Both Millennials and Generation Z can be categorized as digital natives. The way in which they learn reflects that reality. From a learning perspective, a companys learning programs must reflect that also.

Utilizing technologies such as microlearning, which is usually delivered with mobile technology, or machine learning to can engage these individuals in the way they are accustomed to consuming information.

Microlearning is delivering learning in bite-sized pieces. It can take many different forms such an animation or a video. In either case, the information is delivered in a short amount of time; in as little as two to three minutes. In most cases, micro-learning happens on a mobile device or tablet.

When should micro-learning be used?

Think of it as a way to engage employees already on the job. It can be used to deliver quick bits of information that will become immediately relevant to their daily responsibilities. To be more pointed, microlearning is the bridge between formal training and application. At least one study shows after six weeks following a formal training, 85% of the content consumed will have been lost. Microlearning can deliver that information in the interim and can be used at the moment of application.

Microlearning shouldnt be used to replace formal training, but rather as a compliment which makes it perfect for developing and retaining high-quality talent.

Amnesty International piloted a microlearning strategy to launch its global campaign on Human Rights Defenders. The program used the learning approached to build a culture of human rights. It allowed Amnesty to discuss human rights issues in a quick, relevant, and creative manner. As such, learners were taught how to talk to people in everyday life about human rights and human rights defenders.

WEBINAR: L&Ds Role in Enabling the Future of Work with a Skills Focused Strategy

Dell has also used the strategy to implement a digital campaign to encourage 14,000 sales representatives around the world to implement elements of its Net Promoter Score methodology. Using mobile technology and personal computers, the company was able to achieve 11% to 19% uptake in desire among sales reps globally.

Machine learning can also be used as a strategy. Machine learning, which is a branch of artificial intelligence, is an application that provides systems the ability to automatically learn and improve from experience without being programmed to do so.

For the purpose of explanation, the example of an AI-controlled multiple-choice test is relevant. If a person taking the test marked an incorrect answer, AI would then give them a question a bit easier to answer. If the question was answered wrong again, AI would follow with a question lower in difficulty level. When the student began to answer questions correctly, the difficulty of the questions would increase. Similarly, a person answering questions correctly would continue to get more difficult questions. This allows the AI to determine what topics the student understands least. In doing so, learning becomes personalized and specific for the student.

But technology isnt the sole basis for disseminating information. Learning programs should also focus on creating more experience opportunities that offer development in either leadership or talent. Those programs should also prioritize retention. Programs such as mentoring and coaching are great examples.

Dipankar Bandyopadhyay led this charge when he was the Vice President of HR Global R&D and Integration Planning Lead Culture & Change Management for the Monsanto Company. Monsanto achieved this through itsGlobal Leadership Program For Experienced Hires.

A couple of years ago, we realized we had a need to supplement our talent pipeline, essentially in our commercial organization and businesses globally really building talent for key leadership roles within the business, which play really critical influence roles and help drive organizational strategy in these areas. With this intention, we created Global Commercial Emerging Leaders Program, Bandyopadhyay said. Essentially, what it does is focus on getting external talent into Monsanto through different industry segments. This allows us to broaden our talent pipeline, bringing in diverse points of view from very different industry segments (i.e., consumer goods, investment banking, the technology space, etc.) The program selects, onboards, assimilates and develops external talent to come into Monsanto.

Microlearning and machine learning are valuable in developing the workforce, but they are not the only ones available. Additionally, its important to note an organization cant simply provide development and walk away. There has to be data and analysis that tracks employee learning success. There also needs to be strategies in place to make sure workers are retaining that knowledge. Otherwise, it is a waste of money.

NEXT: How L&D Can Help Itself

Want more content faster? Connect with us on Twitter, Facebook and LinkedIn. And don't forget to join our LinkedIn group!

Photo courtesy: Pexels

See original here:
Learning that Targets Millennial and Generation Z - HR Exchange Network

Scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.

The name machine learning was coined in 1959 by Arthur Samuel.[5] Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[6] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[7] In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.

Machine learning tasks are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object (the input), and each image would have a label (the output) designating whether it contained the object. In special cases, the input may be only partially available, or restricted to special feedback.[clarification needed] Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn't have labels.

Classification algorithms and regression algorithms are types of supervised learning. Classification algorithms are used when the outputs are restricted to a limited set of values. For a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. For an algorithm that identifies spam emails, the output would be the prediction of either "spam" or "not spam", represented by the Boolean values true and false. Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.

In unsupervised learning, the algorithm builds a mathematical model from a set of data that contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

Active learning algorithms access the desired outputs (training labels) for a limited set of inputs based on a budget and optimize the choice of inputs for which it will acquire training labels. When used interactively, these can be presented to a human user for labeling. Reinforcement learning algorithms are given feedback in the form of positive or negative reinforcement in a dynamic environment and are used in autonomous vehicles or in learning to play a game against a human opponent.[2]:3 Other specialized algorithms in machine learning include topic modeling, where the computer program is given a set of natural language documents and finds other documents that cover similar topics. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Meta learning algorithms learn their own inductive bias based on previous experience. In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies, and imitation.[clarification needed]

Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM.[8] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[9] The interest of machine learning related to pattern recognition continued during the 1970s, as described in the book of Duda and Hart in 1973. [10] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. [11] As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[12] Probabilistic reasoning was also employed, especially in automated medical diagnosis.[13]:488

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[13]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.[14] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[13]:708710; 755 Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[13]:25

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.[14] It also benefited from the increasing availability of digitized information, and the ability to distribute it via the Internet.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.[15]

Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.[16] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[17] He also suggested the term data science as a placeholder to call the overall field.[17]

Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[18] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[19]

A core objective of a learner is to generalize from its experience.[2][20] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The biasvariance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.[21]

In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[22] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.[23] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.[6]

Supervised learning algorithms include classification and regression.[24] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

In the case of semi-supervised learning algorithms, some of the training examples are missing training labels, but they can nevertheless be used to improve the quality of a model. In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[25]

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics,[26] though unsupervised learning encompasses other domains involving summarizing and explaining data features.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.

Semi-supervised learning

Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov Decision Process (MDP). Many reinforcement learning algorithms use dynamic programming techniques.[27] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

Self-learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). [28] It is a learning with no external rewards and no external teacher advices. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. [29]The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:

It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking behavior, in an environment that contains both desirable and undesirable situations. [30]

Several learning algorithms aim at discovering better representations of the inputs provided during training.[31] Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization[32] and various forms of clustering.[33][34][35]

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors.[36] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.[37]

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately.[38] A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.[39]

In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.[40] Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.[41]

In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.[42]

Three broad categories of anomaly detection techniques exist.[43] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.

Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[44]

Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[45] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliski and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[46] For example, the rule { o n i o n s , p o t a t o e s } { b u r g e r } {displaystyle {mathrm {onions,potatoes} }Rightarrow {mathrm {burger} }} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.

Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[47]

Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[48][49][50] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[51] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.

Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.

Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.

The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[52]

Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.

Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[53] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is oftentimes extended by regularization (mathematics) methods to mitigate overfitting and high bias, as can be seen in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (e.g. used for trendline fitting in Microsoft Excel [54]), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space.

A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[55][56] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[57]

Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model.

Federated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[58]

There are many applications for machine learning, including:

In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1million.[60] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[61] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[62] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[63] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings, and that it may have revealed previously unrecognized influences among artists.[64] In 2019 Springer Nature published the first research book created using machine learning.[65]

Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[66][67][68] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[69]

In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[70] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment.[71][72]

Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[73] Language models learned from data have been shown to contain human-like biases.[74][75] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[76][77] In 2015, Google photos would often tag black people as gorillas,[78] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[79] Similar issues with recognizing non-white people have been found in many other systems.[80] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[81] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[82] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "Theres nothing artificial about AI...Its inspired by people, its created by people, andmost importantlyit impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.[83]

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[84]

In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the False Positive Rate (FPR) as well as the False Negative Rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The Total Operating Characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used Receiver Operating Characteristic (ROC) and ROC's associated Area Under the Curve (AUC).[85]

Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[86] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[87][88] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[89][90]

Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a perpetual ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes in. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[91]

Software suites containing a variety of machine learning algorithms include the following:

Excerpt from:
Machine learning - Wikipedia

SAN FRANCISCO, Jan. 22, 2020 /PRNewswire/ -- Vectorspace AI (VXV) announces datasets that power data engineering, machine learning (ML) and artificial intelligence (AI) systems. Vectorspace AI alternative datasets are designed for predicting unique hidden relationships between objects including current and future price correlations between equities.

