While the applications and capabilities of artificial intelligence and machine learning are rapidly evolving with \u201cnew\u201d uses disrupting entire industries overnight\u2014from building predictive algorithms for marketing trends<\/a> and performance, to AI-powered demand forecasting within the supply chain<\/a>, to transforming the employee hiring process<\/a>\u2014the programming behind them is not new.<\/p>\n The 1956 Dartmouth Summer Research Project on Artificial Intelligence<\/a> effectively launched the field of AI, and IBM researcher Arthur Lee Samuels<\/a> developed one of the first machine learning programs, coining the term in a 1959 research paper. But it\u2019s the recent advancements in modern processor speeds, open-source algorithms, and data storage, availability, and improved visualization that has helped to launch AI and machine learning into broader applications across multiple industries.<\/p>\n As humans continue to push the boundaries of these capabilities and wrestle with their impacts and ramifications, understanding the best uses and applications of these intelligent machines is critical. One important distinction is that machine learning and artificial intelligence are not interchangeable functions; despite clear overlap between the two capabilities, there are also clear differences.<\/p>\n Simply put, artificial intelligence is the science and technology of making machines, especially computer programs, intelligent, with that \u201cintelligence\u201d being the computational ability of these \u201cprograms to solve problems and achieve goals in the world as well as humans,\u201d as explained by computer scientist John McCarthy<\/a>, the \u201cFather of AI\u201d and one of the organizers of the 1956 Dartmouth Conference.<\/p>\n With this computational intelligence, AI machines aren\u2019t programmed to do one single, repeated motion; instead, they adapt to situations and perform tasks in intelligent ways. AI machines are typically specialized, such as automated vehicle or smart stock trading systems. This is referred to as narrow AI<\/a>. Researchers hope a future form of general AI may accommodate multiple tasks carried out in smarter ways\u2014more like a human.<\/p>\n AI can be applied to predict when machines need maintenance or analyze large-scale systems or processes (for example, a factory\u2019s end-to-end manufacturing process) to improve efficiency. AI also has applications in the service industry, like the \u201cJust Walk Out\u201d<\/a> technology employed by Amazon to power its cashier-less Amazon Go stores. The freight arriving at those stores may soon be delivered by driver-less trucks powered by automated vehicle technology, with the potential to replace 2 million American truck drivers<\/a>.<\/p>\n Machine learning is one of four main subsets<\/a> of AI (machine reasoning, automated planning and natural language processing being the other three) and has the dynamic ability to \u201clearn\u201d from data without being programmed to do so. In other words, this is artificial intelligence without the complex codes and decision trees required for programming that AI cannot learn. The key to \u201ctraining\u201d the machine\u2019s intelligence to think logically and understand with a \u201chuman-like\u201d capacity is through massive amounts of data fed through an algorithm that adjusts and improves. For example, if recognition is a key application, over time, machine learning can hone its facial recognition to consistently recognize an individual\u2019s face, even if that person has changed hairstyles, clothes or added someone else to the picture.<\/p>\n While AI has greatly impacted task-oriented jobs in the manufacturing and service industries, knowledge-based tasks and white-collar jobs are also feeling the effects of intelligent machine learning. Look no further than Alexa, Amazon\u2019s ubiquitous voice assistant which continues to get closer to functioning like a human assistant through \u201cactive learning, in which the system identifies areas it needs help from a human expert,\u201d and \u201cself-learning,\u201d where the system is \u201clearning from implicit feedback,\u201d automatically making corrections based on contextual clues from its human interactions, explains Rohit Prasad<\/a>, vice president and chief scientist of Amazon Alexa. This kind of deep learning mirrors the human brain, using Artificial Neural Networks (ANN) in layers, each responsible for one facet of \u201clearning.\u201d<\/p>\n Ultimately, artificial intelligence and machine learning are complementary: Machine learning improves AI by accelerating the learning and increasing its sophistication. The artificial intelligence powered by this machine intelligence makes all this learning worthwhile, packaging the depth of knowledge in a useful application.<\/p>\n The key to effective application is in understanding the areas where artificial intelligence and machine learning applications can boost an organization\u2019s business, and that can mean differentiating between the two based on the business problem to solve. Will the application need ongoing learning, such as predicting customer purchasing behaviors? If so, machine learning may be required to constantly learn from the data and remain up to date. If the need is for efficient customer data processing, a simpler form of AI may fulfill this.<\/p>\nWhat is Artificial Intelligence?<\/h2>\n
What is Machine Learning?<\/h2>\n
Differentiating Artificial Intelligence from Machine Learning<\/h2>\n
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<\/p>\nApplying Artificial Intelligence and Machine Learning<\/h2>\n