This competition was from 2006. The consensus among AI experts (really just everyone) during the 1990s/2000s was that: - AGI could be achieved by giant GOFAI

This competition was from 2006. The consensus among AI experts (really just everyone) during the 1990s/2000s was that: - AGI could be achieved by giant GOFAI (usually expert systems/knowledge bases) projects (like OpenCog and Cyc). - ... or that AGI development is limited by lack of knowledge about key insights (mostly symbolic/rational) into intelligence, not computation/data. (IE AGI was viewed similarly to proving that NP = P or other very-hard math/computer science/psychology/philosophy problems). - ... or that AGI will be achieved through brain scanning/connectomics. - ... or that AGI is impossible. Nobody (except for LeCun and Schmidhuber) paid much attention to neural networks until AlexNet (2012) showed that they could be ran and trained at fast speed and beat the symbolic competition. In the 2000s, only a real, actual psychic would be able to tell you that LLMs would be a valuable path for AGI research. Here is a list of various expert (and "expert") perspectives on AGI during the 1990s/2000s (notice how nobody is talking about neural networks, and they are definitely not talking about anything remotely close to a LLM or transformer): > Copycat is a computer program designed to be able to discover insightful analogies, and to do so in a psychologically realistic way. Copycat's architecture is neither symbolic nor connectionist, no was it intended to be a hybrid other two (although some might see it that way); ... [describes a very symbolic system to our modern day eyes, though it was not really symbolic to 1990s AI researchers] - Douglas Hofstadter and Melanie Mitchell, Fluid Concepts and Creative Analogies (Chapter 5), 1995 > Interviewer: Are you an advocate of furthering AI research? > Dennett: I think that it’s been a wonderful field and has a great future, and some of the directions are less interesting to me and less important theoretically, I think, than others. I don’t think it needs a champion. There’s plenty of drive to pursue this research in different ways. > Dennett (cont): What I don’t think it’s going to happen and I don’t think it’s important to try to make it happen; I don’t think we’re going to have a really conscious humanoid agents anytime in the foreseeable future. And I think there’s not only no good reason to try to make such agents, but there’s some pretty good reasons not to try. Now, that might seem to contradict the fact that I work on a Cog project [sic] with MIT, which was of course is an attempt to create a humanoid agent, cogent, cog, and to implement the multiple drafts model of consciousness; my model of consciousness on it. > Dennett (later): [Cog is intended as a] proof of concept [for AGI]. You want to see what works but then you don’t have to actually do the whole thing. - Daniel Dennett, Daniel Dennett Investigates Artificial Intelligence, Big Think, 2009 > [Context: Marvin Minsky had a speech where he talked about how expert systems don't work, because they do not have any common sense (and the only solution seems to be to create a giant AGI project (without automatic data gathering)).] > Only one researcher has committed himself to the colossal task of building a comprehensive common-sense reasoning system, according to Minsky. Douglas Lenat, through his Cyc project, has directed the line-by-line entry of more than 1 million rules into a commonsense knowledge base. - Mark Baard, AI Founder Blasts Modern Research, Wired, 2003 > Section 1 discusses the conceptual foundations of general intelligence as a discipline, orienting it within the Integrated Causal Model of Tooby and Cosmides; Section 2 constitutes the bulk of the paper and discusses the functional decomposition of general intelligence into a complex supersystem of interdependent internally specialized processes, and structures the description using five successive levels of functional organization: Code, sensory modalities, concepts, thoughts, and deliberation. Section 3 ... [yada yada, this is old, wrong stuff] - Eliezer Yudkowsky, Levels of Organization in General Intelligence, 2007, Machine Intelligence Research Institute I could list more examples, but I have spent way too long on this post. What I will say is that Hutter probably had the most correct idea of how modern semi-general AI would work (from the 2000s). He figured out that compression is a extremely important component of intelligence > 10 years before everybody was doing LLMs. That is impressive. I should probably write a blog post over this.