This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}. It enhances neural networks' ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities' states. These entities' states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.

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Next time you call customer support, the person on the other end of the line may be getting a little help from emotionally intelligent AI software. Some call-center workers are now receiving real-time coaching from software that analyzes their speech and the nature of their dialogue interactions with customers. As they are talking to someone the software might recommend that they talk more slowly or interrupt less often, or warn that the person on the other end of the line seems upset. This gives us a fascinating glimpse of how AI and humans might increasingly work together in the future. Plenty of routine work is becoming automated in call centers and other back office settings, but real human interaction seems likely to resist automation for a long while yet.

The general idea here is that the work done by a Machine Learning engineer can be automated, thus freeing potential users from the tyranny of needing to have specific expertise. Presumably, the ultimate goal of such automations is to make Machine Learning accessible to more people. After all, if a thing can be done automatically, that means anyone who can press a button can do it, right? I'm going to make a three-part argument here that "Machine Learning Automation" is really just a poor proxy for the true goal of making Machine Learning useable by anyone with data. Furthermore, I think the more direct path to that goal is via the combination of automation and interactivity that we often refer to in the software world as "abstraction". By understanding what constitutes a powerful Machine Learning abstraction, we'll be in a better position to think about the innovations that will really make Machine Learning more accessible.

A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we consider efficient learning in large-scale combinatorial semi-bandits with linear generalization, and as a solution, propose two learning algorithms called Combinatorial Linear Thompson Sampling (CombLinTS) and Combinatorial Linear UCB (CombLinUCB). Both algorithms are computationally efficient as long as the offline version of the combinatorial problem can be solved efficiently. We establish that CombLinTS and CombLinUCB are also provably statistically efficient under reasonable assumptions, by developing regret bounds that are independent of the problem scale (number of items) and sublinear in time. We also evaluate CombLinTS on a variety of problems with thousands of items. Our experiment results demonstrate that CombLinTS is scalable, robust to the choice of algorithm parameters, and significantly outperforms the best of our baselines.

Hassaan Ahmed is co-founder of Intellisense Solutions. Machine learning is not new. We have witnessed it since the 1990s, when Amazon introduced a new "recommended for you" section for its users to display more personalized results. When we search for something on Google, machine learning is behind those search results. The "Friends" recommendations or the suggested pages on Facebook or a product recommendation on any e-commerce site all depend on machine learning.

The advocates of machine learning are known to be a fiercely contentious lot, each asserting that its own approach is superior to all others, and that any evidence adduced to the contrary is propaganda, fake news of the worst sort, stemming from jealous advocates of inferior approaches. The closest approximation to the internecine warfare of the machine learning field is the human learning field, in which advocates of public, government-run and union-staffed schools exchange harsh words with advocates of charter schools, with a level of invective and passion that indicates that someone is strongly in favor of hopelessly uneducated machines and/or humans.

The computation model was built on CogSketch, a sketch-understanding system developed in Forbus' laboratory at Northwestern University. Sketching is a natural activity that people do while thinking or trying to communicate an idea, especially when spatial content is involved. Sketching is also heavily used in engineering and geoscience. CogSketch is used to model spatial understanding and reasoning, making it suitable for research based on sketches, but also for testing against a standardized visual intelligence test such as the Raven's Progressive Matrices test.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition." The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus' laboratory.