Continual labeling of training examples is a costly task in supervised learning. Active learning strategies mitigate this cost by identifying unlabeled data that are considered the most useful for training a predictive model. However, sample selection via active learning may lead to an exploitation-exploration dilemma. In online settings, profitable items can be neglected when uncertain items are annotated instead. To illustrate this dilemma, we study a human-in-the-loop customs selection scenario where an AI-based system supports customs officers by providing a set of imports to be inspected. If the inspected items are fraud, officers levy extra duties, and these items will be used as additional training data for the next iterations. Inspecting highly suspicious items will inevitably lead to additional customs revenue, yet they may not give any extra knowledge to customs officers. On the other hand, inspecting uncertain items will help customs officers to acquire new knowledge, which will be used as supplementary training resources to update their selection systems. Through years of customs selection simulation, we show that some exploration is needed to cope with the domain shift, and our hybrid strategy of selecting fraud and uncertain items will eventually outperform the performance of the exploitation strategy.

In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.

The challenge of establishing assurance in autonomy is rapidly attracting increasing interest in the industry, government, and academia. Autonomy is a broad and expansive capability that enables systems to behave without direct control by a human operator. To that end, it is expected to be present in a wide variety of systems and applications. A vast range of industrial sectors, including (but by no means limited to) defense, mobility, health care, manufacturing, and civilian infrastructure, are embracing the opportunities in autonomy yet face the similar barriers toward establishing the necessary level of assurance sooner or later. Numerous government agencies are poised to tackle the challenges in assured autonomy. Given the already immense interest and investment in autonomy, a series of workshops on Assured Autonomy was convened to facilitate dialogs and increase awareness among the stakeholders in the academia, industry, and government. This series of three workshops aimed to help create a unified understanding of the goals for assured autonomy, the research trends and needs, and a strategy that will facilitate sustained progress in autonomy. The first workshop, held in October 2019, focused on current and anticipated challenges and problems in assuring autonomous systems within and across applications and sectors. The second workshop held in February 2020, focused on existing capabilities, current research, and research trends that could address the challenges and problems identified in workshop. The third event was dedicated to a discussion of a draft of the major findings from the previous two workshops and the recommendations.

Here is a sample of our incredibly insightful session topics for 2020 Building a Smarter Artificial Intelligence Strategy for the Enterprise Accelerating Business Innovation with Deep Learning Leading Data Science Teams: A Framework to Help Guide Data Science Project Managers From Cognitive Computing to Artificial Intelligence, the Next 10 Years Democratizing Artificial Intelligence in a Business Context Accelerating AI Development with Deep Learning & Transfer Learning Recent Advances in Machine Learning with Applications to Internet of Things (IoT) Building & Managing World Class Data Science Teams Data Ethnography: Understanding Bias in Machine Learning Models How to Ruin your Business with Data Science & Machine Learning Day 2 & 3 -AI Workshops & Tutorials AI - The One Skill You Need to Ensure Future Success Gain proficiency in essential AI skills & accelerate your insights by attending one of our many introductory level talks & workshops. After attending these sessions, you will better understand the basics of AI & its incredible potential for accelerated growth. Co-located at ODSC West, we have some of the best & brightest speakers on the planet presenting on topics including: AI for Executives Workshop Introduction to Deep Learning Data Science 101 Data Visualization Workshop Introduction Machine Learning Predictive Analytics Workshop Voice AI & Speech Recognition Image Recognition & Machine Vision Why Attend? AI & data science is transforming business. The CxO summit will give you the knowledge & connections to be at the forefront.

Robotic Drives & Physics: Robotics, learn by building III Just in time for Black Friday, this course is being uploaded as you read this! Main lessons will be posted over the months of November and December. Description Please note: Content is being uploaded through November/December. This is a new course. Building on the knowledge you gained in the Analog Electronics and Digital Electronics modules, you'll open even more doors to diverse careers and hobbies by learning how to physically move robots and mechatronics. Robotic drives and physics are intimately intertwined - almost the same topic in fact.

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. The most common use of the softmax function in applied machine learning is in its use as an activation function in a neural network model. Specifically, the network is configured to output N values, one for each class in the classification task, and the softmax function is used to normalize the outputs, converting them from weighted sum values into probabilities that sum to one. Each value in the output of the softmax function is interpreted as the probability of membership for each class. In this tutorial, you will discover the softmax activation function used in neural network models.

In this article, we are going to list the top 10 Machine Learning books that you should read before you start with it. As you know, Machine Learning is a combination of statistic techniques that help computers learn stuff done by humans. This way we are achieving some results with superhuman precision. Now, as we all know, in order to understand the basics of Machine Learning, it's nice to have some knowledge in different areas of "Mathematics". Before you start reading these top 10 books before starting with Machine Learning, we want to show you two other related articles that you will find very helpful.

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library.

In deep learning, Convolutional Neural Networks(CNNs or Convnets) take up a major role. CNNs are widely used in computer vision-based problems, natural language processing, time series analysis, recommendation systems. ConvNet architecture mainly has 3 layers – convolutional layer, pooling layer and fully connected layer. All these layers bring out features of the input by finding some pattern using mathematical calculations. Unlike other neural networks architecture, CNNs have a backpropagation algorithm.

In this article, we explore the basics of natural language processing (NLP) with code examples. We dive into the natural language toolkit (NLTK) library to present how it can be useful for natural language processing related-tasks. Afterward, we will discuss the basics of other Natural Language Processing libraries and other essential methods for NLP, along with their respective coding sample implementations in Python. Check out our tutorial on the Bernoulli distribution with code examples in Python. Computers and machines are great at working with tabular data or spreadsheets. However, as human beings generally communicate in words and sentences, not in the form of tables. Much information that humans speak or write is unstructured. So it is not very clear for computers to interpret such. In natural language processing (NLP), the goal is to make computers understand the unstructured text and retrieve meaningful pieces of information from it.