Vectorspace AI enables data, ML and Natural Language Processing/Understanding (NLP/NLU) engineers and scientists to save time by testing a hypothesis or running experiments faster to achieve an improvement in bottom line revenue and information discovery. Vectorspace AI datasets underpin most of ML and AI by improving returns from R&D divisions of any company in discovering hidden relationships in drug development.

"We are happy to be working with Vectorspace AI based on their most recent collaboration with us based on the article we published titled 'Generating and visualizing alpha with Vectorspace AI datasets and Canvas'. They represent the tip of the spear when it comes to advances in machine learning and artificial intelligence. Our customers and partners will certainly benefit from our continued joint development efforts in ML and AI," Shaun McGough, Product Engineering, Elastic.

Increasing the speed of discovery in every industry remains the aim of Vectorspace AI, along with a particular goal which relates to engineering machines to trade information with one another, connected to exchanging and transacting data in a way that minimizes a selected loss function. Data vendors such as Neudata.co, asset management companies and hedge funds including WorldQuant, use Vectorspace AI datasets to improve and protect 'alpha'.

Limited releases of Vectorspace AI datasets will be available in partnership with Amazon and Microsoft.

About Vectorspace AI (vectorspace.ai)

Vectorspace AI focuses on context-controlled NLP/NLU (Natural Language Processing/Understanding) and feature engineering for hidden relationship detection in data for the purpose of powering advanced approaches in Artificial Intelligence (AI) and Machine Learning (ML). Our platform powers research groups, data vendors, funds and institutions by generating on-demand NLP/NLU correlation matrix datasets. We are particularly interested in how we can get machines to trade information with one another or exchange and transact data in a way that minimizes a selected loss function. Our objective is to enable any group analyzing data to save time by testing a hypothesis or running experiments with higher throughput. This can increase the speed of innovation, novel scientific breakthroughs and discoveries. For a little more on who we are, see our latest reddit AMA on r/AskScience or join our 24 hour communication channel here. Vectorspace AI offers NLP/NLU services and alternative datasets consisting of correlation matrices, context-controlled sentiment scoring, and other automatically engineered feature attributes. These services are available utilizing the VXV token and VXV wallet-enabled API. Vectorspace AI is a spin-off from Lawrence Berkeley National Laboratory (LBNL) and the U.S. Dept. of Energy (DOE). The team holds patents in the area of hidden relationship discovery.

SOURCE Vectorspace AI

vectorspace.ai

More here:
Vectorspace AI Datasets are Now Available to Power Machine Learning (ML) and Artificial Intelligence (AI) Systems in Collaboration with Elastic -...

If you want to be competitive in an increasingly data-driven world, you need to have at least a baseline understanding of AI and machine learningthe driving forces behind some of todays most important technologies.

The Essential AI & Machine Learning Certification Training Bundle will introduce you to a wide range of popular methods and tools that are used in these lucrative fields, and its available for over 90 percent off at just $39.99.

This 4-course bundle is packed with over 280 lessons that will introduce you to NLP, computer vision, data visualization, and much more.

After an introduction to the basic terminology of the field, youll explore the interconnected worlds of AI and machine learning through instruction that focusses on neural networks, deep architectures, large-scale data analysis, and much more.

The lessons are easy to follow regardless of your previous experience, and there are plenty of real-world examples to keep you on track.

Dont get left behind during the AI and machine learning revolution. The Essential AI & Machine Learning Certification Training Bundle will get you up to speed for just $39.99over 90 percent off for a limited time.

Prices are subject to change.

This is a promotional article about one of Interesting Engineering's partners. By shopping with us, you not only get the materials you need, but youre also supporting our website.

View post:
Uncover the Possibilities of AI and Machine Learning With This Bundle - Interesting Engineering

Add to favorites

The data aspect in particular is something that we often see overlooked

Open source enterprise software firm Red Hat now a subsidiary of IBM have conducted its annual survey of its customers which highlights just how prevalent artificial intelligence and machine learning is becoming, while a talent and skill gap is still slowing down companies ability to enact digital transformation plans.

Here are the top three takeaways from Red Hats customer survey;

When asked to best describe their companies approach to cloud infrastructure 31 percent stated that they run a hybrid cloud, while 21 percent said their firm has a private cloud first strategy in place.

The main reason cited for operating a hybrid cloud strategy was the security and cost benefits it provided. Some responders noted that data integration was easier within a hybrid cloud.

Not everyone is fully sure about their approach yet, as 17 percent admitted they are in the process of establishing a cloud strategy, while 12 percent said they have no plans at all to focus on the cloud.

When it comes to digital transformation there has been a notable rise in the amount of firms that undertaken transformation projects. In 2018; under a third of responders (31 percent) said they were implementing new processes and technology, this year that number has nearly doubled as 58 percent confirm they are introducing new technology.

Red Hat notes that: The drivers for these projects vary. And the drivers also vary by the role of the respondent. System administrators care most about simplicity. IT architects focus on user experience and innovation. For managers, simplicity, user experience, and innovation are all tied for top priority. Developers prioritize innovationwhich, overall, was cited as the most important reason to do digital transformation projects.

However, one in ten surveyed said they are facing a talent and skillset gap that is slowing down the pace at which they can transform their business. The skillset is being made worse by the amount of new technologies that are being brought to market such as artificial intelligence, machine learning and containerisation, the use of which is expected to grow significantly in the next 24 months.

Artificial intelligence, machine learning models and processes is the clear emerging technology for firms in 2019, as 30 percent said that they are planning to implement an AI or ML project within the next 12 months.

However, enterprises are worried about the compatibility and complexity of implementing AI or ML, with 29 percent stating they are worried about evolving software stacks.

One in five (22 percent) responders are worried about getting access to the right data. The data aspect in particular is something that we often see overlooked; obtaining relevant data and cleansing or transforming it in ways that its a useful input for models can be one of the most challenging aspects of an AI project, Red Hat notes.

Red Hats survey was created by compiling 876 qualified responses from Red Hat customers during August and September of 2019.

Read the original post:
Red Hat Survey Shows Hybrid Cloud, AI and Machine Learning are the Focus of Enterprises - Computer Business Review

MOUNTAIN VIEW, Calif. (PRWEB) January 22, 2020

The New York Institute of Finance (NYIF) and Google Cloud announced a new Machine Learning for Trading Specialization available exclusively on the Coursera platform. The Specialization helps learners leverage the latest AI and machine learning techniques for financial trading.

Amid the Fourth Industrial Revolution, nearly 80 percent of financial institutions cite machine learning as a core component of business strategy and 75 percent of financial services firms report investing significantly in machine learning. The Machine Learning for Trading Specialization equips professionals with key technical skills increasingly needed in the financial industry today.

Composed of three courses in financial trading, machine learning, and artificial intelligence, the Specialization features a blend of theoretical and applied learning. Topics include analyzing market data sets, building financial models for quantitative and algorithmic trading, and applying machine learning in quantitative finance.

As we enter an era of unprecedented technological change within our sector, were proud to offer upskilling opportunities for hedge fund traders and managers, risk analysts, and other financial professionals to remain competitive through Coursera, said Michael Lee, Managing Director of Corporate Development at NYIF. The past ten years have demonstrated the staying power of AI tools in the finance world, further proving the importance for both new and seasoned professionals to hone relevant tech skills.

The Specialization is particularly suited for hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in constructing effective trading strategies using machine learning. Prerequisites include basic competency with Python, familiarity with pertinent libraries for machine learning, a background in statistics, and foundational knowledge of financial markets.

Cutting-edge technologies, such as machine and reinforcement learning, have become increasingly commonplace in finance, said Rochana Golani, Director, Google Cloud Learning Services. Were excited for learners on Coursera to explore the potential of machine learning within trading. Looking beyond traditional finance roles, were also excited for the Specialization to support machine learning professionals seeking to apply their craft to quantitative trading strategies.

The Specialization features renowned data-driven finance experts Ram Seshadri (Google) and Jack Farmer (NYIF). Upon successful completion of the Specialization, paying learners will receive a certificate from NYIF and Google Cloud Platform to display on their LinkedIn profile or resume.

