Based in Perth, Australia this a Blog of Associate Professor AmandeeP Sidhu. He focuses on everything Artificial Intelligence, Big Data and Biomedical Informatics.

AI: "Artificial" or Augmented" Intelligence

"Intelligence amplification (IA) (also referred to as cognitive augmentation and machine augmented intelligence) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s. IA is sometimes contrasted with AI (artificial intelligence), that is, the project of building a human-like intelligence in the form of an autonomous technological system such as a computer or robot. AI has encountered many fundamental obstacles, practical as well as theoretical, which for IA seem moot, as it needs technology merely as an extra support for an autonomous intelligence that has already proven to function. Moreover, IA has a long history of success, since all forms of information technology, from the abacus to writing to the Internet, have been developed basically to extend the information processing capabilities of the human mind."

What is Augmented Intelligence (IA)?
How IA is different from Artificial Intelligence (AI)?

The term “Artificial Intelligence” (AI) brings to mind to the notion of replacing human intelligence with something synthetic. "Augmented Intelligence" (IA) describes a combination of the human brain and the most reliable facets of artificial intelligence. This means that IA aims to build systems that enhance and scale human expertise and skills rather than replacing them.

An approach using IA plays to the strengths of both human analysts and machines, assuming that, between the two, humans tend to be the better decision makers while machines tend to be faster analysts. It also assumes that the human analyst should always be in the driver’s seat, and that the machine exists to assist, not replace. In IA the human analyst is fundamental to the final output, but is now able to deliver at a speed and scope that could only be achieved with the aid of intelligent machines.

IA is comprehensive set of capabilities based on technologies which include AI, but that go far beyond it. It comprises the fields of machine learning, reasoning and decision technologies, language, speech and vision recognition and processing technologies, human interface technologies, distributed and high-performance computing, and new computing architectures and devices. When purposefully integrated, these capabilities are designed to solve a wide range of practical problems, boost productivity, and foster new discoveries across many industries. The following Figure from CognitiveScale is a very good representation of the current AI and IA landscape.


History of Augmented Intelligence (IA):

Douglas C. Engelbart in his October 1962 report entitled: "Augmenting Human Intellect: A Conceptual Framework" proposed a research framework for IA:

"By augmenting human intellect we mean increasing the capability of man to approach complex problem situation to gain comprehension to suit his particular needs and to derive solutions to problems. Accepting the term intelligence amplification does not imply any attempt to increase native human intelligence. The term intelligence amplification seems applicable to our goal of augmenting the human intellect in that the entity to be produced will exhibit more of what can be called intelligence than an unaided human could we will have amplified the intelligence of the human by organizing his intellectual capabilities into higher levels of synergistic structuring. In amplifying our intelligence we are applying the principle of synergistic structuring that was followed by natural evolution in developing the basic human capabilities. What we have done in the development of our augmentation means is to construct superstructure that is synthetic extension of the natural structure upon which it is built."


What's Next for Augmented Intelligence (IA)?

Any proposed Augmented Intelligence (IA) solution should have each of following three features:

  1. Scalability: To search across all possible publically available data sets.
  2. Data Quality & Enrichment: Use currently available components of Artificial Intelligence (AI) to sort through all of that data, enriching it in a way that makes it more searchable and effective.
  3. Simple Interface: Front-end search and analysis interface with broad design target to cover every possible application domain.

Machine Learning tools are currently used in these areas (Figure from CognitiveScale):


AI shaping workforce of 2030

It's all about Data