What can artificial intelligence learn from dogs? Quite a lot, say researchers from the University of Washington and Allen Institute for AI. They recently trained neural networks to interpret and predict the behavior of canines. Their results, they say, show that animals could provide a new source of training data for AI systems -- including those used to control robots. To train AI to think like a dog, the researchers first needed data. They collected this in the form of videos and motion information captured from a single dog, a Malamute named Kelp.

"With devices like Alexa Echo, Apple HomePod and SmartLock in our homes, the'intelligent personal assistant' SIRI and Google in our pockets, we have to become more aware that we are giving devices an incredible amount of personal data and control to machines. Our young people are growing up where this is normal practice and more and more common. This initiative is about giving students the opportunity to reflect on the control and information that we freely hand over, and about examining some of the potential outcomes in society as we give more and more to machines and machine learning. HAL was a concept 50 years ago, a warning of sorts, that is still valid today. We want the students to have fun, and to see the opportunities as well as the challenges of the incredible technology we have in our world today and potentially tomorrow," said Valerie Cowman, Chair of the Skills & Education Committee of the CEIA – Cork's Technology Network.

Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the changing world states. To supply this knowledge, we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the preconditions and effects of actions, and use it along with commonsense knowledge of persistence to answer questions about change. Our evaluation shows that our system, ProComp, significantly outperforms two strong reading comprehension (RC) baselines. Our contributions are two-fold: the Semantic Lexicon rulebase itself, and a demonstration of how a simulation-based approach to machine reading can outperform RC methods that rely on surface cues alone. Since this work was performed, we have developed neural systems that outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the Semantic Lexicon remains a novel and potentially useful resource, and its integration with neural systems remains a currently unexplored opportunity for further improvements in machine reading about processes.

Artificial intelligence (AI) is a field of technology that is surrounded by both hype and misconceptions. It is predicted that $60 billion will be spent by brands on AI technology by 2025, so this hype is having a direct impact on where companies allocate their budgets. A significant difficulty in defining the size of the AI market is in defining exactly where its boundaries lie. Although we tend to imagine eerily human robots that mimic our mannerisms, AI is actually a very broad field that encompasses a range of disciplines – some more relevant to search marketing than others. More often than not, it is embedded in software that can process vast amounts of data to make or inform more intelligent decisions.

As e-learning systems become more prevalent, there is a growing need for them to accommodate individual differences between students. This paper addresses the problem of how to personalize educational content to students in order to maximize their learning gains over time. We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits. Given a set of target questions MAPLE estimates the expected learning gains for each question and uses an exploration-exploitation strategy to choose the next question to pose to the student. It maintains a personalized ranking over the difficulties of question in the target set which is used in two ways: First, to obtain initial estimates over the learning gains for the set of questions. Second, to update the estimates over time based on the students responses. We show in simulations that MAPLE was able to improve students' learning gains compared to approaches that sequence questions in increasing level of difficulty, or rely on content experts. When implemented in a live e-learning system in the wild, MAPLE showed promising results. This work demonstrates the efficacy of using stochastic approaches to the sequencing problem when augmented with information about question difficulty.

Many companies use the term artificial intelligence, or AI, as a way to generate excitement for their products and to present themselves as on the cutting edge of tech development. But what exactly is artificial intelligence? And how will it help the development of future generations? Find out the answers to these questions and more in AI 101, a brand new FREE report from Business Insider Intelligence, Business Insider's premium research service, that describes how AI works and looks at its present and potential future applications.

TensorFlow has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning. This Learning Path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient deep learning. So, if you are interested to acquire complete knowledge on deep learning with TensorFlow, then you should surely go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a look at your learning journey.

Keras is a deep learning library for fast, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time. This course will show you how to leverage the power of Keras to build and train high performance, high accuracy deep learning models, by implementing practical projects in real-world domains.Spanning over three hours, this course will help you master even the most advanced concepts in deep learning and how to implement them with Keras. You will train CNNs, RNNs, LSTMs, Autoencoders and Generative Adversarial Networks using real-world training datasets. These datasets will be from domains such as Image Processing and Computer Vision, Natural Language Processing, Reinforcement Learning and more.By the end of this highly practical course, you will be well-versed with deep learning and its implementation with Keras.

One-class Support Vector Machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and widely adopted in both research as well as industrial applications. The biggest issue for OC-SVM is, however, the capability to operate with large and high-dimensional datasets due to inefficient features and optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or auto-encoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class SVM (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descend to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.

In this work, we propose an automated method to identify semantic bugs in student programs, called ATAS, which builds upon the recent advances in both symbolic execution and active learning. Symbolic execution is a program analysis technique which can generate test cases through symbolic constraint solving. Our method makes use of a reference implementation of the task as its sole input. We compare our method with a symbolic execution-based baseline on 6 programming tasks retrieved from CodeForces comprising a total of 23K student submissions. We show an average improvement of over 2.5x over the baseline in terms of runtime (thus making it more suitable for online evaluation), without a significant degradation in evaluation accuracy.