A simple question shows the limits of current AI systems.
AdvertisementUltimately, AI systems should help humans make better, more accurate decisions. Yet even the most impressive and flexible of today’s AI tools – such as the large language models behind the likes of ChatGPT – can have the opposite effect.Why? They have two crucial weaknesses. They do not help decision-makers understand causation or uncertainty.
However, these approaches require huge amounts of data. As a result, they encourage organisations to assemble enormous repositories of data – or trawl through existing datasets collected for other purposes. Dealing with “big data” brings considerable risks around security, privacy, legality and ethics.In low-stakes situations, predictions based on “what the data suggest will happen” can be incredibly useful. But when the stakes are higher, there are two more questions we need to answer.
The first is about how the world works: “what is driving this outcome?” The second is about our knowledge of the world: “how confident are we about this?”Perhaps surprisingly, AI systems designed to infer causal relationships don’t need “big data”. Instead, they need. The usefulness of the information depends on the question at hand, the decisions we face, and the value we attach to the consequences of those decisions.is approximately constant irrespective of the volume of data we collect.
So, what is the solution? The process starts with developing AI techniques that tell us what we genuinely don’t know, rather than producing variations of existing knowledge.Why? Because this helps us identify and acquire the minimum amount of valuable information, in a sequence that will enable us to disentangle causes and effects.
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