Does automatic and seamless learning occur with machine intelligence? The world of machine intelligence spans industries, platforms and functions which can add value to your business.\nAutonomous systems have been used in the air, ground and sea with far-reaching industrial applications. Many of these were governmental, and while they could be applied to the private sector, there wasn\u2019t a strong demand. The private sector is starting to ask more questions. We\u2019ll cover a few today: What is machine learning? What are the approaches for machine learning? And where is machine learning practically being used?\nWhat is machine learning?\nComputers don\u2019t learn on their own; they must be taught. Statistical methods drove the pioneering of machine learning using basic algorithms. Initial models had questionable effectiveness during the\u00a0A.I. winter\u00a0of the 1970s. This period marked the peak of the hype cycle for machine learning; big things were thought to be just ahead and around the corner. Despite driving faster around those corners, it wasn\u2019t until 2010 that machine learning achieved the level of respect it deserved.\nThe catalyst for machine learning was a resurgence of\u00a0backpropagation, a method of training or teaching artificial neural networks. This technique is combined with optimization methods. The value of this process was that it redefined learning as an optimization problem for neural networks.\nNeural networks are a computer system modeled on the human brain and nervous system. This algorithm was introduced in 1970 but wasn't made famous until 1986, in an article in Nature by\u00a0David Rumelhart,\u00a0Geoffrey Hinton and\u00a0Ronald Williams. The crux of the paper described how neural nets could be used to self-organize to solve previously unsolvable problems. It\u2019s a heavily mathematical paper that highlights how computers learn and why learning is the backbone of neural networks.\nMachine learning still requires learning, and that learning is done through algorithms. The better the algorithm, the faster the computer learns.\u00a0When you purchase machine learning products, services or interactions you\u2019re buying a machine learning engine. Try to buy a smart engine.\nWhat are the approaches to machine learning?\nWhether you\u2019re trying to get a machine to recognize an apple or process claims with machine intelligence, there are 15 approaches to help machines learn.\nFirst, decision tree learning uses decision trees and predictive models to map observations. Second, association rule learning helps to discover the relationships between arguments in large data sets. Third, artificial neural networks are learning algorithms, modeled off the biological neural networks to aid in connections and computations. Fourth, deep learning is used to process more complex environments such as sight and sound. Fifth, inductive logic programming uses logic as the base input for hypotheses. Sixth, support vector machines are a set of related supervised learning methods used for classification and regression. Seventh, group observations are clustered into subsets (called clusters) to more easily represent similar or dissimilar observations, driven by statistical data analysis. Eighth, Bayesian networks derive from probabilistic graphical models that represent relationships. Ninth, reinforcement learning connects actions to an environment tied to a reward. Tenth, representation learning seeks to discover better representations provided during training. Eleventh, similarity and metric learning use pairs that are similar and less similar to predict if new objects are similar. Twelfth, sparse dictionary learning represents datum as a linear combination of basic functions. Thirteenth, genetic algorithms mimic natural selections with search heuristics to find real solutions to problems using concepts such as mutation and crossover to generate a new genotype to solve problems. Fourteenth, rule-based machine learning identifies, learns or evolves rules to store, manipulate or apply knowledge. Fifteenth, learning classifier systems are collections of rule-based machine learning algorithms that have a discovery component and learning component (supervised learning, reinforcement learning or unsupervised learning).\nWhere is machine learning being used?\nMachine intelligence is being used by\u00a0Facebook\u00a0and API.ai\u00a0to expand your personal space.\u00a0Howdy, X.ai,\u00a0Clara and\u00a0Kasisto\u00a0are applying machine intelligence to stretch your professional space. Commercial platforms are also emerging, including research, full stack capabilities, industrial IOT, audio, vision and data enrichment.\nIt\u2019s exciting to consider the impact to healthcare. Every industry is getting curious about how it can leverage machine learning, including advertising technology (AdTheorent, Dstillery, Tapad), agriculture (BlueRiver, Tule, TerrAvion), retail (InVenture,\u00a0Earnest Machine,\u00a0Lenddo), legal (Everlaw,\u00a0Ravel Law,\u00a0Seal Software), manufacturing (Zymergen,\u00a0Ginkgo Bioworks,\u00a0Sight Machine\u00a0), education (Knewton,\u00a0Udacity,\u00a0Gradescope), transportation and logistics (Preteckt,\u00a0ClearMetal,\u00a0Nauto) and finance (Quantopian,\u00a0Kensho,\u00a0iSentium).\nMachine learning is becoming the default tool for data science, giving organizations new insights into customer behavior.