How can we compare education and artificial intelligence? Well, some of you may say that we should include more courses related to artificial intelligence (AI). Yes, we should include artificial intelligence in the course, but I'm not talking about this type of connection here. I'm trying to emphasize on the advancement of AI here, and why education should be like AI in this article. AI is everywhere nowadays from our cars to our pockets.

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 The platform has the ability to solve visual problems and understand sketches in order to give immediate, interactive feedback.

Goal of the meetup is to dive into the Deep learning space. To start off with we will be going through the lectures of a Deep learning course on Udacity and working on the assignments (of course, we will maintain the "honor of code"). Once we are done with that we will take off with reading popular deep learning papers and implementing them. Currently this meetup is mostly for people who have some knowledge of machine learning but not deep learning. If you are an expert in deep learning then you are most welcome to join but we may not have much to offer, unless you want to brush up your DL skills or are interested in guiding DL enthusiasts.

Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

Many of us know Jack and Jill went up a hill to fetch a pail of water. But did you know that Jill then went to the Georgia Institute of Technology? That's right -- Jill went on to college and is now a teaching assistant in a course on artificial intelligence (AI) in Georgia Tech's computer science program. Jill assists Ashok Goel, professor in the School of Interactive Computing. Jill, implemented on IBM's Watson platform, was first used during the spring 2016 semester to successfully answer frequently asked student questions without the help of humans.

As schools (and the Department of Education) encourage children to pursue in science, technology, engineering and math, the toy industry has been looking for ways to both assist and capitalize on STEM's popularity. And they're finding that there's a lot of fun to be had in teaching kids that science and math are more than just memorizing tables and formulas. The hiccup is, if you're looking for a STEM toy for your son or daughter, it can be overwhelming. One option is Amazon's just-announced subscription program called STEM Club, which delivers hand-picked, age-appropriate toys that encourage kids to learn as they play. At just under $20 per month, it guarantees a steady flow of items, but early customer reviews have been mixed. If you're more of a take-charge parent who would rather pick and choose STEM toys yourself, we've got a few suggestions that will not only engage your kids, but could keep you up late playing with the toys yourself.

Home Depot uses it to show which bathtubs in its huge inventory will fit someone's oddly shaped bathroom. Apple uses it to present customers with relevant apps from the app store. Intuit uses it to display the right help page when a user is filling out a particular tax form. And organizations are turning to it in droves to differentiate and innovate their offerings. In a recent interview, Gartner Fellow and Vice President Tom Austin noted that about half of large enterprises are experimenting with "smart computing" projects.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

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.