AI is more than machine/deep learning

How do we approach it?

Artificial Intelligence (AI) was first established as a scientific discipline back in 1956, when it was given the name it still has today at the Dartmouth Workshop. For over 60 years, AI has been researched and developed through multiple hype cycles with up periods of heavy investment and down cycles of lower investment (a.k.a AI winters). There has been, and still are, multiple philosophies and camps that approach AI quite differently. For example, one can categorize the approaches to AI by dividing those according to thinking vs. acting and human like vs. rational.

In our work we do not (at least consciously) position ourselves into a specific school of thought but rather approach AI as open technologies that emulate natural intelligence on a range of tasks exhibited by people, animals, and organizations across a wide range of contexts. Referring to the categorization example, this includes all the categories, as humans are known to be able to both think and act, as well as think rationally and irrationally/creatively.

The mainstream of AI research and development is currently focused in machine learning and its subset of deep learning, which has recently enabled conceptually narrow AI applications to reach or outperform human capabilities in some tasks. Examples of this kind of narrow AI tasks include playing chess (IBM Deep Blue, 1996), Jeopardy! (IBM Watson, 2011) and Go (Google AlphaGo, 2016).

However, on tasks requiring general or conceptually wider intelligence, including learning and reasoning over wide variety of abstract concepts and everyday objects in a given context, the research and development is still in its infancy and not in the level of human intellectual performance. This field of AI, known as Artificial General Intelligence (AGI), focuses in research and development of artificial intelligence that could perform any intellectual task that human can. The AGI R&D is closely related to Cognitive Science; AGI R&D is focused in studying machine realization of human cognition studied by Cognitive Science.

The current state and progress of AI could be summarized by stating that AI is matching human performance in conceptually narrow tasks and the quest is towards a wider AI, perhaps one day matching the general intellectual capability of humans. Interesting ideas, theories, presentations and publications on this subject can be found from e.g. Danko Nikolic, Joscha Bach and Peter Gärdenfors.

Our approach to Opentech AI in our R&D work and in this blog could be characterized also as a quest for wider AI. We acknowledge the power of machine learning and deep learning in narrow AI tasks, but at the same time remain open and curious about all approaches and solutions that have potential for improving the intellectual wideness in AI applications. AI is often contradicted and compared against humans, however the combination of human and machine intelligence has been found to be a very powerful combination and is setting the direction of AI applications towards cognitive intelligent assistants for improving human capability and productivity.

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