Most AI has limited memory, where machines use large volumes of data for deep learning. With this deep learning, programmes offer personalised AI experiences like virtual assistants or search engines that store your data and personalise your future experiences. Apple’s SIRI is an example of narrow AI.

02 Artificial general intelligence

• Far stronger and more flexible. An advanced form of artificial narrow intelligence.
• Theoretical at this stage, this is a computer model that can perform a multitude of ‘human’ cognitive tasks. Recognising images, sounds as well as solving logic problems and data-pattern recognition. It should be as good or better than humans at solving problems in many areas requiring intelligence.

03 Artificial super intelligence

• In some ways the end game. A computer can be turned to a plethora of tasks and perform them far ahead of what a human would be capable of doing.

ChatGPT is an advanced form of artificial narrow intelligence. Commonly referred to as ‘generative AI’, it represents a step change in capabilities within the category of narrow artificial intelligence. Generative AI can take a given set of inputs and produce sophisticated outputs, not just in text but also as images, audio and synthetic data. This is starting to push the boundaries between narrow and general intelligence. But for now, artificial general intelligence remains a theoretical objective.