What is Artificial Intelligence?
AI, Technology, Philosophy, Self.

What is Artificial Intelligence?

A Starting Point for Exploring the technology and Social Implications of AI.

Not AI. Photo by Rudi Endresen on Unsplash

Seriously, What Do We Think When Some Say “AI”?

Are we just mystified by AI or perhaps we just ignore it? Maybe some have visions of killer robots or totalitarian regimes controlling our minds? Misinformation, hype and ignorance abound and I decided to ignore all this rubbish and start with the original explanation of “Artificial Intelligence”.

On August 31, 1955, J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon proposed that a

“study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”

The proposal outlines the interests of participants and areas of work. The precise description would be mathematical in nature and based on the concepts under development by the participants. The implication of the words “simulate it” are key to a faithful interpretation of AI.

“Simulate” and “Precisely Described”.

Simulation is key to our discussion. AI does not have an objective to create intelligence but rather to “simulate it”. The success of the simulation depends upon the precision of the description of human intelligence and the method adopted for validating the simulation.

The Turing test dating to 1950 was adopted as an early validation technique. Here, a human would judge conversations between a human and a machine designed to generate human-like responses. If the judge could not tell the difference between human and machine, then the simulation would be considered valid. The Turing test and its variants are still the subjects of much controversy.

The phrase “precisely described” implies a reductionist approach to the study of intelligence. However, intelligence emerges from the complex electrochemical interactions between the brain’s neurons. Using reductionism to study intelligence means separating and simplifying phenomena and ignoring the neuronic interactions.

Reductionism fails in the goal of providing a precise description of intelligence. This explains why after nearly 60 years of intensive research booms and busts the optimism of the 1956 participants has not been realised. The phrase “Artificial Intelligence” has been shown to be a misnomer, but the term has penetrated the common lexicon to such a degree that it’s here to stay.

The mathematical tools developed as subsets of AI are extremely useful and valuable in the way they underpin our modern interconnected society.

The Tools of AI: Machine Learning and Deep Learning

While “Artificial Intelligence” (AI) is concerned with human intelligence, the commercially successful tools of AI research focus on data parsing and pattern matching. These tools are categorised under the broad church of “Machine Learning”. Machine Learning has been widely deployed in fields including facial recognition; cargo distribution; sales and marketing; climate science; social media; and financial trading.

Machine Learning algorithms can be deployed to many applications without the need for changing any code. Instead, standard sets of labelled data are used to train its algorithms to recognise desired patterns. These “trained” algorithms can then be applied to new data sets to discover the desired patterns.

A Machine Learning technique called Artificial Neural Networks is designed to mimic the electrical interactions between neurons of the human brain. In Machine Learning, the set of algorithms of ANN’s consist of three levels: input, parsing and output.

“Deep Learning” is applied when a neural network has more than three layers. Some of the modern Deep Learning neural networks have many thousands of layers. Every layers can be optimised through a technique called backpropagation that gives Deep Learning the ability to independently learn and make decisions on pattern recognition. Deep learning is ideal for large unstructured data sets.

Going Forward

This introductory article establishes the framework for my articles on AI. Future articles will give examples for programming in Machine Learning with a view to “learn by doing” and showing exactly what AI can and can’t do.

I want to discuss the social impacts of AI, particularly in view of the new data laws and privacy considerations for protecting people’s rights to own their own data.

If you have any questions or suggests please comment on this article.

Acknowledgement

I want to thank Dr Mehmet Yildiz for his vision in creating the new Technology publication. I have always wanted a broad platform of technologists to share and learn new knowledge.
['Dr John Rose']
['Philosophy', 'Neural Networks', 'Deep Learning', 'Technology', 'AI']