Learners everywhere from the heart of Wall Street to a rural town on the other side of the globe now have access to seasoned instructors from leading institutions like NYIF and Google, said Dil Sidhu, Chief Content Officer at Coursera. With their newfound tech skills, finance professionals can continue to succeed in todays increasingly digital and AI-first economy.

Learners worldwide can enroll in the first two courses of the Specialization on Coursera today, with the third course launching next month. For more information, please visit here.

###

About New York Institute of Finance (NYIF)The New York Institute of Finance (http://www.NYIF.com), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes.

About CourseraCoursera was founded by Daphne Koller and Andrew Ng with a vision of providing life-transforming learning experiences to anyone, anywhere. It is now a leading online learning platform for higher education, where more than 47 million learners from around the world come to learn skills of the future. 200 of the worlds top universities and industry educators partner with Coursera to offer courses, Specializations, certificates, and degree programs. 2,100 companies trust the companys enterprise platform Coursera for Business to transform their talent. Coursera for Government equips government employees and citizens with in-demand skills to build a competitive workforce. Coursera is backed by leading investors that include Kleiner Perkins, New Enterprise Associates, GSV Capital, Learn Capital, and SEEK Group.

Share article on social media or email:

Continue reading here:
New York Institute of Finance and Google Cloud Launch A Machine Learning for Trading Specialization on Coursera - PR Web

Software development is a massive part of the tech industry that is absolutely set to stay. Its importance is elemental, supporting technology from the root. Its unsurprisingly a massive industry, with lots of investment and millions of jobs that help to propel technology on its way with great force. Software testing is one of the vital cogs in the software development machine, without which faulty software would run amuck and developing and improving software products would be a much slower and much more inefficient process. Software testing as its own field has gone through several different phases, most recently landing upon the idea of using machine learning. Machine learnings importance is elemental to artificial intelligence, and is a method of freeing up the potential of computers through the use of data feeding. Effective machine learning can greatly improve software testing.

Lets take a look at how that is the case.

As well as realizing the immense power of data over the last decade, we have also reached a point in our technological, even sociological evolution in which we are producing more data than ever, proposes Carl Holding, software developer at Writinity and ResearchPapersUK. This is significant in relation to software testing. The more complex and widely adopted software becomes, the more data that is generated about its use. Under traditional software testing conditions, that amount of data would actually be unhelpful, since it would overwhelm testers. Conversely, machine learning computers hoover up vast data sets as fuel for their analysis and their learning pattern. Not only do the new data conditions only suit large machine learning computers, its also precisely what makes large machine learning computers most successful.

Everyone makes mistakes, as the old saying goes. Except, thats not true: machine learning computers dont. Machine learning goes hand in hand with automation, something which has become very important for all sorts of industries. Not only does it save time, it also gets rid of the potential for human mistakes, which can be very damaging in software testing, notes Tiffany Lee, IT expert at DraftBeyond and LastMinuteWriting. It doesnt matter how proficient a human being is at this task, they will always slip up, especially under the increased pressure put on them with the volume of data that now comes in. A software test sullied by human error can actually be even worse than if no test had been done at all, since getting misinformation is worse than no information. With that in mind, its always just better to leave it to the machines.

Business has always been about getting ahead, regardless of the era or the nature of the products and services. Machine learning is often looked to as a way to predict the future by spotting trends in data and feeding those predictions to the companies that want it most. Software is by no means an industry where this is an exception. In fact, given that it is within the tech sector, its even more important to software development than other industries. Using a machine learning computer for software testing can help to quickly identify the way things are shaping up for the future which means that you get two functions out of your testing process, for the price of one. This can give you an excellent competitive edge.

That machine learning computers save you time should be a fairly obvious point at this stage. Computers handle tasks that take humans hours in a matter of seconds. If you add the increased accuracy advantage over traditional methods then you can see that using this method of testing will get better products out more quickly, which is a surefire way to start boosting your sales figures with ease.

Overall, its a no-brainer. And, as machine learning computers become more affordable, you really have no reason to opt for any other method beyond it. Its a wonderful age for speed and accuracy in technology and with the amount that is at stake with software development, you have to be prepared to think ahead.

The rest is here:
Looking at the most significant benefits of machine learning for software testing - The Burn-In

For open source development at cloud scale with a code-first experience. Basic + UI capabilities + secure and comprehensive machine learning lifecycle management for all skill levels. Automated machine learning Create and run experiments in notebooks Available Available Create and run experiments in studio web experience Not available Available Industry leading forecasting capabilities Not available Available Support for deep learning and other advanced learners Not available Available Large data support (up to 100GB) Not available Available Interpretability in UI Not available Available Machine Learning Pipelines Create, run, and publish pipelines using the Azure ML SDK Available Available Create pipeline endpoints using the Azure ML SDK Available Available Create, edit, and delete scheduled runs of pipelines using the Azure ML SDK Available Available Create and publish custom modules using the Azure ML SDK Available Available View pipeline run details in studio Available Available Create, run, visualize, and publish pipelines in Azure ML designer Not available Available Create pipeline endpoints in Azure ML designer Not available Available Create, edit, and delete scheduled runs of pipelines in Azure ML designer Not available Available Create and publish custom modules in Azure ML designer Not available Available Integrated notebooks Workspace notebook and file sharing Available Available R and Python support Available Available Notebook collaboration Available Available Compute instance Managed compute Instances for integrated Notebooks Available Available Sharing of compute instances Available Available Collaborative debugging of models Available Available Jupyter, JupyterLab, Visual Studio Code Available Available Virtual Network (VNet) support for deployment Available Available SDK Support R and Python SDK support Available Available Security Role Based Access Control (RBAC) support Available Available Virtual Network (VNet) support for training Available Available Virtual Network (VNet) support for inference Available Available Scoring endpoint authentication Available Available Compute Cross workspace capacity sharing and quotas Not available Available Data for machine learning Create, view or edit datasets and datastores from the SDK Available Available Create, view or edit datasets and datastores from the UI Available Available View, edit, or delete dataset drift monitors from the SDK Available Available View, edit, or delete dataset drift monitors from the UI Not available Available MLOps Create ML pipelines in SDK Available Available Batch inferencing Available Available Model profiling Available Available Interpretability in UI Not available Available Labeling Labeling Project Management Portal Available Available Labeler Portal Available Available Labeling using private workforce Available Available

Link:
Pricing - Machine Learning | Microsoft Azure

Human resources has been slower to come to the table with machine learning and artificial intelligence than other fieldsmarketing, communications, even health care. But the value of machine learning in human resourcescan now be measured, thanks to advances in algorithms that can predict employee attrition, for example, or deep learning neural networks thatare edging toward more transparent reasoning in showing why a particular result or conclusion was made.

The value beyond numbers for CEOs and managersis the power inunderstanding whats actually happening within acompany i.e. withtheir people. AsGlintsJustin Black articulated in awebinar for the Human Capital Institute(HCI), executives and leaders need information that helps them point people in the right direction; informationsales data, KPIs, etc.change over time, and machine learning can react faster than people in helping draw out the insights and inferences that might otherwise take reams of manpower or not be uncovered at all.

Though not an exhaustive list, belowis an outline of solid examples of machine learning and artificial intelligence applications at work in human resources today, along with developing and near-future applications.

Applicant Tracking & Assessment

Applicant tracking and assessment has topped the list in early machine learning applications, especially for companies and roles that receive high volumes of applicants.Glintis not an AI company, but they use AI tools to help companies save money and provide a better work experience. Machine learning tools help HR and management personnel hirenew team members bytrackinga candidates journey throughout the interview process and helping speed up the processof getting streamlined feedback to applicants.

Peopliseis another solutionfor helpingcompanies calculate fit score for new talent, combining tools like digital screening and online interview results to help hiring managers arrive at decisions.

While competition for the best people has driven many HR departments to use algorithmic-based assessments, aCEBarticle on using machine learning to eliminate bias cautions that human oversight isstill of paramount importance. Its not enough to act directly on data insights, but to use this information in tandem with driving question such as: 1) how I can link applicant traits to business outcomes; 2) which outcomes should be our focus when hiring; and 3) can predictions (hiring and otherwise) be made in an unbiased way.

