6 1 Introduction
1.6 Potential Approaches to AGI
In principle, there is a large number of approaches one might take to building an AGI, starting from the knowledge, software and machinery now available. This is not the place to review them in detail, but a brief list seems apropos, including commentary on why these are not the approaches we have chosen for our own research. Our intent here is not to insult or dismiss these other potential approaches, but merely to indicate why, as researchers with limited time and resources, we have made a different choice regarding where to focus our own energies.
1.6.1 Build AGI from Narrow AI
Most of the AI programs around today are “narrow AI” programs — they carry out one particular kind of task intelligently. One could try to make an advanced AGI by combining a bunch of enhanced narrow AI programs inside some kind of overall framework.
However, we’re rather skeptical of this approach because none of these narrow AI programs have the ability to generalize across domains — and we don’t see how combining them or ex- tending them is going to cause this to magically emerge.
1.6.2 Enhancing Chatbots
One could seek to make an advanced AGI by taking a chatbot, and trying to improve its code to make it actually understand what it’s talking about. We have some direct experience with this route, as in 2010 our AI consulting firm was contracted to improve Ray Kurzweil’s online chatbot "Ramona". Our new Ramona understands a lot more than the previous Ramona version or a typical chatbot, due to using Wikipedia and other online resources, but still it’s far from an AGI.
A more ambitious attempt in this direction was Jason Hutchens’ a-i.com project, which sought to create a human child level AGI via development and teaching of a statistical learning based chatbot (rather than the typical rule-based kind). The difficulty with this approach, however, is that the architecture of a chatbot is fundamentally different from the architecture of a generally intelligent mind. Much of what’s important about the human mind is not directly observable in conversations, so if you start from conversation and try to work toward an AGI architecture from there, you’re likely to miss many critical aspects.
1.6.3 Emulating the Brain
One can approach AGI by trying to figure out how the brain works, using brain imaging and other tools from neuroscience, and then emulating the brain in hardware or software.
One rather substantial problem with this approach is that we don’t really understand how the brain works yet, because our software for measuring the brain is still relatively crude. There is no brain scanning method that combines high spatial and temporal accuracy, and none is
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