How Does Artificial Intelligence Work?

AI software development has phenomenal potential in healthcare and medicine. But how artificial intelligence systems work is not something that's entirely intuitive. Algorithms are generally understood to produce very specific results based upon input. Typical computer programs do not demonstrate the deductive reasoning, pattern recognition, anticipation, and decision-making that are hallmarks of human intelligence in action. Artificial intelligence programming allows for this higher level information processing through various systems, allowing intellectual work that typically would require full human control to be automated by computers. Not only does this save time and labor, but it also offers the opportunity to discover patterns in expansive data sets that might otherwise go unnoticed by the human eye.

Fundamentals of Artificial Intelligence

Artificial intelligence was first developed using symbolic learning. The earliest applications were used for robotics, allowing for computer vision and movement to achieve specific tasks within a contained environment. Other common symbolic learning applications used formal logic to achieve easily defined outcomes, such as playing games like chess and checkers. In these applications, the artificial intelligence systems base their actions upon the observed environment. More advanced forms of AI add new heuristics (guidelines) or even new algorithms based upon observed success of existing AI to improve over time. The addition of these heuristics to artificial intelligence applications constitute rule-based machine learning.

What is Machine Learning?

AI machine learning focuses on development of capacity for AI to improve its responses to given conditions through time by experience. Machine learning also encompasses deep learning features that allow for multidimensional pattern recognition from complex data sets. The development of machine learning has been crucial to growing artificial intelligence capacities in ways that make AI much more recognizably cognizant.

Rule-Based Machine Learning

For example, in rule-based machine learning, functioning is determined through a growing series of heuristics based upon the knowledge captured by the system in its performance. These rules typically qualify how information will be processed based on observed conditions in if-and-then logic gates. The AI application will identify for itself the useful rules to be applied to enhance its functionality in supervised learning development, thus allowing it to enhance its functional understanding over time. Rule-based machine learning allows for continuous autonomous improvement to intellectual output, allowing AI to mimic how real brains work.

Computer Vision

The improvements in computer vision that machine learning allows over the older generation of symbolic learning is a good example of how machine learning advanced the capabilities of AI. Previous computer vision was relatively clumsy, and required much more exact matches of images. With machine learning, computer vision becomes more general, judgment-oriented, and intuitive. Shapes and other factors need not line up exactly against a defined range, but rather algorithms can spot similarity to previously categorized images of that type to base decisions on overall similarity. These judgments can be corrected by code developers in supervised learning, which labels the machine learning output and allows the code to adjust its future output based on previous output accuracy.

Deep Machine Learning with Artificial Neural Networks

Artificial neural networks represent the pinnacle of machine learning. Starting with convolutional neural networks (CNN), and evolving towards recurrent neural networks (RNN) with long short-term memory (LSTM) and/or reinforcement learning, deep learning algorithms empower the most advanced artificial intelligence systems available today. In artificial neural networks, inputs are passed through several functional parameters arranged as networks webbed together in multiple layers. Each output is a reflection of the relative "weight" of individual neurons within that layer.

Natural Language Processing

Today's most advanced natural language processing leverages artificial neural networks to accomplish its tasks. Since context determines much of the semantics of language, having an artificial neural network allows natural language processing AI applications to make crucial judgments on meaning based on the surrounding information. Here, recurrent neural networks, which continually reprocess information temporally using recursive information flows that refer processed information back to previous neuronal layers before passing outputs to the next layer, are particularly helpful to ensure that each word is interpreted within the context of the content that both precedes and proceeds it.

Supervised and Unsupervised Learning

This weight of individual and neuronal layer input may be adjusted through a process of supervised learning, where the algorithm is trained based upon observer review of outputs. Artificial neural networks can also be used in unsupervised deep learning, in which the AI application is free to identify any patterns it may find across complex data sets. Unsupervised learning with artificial neural networks can be particularly useful when seeking to find correlations in healthcare outcomes based on demographics and treatment histories, as AI is better able to detect more subtle and complex correlational patterns than humans typically can, and the automated process helps speed up the research.

Artificial Intelligence and Healthcare

AI software development in healthcare and medicine shows exceptional potential to increase efficiencies and accelerate workflows in ways that will help reduce costs and increase productivity. Let ArtificialIntelligence.health be the AI software development company to find the best artificial intelligence applications for your medical organization. Contact ArtificialIntelligence.health today to learn more.