How Does Artificial Intelligence Work?
AI software development is poised to become one of the most important technological changes to the business-to-business sector in some time. But B2B business managers struggle to understand how artificial intelligence systems work. Algorithms produce very specific results based upon the input they receive. Pattern recognition, anticipation, deductive reasoning, and decision-making are the hallmarks of human intelligence, but are not expected of typical computer programs. This higher level of information processing that would typically depend upon human intellectual labor can be automated by computers through artificial intelligence programming. In addition to helping businesses save on time and labor, AI software can also allow for the discovery of patterns in exceptionally large and complex data sets that might otherwise go unnoticed by people. In the era of big data, this enhanced insight offered by AI can prove to be vital in giving businesses a competitive edge within their markets.
Fundamentals of Artificial Intelligence
Symbolic learning was the first form of artificial intelligence developed. Initially used for robotics, this AI allows for computer vision and movement to direct robots to achieve very specific tasks within a regular and contained environment, such as on a manufacturing assembly line. Virtual chess and checkers players are other examples of symbolic learning applications using formal logic to achieve clearly defined outcomes. In these types of AI applications, actions are based upon the observed environment and target goal. Heuristics (guidelines) or the addition of new algorithms in response to past experience make for more advanced AI applications that improve over time. It is the addition of these heuristics to artificial intelligence systems that constitute the creation of rule-based machine learning.
What is Machine Learning?
Machine learning is a form of AI that focused on the development of the capacity of the AI system to improve its functioning based on prior experience. Deep learning features are a component of advanced machine learning systems that allow for multidimensional pattern recognition from complex data sets, which generally mix qualitative and quantitative information with varying degrees and consistency of structuring. It is the development of machine learning that has grown artificial intelligence capabilities in ways that make AI much more recognizably cognizant.
Rule-Based Machine Learning
In rule-based machine learning, functionality is governed by a growing set of heuristics based upon past performance metrics captured by the system. These rules determine how AI programs process information given certain conditions using if-and-then logic gates. In supervised learning AI development, the artificial intelligence system identifies the rules it will apply to enhance its own functionality over time. This type of machine learning allows AI to mimic how real brains learn by allowing for continuous autonomous improvement of intellectual output through acquired experience.
Computer Vision
Machine learning allows for great advances in computer vision over the older generation of symbolic learning-based AI. The first generation of computer vision sensed much less detail and required much more exacting matches to stored information to function. Computer vision becomes much more general using machine learning, capable of producing judgments about recorded images based on a broader understanding. Instead of relying on shapes, measurements, and other factors coinciding within specific parameters, the latest generation of computer vision can spot correspondences of many kinds between image sets within a specific category. For example, while a shot of a dog's face may appear very different to its side, with enough classified information an AI computer vision program may be able to classify both images appropriately as belonging to the class of dog. Using supervised learning, developers can correct the output of computer vision AI programs and let the code autonomously adjust its own functioning to improve accuracy in subsequent exercises.
Deep Machine Learning with Artificial Neural Networks
Artificial neural networks are the ultimate in machine learning. Beginning with convolutional neural networks (CNN), and evolving to recurrent neural networks (RNN) with long short-term memory (LSTM) and/or reinforcement learning, the most advanced AI programs being developed today are empowered by deep machine learning algorithms. In artificial neural networks, functional parameters (or neurons) are webbed together in multiple layers comprising the network. Input passes through each neuron and layer of the network, with each output of each layer a reflection of the relative weight each neuron within the layer contributes to the output. It is this weighing capacity of the output of multiple functions that can be adjusted based on performance parameters to improve functional output through artificial neural networks over time.
Natural Language Processing
Artificial neural networks are crucial to the functioning of the most advanced natural language processing software available today. Here, artificial neural networks help determine semantics of language through its context, using AI to make the judgments needed to determine meaning and intent of human language. Recurrent neural networks (RNNs) are particularly important to AI natural language processing. In RNNs, information is continuously reprocessed recursively, with each subsequent neural layer referring back to the previous before passing its own outputs to the next layer. The unique information processing of recurrent neural networks allows each word to be deciphered in the context of the words both preceding and proceeding it, just as human listeners understand spoken and written language.
Supervised and Unsupervised Learning
Supervised learning allows for the adjustment of the output of each neuron and neural layer, where the algorithm is refined by its programmers based upon the relative success of its observed output. Unsupervised learning is another option for refinement of artificial neural networks. Here, the AI application is free to identify any and all patterns it may find across complex data sets. This can be useful for businesses to determine impacts of complex correlations. A couple examples include how delivery times may affect future purchase amounts and types, or how production processes of specific items may correlate to positive or negative product reviews. In these instances, AI may be better able to detect subtle correlational patterns than humans can, and help businesses see the broader impacts of each decision and each part of the production process for a more holistic understanding of their operations and their opportunities for improvement.
Artificial Intelligence and the Business-to-Business Sector
Artificial intelligence technologies in B2B businesses will offer a substantial competitive advantage to those companies that adopt them, helping to increase business intelligence (BI) analytics, improve marketing and sales, raise productivity, and reduce costs. Let ArtificialIntelligenceB2B.com guide your business into the world of AI, finding the right artificial intelligence solution to achieve your goals. Learn more about how AI will impact the B2B sector by exploring this website further, or if you are interested in discussing where AI can help with your business goals, contact ArtificialIntelligenceB2B.com today.
