Businesses of today are constantly on the lookout for ways to radically transform their operational efficiencies. Digitization of business processes, technologies and products has enabled them to integrate methods to not only lower their costs, but to meet customer expectations in novel and customized ways. The one driver that many enterprises are wholeheartedly adopting now is Robotic Process Automation (RPA). With the help of robotics, enterprises can improve the physically measurable outcomes of manufacturing facilities, and also enhance their virtual operations to deliver the best results. In the simplest terms, RPA is the application of software robots that can interpret the functioning of existing applications and then imitate them with higher precision and fewer errors.

London – Experts from two US colleges have found the individual neuronal activity responsible for observational learning, a study published Tuesday in "Nature Communications" revealed. Researchers from the University of California - Los Angeles (UCLA) and the California Technical Insitute (Caltech) studied the activity of individual neurons with the aim of discovering the system through which the human brain learns from observing other individuals. According to research group leader Michael Hill, the study has managed to "transcend different levels of neuroscience," including the abstract level of computational models _ reflected in the activity of individual neurons _ and human behavior and interaction. The researchers studied brain activity by implanting chronic depth electrodes in ten patients suffering from epilepsy, who were then instructed to play a card game. During the game, information registered by the electrodes reflected changes in neuronal behavior caused by the process of learning through the observation of other players.

In this guide we are going to discuss how to install and use the Datumbox Machine Learning framework in your Java projects. Since almost all of the code is written in Java, using it is as simple as including it as dependency in your Java project. Nevertheless a couple of classes (DataEnvelopmentAnalysis and LPSolver) use an external C library called lpsolve (Linear Programming Solver). Note that if you don't plan to use those 2 classes you are not required to install any binary libraries on your system. Nevertheless if you want to explore all the supported algorithms it is recommended to do the full installation as described below.

Gradient boosting involves the creation and addition of decision trees sequentially, each attempting to correct the mistakes of the learners that came before it. This raises the question as to how many trees (weak learners or estimators) to configure in your gradient boosting model and how big each tree should be. In this post you will discover how to design a systematic experiment to select the number and size of decision trees to use on your problem. How to Tune the Number and Size of Decision Trees with XGBoost in Python Photo by USFWSmidwest, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

As humans cede more and more control to algorithms, whether in the courtroom or on social media, the way they are built becomes increasingly important. The foundation of machine learning is data gathered by humans, and without careful consideration, the machines learn the same biases of their creators. Sometimes bias is difficult to track, but other times it's clear as the nose on someone's face--like when it's a face the algorithm is trying to process and judge. An online beauty contest called Beauty.ai, run by Youth Laboratories (that lists big names in tech like Nvidia and Microsoft as "partners and supporters" on the contest website), solicited 600,000 entries by saying they would be graded by artificial intelligence. The algorithm would look at wrinkles, face symmetry, amount of pimples and blemishes, race, and perceived age. However, race seemed to play a larger role than intended; of the 44 winners, 36 were white.

It has been dubbed the'quantum enigma machine' - and has been used for a groundbreaking new form of unbreakable encrypted messaging for the first time. The researchers proved a message could be sent with a key that's shorter than the message itself, breaking the conditions defined decades ago by the'father of information theory,' Claude Shannon. This encryption method, known as quantum data locking, could one day make for super-secure systems in which it is virtually impossible for a third party to obtain and translate the message. Using a device dubbed the'quantum enigma machine,' researchers have demonstrated a new form of unbreakable encrypted messaging for the first time. In an example explaining how this system works, a hypothetical'Alice' is sending an encrypted message to'Bob,' with'Eve' being the third party The work also taps into the fundamental uncertainty of quantum measurements, which states that the more we know about one property of a particle, the less we know about another.

This paper describes a new mechanism that might help with defining pattern sequences, by the fact that it can produce an upper bound on the ensemble value that can persistently oscillate with the actual values produced from each pattern. With every firing event, a node also receives an on/off feedback switch. If the node fires, then it sends a feedback result depending on the input signal strength. If the input signal is positive or larger, it can store an 'on' switch feedback for the next iteration. If the signal is negative or smaller, it can store an 'off' switch feedback for the next iteration. If the node does not fire, then it does not affect the current feedback situation and receives the switch command produced by the last active pattern event for the same neuron. The upper bound therefore also represents the largest or most enclosing pattern set and the lower value is for the actual set of firing patterns. If the pattern sequence repeats, it will oscillate between the two values, allowing them to be recognised and measured more easily, over time. Tests show that changing the sequence ordering produces different value sets, which can also be measured.

This paper extends the standard chaining technique (e.g., Pollard, 1990; Dudley, 1999; Györfi et al., 2002; Boucheron et al., 2012) to prove high-probability excess risk upper bounds for empirical risk minimization (ERM) for random design settings even if the magnitude of the noise and the estimates is unbounded. Our result (Theorem 1) covers bounded settings (Bartlett et al., 2005; Koltchinskii, 2011), extends to sub-Gaussian or even subexponential noise(van de Geer, 2000; Györfi and Wegkamp, 2008), and handles hypothesis classes with unbounded magnitude (Lecué and Mendelson, 2013; Mendelson, 2014; Liang et al., 2015). Furthermore, it applies to many loss functions besides the squared loss, and does not need additional statistical assumptions such as the bounded kurtosis of the transformed covariates over the hypothesis class, which prevent the latest developments to provide tight excess risk bounds for many sub-Gaussian cases (Section 1.2). To demonstrate the effectiveness of our method for such unbounded settings, we use our general excess risk bound (Theorem 1) to provide a detailed analysis for linear least squares estimators using quadratic slope constraint and penalty with sub-Gaussian noise and domain for the random design, nonrealizable setting(Section 3). Our result for the slope constrained case extends Theorem A of Lecué and Mendelson (2013) and nearly proves the conjecture of Shamir (2015), while our treatment for the penalized case (ridge regression) is comparable to the work of Hsu et al. (2014). The rest of this section introduces our notation through the formal definition of the regression problem and ERM estimators (Section 1.1), and discusses the limitations of 1 current excess risk upper bounds in the literature (Section 1.2). Then, we provide our main result in Section 2 to upper bound the excess risk of ERM estimators, and discuss its properties for various settings including many loss functions besides the squared loss. Next, Section 3 provides a detailed analysis for linear least squares estimators including the slope constrained case (Section 3.1) and ridge regression (Section 3.2). Finally, Section 4 proves our main result (Theorem 1).

Estimators of information theoretic measures such as entropy and mutual information are a basic workhorse for many downstream applications in modern data science. State of the art approaches have been either geometric (nearest neighbor (NN) based) or kernel based (with a globally chosen bandwidth). In this paper, we combine both these approaches to design new estimators of entropy and mutual information that outperform state of the art methods. Our estimator uses local bandwidth choices of $k$-NN distances with a finite $k$, independent of the sample size. Such a local and data dependent choice improves performance in practice, but the bandwidth is vanishing at a fast rate, leading to a non-vanishing bias. We show that the asymptotic bias of the proposed estimator is universal; it is independent of the underlying distribution. Hence, it can be pre-computed and subtracted from the estimate. As a byproduct, we obtain a unified way of obtaining both kernel and NN estimators. The corresponding theoretical contribution relating the asymptotic geometry of nearest neighbors to order statistics is of independent mathematical interest.

We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective in the M-step. The EM-like method is a modification of alternating minimization. We prove that EM-like method will be the exactly same with contrastive divergence in restricted Boltzmann machine if the M-step of this method adopts special approximation. We also propose a new measure to assess the performance of Boltzmann machine as generative models of data, and its computational complexity is O(Rmn). Finally, we demonstrate the performance of EM-like method using numerical experiments.