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) …
Oracle on Tuesday is announcing three new products within its Customer Experience (CX) Cloud Suite: an analytics application called Infinity; a powerful, easy-to-use segmentation tool for marketers called CX Audience; and a new cloud service called the Loyalty Cloud. In the last couple of years, most enterprises have come to recognize they need to undergo a digital transformation to react to customer expectations with more self-serve digital and mobile experiences, according to Des Cahill, Oracle VP and head CX evangelist. Now, he told ZDNet, "The conversation is more about where you are in the transformation of your business." The products that Oracle is rolling out this week, Cahill said, enable that process in three ways: by connecting customer data, applying intelligence, and ultimately helping to enhance the customer experience. The CX Audience and Infinity tools are part of the Oracle Marketing Cloud (within the CX Suite).
Many popular random partition models, such as the Chinese restaurant process and its two-parameter extension, fall in the class of exchangeable random partitions, and have found wide applicability in model-based clustering, population genetics, ecology or network analysis. While the exchangeability assumption is sensible in many cases, it has some strong implications. In particular, Kingman's representation theorem implies that the size of the clusters necessarily grows linearly with the sample size; this feature may be undesirable for some applications, as recently pointed out by Miller et al. (2015). We present here a flexible class of non-exchangeable random partition models which are able to generate partitions whose cluster sizes grow sublinearly with the sample size, and where the growth rate is controlled by one parameter. Along with this result, we provide the asymptotic behaviour of the number of clusters of a given size, and show that the model can exhibit a power-law behavior, controlled by another parameter. The construction is based on completely random measures and a Poisson embedding of the random partition, and inference is performed using a Sequential Monte Carlo algorithm. Additionally, we show how the model can also be directly used to generate sparse multigraphs with power-law degree distributions and degree sequences with sublinear growth. Finally, experiments on real datasets emphasize the usefulness of the approach compared to a two-parameter Chinese restaurant process.
I discuss here off-the-beaten-path beautiful, even spectacular results from number theory: not just about prime numbers, but also about related problems such as integers that are sum of two squares. The connection between these numbers and prime numbers will appear later in this article. A few important unsolved mathematical conjectures are presented in a unified approach, and some new research material is also introduced, especially an attempt at generalizing and unifying concepts related to data set density and limiting distributions. The approach is very applied, focusing on algorithms, simulations, and big data, to help discover fascinating results. Even though some of the most exciting topics of mathematics are discussed here (including fundamental, century-old problems still unresolved as well as brand new hypotheses), most of the article can be understood by the layman. Among other things, you will learn some new ways to estimate Pi based on non-traditional experiments, or how a conjecture for prime numbers somehow generalizes to apply to Fibonacci numbers as well.
If you spend enough time reading about programming or computer science, there's a good chance that you'll encounter the same ideas, terms, concepts, and names, time and again. Some of them start to become more familiar with time. Naturally, organically, and sometimes without too much effort on your part, you start to learn what all of these things mean. This happens because either you've slowly begun to grasp the concept, or you've read about a phrase enough times that you start to truly understand its meaning.
Interestingly, I started to research this topic by trying to apply the notorious central limit theorem (CLT) to non-random (static) variables -- that is, to fixed sequences of numbers that look chaotic enough to simulate randomness. While this function produces a sequence of numbers that seems fairly random, there are major differences with truly random numbers, to the point that CLT is no longer valid. Note that oscillations are expected (after all, U(n) is supposed to converge to a statistical distribution, possibly the bell curve, even though we are dealing with non-random sequences) but such large-scale, smooth oscillations, are suspicious. Confidence intervals (CI) can be empirically derived to test a number of assumptions, as illustrated in figure 1: in this example, based on 8 measurements, it is clear that maximum gap CI's for a-sequences are very different from those for random numbers, meaning that a-sequences do not behave like random numbers.
Have you ever thought about how strong a prior is compared to observed data? In order to alleviate this trouble I will take you through some simulation exercises. These are meant as a fruit for thought and not necessarily a recommendation. However, many of the considerations we will run through will be directly applicable to your everyday life of applying Bayesian methods to your specific domain. We will start out by creating some data generated from a known process.
While having myself a strong mathematical background, I have developed an entire data science and machine learning framework (mostly for data science automation) that is almost free of mathematics, and known as deep data science. You will see that you can learn serious statistical concepts (including limit theorems) without knowing mathematics, much less probabilities or random variables. Anyway, for algorithms processing large volume of data in nearly real-time, computational complexity is still very important: read my article about how bad so many modern algorithms are and could benefit from some lifting, with faster processing time allowing to take into account more metrics, more data, and more complicated metrics, to provide better results. It looks like f(n), as n tends to infinity, is infinitely smaller than log n, log(log n), log(log(log n))), and so on, no matter how many (finite number of) nested log's you have.
Check out Jana Eggers's sessions, "AI for business" and "It's the organization, stupid" at the Artificial Intelligence Conference, September 17-20, 2017, in San Francisco to learn more about implementing AI in your organization. Like infinity, artificial intelligence is an abstract concept. AI commercials show floating orbs and a sprinkling of fairy dust providing magical answers to our questions--even those we didn't know to ask. These presentations of AI remind me of an episode from South Park's second season called "Underpants Gnomes." In this episode, gnomes collect underpants and make a profit.