Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems. However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking systems that work across a variety of domains. Existing approaches generally require some dialogue data with state information and their ability to generalize to unknown domains is limited. In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets. We divide the slot types in dialogue state into categorical or extractive to borrow the advantages from both multiple-choice and span-based reading comprehension models. Our method achieves near the current state-of-the-art in joint goal accuracy on MultiWOZ 2.1 given full training data. More importantly, by leveraging machine reading comprehension datasets, our method outperforms the existing approaches by many a large margin in few-shot scenarios when the availability of in-domain data is limited. Lastly, even without any state tracking data, i.e., zero-shot scenario, our proposed approach achieves greater than 90% average slot accuracy in 12 out of 30 slots in MultiWOZ 2.1.

The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new advance reaches commercial production can span 20 years. This tutorial is aimed to provide experimental scientists with a foundation in the science of making decisions. Using numerical examples drawn from the experiences of the authors, the article describes the fundamental elements of any experimental learning problem. It emphasizes the important role of belief models, which include not only the best estimate of relationships provided by prior research, previous experiments and scientific expertise, but also the uncertainty in these relationships. We introduce the concept of a learning policy, and review the major categories of policies. We then introduce a policy, known as the knowledge gradient, that maximizes the value of information from each experiment. We bring out the importance of reducing uncertainty, and illustrate this process for different belief models.

This article is a guest post on NoCamels and has been contributed by a third party. NoCamels assumes no responsibility for the content, including facts, visuals, and opinions presented by the author(s). Ryan E. Long is a non-residential fellow of Stanford Law School's Center for Internet and Society and Vice-Chair of the CA Lawyers Association, IP Licensing Interest Group. In addition, he has written for or been interviewed by publications such as The Nordic Blockchain Association, El Pais, Cognitive Times, and Digital Trends about new tech subjects such as artificial intelligence, blockchain and "deep fake" videos. Currently, he is an adjunct professor of media law at Pepperdine Law School in Malibu, California.

Artificial intelligence is currently one of the hottest buzzwords in tech -- with good reason. In the last few years, we have seen several technologies previously in the realm of science fiction transform into reality. Experts look at artificial intelligence as a factor of production, that has the potential to introduce new sources of growth and change the way work is done across industries. In fact, AI technologies could increase labour productivity by 40% or more by 2035, according to a recent report by Accenture. This could double economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this field.

The way to set your children up for success in this day and age is to ensure they learn about artificial intelligence, according to the billionaire tech entrepreneur Mark Cuban. "Give your kids an edge, have them sign up [and] learn the basics of Artificial Intelligence," Cuban tweeted on Monday. Cuban, who is a star on the hit ABC show "Shark Tank" and the owner of the Dallas Mavericks NBA basketball team, was promoting a free, one-hour virtual class his foundation is teaching an introduction to artificial intelligence in collaboration with A.I. For Anyone, a nonprofit organization that aims to improve literacy of artificial understanding. "Parents, want your kids to learn about artificial intelligence while you're stuck in quarantine," Cuban says on his LinkedIn account.

Today, we are bringing you the king of our AI courses...: Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.

This thesis takes inspiration from quantum physics to investigate mathematical structure that lies at the interface of algebra and statistics. The starting point is a passage from classical probability theory to quantum probability theory. The quantum version of a probability distribution is a density operator, the quantum version of marginalizing is an operation called the partial trace, and the quantum version of a marginal probability distribution is a reduced density operator. Every joint probability distribution on a finite set can be modeled as a rank one density operator. By applying the partial trace, we obtain reduced density operators whose diagonals recover classical marginal probabilities. In general, these reduced densities will have rank higher than one, and their eigenvalues and eigenvectors will contain extra information that encodes subsystem interactions governed by statistics. We decode this information, and show it is akin to conditional probability, and then investigate the extent to which the eigenvectors capture "concepts" inherent in the original joint distribution. The theory is then illustrated with an experiment that exploits these ideas. Turning to a more theoretical application, we also discuss a preliminary framework for modeling entailment and concept hierarchy in natural language, namely, by representing expressions in the language as densities. Finally, initial inspiration for this thesis comes from formal concept analysis, which finds many striking parallels with the linear algebra. The parallels are not coincidental, and a common blueprint is found in category theory. We close with an exposition on free (co)completions and how the free-forgetful adjunctions in which they arise strongly suggest that in certain categorical contexts, the "fixed points" of a morphism with its adjoint encode interesting information.

As people try to make the most of their time in quarantine, we've received a ton of demand from educators to host an online workshop, teaching students the fundamentals of artificial intelligence. For Anyone is excited to host a Learn AI in 60 Minutes virtual workshop in partnership with the Mark Cuban Foundation. During this session, we will cover the following topics, host a Q&A with AI experts, and share resources that will enable you to teach AI to others.

Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated], 1 more Created by Lazy Programmer Inc. When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible.