And, it was first published on Dataconomy on Dec 21, 2016; I'm reposting here for ease of access. In May 2017 I gave an updated talk; here's the slides & video.] In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB.

And, it was first published on Dataconomy on Dec 21, 2016; I'm reposting here for ease of access. In May 2017 I gave an updated talk; here's the slides & video.] In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB.

If you search around the internet looking for applying Naive Bayes classification on text, you'll find a ton of articles that talk about the intuition behind the algorithm, maybe some slides from a lecture about the math and some notation behind it, and a bunch of articles I'm not going to link here that pretty much just paste some code and call it an explanation. So I'm going to try to do a little more here, by hopefully writing and explaining enough, is let you yourself write a working Naive Bayes classifier. There are three sections here. First is setup, and what format I'm expecting your text to be in for the classification. Second, I'll talk about how to run naive Bayes on your own, using slow Python data structures.

Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common challenges related to supervised learning algorithms by using mixture probability distribution functions. With this modeling strategy, we identify sub-labels and generate synthetic data in order to reach better classification accuracy. It means we focus on increasing the training data synthetically to increase the classification accuracy.

Very frequently, tensor mining is done in an entirely unsupervised way, since ground truth and labels are either very expensive or hard to obtain. Our problem, thus, is: given a potentially very large and sparse tensor, and its R-component decomposition, compute a quality measure for that decomposition. Subsequently, using that quality metric, we would like to identify a "good" number R of components, and ultimately minimize human intervention and trial-and-error fine tuning. This problem is extremely hard. In fact, even computing the rank of a tensor has been shown to be an NPhard problem, in stark contrast to the matrix rank which can be easily computed in polynomial time. Fortunately, there exist heuristics that are able to assist with the above problem and have been shown to work well in practice, in the field of Chemometrics. Such a powerful and intuitive heuristic is the so-called "Core Consistency Diagnostic" [1], which given a tensor and its PARAFAC decomposition, provides a quality measure, which we can in turn use as a proxy of how interpretable our results are.

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.

In this article, we look at two machine learning (ML) techniques, Naive Bayes classifier and neural networks, and demystify how they work. With all the hype surrounding self-driving cars and video-game-playing AI robots, it's worth taking a step back and reminding ourselves how machine learning programs actually "learn". In this article, we look at two machine learning (ML) techniques–spam filters and neural networks–and demystify how they work. And if you're not sure what machine learning even is, read about the difference between artificial intelligence, machine learning, and deep learning. One common machine learning algorithm is the Naive Bayes classifier, which is used for filtering spam emails.

The "one-time-only" boxing match between a 40-year-old who retired two years ago and an Irishman making his pro debut in the sport is positioned to become the greatest-selling pay-per-view fight of all time Friday. Showtime Executive Vice President Stephen Espinoza said "it's too early to declare a hard number" but Saturday's Floyd Mayweather Jr.-Conor McGregor fight is "tracking in the mid-to-high 4 million pay-per view buys." "If we don't reach the record, we're going to be very, very close," and "we consider it a massive success." "It was an exciting, entertaining fight and there was massive interest," in it, Espinoza told The Times, crediting strong digital sales to boost the overall domestic sales. The bout is also expected to surpass the $600 million generated in total revenue by Mayweather's less-entertaining unanimous-decision triumph over seven-division champion Manny Pacquiao, with final pay-per-view numbers expected by next week.

This is great, in the sense that you don't have to worry about accidently forgetting to carry the 1 or remember how each rule in calculus operates. It is still great to have a general understanding of some of the equations you can utilize, distributions you can model and general statistics rules that can help clean up your data! We need to quickly lay out some definitions. In this post we will talk about discrete variables. If you have not heard the term before this references variables that are of a limited set. It actually could include numbers that are decimals pending on the set of variables you are using. However, these rules need to be established. For instance, you can't have 3.5783123 medical procedures in real life.

This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.