Its interesting but ultimately to really'under' 'stand' language in a deep way, the systems will need representations based on lower-level (possibly virtual) sensory inputs. That is one of the main enablers for truly general intelligence because its based on this common set of inputs over time, i.e. senses. The domain is sense and motor output and this is a truly general domain. Its also a domain that is connected to the way the concepts map to the real physical world. So when the advanced agent NN systems are put through their paces in virtual 3d worlds by training on simple words, phrases, commands, etc. involving'real-world' demonstrations of the concepts then we will see some next-level understanding.
Report on the Third Conference on Artificial General Intelligence Abstract During March 5-8, 2010, around 75 researchers from various disciplines converged at the University of Lugano for the Third Conference on Artificial General Intelligence (AGI-10). During March 5-8, 2010, around 75 researchers from various disciplines converged at the University of Lugano for the Third Conference on Artificial General Intelligence (AGI-10).
Artificial general intelligence (AGI) (also referred to as "general artificial intelligence" - GAI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and a common topic in science fiction and futurism. Artificial general intelligence is also referred to as "strong AI", "full AI" or as the ability of a machine to perform "general intelligent action". Some references emphasize a distinction between strong AI and "applied AI" (also called "narrow AI" or "weak AI"): the use of software to study or accomplish specific problem solving or reasoning tasks. Weak AI, in contrast to strong AI, does not attempt to perform the full range of human cognitive abilities.
It is argued that any real-world, limited-resources general intelligence is going to manifest a mixture of general principles such as Solomonoff induction and complex self-organizing adaptation, with specific structures and dynamics that reflect corresponding structures and dynamics in the tasks and environments in whose context it was created. This interplay between the general and the specific will play out differently in each type of intelligent system. A number of ideas drawn from previous publications are reviewed here -- e.g.
Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.