AGI Innovations Inc, (www.AGi-3.com ) an R and D company focused on advancing Artificial General Intelligence (AGI) has secured $4 million in funding to support research on long-term AGI development. AGI is the AI discipline concerned with developing systems with human-like cognitive abilities such as general learning, reasoning, and problem solving. AGI Innovations Inc, also known as AGi3, was formed early in 2014 to continue research originally spearheaded by Adaptive A.I. Inc (a2i2), an AGI R and D company formed in 2001. On completing its first generation AGI engine in 2008, a2i2 launched.
SmartAction Company to commercialize its technology by providing automated support calls utilizing intelligent, natural language conversation (http://www.smartaction.com/resources/audio-samples).
UPDATE – Some people are dismissing the $4 million in funding. A reminder that Deepmind (artificial intelligence company focused on Deep Learning) had $50 million in funding and then was purchased by Google for $650 million. Baidu, Facebook and other companies are also in the hunt to buyout companies in artificial intelligence.
Peter Voss, founder of these companies, explains that while commercialization helped to validate the AGI approach taken by the company, it also totally shifted focus away from general AI to providing specific solutions to its customers. “Our AGI research was essentially halted.
Fortunately, we now have a new dedicated and funded company focused entirely on long-term AGI development” Voss said.
“Over the past year we have assembled a strong 10-person team of programmers and ‘AI Psychologists’, and are now in full swing. We are, however, still seeking additional key personnel.”
AGi3 is currently engaged in a 30-months cognitive architecture project to consolidate and extend its various prior research results into a next-generation AGI engine prototype. This development aims to advance state-of-the-art deep natural language understanding (NLU) integrated with perceptual grounding, and exploration of meta-cognitive processes.
While AGi3 has a pure R and D focus, it expects aspects of its new technology to be commercialized separately in the coming years.
Imagine if computers could learn and think. If machines were truly ‘intelligent.’ If software was more flexible and adaptable to work the way you want it to. If you could converse with your computer in plain English. If your business could operate more efficiently and effectively, with lower cost and higher customer satisfaction, by using more intelligent IT systems.
This optimistic vision is rapidly moving closer to reality. The foundational knowledge and technology to build computers with human-level learning and thinking ability are now finally emerging. Recent advances in computer technology combined with insights from fields as varied as psychology, philosophy, evolution, brain physiology, and information theory allow us to finally solve the previously intractable problems of creating real AI. The long-promised power of truly intelligent machines will soon be available to help us solve the many problems facing mankind.
We expect skepticism. Haven’t we been promised real artificial intelligence for 30 years or more? Yet all we see around us are ‘stupid’ computer programs that don’t understand what we actually want to do, and respond with cryptic error messages when things go wrong. What is more, they cannot adapt to changing circumstances or requirements, and they don’t learn from their mistakes.
A new approach to AI, called ‘artificial general intelligence’, or AGI, has emerged. It promises to finally overcome the limitations of traditional AI, and usher in a new era of vastly superior computer systems and tools.
What exactly is AGI, and how does it differ from conventional AI?
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.