Attracting Talent

Attracting talent beforehiring has also seen an upswingin machine-learning based applications in the past few years. Black, who is Glints senior director of Organizational Development, named LinkedIn as an example of a company using one of the most common versions of basic machine learningrecommendingjobs. Other job-finding sites, including Indeed, Glassdoor, and Seek use similar algorithms to build interaction mapsbased on users data from previous searches, connections, posts, and clicks.

PhenomPeople is one example of a suite of machine learning-based toolsthat helps leadpotential talent to a companys career site through multiple social media and job search channels. Black notes that this is really just one step past a keyword search, albeit a big step computationally, as theres a lot more to do.

Attrition Detection

Understanding people and why they decide to stay at or leave a job is arguably one of the most important questions for HR to answer. Identifying attrition risk calls for advancedpattern recognition in surveying an array of variables.

In the earlier mentioned HCI webinar, Black describes a hypotheticalsituation of identifying specific risk factors based on scores to an employee survey. If a human were to try and detect attrition risk among female engineers in Palo Alto with less than 2 years of tenure, the variance analyses to reach that conclusion are innumerable, like finding a needle in haystack, but machine learning allows us to connect these dots in seconds, freeing HR representatives to spend time supporting teams instead of analyzing data.

Glints employee engagement platform

Advances in NLP have included the ability to process large amounts of unstructured data, and algorithms can also do things like identify emotional activity in comments and tease out prescriptive comments, or actionable suggestions. Black describesprototypicality algorithms that can pull out individual comments thatrepresent the sum of what everyones saying, allowing companies to get a broadly inclusive but digestible pulse on company processes and specific issues.

JPMorgan is apparently one of several financial institutions that hasalso put into place algorithms that can survey employee behavior and identify rogue employees before any criminal activity takes place, an obviously more insidious form of attrition with dire consequenceswatch the interview with Bloomberg Reporter Hugh Son as hediscussesthese new safeguards with Bloomberg Technology.

Individual Skills Management/Performance Development

Machine learning is showing its potential inboosting individual skill management and development. While there is definitely room for growth in this arena, platforms that cangive calibrated guidance without human coaches save time and provide the opportunity for more people togrow in their careers and stay engaged.Workday is just one example of a company building personalized training recommendations for employees based on a companys needs, market trends, and employee specifics.

Black elaborates that these types of performance development assessments are useful when actually read, which is why this type of machine-based feedback has been successful for individuals. But this becomes more difficult at the level of the organization, where its almost impossible to make sense of enormous amounts of varying data; this is an area wheremachine learning is evolving, with an increased focus on the overall performance of the corporate lattice.

Enterprise Management

As alluded to in the last example, enterprise management and engagement based on machine learning insights is already here in early forms but has yet to be taken to scale. KPMG promotes its customized Intelligent Enterprise Approach, leveraging predictive analytics and big data management to help companies make business decisions that optimize key KPIs and other metrics.re:Work, which provides best workplace practices and ideas from Google and other leading organizations (including KPMG), is an excellent resource for staying up-to-date on new tools and case studies in this space.

Googles People Analytics department has been a pioneer in building performance-management engines at the enterprise level. From an early stage, the team (led by Prasad Setty) posed existing questionsfor example, whats the ideal size for a given team or departmentbut focused on finding new ways to use data in order to help answer these questions. In turn, People Analytics has helped pave the way for solving fundamental business problems related to the employee life cycle, with afocus on improving Googlers'production and overall wellness. Asoutline by Chris Derose for The Atlantic, over the last half of a decade, the team has produced insights that have led to improvements in company-wide actions, such as:

Post-Hire Outcome Algorithms

CEB notes that theideal hiring algorithm would predicta post-hire outcome (for example, reducing time taking customer service calls while keeping customer satisfaction high) rather than just matching job requirements with items on an employees resume or pre-hire assessment results.

The article goes on to note that its sometimes the counterintuitive aspectsthat predict job performance, informationthat a machine is better at findingthrough analysis than human inference. For example, CEB describes a model created for a call center representative role that linked call center experience to resultingpoor performance. While a link to the source or actual model would be helpful, the idea is interesting and reflects machine learnings strengths in invisiblepattern recognition

Internal Management

WhenTalent AnalyticsChief Scientist Pasha Roberts discussed the role of predictive analytics in human resource management with Emerj in 2016, he brought up the internal movement of employees within a company as an issue unique to HR and analytics. You can use agent-based modeling to simulate and look at how people can move within a companyand be better able to hire a person at the entry-level that will be likely to move through corporate ladder, said Roberts. While there are early systems in place, more data over time should lead to a more robust and scalable model for internal management over the nextfive years.

Increased Behavior Tracking and Data-Based Decision Making

Ben Waber, president and CEO ofHumanyzeand also a past guest on Emerj, talked about the increasing use of IoT wearable data in the workplace. These types of gadgets are more common at the enterprise levelbluetooth headphones and smart ID badges, for exampleand companies are continuing toadd sensor technology to the workplace in order to collect data. This is an area that Waber researched while serving as a visiting scientist at the MIT Media Lab, using data collected from smart badges to look at things like employee dialogue, interaction, networks within a company, where people spent their time, etc. It would seem that privacy might be a concern, but technologies like smart badges are starting to proliferate quickly (with vendors like Atmel, in the below video, introducing new and updated apps for Android phones). This type of data, says Waber, allows us to pose and answer crucial business-driving questions that we couldnt ask before, such as how much does my sales team talk to my engineering team?

Things to Keep in Mind:Machine Learning in Human Resources

Google People Analytics Lead, Ian OKeefe, told a story at the People Analytics & Future of Work conference inJanuary 2016 about his teams efforts to quantify things like efficiency, effectiveness and employee experience by looking at hiring decisions, teamclimate, and personal development. In the end, his team found that people armed with better data make better decisions than algorithms alone can do.

Well-designed AI applications, says Black, have three main cross functions: main expertise, data science expertise, and design/user experience expertise. At present, very few providers do all three of these well. The best solutions today and in the near futuredont replace humans, but emphasize scaling better decision making with the use of machines as a tool and collaborator.

Our survey of machine learning in human resources illuminates the development of a more people-centric approach, paving the way for more more valuable programs and less wasted time; reduced bias in programs; less administration and more individual development; and the ability to act proactively rather than reactively, moving seamlessly fromthe level of the individual to the organization and back again.

Image credit: Corporate IT

Read the original here:
Machine Learning in Human Resources Applications and ...

As we reached the digital era, where computers became an integral part of the everyday lifestyle, people cannot help but be amazed at how far we have come since the time immemorial. The creation of the computers, as well as the internet, had led us into a more complex thinking, making information available to us with just a click. You just type in the words and information will be readily available for you.

However, as we approached this era, a lot of inventions and terms became confusing. Have you heard about Artificial intelligence? How about Deep Learning? Moreover, Machine Learning? These three words are familiar to us and can be used interchangeably, however, the exact meaning of this becomes uncertain. The more people used it, the more confusing it gets.

Also Read:Top 5 Data Science and Machine Learning Courses

Deep Learning and Machine Learning are words that followed after Artificial Intelligence was created. It is like breaking down the function of AI and naming them Deep Learning and Machine Learning. But before this gets more confusing, let us differentiate the three starting off with Artificial Intelligence.

AI is the like creating intelligence artificially. Artificial Intelligence is the broad umbrella term for attempting to make computers think the way humans think, be able to simulate the kinds of things that humans do and ultimately to solve problems in a better and faster way than we do. The AI itself is a rather generic term for solving tasks that are easy for humans, but hard for computers. It includes all kinds of tasks, such as doing creative work, planning, moving around, speaking, recognizing objects and sounds, performing social or business transactions and a lot more.

Digital era, brought an explosion of data in all forms and from every region of the world. This data, known simply as Big Data, is drawn from sources like social media, internet search engines, e-commerce platforms, online cinemas, etc. This enormous amount of data is readily accessible and can be shared through various applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to Artificial Intelligence (AI) systems for automated support.

More and more plans to try different approaches to use AI leads to the most promising and relevant area which is the Machine Learning. The most common way to process the Big Data is called Machine Learning. It is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with newly added data.

For example, if a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.

Deep learning, on the other hand, is a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning non-linear technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature that is likely to make up a fraudulent activity.

Thus, these three are like a triangle where the AI to be the top that leads to the creation of Machine Learning with a subset of Deep Learning. These three had made our life easier as time goes by and helped make a faster and better way of gathering information that cannot be done by humans because of the enormous amount of information available.

