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Artificial General Intelligence - What Does It Mean And Should We Be Worried About It? Posted on : May 14 - 2020

Artificial General Intelligence (AGI) - a hypothetical machine capable of any and all of the intellectual tasks performed by humans - is considered by many to be a pipe dream. A long-standing feature of science fiction, AGI has achieved a cultural reputation of both reverence and fear, but above all an appreciation for the possibilities it presents. However, despite what the movies might suggest, there is still considerable debate around what constitutes general intelligence in humans, let alone machines.

Before diving into AGI, it’s worth establishing what has become the accepted meaning of ‘general intelligence’. The term ‘general intelligence’ is an evolving term. When the first electronic computers were created, many leaders in the field attributed their ability to do complicated sums as evidence of a higher intelligence than was previously known. What followed was the ability to best humans at strategy games like chess, and eventually speech and image recognition. It seems likely that this evolution will too apply to Artificial General Intelligence, particularly as the concept becomes increasingly abstract.

However, as it stands, there are some generally accepted factors which determine if a human or machine is capable of artificial general intelligence. First, that they must have the ability to learn from a limited amount of data or experience - often referred to as few shot learning. Secondly, to be able to learn, and improve its ability to learn, from a wide variety of contexts, known as meta learning. This directly feeds into the final factor: causal inference. This is the capability for scenario generation: to be able to plan for future events, or non-events, through an understanding of cause and effect.

Of course, many artificial intelligence machines, whilst not exhibiting general intelligence, are enormously capable for specific uses. These machines are referred to as ‘narrow AI’ and are in some ways more useful than AGI would be, as they are designed to solve very specific problems. The most common example of this is the recommender systems that tech companies use for their social networks and e-commerce platforms. This is often for deciding what piece of content should be shown next, whether that's an ad, news or video - or alternatively what product a user might want to buy based on their previous spending patterns and those of similar users.

While specific, these narrow AI systems can be extremely complex, solving incredibly challenging problems. A good example of this is Causalens which has become a pioneer in causal inference, meaning they can more accurately and robustly model trends in time-series - such as indices for the global economy, or predict how shocks in one sector might affect another. Another interesting use case is natural language understanding. Wluper are a pioneer in this area, by being able to understand sentiment and intentions, understanding deeper meaning in questions and answers and even producing written content on demand. View More