Insights
Knowledge items to support your research
Behind “Artificial Intelligence”
The general concept behind “artificial intelligence” dates far back to the times of ancient history with traces found in Greek mythology’s Talos, a gigantic bronze mechanical robot-like heroic figure with human intelligence that served as a guardian to the island of Crete against ill-intentioned outsiders and invaders. The origination of modern artificial intelligence is considered to have transpired in the 1950s alongside the dawn of the computer age. In 1950 Alan Turing published a seminal paper titled “Computing Machinery and Intelligence” where Turing discusses the potential for a scenario where humans create a scenario in which “machines can think.”
Machine Learning
It’s important to note that some do not consider machine learning to be AI, but rather solely a field of computer science. However, the term is commonly used in conjunction with artificial intelligence. In that vein, we can (more or less) consider machine learning to be a branch stemming from both computer science and artificial intelligence. Machine learning strives to educate systems, using structured and/or labeled data, on how to absorb information and perform a specific task without requiring categorical programming. It is a method of data analysis that comprises constructing and amending models that permit programs to “learn” through experience and repetition. A few examples of instances wherein machine learning is utilized include image/speech recognition, financial services such as spend tracking, spam/malware email filtering, customer service chatbots, and many more.
Deep Learning
Deep learning is a division of machine learning that employs numerous “levels” of neural networks, built to function in an unsupervised learning manner that emulates a human brain with the ability to “learn” from vast quantities of data, regardless of whether or not that data is unstructured, unlabeled, missing, or otherwise. Every layer of the neural network contains deep learning algorithms that carry out computations and make forecasts in repetition to learn and progressively boost the precision of the results/recommendations as time goes on. A few examples of instances wherein deep learning is utilized include digital assistants, financial fraud detection, self-driving vehicles, and many more.
Neural Networks
Neural networks are structures of neurons that can adjust with variable data inputs, composed of a sequence of algorithms that seek to identify core connections within a group of data in a procedure that simulates how a human brain might function in order to identify and recognize patterns. “Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems,” stated Pratik Shukla and Roberto Iriondo for Towards AI, a Medium publication.
Cognitive AI
Cognitive AI is feasibly the most advanced form of artificial intelligence to date. It is a hybrid of conventional numeric AI (machine learning, neural networks, and deep learning), used in conjunction with symbolic AI to enable a system to produce transparent recommendations. Cognitive AI is an intelligent system that comprehends large quantities of variable data while applying situational awareness and codified expert human knowledge, expertise, and best practices to identify problems and recommend solutions to real-world challenges. “This unique hybrid AI combines the best of numerical/statistical approaches with the best of symbolic/logical techniques to become greater than the sum of its parts” stated VentureBeat and Beyond Limits in a 2019 VBLab article.