Humans will take forever just to get a single information while these AI will only take minutes. As we become more and more comfortable using technology, the better humans can develop them into a better version of itself. You should also check our latest article:5 Best Programming Languages for Artificial Intelligence Systems

See original here:
Difference between AI, Machine Learning and Deep Learning

SAN FRANCISCO, Jan. 13, 2020 /PRNewswire/ -- The global machine learning marketsize is expected to reach USD 96.7 billion by 2025, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 43.8% from 2019 to 2025. Production of massive amounts of data has increased the adoption of technologies that can provide a smart analysis of that data.

Key suggestions from the report:

Read 100 page research report with ToC on "Machine Learning Market Size, Share & Trends Analysis Report By Component, By Enterprise Size, By End Use (Healthcare, BFSI, Law, Retail, Advertising & Media), And Segment Forecasts, 2019 - 2025" at: https://www.grandviewresearch.com/industry-analysis/machine-learning-market

Technologies such as Machine Learning (ML) are being rapidly adopted across various applications in order to automatically detect meaningful patterns within a data set. Software based on ML algorithms, such as search engines, anti-spam software, and fraud detection software, are being increasingly used, thereby contributing to market growth.

The rapid emergence of ML technology has increased its adoption across various application areas. It provides cloud computing optimization along with intelligent voice assistance. In healthcare, it is used for the diagnosis of individuals. In case of businesses, the use of ML models that are open source and have a standards-based structure has increased in recent years. These models can be easily deployed in various business programs and can help companies bridge the skills gap between IT programmers and information scientists.

Developments such as fine-tuned personalization, hyper-targeting, searching engine optimization, no-code environment, self-learning bots, and others are projected to change the machine learning landscape. The development of capsule network has replaced neural networks in order to provide more accuracy in pattern detection, with fewer errors. These advanced developments are anticipated to proliferate market growth in the foreseeable future.

Grand View Research has segmented the global machine learning market based on component, enterprise size, end use, and region:

Find more research reports on Next Generation Technologies Industry, by Grand View Research:

Gain access to Grand View Compass, our BI enabled intuitive market research database of 10,000+ reports

About Grand View Research

Grand View Research, U.S.-based market research and consulting company, provides syndicated as well as customized research reports and consulting services. Registered in California and headquartered in San Francisco, the company comprises over 425 analysts and consultants, adding more than 1200 market research reports to its vast database each year. These reports offer in-depth analysis on 46 industries across 25 major countries worldwide. With the help of an interactive market intelligence platform, Grand View Research helps Fortune 500 companies and renowned academic institutes understand the global and regional business environment and gauge the opportunities that lie ahead.

Contact:

Sherry JamesCorporate Sales Specialist, USA Grand View Research, Inc. Phone: +1-415-349-0058 Toll Free: 1-888-202-9519 Email: sales@grandviewresearch.comWeb: https://www.grandviewresearch.comFollow Us: LinkedIn| Twitter

SOURCE Grand View Research, Inc.

Read this article:
Machine Learning Market Size Worth $96.7 Billion by 2025 ...

Faced with mounting compliance costs and regulatory pressures, financial institutions are rapidly adopting Artificial Intelligence (AI) solutions, including machine learning and robotic process automation (RPA) to combat sophisticated and evolving financial crimes.

Over one third of financial institutions have deployed machine learning solutions, recognizing that AI has the potential to improve the financial services industry by aiding with fraud identification, AML transaction monitoring, sanctions screening and know your customer (KYC) checks (Financier Worldwide Magazine).

When deployed in financial crime management solutions, analytical agents that leverage machine learning can help to reduce false positives, without compromising regulatory or compliance needs.

It is well known that conventional, rules-based fraud detection and AML programs generate large volumes of false positive alerts. In 2018, Forbes reported With false positive rates sometimes exceeding 90%, something is awry with most banks legacy compliance processes to fight financial crimes such as money laundering.

Such high false positive rates force investigators to waste valuable time and resources working through large alert queues, performing needless investigations, and reconciling disparate data sources to piece together evidence.

The highly regulated environment makes AML a complex, persistent and expensive challenge for FIs but increasingly, AI can help FIs control not only the complexity of their AML provisions, but also the cost (Financier Worldwide Magazine).

In an effort to reduce the costs of fraud prevention and BSA/AML compliance efforts, financial institutions should consider AI solutions, including machine learning analytical agents, for their financial crime management programs.

Machine learning agents use mathematical and statistical models to learn from data without being explicitly programmed. Financial institutions can deploy dynamic machine learning solutions to:

To effectively identify patterns, machine learning agents must process and train with a large amount of quality data. Institutions should augment data from core banking systems with:

When fighting financial crime, a single financial institution may not have enough data to effectively train high-performance analytical agents. By gathering large volumes of properly labeled data in a cloud-based environment, machine learning agents can continuously improve and evolve to accurately detect fraud and money laundering activities, and significantly improve compliance efforts for institutions.

Importing and analyzing over a billion transactions every week in our Cloud environment, Verafins big data intelligence approach allows us to build, train, and refine a proven library of machine learning agents. Leveraging this immense data set, Verafins analytical agents outperform conventional detection analytics, reducing false positives and allowing investigators to focus their efforts on truly suspicious activity. For example:

With proven behavior-based fraud detection capabilities, Verafins Deposit Fraud analytics consistently deliver 1-in-7 true positive alerts.

By deploying machine learning, Verafin was able to further improve upon these high-performing analytics resulting in an additional 66% reduction in false positives. Training our machine learning agents on check returns mapped as true fraud in the Cloud, the Deposit Fraud detection rate improved to 1-in-3 true positive alerts, while maintaining true fraud detection.

These results clearly outline the benefits of applying machine learning analytics to a large data set in a Cloud environment. In todays complex and costly financial crime landscape, financial institutions should deploy financial crime management solutions with machine learning to significantly reduce false positives, while maintaining regulatory compliance.

In an upcoming article, we will explore how and when robotic process automation can benefit financial crime management solutions.

Continued here:
Machine Learning: Higher Performance Analytics for Lower ...

What Is Machine Learning?

Machine learning is theconcept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computers built-in algorithms current regardless of changes in the worldwide economy.

Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning.

The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the model shouldnt change.

Machine learning is used in different sectors for various reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be tuned to provide accurate and personalized recommendations to their users based on the users internet search history or previous transactions. Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model. Information hubs can use machine learning to cover huge amounts of news stories from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital-savvy era is endless as businesses and governments become more aware of the opportunities that big data presents.

How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. However, some pertinent information may not be widely publicized by the media and may be privy to only a select few who have the advantage of being employees of the company or residents of the country where the information stems from. In addition, theres only so much information humans can collect and process within a given time frame. This is where machine learning comes in.

An asset management firm may employ machine learning in its investment analysis and research area. Say the asset manager only invests in mining stocks. The model built into the system scans the web and collects all types of news events from businesses, industries, cities, and countries, and this information gathered makes up the data set. The asset managers and researchers of the firm would not have been able to get the information in the data set using their human powers and intellects. The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set. Saya mining company XYZ just discovered a diamond mine in a small town in South Africa, the machine learning app would highlight this as relevant data. The model could then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period, or which mining stocks are likely to increase in value at a certain time. This information is relayed to the asset manager to analyze and make a decision for his portfolio. The asset manager may make a decision to invest millions of dollars into XYZ stock.

In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit his losses by exiting the stock.

Read more here:
Machine Learning Definition

What IT team wouldnt like to have a crystal ball that could predict the IT future, letting them fix application and infrastructure performance problems before they arise? Well, the current shortage ofcrystal balls makes the union of artificial intelligence (AI), machine learning (ML), and utilisation forecasting the next best thing for anticipating and avoiding issues that threaten the overall health and performance of all IT infrastructure components. The significance of AI has not been lost to organisations in the United Kingdom, with 43 per cent of them believing that AI will play a big role in their operations.

Utilisation forecasting is a technique that applies machine learning algorithms to produce daily usage forecasts for all utilisation volumes across CPUs, physical and virtual servers, disks, storage, bandwidth, and other network elements, enabling networking teams to manage resources proactively. This technique helps IT engineers and network admins prevent downtime caused by over-utilisation.

