Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

Random forests are an excellent "out of the box" tool for machine learning with many of the same advantages that have made neural nets so popular. They are able to capture non-linear and non-monotonic functions, are invariant to the scale of input data, are robust to missing values, and do "automatic" feature extraction. Additionally, they have other benefits that neural nets do not. What follows is a look into how random forests work, how they may be usefully applied, and a discussion of some situations in which they may be preferable to neural networks. So how do random forests work?

But cars more fully integrated into the so-called internet of things -- everyday devices able both to send and receive data -- could become more of a seamless piece of the daily digital fabric of people's lives. Even now, Amazon's voice-activated home assistant, Alexa, can order up an Uber ride or find out how much gas is in a car's tank while the driver is still in the house. BMW announced this month that its Connected services would enable Alexa owners to lock the car doors and check car battery levels from the comfort of their sofas. Ford Motor plans to introduce Alexa integration into vehicles, including the Escape and Fusion, before the end of this year, said James A. Buczkowski, who oversees advanced engineering at Ford. "Your spouse could add things to the shopping list, which your car would alert you to," Mr. Buczkowski said.

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The artificial intelligence revolution has arrived. Companies are racing to deliver AI capabilities that make sense of the massive influx of data created in the last several years as a result of the cloud, mobile, social and IoT trends. But cracking the AI nut is difficult. The technical requirements, resources and expertise needed to deliver AI are enormous, and until recently it was only feasible for the largest companies. But as intelligence becomes the new currency in business, it is vital that these barriers be knocked down and that the power of AI reaches the hands of employees across organizations, in every industry and of all sizes.

In 1962, one year after the first industrial robot joined a production line at General Motors, the animated sitcom The Jetsons debuted. For just one season, the show forecast a future when people could have whatever they wanted (a gourmet dinner, a clean house, a flying car that folds into a briefcase) by pushing a button. It was fantasy then, and much of it still is today--progress is sometimes slow. The Hunter-Gatherer Age lasted a couple million years, the Agricultural Age lasted several thousand years, and the Industrial Age lasted a couple of centuries. Then the Information Age came along and dramatically accelerated the speed at which we evolve, at least technologically.

The advancement could mark a major breakthrough in the development of AI, as the "differentiable neural computer" (DNC) can solve problems without any prior knowledge. Instead, the DNC learns to use its own memory to answer questions about complex data. In a study published in the journal Nature, the technology also demonstrated it can solve a block puzzle game using reinforcement learning. What makes the DNC impressive is that it can learn to form and navigate complex data structures all on its own. The researchers demonstrated how the program can analyze a description of an arbitrary graph and answer questions about it.

Once these data subsets are created from the primary dataset, a predictive model or classifier is trained using the training data, and then the model's predictive accuracy is determined using the test data. As mentioned, machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. In a nutshell, machine learning is all about automatically learning a highly accurate predictive or classifier model, or finding unknown patterns in data, by leveraging learning algorithms and optimization techniques. The columns in this case, and the data contained in each, represent the features (values) of the data, and may include feature data such as game date, game opponent, season wins, season losses, season ending divisional position, post-season berth (Y/N), post-season stats, and perhaps stats specific to the three phases of the game: offense, defense, and special teams.

Word embedding is a technique that treats words as vectors whose relative similarities correlate with semantic similarity. This technique is one of the most successful applications of unsupervised learning. Natural language processing (NLP) systems traditionally encode words as strings, which are arbitrary and provide no useful information to the system regarding the relationships that may exist between different words. Word embedding is an alternative technique in NLP, whereby words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size, and the similarities between the vectors correlate with the words' semantic similarity. For example, let's take the words woman, man, queen, and king.

When someone can take a very challenging present-day problem and translate it into a problem that has been studied for centuries, the result can be amazing. Such is the case with Word2Vec, a method for transforming words into vectors. Text is unstructured data and has been explored mathematically far less than vectors, both historically and today. Physicist and mathematician Sir Isaac Newton may have been the first person to study vectors in the context of forces in physics. The concept of vectors has almost three centuries of scientific maturity.