Artificial Intelligence – Brief History & Current Reality
The term Artificial Intelligence (AI) was first mentioned at a conference in Dartmouth College in 1956 named “A Proposal for the DartMouth Summer Research Project on Artificial Intelligence“.
For some years after the conference, AI promised a great deal and millions of USD was invested in research. However disappointment followed and many governments withdrew funds.
With advancements over the last 20 years in computer hardware and the availability of programming languages such as Python, R, MATLAB etc., AI emerged again as a serious aspect of general Information Technology (IT).
More recently the European Commission’s High-Level Expert group on Artificial Intelligence (2019) defined AI as “systems that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals”.
General AI vs. Narrow AI
General AI also known as “strong AI” usually refers to systems that mimic human like intelligence. At present (2020), experts disagree when general AI will be mainstream or if it can ever be achieved.
In contrast Narrow AI focuses on performing a very specific tasks intelligently. Examples of Narrow AI in the public services are available through the links below.
- Computer vision is the ability to process and synthesize visual data. This includes being able to detect and classify objects based on image analysis. The link below is an example from the UK of using computer vision to detect cancer from MRI scans.
- Another example is facial recognition. Security forces around the world are using this to detect people of interest from images captured from CCTV or public surveillance cameras.
- Natural Language Processing (NLP) refers to the ability interpret human language and can include text analysis or translation. Coupled with the field of Speech Recognition which focuses on the ability to understand speech and if required, translate this into text, NLP systems can be linked to speech recognition systems to deliver intelligent chatbots. An example from the Revenue Commissioners in the Irish Civil Service is linked to below.
- Another example from Smart Dublin describes how comments made by citizens about the city on social media sites can give insights about their views in civic issues.
- Anomoly detection is an example of Machine Learning. One method of model development named Supervised Learning uses labelled data to learn what valid and fraudulent claims looks like and then applies this knowledge to future cases in an effort to detect fraud. Another method of model developlement Unsupervised Learning applies algorithms to large amounts of data in order to detect any underlying structures that may exist. Data items that seem to be outliers can be referred for further examination.
Issues with AI
While there is great potential for AI to deliver on the new technologies outlined above, it is not without it’s issues. Essentially AI is a combination of algorithms and data, and there is potential for bias and human error in both.
For example a data set that is inaccurately labelled or does not fully represent the domain it is supposed to represent, can result in a biased solution being rolled out. Likewise a bug in a human developed Machine Learning (ML) algorithm can cause sub-optimal operation. In addition, another aspect of AI known as Deep Learning, uses Artificial Neural Networks (ANN) to come to it’s own understanding of sometimes unstructured data. This can make determining where a fault lies very difficult especially in AI applications where there is an integrated suite of ANN’s.
EU bodies are actively trying to come to terms with the above in order to protect citizens from the adverse effects of errant AI. This is especially important in high-risk critical systems such as medical applications or autonomous vehicles.
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