The AI/ML driving forecasting solution produces intelligent and reliable reports by taking advantage of the current availability of ample historic records and high-performance computing algorithms. Without AI/ML, utilisation forecasting relies on reactive monitoring. You set predefined thresholds for given metrics such as uptime, resource utilisation, network bandwidth, and hardware metrics like fan speed and device temperature. When a threshold is exceeded, an alert is issued. However, that reactive approach will not detect the anomalies that happen below that threshold and create other, indirect issues. Moreover, it will not tell you when you will need to upgrade your infrastructure based on current trends.

To forecast utilisation proactively, you need accurate algorithms that can analyze usage patterns and to detect anomalieswithout false positivesin daily usage trends. Thats how you predict usage in the future. Let us take a look at a simple use case.

SEE ALSO:

With proactive, AI/ML-driven utilisation forecasting, you can find a minor increase in your officebandwidth usage during the World Series, the FIFA World Cup, and other sporting events. Thatanomalous usage can be detected even if you have a huge amount of unused internet bandwidth. Similarly, proactive utilisation forecasting lets you know when to upgrade your infrastructure based on new recruitment and attrition rates.

A closer look at the predictive technologies reveals the fundamental difference between proactive and reactive forecasting. Without AI and ML, utilisation forecasting uses linear regression models to extrapolate and provide prediction based on existing data. This method involves no consideration of newly allocated memory or anomalies in utilisation patterns. Also, pattern recognition is a foreign concept. Although useful, linear regression models do not give IT admins complete visibility.

AI/ML-driven utilisation forecasting, on the other hand, uses the Seasonal and Trend decomposition using Loess (STL) method. STL lets you study the propagation and degradation of memory as well as analyze pattern matching whereby periodic changes in the metric configuration will be automatically adjusted. Bottom line, STL dramatically improves accuracy thanks to those dynamic, automated adjustments. And if any new memory is allocated, or if memory size is increased or decreased for the device, the prediction will change accordingly. This option was not possible with linear regression.

Beyond forecasting, ML can be used to improve anomaly detection. Here, adaptive thresholds for different metrics are established using ML and analysis of historic data will reveal any anomalies and trigger appropriate alerts. Other application and infrastructure monitoring functions will also be improved when enhanced with AI and ML technologies. Sometime in the not-too-distant future, AI/ML-driven forecasting and monitoring will rival the predictive powers of the fabled crystal ball.

by Rebecca D'Souza, Product Consultant, ManageEngine

The rest is here:
Optimising Utilisation Forecasting with AI and Machine Learning - Gigabit Magazine - Technology News, Magazine and Website

Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission.There are some 288 lessons included in this online training course.

Image: pexels

By StackCommerceMashable Shopping2020-01-16 19:44:17 UTC

TL;DR: Jump into the world of AI with the Essential AI and Machine Learning Certification Training Bundle for $39.99, a 93% savings.

From facial recognition to self-driving vehicles, machine learning is taking over modern life as we know it. It may not be the flying cars and world-dominating robots we envisioned 2020 would hold, but it's still pretty futuristic and frightening. The good news is if you're one of the pros making these smart systems and machines, you're in good shape. And you can get your foot in the door by learning the basics with this Essential AI and Machine Learning Certification Training Bundle.

This training bundle provides four comprehensive courses introducing you to the world of artificial intelligence and machine learning. And right now, you can get the entire thing for just $39.99.

These courses cover natural language processing, computer vision, data visualization, and artificial intelligence basics, and will ultimately teach you to build machines that learn as they're fed human input. Through hands-on case studies, practice modules, and real-time projects, you'll delve into the world of intelligent systems and machines and get ahead of the robot revolution.

Here's what you can expect from each course:

Access 72 lectures and six hours of content exploring topics like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep architectures using TensorFlow. Ultimately, you'll build a foundation in both artificial intelligence, which is the concept in which machines develop the ability to simulate natural intelligence to carry out tasks, and machine learning, which is an application of AI aiming to learn from data and build on it to maximize performance.

Through seven hours of content, you'll learn how to arrange critical data in a visual format think graphs, charts, and pictograms. You'll also learn to deploy data visualization through Python using Matplotlib, a library that helps in viewing the data. Finally, you'll tackle actual geographical plotting using the Matplotlib extension called Basemap.

In just 5.5 hours, this course gives you a more in-depth look at the role of CNNs, the knowledge of transfer learning, object localization, object detection, and using TensorFlow. You'll also learn the challenges of working with real-world data and how to tackle them head-on.

Natural language processing (NLP) is a field of AI which allows machines to interpret and comprehend human language. Through 5.5 hours of content, you'll understand the processes involved in this field and learn how to build artificial intelligence for automation. The course itself provides an innovative methodology and sample exercises to help you dive deep into NLP.

Originally $656, you can slash 93% off and get a year's worth of access to the Essential AI and Machine Learning Bundle for just $39.99 right now.

Prices subject to change.

Read the original:
Don't want a robot stealing your job? Take a course on AI and machine learning. - Mashable

Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a data scientist in a box.

Ill start by saying that machine learning and artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways.

The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a nave Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didnt have real data and only had a conceptual problem to be solved.

The second go was about six months later when another colleague in the same team came up with a way to classify data sets and in fact developed a flexible training engine and data tagging approach to determining whether certain columns in data sets were likely to be names, addresses, phone numbers and email addresses. On face value you would think this to be something simple but in reality, it is of course only as good as the training data and in this instance we could easily confuse the system and the data tagging with things like social security numbers that looked like phone numbers, postcodes that were simply numbers and ultimately could be anything and so on. Names were only as good as the locality from which the names training data was sourced and cities, towns. Streets and provinces all proved to most work ok but almost always needed region-specific training data. At any rate, this method of classifying contact data for the most part met the rough objectives of the task at hand and so we soldiered on.

A few months later I was called over to a developers desk and asked for my opinion on a side project that one of the senior developers and architects had been working on. The objective was ambitious but impressive. The solution had been built in response to three problems in the field. The first problem to be solved was decoding why certain records were deemed to be related to one another when with the naked eye they seemed to not be, or vice versa. While this piece didnt involve any ML per se, the second part of the solution did, in that it self-configured thousands of combinations of alternative fuzzy matching criteria to determine an optimal set of duplicate record matching rules.

This was understandably more impressive and practically understandable almost self-explanatory. This would serve as a great utility for a consultant, a data analyst or a relative layperson to find explainability in how potential duplicate records were determined to have a relationship. This was specifically important because it immediately could provide value to field services personnel and clients. In addition, the developer had cunningly introduced a manual matching option that allowed a user to evaluate two records and make a decision through visual assessment as to whether two records could potentially be considered related to one another.

In some respects what was produced was exactly the way that I like to see products produced. The field describes the problem; the product management organization translates that into more elaborate stories and looks for parallels in other markets, across other business areas and for ubiquity. Once those initial requirements have been gathered it is then to engineering and development to come up with a prototype that works toward solving the issue.

The more experienced the developer of course the more comprehensive the result may be and even the more mature the initial iteration may be. Product is then in a position to pitch the concept back at the field, to clients and a selective audience to get their perspective on the solution and how well it matches the for solving the previously articulated problem.

The challenge comes when you have a less tightly honed intent, a less specific message and a more general problem to solve and this comes now to the latest aspect of machine learning and artificial intelligence that I picked up.

One of the elements with dealing with data validation and data preparation is the last mile of action that you have in mind for that data. If your intent is as simple as one of, lets evaluate our data sources, clean them up and makes them suitable for online transaction processing then thats a very specific mission. You need to know what you want to evaluate, what benchmark you wish to evaluate them against and then have some sort of remediation plan for them so that they support the use case for which theyre intended say, supporting customer calls into a call centre. The only areas where you might consider artificial intelligence and machine learning for applicability in this instance might be for determining matches against the baseline but then the question is whether you simply have a Boolean decision or whether in fact, some sort of stack ranking is relevant at all. It could be argued either way, depending on the application.

When youre preparing data for something like a decision beyond data quality though, the mission is perhaps a little different. Effectively your goal may be to cut the cream of opportunities off the top of a pile of contacts, leads, opportunities or accounts. As such, you want to use some combination of traits within the data set to determine influencing factors that would determine a better (or worse) outcome. Here, linear regression analysis for scoring may be sufficient. The devil, of course, lies in the details and unless youre intimately familiar with the data and the proposition that youre trying to resolve for you have to do a lot of trial and error experimentation and validation. For statisticians and data scientists this is all very obvious and you could say, is a natural part of the work that they do. Effectively the challenge here is feature selection. A way of reducing complexity in the model that you will ultimately apply to the scoring.

