|Information & Knowledge Management: Artificial Intelligence|
Artificial Intelligence (AI) involves using machines (i.e., computers) to do things that traditionally require human intelligence. This means creating algorithms to classify, analyse, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time. Common AI applications include speech recognition, natural language processing, machine vision (which is similar to voice recognition but enables a computer to see and interpret), and expert systems, i.e., a software application using a database of expert knowledge capable of offering advice to facilitate decision making.
In This Report
The StageThe term “artificial intelligence” was first coined by John McCarthy in 1956. As one of the founders of AI, he and a group of research scientists started to clarify the role and concept of “thinking machines” at a workshop called the Dartmouth Summer Research Project. McCarthy proposed the workshop “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.”
Ever since, AI has been evolving and now has an impact on almost every aspect of life. It is seemingly everywhere. It is in your home (in the guise of Siri or Alexa, for example), at the train station, in public spaces (facial recognition technology), when you use your credit card, and anytime you use Google to search the internet. AI is here to stay; so, we might as well embrace it, and try to understand it better.
There are two main types of AI: machine learning, and deep learning. Machine learning is the ability to process large amounts of data very quickly. In a manufacturing plant, for example, the machinery is hooked up to a complicated network being fed data, functionality, and production. The machine learning algorithm can rapidly analyse data and detect patterns or anomalies, notifying decision makers of non-optimised production levels or preventative maintenance issues. Deep learning is a widely used version of machine learning that involves multiple “brain-like” networks (neural networks), to engage in non-linear reasoning. Banks use it to detect fraud, and Tesla uses it for its self-driving cars. While machine learning is limited once a certain amount of data has been captured, deep learning is far more scalable. Therefore, deep learning models will be far more prolific.
Businesses today are increasingly reliant on AI to gain an edge in sectors as diverse as banking, manufacturing, retail, health care, security, and farming. Most industry players use AI to identify, make decisions and, in some cases, predict trends and opportunities. One of the biggest advantages of the AI system is speed: it can outdo humans in processing large amounts of data, and present synthesised courses of action to human users. However, it has difficulty completing common-sense tasks, especially those that involve value-driven decisions.
AI is increasingly affordable and accessible to businesses and the public, and there is a rapidly expanding range of ready-to-use services for all types and sizes of business. The question is, will AI take over jobs and make human input obsolete? Experts are not quite sure how or to what extent these algorithms will automate existing jobs, but they do agree that manual and managerial jobs will both be affected. New jobs, however, will also be created. Finally, the growing sophistication and ubiquity of AI systems has raised many ethical concerns such as bias, fairness, transparency, safety, and accountability. The algorithm, while being able to serve a wide variety of purposes, can never guarantee ethical decision making by a robot. After all, they have been taught by humans.
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