Morever, these algorithms are robust, so don't require problem-specific hand-tuning. One powerful example is sampling from an arbitrary probability distribution, which we need to do often (and efficiently!) when doing inference. The brute force approach, rejection sampling, is problematic because acceptance rates are low: as only a tiny fraction of attempts generate successful samples, the algorithms are slow and inefficient. See this post by Jeremey Kun for further details. Until recently, the main alternative to this naive approach was Markov Chain Monte Carlo sampling (of which Metropolis Hastings and Gibbs sampling are well-known examples). If you used Bayesian inference in the 90s or early 2000s, you may remember BUGS (and WinBUGS) or JAGS, which used these methods. These remain popular teaching tools (see e.g.
The commercial Internet has now been around for twenty some years and the overall experience hasn't changed much from the days of "You've Got Mail." The Internet started out as a research tool between government, universities and corporations. With the advent of hyperlinks, the Internet has been transformed into a commercial vehicle for the sale of good and services. The Internet of today as a research tool is pathetic and has taken on a bias of consumerism. Take this example: "show me all printers that use HP 950 Ink cartridges."
Text clustering is a widely used techniques to automatically draw out patterns from a set of documents. This notion can be extended to customer segmentation in the digital marketing field. As one of its main core is to understand what drives visitors to come, leave and behave on site. One simple way to do this is by reviewing words that they used to arrive on site and what words they used ( what things they searched) once they're on your site. Another usage of text clustering is for document organization or indexing (tagging).
High-tech entrepreneur Elon Musk has launched an open-source training "gym" for artificial-intelligence programmers. It's an interesting move for a man who in 2014 said artificial intelligence, or A.I., will pose a threat to the human race. "I think we should be very careful about artificial intelligence," Musk said about a year and a half ago during an MIT symposium. "If I were to guess at what our biggest existential threat is, it's probably that... with artificial intelligence, we are summoning the demon. In all those stories with the guy with the pentagram and the holy water, and he's sure he can control the demon.
Machine learning is on a steep adoption curve and making its inroads in our daily lives and work. The application of the technology won't be an issue at all. There's an abundance of meaningful value propositions for many functional areas, business processes and roles across multiple industries. Software vendors of enterprise business solutions are focusing their product development on machine learning and other related artificial intelligence technologies. CEO Bill McDermott of SAP said that intelligent applications will fundamentally change the way you do work in the enterprise in the next decade.