The journey I am on right now with a technology partner, focuses on ways to actually optimise the features in a way that only the most necessary and optimised features will need to be considered. This, in turn, makes the model potentially simpler and faster to execute, particularly at scale. So while the regression analysis still needs to be done, determining what matters, what has significance and what should be retained vs discarded in terms of the model design, is being all factored into the model building in an automated way. This doesnt necessarily apply to all kinds of AI and ML work but for this specific objective it is perhaps more than adequate and one that doesnt require a data scientist to start delivering a rapid yield.

More here:
Adventures With Artificial Intelligence and Machine Learning - Toolbox

When deciding on a machine learning conference, why go to Machine Learning Week 2020? This five-conference event May 31 June 4, 2020 at Caesars Palace, Las Vegas delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques. In this video, Predictive Analytics World Founder Eric Siegel spills on the details and lists five reasons this is the most valuable machine learning event to attend this year.

Note: This article is based on the transcript of a special episode of The Dr. Data Show click here to view.

In this article, I give five reasons that Machine Learning Week May 31 June 4, 2020 at Caesars Palace, Las Vegas is the most valuable machine learning event to attend this year. MLW is the largest annual five-conference blow-out part of the Predictive Analytics World conference series, of which I am the founder.

First, some background info. Your business needs machine learning to thrive and even just survive. You need it to compete, grow, improve, and optimize. Your team needs it, your boss demands it, and your career loves machine learning.

And so we bring you Predictive Analytics World, the leading cross-vendor conference series covering the commercial deployment of machine learning. By design, PAW is where to meet the whos who and keep up on the latest techniques.

This June in Vegas, Machine Learning Week brings together five different industry-focused events: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, and Deep Learning World. This is five simultaneous two-day conferences all happening alongside one another at Caesars Palace in Vegas. Plus, a diverse range of full-day training workshops, which take place in the days just before and after.

Machine Learning Week delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learning deployment, and the hottest topics and techniques.

This mega event covers all the bases for both senior-level expert practitioners as well as newcomers, project leaders, and executives. Depending on the topic, sessions and workshops are either demarcated as the Expert/practitioner level, or for All audiences. So, you can bring your team, your supervisor, and even the line-of-business managers you work with on model deployment. About 60-70% of attendees are on the hands-on practitioner side, but, as you know, successful machine learning deployment requires deep collaboration between both sides of the equation.

PAW and Deep Learning World also takes place in Germany, and Data Driven Government takes place in Washington DC but this article is about Machine Learning Week, so see predictiveanalyticsworld.com for details about the others.

Here are the five reasons to go.

Five Reasons to Go to Machine Learning Week June 2020 in Vegas

1) Brand-name case studies

Number one, youll access brand-name case studies. At PAW, youll hear directly from the horses mouth precisely how Fortune 500 analytics competitors and other companies of interest deploy machine learning and the kind of business results they achieve. More than most events, we pack the agenda as densely as possible with named case studies. Each day features a ton of leading in-house expert practitioners who get things done in the trenches at these enterprises and come to PAW to spill on the inside scoop. In addition, a smaller portion of the program features rock star consultants, who often present on work theyve done for one of their notable clients.

2) Cross-industry coverage

Number two, youll benefit from cross-industry coverage. As I mentioned, Machine Learning Week features these five industry-focused events. This amounts to a total of eight parallel tracks of sessions.

Bringing these all together at once fosters unique cross-industry sharing, and achieves a certain critical mass in expertise about methods that apply across industries. If your work spans industries, Machine Learning Week is one-stop shopping. Not to mention that convening the key industry figures across sectors greatly expands the networking potential.

The first of these, PAW Business, itself covers a great expanse of business application areas across many industries. Marketing and sales applications, of course. And many other applications in retail, telecommunications, e-commerce, non-profits, etc., etc.

The track topics of PAW Business 2020

PAW Business is a three-track event with track topics that include: analytics operationalization & management i.e., the business side core machine learning methods and advanced algorithms i.e., the technical side innovative business applications covered as case studies, and a lot more.

PAW Financial covers machine learning applications in banking including credit scoring insurance applications, fraud detection, algorithmic trading, innovative approaches to risk management, and more.

PAW Industry 4.0 and PAW Healthcare are also entire universes unto themselves. You can check out the details about all four of these PAWs at predictiveanalyticsworld.com.

And the newer sister event Deep Learning World has its own website, deeplearningworld.com. Deep learning is the hottest advanced form of machine learning with astonishing, proven value for large-signal input problems, such as image classification for self-driving cars, medical image processing, and speech recognition. These are fairly distinct domains, so Deep Learning World does well to complement the four Predictive Analytics World events.

3) Pure-play machine learning content

Number three, youll get pure-play machine learning content. PAWs agenda is not watered down with much coverage of other kinds of big data work. Instead, its ruthlessly focused specifically on the commercial application of machine learning also known as predictive analytics. The conference doesnt cover data science as a whole, which is a much broader and less well-defined area, that, for example, can include standard business intelligence reporting and such. And we dont cover AI per se. Artificial intelligence is at best a synonym for machine learning that tends to over-hype, or at worst an outright lie that promises mythological capabilities.

4) Hot new machine learning practices

Number four, youll learn the latest and greatest, the hottest new machine learning practices. Now, we launched PAW over a decade ago, so far delivering value to over 14,000 attendees across more than 60 events. To this day, PAW remains the leading commercial event because we keep up with the most valuable trends.

For example, Deep Learning World, which launched more recently in 2018 covers deep learnings commercial deployment across industry sectors. This relatively new form of neural networks has blossomed, both in buzz and in actual value. As I mentioned, it scales machine learning to process, for example, complex image data.

And what had been PAW Manufacturing for some years has now changed its name to PAW Industry 4.0. As such, the event now covers a broader area of inter-related work applying machine learning for smart manufacturing, the Internet of Things (IoT), predictive maintenance, logistics, fault prediction, and more.

In general, machine learning continues to widen its adoption and apply in new, innovative ways across sectors in marketing, financial risk, fraud detection, workforce optimization, and healthcare. PAW keeps up with these trends and covers todays best practices and the latest advanced modeling methods.

5) Vendor-neutral content

And finally, number five, youll access vendor-neutral content. PAW isnt run by an analytics vendor and the speakers arent trying to sell you on anything but good ideas. PAW speakers understand that vendor-neutral means those in attendance must be able to implement the practices covered and benefit from the insights delivered without buying any particular analytics product.

During the event, some vendors are permitted to deliver short presentations during a limited minority of demarcated sponsored sessions. These sessions often are also substantive and of great interest. In fact, you can access all the sponsors and tap into their expertise at will in the exhibit hall, where theyre set up for just that purpose.

By the way, if youre an analytics vendor yourself, check out PAWs various sponsorship opportunities. Our events bring together a great crowd of practitioners and decision makers.

Summary Five Reasons to Go

1) Brand-name case studies

2) Cross-industry coverage

3) Pure-play machine learning content

4) Hot new machine learning practices

5) Vendor-neutral content

and those are the reasons to come to Machine Learning Week: brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques.

Machine Learning Week not only delivers unique knowledge-gaining opportunities, its also a universal meeting place the industrys premier networking event. It brings together the whos who of machine learning and predictive analytics, the greatest diversity of expert speakers, perspectives, experience, viewpoints, and case studies.

This all turns the normal conference stuff into a much richer experience, including the keynotes, expert panels, and workshop days, as well as opportunities to network and talk shop during the lunches, coffee breaks, and reception.

I encourage you to check out the detailed agenda see all the speakers, case studies, and advanced methods covered. Each of the five conferences has its own agenda webpage, or you can also view the entire five-conference, eight-track mega-agenda at once. This view pertains if youre considering registering for the full Machine Learning Week pass, or if youll be attending along with other team members in order to divide and conquer.

Visit our website to see all these details, register, and sign up for informative event updates by email.

Or to learn more about the field in general, check out our Predictive Analytics Guide, our publication The Machine Learning Times, which includes revealing PAW speaker interviews, and, episodes of this show, The Dr. Data Show which, by the way, is generally about the field of machine learning in general, rather than about our PAW events.

