If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Two-thirds of Americans believe that, in 50 years, robots and computers will do much of the work humans now do. The World Economic Forum's 2016 report, The Future of Jobs, estimates that 5 million jobs will be lost to automation by 2020 and that the number will keep growing. Jobs that once seemed like "safe bets"--office workers and administrative personnel, manufacturing, and even law--will be hit hardest, the report estimates. "There are some overarching shifts poised to change the nature of work itself over the next decade," says Devin Fidler, research director at Institute for the Future, a nonprofit research center focused on long-term forecasting. So what do you need to work on to be marketable in 2025?
Almost a week after being shut down for spewing racist and sexist comments on Twitter, Microsoft Corp's artificial intelligence'chatbot' called Tay briefly rejoined Twitter on Wednesday only to launch a spam attack on its followers. "Tay remains offline while we make adjustments," a Microsoft representative said in an email. It was taken offline following the incident, according to a Microsoft representative, in an effort to make "adjustments" to the artificial intelligence profile. According to its Twitter profile, Tay is "an artificial intelligent chatbot developed by Microsoft's Technology and Research and Bing teams to experiment with and conduct research on conversational understanding."
Almost a week after being shut down for spewing racist and sexist comments on Twitter, Microsoft Corp's artificial intelligence'chatbot' called Tay briefly rejoined Twitter on Wednesday only to launch a spam attack on its followers. The incident marks another embarrassing setback for the software company as it tries to get ahead of Alphabet Inc's Google, Facebook Inc and other tech firms in the race to create virtual agents that can interact with people and learn from them. The TayTweets (@TayandYou) Twitter handle was made private and the chatbot stopped responding to comments Wednesday morning after it fired off the same tweet to many users. "You are too fast, please take a rest...," tweeted Tay to hundreds of Twitter profiles, according to screen images published by technology news website The Verge. Tay's Twitter account was accidentally turned back on while the company was fixing the problems that came to light last week, Microsoft said on Wednesday.
In this fourth part of the tutorial we will discuss the ROC curve. The ROC curve is one of the methods for visualizing classification quality, which shows the dependency between TPR (True Positive Rate) and FPR (False Positive Rate). The more convex the curve, the better the classifier. In the example below, the „green" classifier is better in area 1, and the „red" classifier is better in area 2. AUC 1 means a perfect classifier, AUC 0.5 is obtained for purely random classifiers. AUC 0.5 means the classifier performs wor
In "Teaching Data Science in English (Not in Math)", the Feb-08-2016 entry of his Web log, "The Datatist", Charles Givre criticizes the use of specialized math symbols (capital sigma for summation, etc.) and Greek letters as being confusing, especially to newcomers to the field. He suggests that "English" (pseudo-code) be used instead, such as the following: Although there are some flaws in this particular comparison (1. the first example includes an unnecessary middle portion, 2. it also features the linear model definition, while the pseudo-code example does not, and 3. the indexing variable in the summation is straightforward in this case, but is not always so), I tend to agree. Despite my own familiarity with the math jargon, I agree with him that, in many cases, pseudo-code is easier to understand. Pseudo-code is certainly simpler syntactically since it tends to make heavy use of (sometimes deeply-nested) functions, as opposed to floating subscripts, superscripts and baroque symbols. Pseudo-code often employs real names for parameters, variables and constants, as opposed to letters.
Machine learning is an artificial intelligence branch that has been well applied and recognized as an effective tool to handle a wide range of real situations. In the last few years, we have witnessed the explosion of Big Data, which has enables researchers to store data for analysis in an unprecedented way. This explosion in data available for analysis is as evident in healthcare as anywhere else. In particular, this special issue is focused on the areas of bioinformatics and biomedical engineering. These are two of the fastest developing research fields in the last few decades, since the biological data used to provide information is rapidly generated, and it is mandatory to be able to extract information and knowledge from them, as technological innovation in these fields is to be probably one of the most important developments in the next coming years.
Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning. This post was written for developers interested in applied machine learning, specifically predictive modeling. You do not need to have a background in linear algebra or statistics.
Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive users, and real-time performance that make the problem of creating AI approaches for these domains harder.