Cognilytica is running our Artificial Intelligence (AI) and Machine Learning (ML) training and certification based on the best practices CPMAI methodology, with a live virtual training starting the week of March 25, 2020 that will teach you how to apply the CPMAI Methodology for your projects. This live, instructor-led training is conducted completely online at designated times with live trainers. The course is run as a series of eight (8) sessions conducted over four (4) weeks. This vendor neutral course is an intensive, interactive, real-world based "fire hose" that prepares you to succeed with your AI & ML efforts, whether you're just beginning them or are well down the road with implementation, reflecting the best thinking and research that Cognilytica produces. Cognilytica's CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise.

As Covid-19 continues to spread, the world has gone digital on an unprecedented scale. Tens of thousands of employees are working from home, and huge conferences, like the Google I/O and Apple WWDC software extravaganzas, plan to experiment with digital events. Universities too are sending students home. This might have meant an extended break from school not too long ago. As lecture halls go empty, an experiment into digital learning at scale is ramping up.

In this paper, we propose a general theoretical method for analyzing the risk bound in the presence of adversaries. Specifically, we try to fit the adversarial learning problem into the minimax framework. We first show that the original adversarial learning problem can be transformed into a minimax statistical learning problem by introducing a transport map between distributions. Then, we prove a new risk bound for this minimax problem in terms of covering numbers under a weak version of Lipschitz condition. Our method can be applied to multi-class classification and popular loss functions including the hinge loss and ramp loss.

Compressive learning is an effective method to deal with very high dimensional datasets by applying learning algorithms in a randomly projected lower dimensional space. In this paper, we consider the learning problem where the projected data is further compressed by scalar quantization, which is called quantized compressive learning. Generalization error bounds are derived for three models: nearest neighbor (NN) classifier, linear classifier and least squares regression. Besides studying finite sample setting, our asymptotic analysis shows that the inner product estimators have deep connection with NN and linear classification problem through the variance of their debiased counterparts. By analyzing the extra error term brought by quantization, our results provide useful implications to the choice of quantizers in applications involving different learning tasks.

In this post, you will discover the top 10 books available right now on Artificial Intelligence. There are quite a few available online in which you may purchase. Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

"A chemical reaction is a highly complex system", explains Frederik Sandfort, PhD student at the Institute of Organic Chemistry and one of the lead authors of the publication. "In contrast to the prediction of properties of individual compounds, a reaction is the interaction of many molecules and thus a multidimensional problem," he adds. Moreover, there are no clearly defined "rules of the game" which, as in the case of modern chess computers, simplify the development of AI models. For this reason, previous approaches to accurately predicting reaction results such as yields or products are mostly based on a previously gained understanding of molecular properties. "The development of such models involves a great deal of effort. Moreover, the majority of them are highly specialized and cannot be transferred to other problems," Frederik Sandfort adds.

I still learn new knowledge everyday with my growing passion in Data Science field. To pursue different career track as a graduating physics student there must be'Why' and'How' questions to be answered. Having been asked by a number of people about my transition from academia -- Physics to Data Science, I hope my story could answer the questions on why I decided to become a Data Scientist and how I pursued the goal, and ultimately encourage as well as inspire more people to pursue their passion. The CERN Summer Student Programme offers once-in-a-lifetime opportunity for undergraduate students of physics, computing and engineering to join one of their research projects with top scientists in multicultural teams at CERN in Geneva, Switzerland. In June 2017, I was very fortunate to be accepted to join the programme.

In the latest McKinsey Global Survey on AI we noted a significant year-over-year jump in companies using AI across multiple areas of the business. And while most survey respondents said their companies have gained value from AI, some are attaining greater scale, revenue increases, and cost savings than the rest. Based on our research and experience, this is no accident; how companies build their business strategy, what foundations they put in place, and how they tackle AI adoption in the workplace can all impact their potential for transformation. Many companies that have spent years developing AI technologies are facing the stark reality that successfully scaling AI requires more than just deploying AI technology. We find that those companies finding more success in scaling efforts are more likely than others to apply a core set of practices.

Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at https://github.

We propose a new application of embedding techniques for problem retrieval in adaptive tutoring. The objective is to retrieve problems whose mathematical concepts are similar. There are two challenges: First, like sentences, problems helpful to tutoring are never exactly the same in terms of the underlying concepts. Instead, good problems mix concepts in innovative ways, while still displaying continuity in their relationships. Second, it is difficult for humans to determine a similarity score that is consistent across a large enough training set. We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of abstraction and embedding steps. Prob2Vec achieves 96.88\% accuracy on a problem similarity test, in contrast to 75\% from directly applying state-of-the-art sentence embedding methods. It is interesting that Prob2Vec is able to distinguish very fine-grained differences among problems, an ability humans need time and effort to acquire. In addition, the sub-problem of concept labeling with imbalanced training data set is interesting in its own right. It is a multi-label problem suffering from dimensionality explosion, which we propose ways to ameliorate. We propose the novel negative pre-training algorithm that dramatically reduces false negative and positive ratios for classification, using an imbalanced training data set.