This article is based on a transcript from The Dr. Data Show.

CLICK HERE TO VIEW THE FULL EPISODE

About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics. Click here to view more episodes and to sign up for future episodes of The Dr. Data Show.

About the Author

Eric Siegel, Ph.D., founder of the Predictive Analytics Worldand Deep Learning World conference series and executive editor ofThe Machine Learning Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning bookPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, the host of The Dr. Data Show web series, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. Follow him at @predictanalytic.

Read more:
Five Reasons to Go to Machine Learning Week 2020 - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

If you want to know the future of networking, follow the money right to the edge.

Applications are expected to move from data centers to edge facilities in record numbers, opening up a huge new market opportunity. The edge computing market is expected to grow at a compound annual growth rate of 36.3 percent between now and 2022, fueled by rapid adoption of the internet of things, autonomous vehicles, high-speed trading, content streaming and multiplayer games.

What these applications have in common is a need for near zero-latency data transfer, usually defined as less than five milliseconds, although even that figure is far too high for many emerging technologies.

The specific factors driving the need for low latency vary. In IoT applications, sensors and other devices capture enormous quantities of data, the value of which degrades by the millisecond. Autonomous vehicles require information in real-time to navigate effectively and avoid collisions. The best way to support such latency-sensitive applications is to move applications and data as close as possible to the data ingestion point, therefore reducing the overall round-trip time. Financial transactions now occur at sub-millisecond cycle times, leading one brokerage firm to invest more than $100 million to overhaul its stock trading platform in a quest for faster and faster trades.

As edge computing grows, so do the operational challenges for telecommunications service provider such as Verizon Communications Inc., AT&T Corp. and T-Mobile USA Inc. For one thing, moving to the edge essentially disaggregates the traditional data center. Instead of massive numbers of servers located in a few centralized data centers, the provider edge infrastructure consists of thousands of small sites, most with just a handful of servers. All of those sites require support to ensure peak performance, which strains the resources of the typical information technology group to the breaking point and sometimes beyond.

Another complicating factor is network functions moving toward cloud-native applications deployed on virtualized, shared and elastic infrastructure, a trend that has been accelerating in recent years. In a virtualized environment, each physical server hosts dozens of virtual machines and/or containers that are constantly being created and destroyed at rates far faster than humans can effectively manage. Orchestration tools automatically manage the dynamic virtual environment in normal operation, but when it comes to troubleshooting, humans are still in the drivers seat.

And its a hot seat to be in. Poor performance and service disruptions hurt the service providers business, so the organization puts enormous pressure on the IT staff to resolve problems quickly and effectively. The information needed to identify root causes is usually there. In fact, navigating the sheer volume of telemetry data from hardware and software components is one of the challenges facing network operators today.

A data-rich, highly dynamic, dispersed infrastructure is the perfect environment for artificial intelligence, specifically machine learning. The great strength of machine learning is the ability to find meaningful patterns in massive amounts of data that far outstrip the capabilities of network operators. Machine learning-based tools can self-learn from experience, adapt to new information and perform humanlike analyses with superhuman speed and accuracy.

To realize the full power of machine learning, insights must be translated into action a significant challenge in the dynamic, disaggregated world of edge computing. Thats where automation comes in.

Using the information gained by machine learning and real-time monitoring, automated tools can provision, instantiate and configure physical and virtual network functions far faster and more accurately than a human operator. The combination of machine learning and automation saves considerable staff time, which can be redirected to more strategic initiatives that create additional operational efficiencies and speed release cycles, ultimately driving additional revenue.

Until recently, the software development process for a typical telco consisted of a lengthy sequence of discrete stages that moved from department to department and took months or even years to complete. Cloud-native development has largely made obsolete this so-called waterfall methodology in favor of a high-velocity, integrated approach based on leading-edge technologies such as microservices, containers, agile development, continuous integration/continuous deployment and DevOps. As a result, telecom providers roll out services at unheard-of velocities, often multiple releases per week.

The move to the edge poses challenges for scaling cloud-native applications. When the environment consists of a few centralized data centers, human operators can manually determine the optimum configuration needed to ensure the proper performance for the virtual network functions or VNFs that make up the application.

However, as the environment disaggregates into thousands of small sites, each with slightly different operational characteristics, machine learning is required. Unsupervised learning algorithms can run all the individual components through a pre-production cycle to evaluate how they will behave in a production site. Operations staff can use this approach to develop a high level of confidence that the VNF being tested is going to come up in the desired operational state at the edge.

AI and automation can also add significant value in troubleshooting within cloud-native environments. Take the case of a service provider running 10 instances of a voice call processing application as a cloud-native application at an edge location. A remote operator notices that one VNF is performing significantly below the other nine.

The first question is, Do we really have a problem? Some variation in performance between application instances is not unusual, so answering the question requires a determination of the normal range of VNF performance values in actual operation. A human operator could take readings of a large number of instances of the VNF over a specified time period and then calculate the acceptable key performance indicator values a time-consuming and error-prone process that must repeated frequently to account for software upgrades, component replacements, traffic pattern variations and other parameters that affect performance.

In contrast, AI can determine KPIs in a fraction of the time and adjust the KPI values as needed when parameters change, all with no outside intervention. Once AI determines the KPI values, automation takes over. An automated tool can continuously monitor performance, compare the actual value to the AI-determined KPI and identify underperforming VNFs.

That information can then be forwarded to the orchestrator for remedial action such as spinning up a new VNF or moving the VNF to a new physical server. The combination of AI and automation helps ensure compliance with service-level agreements and removes the need for human intervention a welcome change for operators weary of late-night troubleshooting sessions.

As service providers accelerate their adoption of edge-oriented architectures, IT groups must find new ways to optimize network operations, troubleshoot underperforming VNFs and ensure SLA compliance at scale. Artificial intelligence technologies such as machine learning, combined with automation, can help them do that.

In particular, there have been a number of advancements over the last few years to enable this AI-driven future. They include systems and devices to provide high-fidelity, high-frequency telemetry that can be analyzed, highly scalable message buses such as Kafka and Redis that can capture and process that telemetry, and compute capacity and AI frameworks such as TensorFlow and PyTorch to create models from the raw telemetry streams. Taken together, they can determine in real time if operations of production systems are in conformance with standards and find problems when there are disruptions in operations.

All that has the potential to streamline operations and give service providers a competitive edge at the edge.

Sumeet Singh is vice president of engineering at Juniper Networks Inc., which provides telcos AI and automation capabilities to streamline network operations and helps them use automation capabilities to take advantage of business potential at the edge. He wrote this piece for SiliconANGLE.

Show your support for our mission with our one-click subscription to our YouTube channel (below). The more subscribers we have, the more YouTube will suggest relevant enterprise and emerging technology content to you. Thanks!

Support our mission: >>>>>> SUBSCRIBE NOW >>>>>> to our YouTube channel.

Wed also like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we dont have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary onSiliconANGLE along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams attheCUBE take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.

If you like the reporting, video interviews and other ad-free content here,please take a moment to check out a sample of the video content supported by our sponsors,tweet your support, and keep coming back toSiliconANGLE.

Read the rest here:
How machine learning and automation can modernize the network edge - SiliconANGLE

One of the biggest machine learning events is taking place in Las Vegas just before summer, Machine Learning Week 2020

This five-day event will have 5 conferences, 8 tracks, 10 workshops, 160 speakers, more than 150 sessions, and 800 attendees.

If there is anything you want to know about machine learning for your small business, this is the event. Keynote speakers from Google, Facebook, Lyft, GM, Comcast, WhatsApp, FedEx, and LinkedIn to name just some of the companies that will be at the event.

The conferences will include predictive analytics for business, financial services, healthcare, industry and Deep Learning World.

Training workshops will include topics in big data and how it is changing business, hands-on introduction to machine learning, hands-on deep learning and much more.

Machine Learning Week will take place from May 31 to June 4, 2020, at Ceasars Palace in Las Vegas.

Click the red button and register.

Register Now

This weekly listing of small business events, contests and awards is provided as a community service by Small Business Trends.

You can see a full list of events, contest and award listings or post your own events by visiting the Small Business Events Calendar.

Image: Depositphotos.com

Read this article:
Educate Yourself on Machine Learning at this Las Vegas Event - Small Business Trends