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) …
We could not so far claim that deep networks trained with stochastic gradient descent are Bayesian. And it may be because SGD biases learning towards flat minima, rather than sharp minima. It turns out, (Hochreiter and Schmidhuber, 1997) motivated their work on seeking flat minima from a Bayesian, minimum description length perspective. Seeking flat minima makes sense from a minimum description length perspective.
When it comes to bringing intelligence to real-time engineering systems, the world of finance has been hindered by its legacy. Compared to things like self-driving cars, incumbent financial infrastructure takes a very long time to update, and is siloed into systems that cannot really talk to each other. Paul Bilokon, founder of Thalesians, an organization to promote deeper thinking and philosophy within finance, points out that many nonfinancial systems are using software techniques that are far ahead. But he also sees this changing thanks to improved infrastructure tools and advancements in machine learning within finance. "Look at all these techniques that people use outside finance," Bilokon said.
Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. It's an interesting question, the extent to which the latest artificial intelligence and deep learning techniques can crossover into finance. Financial data modeling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet.
What better way to enjoy this spring weather than with some free machine learning and data science ebooks? Here is a quick collection of such books to start your fair weather study off on the right foot. The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together. A mix of classic and contemporary titles, hopefully you find something new (to you) and of interest here. Think Stats is an introduction to Probability and Statistics for Python programmers.
MIT loves numbers, so let me drop a few to illustrate the popularity of a course called 6.036. It is overseen by four instructors and 15 teaching assistants. Lectures are held in 26-100, the school's largest auditorium, with 566 seats. But this semester, about 700 students signed up for the course -- also known as Introduction to Machine Learning -- according to Tommi Jaakkola, the computer science professor who created it. So at the first lecture, more than 100 students watched on a video screen in an overflow room.
Hyperband is a method for tuning iterative algorithms. It uses random sampling and attempts to gain the edge by using time spent optimizing in the best way. We explain a few things that were not clear to us right away, and try the algorithm in practice. Come back later for la version final. For us, the name conjures an idea of some fancy topological construct.
An Introduction to Statistical Learning: with Applications in R Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language. Modeling With Data This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information. Big Data, Data Mining, and Machine Learning On this resource the reality of big data is explored, and its benefits, from the marketing point of view.
Machine learning has become a buzzword in the media these days. Recently Science magazine published a cover paper on Human-level concept learning through probabilistic program induction and shortly after Nature magazine devoted its cover story to AlphaGo, an AI program that defeated European Go Championship winner. Late on Tuesday night, Google's DeepMind AI group will play one of the world's best human Go players, Lee Se-dol of South Korea. The game will be live streamed on YouTube, and the stream is embedded at the end of this story. Many are now discussing the potential of artificial intelligence, asking questions such as "Can machines learn like a human?", "Will artificial intelligence surpass human intelligence?",
Bayesian statistics, which allows researchers to use everything from hunches to hard data to compute the probability that a hypothesis is correct (see p. 1461), is experiencing a renaissance in fields of science ranging from astrophysics to genomics and in real-world applications such as testing new drugs and setting catch limits for fish. Advances in computers and the limitations of traditional statistical methods are part of the reason for the new popularity of this approach, first proposed in a 1763 paper by the Reverend Thomas Bayes. In addition, advocates say it produces answers that are easier to understand and forces users to be explicit about biases obscured by reigning "frequentist" approaches. Detractors, on the other hand, fear that because Bayesian analysis can take into account prior opinion, it could spawn less objective evaluations of experimental results.
Paul Graham popularized the term "Bayesian Classification" (or more accurately "Naïve Bayesian Classification") after his "A Plan for Spam" article was published (http://www.paulgraham.com/spam.html). In fact, text classifiers based on naïve Bayesian and other techniques have been around for many years. Companies such as Autonomy and Interwoven incorporate machine-learning techniques to automatically classify documents of all kinds; one such machine-learning technique is naïve Bayesian text classification. Naïve Bayesian text classifiers are fast, accurate, simple, and easy to implement. In this article, I present a complete naïve Bayesian text classifier written in 100 lines of commented, nonobfuscated Perl.