What is AGI?
Computer systems based on AGI technology ('AGIs') are specifically engineered to be able to learn. They are able to acquire a wide range of knowledge and skills via learning similar to the way we do. Unlike current computer systems, AGIs do not need to be programmed to do new tasks. Instead, they are simply instructed and taught by humans. Additionally, these systems can learn by themselves both implicitly 'on-the-job', and explicitly by reading and practicing. Furthermore, just like humans, they resiliently adapt to changing circumstances.
This general ability to learn through natural interaction with the environment as well as from teachers, allows them to autonomously expand and adapt their abilities over time they become ever more knowledgeable, smarter, and more useful.
In addition to their intrinsic learning ability, AGIs are also designed to function in a goal-directed manner. This means that they automatically focus their attention on information and activities that are likely to help solve problems they have been given. For example, an AGI trained and instructed to look for inconsistencies in arthritis medication studies will spend its time perusing relevant articles, news, and background information, and request pertinent additional information or clarification from other researchers. On the other hand, an AGI assigned to be a personal assistant will seek out knowledge and skills necessary for that job, such as learning how to deal with various types of business associates, schedules, priorities, and travel arrangements, as well as the personal preferences of its boss.
AGIs learn both conceptually and contextually. Conceptual learning implies that knowledge is assimilated in a suitably generalized and abstract form: Skills acquired for one task are available for similar, but non-identical tasks, while at the same time making the system much more useful and robust when coping with environmental changes. Context, on the other hand, allows the system to utilize relevant background information to appropriately tailor its responses to each specific situation. It can take into account such crucial factors as recent actions and events, current goals and priorities, who it is communicating with, and anything else that affects its current actions.
Other central AGI features include an ability to anticipate events and outcomes, and the ability to introspect to be aware of its own cognitive states (such as novelty, confusion, certainty, its level of ability, etc). These design features, combined with the fact that AGIs directly perceive their environments via built-in senses, endow them with human-like understanding of facts and situations.
In contrast, systems based on conventional AI technology provide little or no learning capability beyond their initial one-time training phase (if any). Traditional computer programs are designed for specific applications, and are incapable of being used for any other purpose. In fact, even within their given domain any new requirements or changes to their operating environment require costly program changes.
To use a human analogy to highlight the difference, imagine an entirely unschooled person. If we wanted to put them to work on an assembly line, we could instruct them with a very detailed script for a specific set of actions; in other words, rote learning, with no real understanding (like programming an 'expert system'). Or, we could take on the much more difficult task of teaching them to read and write, to think logically and to learn. This would enable them to learn and re-learn any number of jobs in the factory and elsewhere; and to perform them much more intelligently with understanding. This is the AGI approach. Furthermore, an educated person (or AGI) can also manage other entities with low-level skills, or those that possess highly specialized knowledge, thereby greatly increasing their own productivity.
In summary, an AGI's ability to learn implies a number of advantages over conventional AI technology: It can be taught, instead of having to be programmed; it learns from experience and can learn by itself; it can deal with ambiguity and unknown situations, know when to ask for help, and recover from errors resiliently and autonomously.
Note that all these advantages are in addition to computer systems' natural strengths: large 'photographic' memories, high speed, accuracy, upgradeability, seamless interfacing with other systems, etc. Another key feature of such trainable/ trained systems is that, unlike skilled humans, they can be duplicated, and efficiently pool knowledge and experience. These capabilities allow for rapid up-scaling of production. For example, various AGIs, after having been trained in particular specialties, could pool their knowledge and then be duplicated hundreds of times imbuing each one of them with their combined knowledge. From there on these AGIs can pursue coordinated, yet individual paths, while regularly updating each other.