Toy makers are coming up with more and more ways to encourage children to learn STEM skills, and Hasbro is trying to do that in a somewhat futuristic way. The company is releasing a $120 robotic dog toy called Proto Max as part of its FurReal Friends line of animatronic pets that children can customize via an app. To be clear, you'll be tweaking this robo-dog's behavior and character, not the colors of its eyes or fur or the shape of its nose or face. That initially sounded a bit too much like pet Westworld to me, but after a brief demonstration, I was persuaded to quiet my internal ethics police. Proto Max is designed for kids ages six and older to learn the basics of how programming works, but even those who don't want to deal with figuring out how to customize a robot can still play with it.

First you do not always need to normalize (standardize) the input vectors (feature vectors), sometimes is good, sometimes is bad. In general you scale your feature vector when the magnitude of a feature dominates the others, so the model cannot pick up the contribution of the smaller magnitude features. Read here for a detailed explanation. Second there are two general classes of machine learning problems: classification and regression. In a classification type problem the output (dependent variable) is discrete, so you do not need to normalize it.

Job openings often go unfulfilled despite a surfeit of unemployed or underemployed workers. One of the main reasons for this is a mismatch between the skills required by employers and the skills that workers possess. This mismatch, also known as the skills gap, can pose socio-economic challenges for an economy. A first step in alleviating the skills gap is to accurately detect skills in human capital data such as resumes and job ads. Comprehensive and accurate detection of skills facilitates analysis of labor market dynamics. It also helps bridge the divide between supply and demand of labor by facilitating reskilling and workforce training programs. In this paper, we describe SKILL, a Named Entity Normalization (NEN) system for occupational skills. SKILL is composed of 1) A skills tagger which uses properties of semantic word vectors to recognize and normalize relevant skills, and 2) A skill entity sense disambiguation component which infers the correct meaning of an identified skill by leveraging Markov Chain Monte Carlo (MCMC) algorithms. Data-driven evaluation using end-user surveys demonstrates that SKILL achieves 90% precision and 73% recall for skills tagging. SKILL is currently used by various internal teams at CareerBuilder for big data workforce analytics, semantic search, job matching, and recommendations.

Our research aims at developing interactive, social agents that can coach people to learn new tasks, skills, and habits. In this paper, we focus on coaching sedentary, overweight individuals to exercise regularly. We employ adaptive goal setting in which the coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally more difficult as the trainee progresses through the training program. Our approach is model-based - the coach maintains a parameterized model of the trainee's aerobic capability that drives its expectation of the trainee's performance. The model is continually revised based on interactions with the trainee. The coach is embodied in a smartphone application which serves as a medium for coach-trainee interaction. We show that our approach can adapt the trainee program not only to several trainees with different capabilities but also to how a trainee's capability improves as they begin to exercise more. Experts rate the goals selected by the coach better than other plausible goals, demonstrating that our approach is effective.

Bees pollinate more than $15 billion worth of crops in the US each year, but with the population dwindling, experts are searching for new ways to help perform the task. Now, a student has unveiled a personal robotic bee that mimics how the insects pollinate plants. Called'Plan Bee', the drone is a hand-sized yellow-and-black device that stores pollen in its body cavity and releases it later for cross-pollination. A student has unveiled a personal robotic bee that mimics how the insects pollinate plants. Called'Plan Bee', the drone is a hand-sized yellow-and-black device that stores pollen in its body cavity and releases it later for cross-pollination A student at Savannah College designed a'bee drone' to raise awareness of the important role bees play in our food system.

Self-driving cars require self-driving car software, and Udacity's helping to feed that need with its nanodegree program in the field. Now, the online education company is also making available its self-driving car simulator via open source license, allowing anyone with a working knowledge of Unity to gab the assets, load its preexisting scenes and create their own tracks for virtual testing. If you weren't already aware, a lot of the'education' of self-driving vehicle software happens in virtual environments, since it's still relatively expensive to build an actual self-driving test vehicle, and a bit complicated on the regulatory side to find somewhere willing to let you test in real-world conditions – plus you have to prove you can do so with a reasonable expectation of safety. Udacity committed to building an open source autonomous car as part of its effort to offer its self-driving car nanodegree program, which will use code written by hundreds of its students from around the world (which will be available publicly via open source license). The company shared more details around this plan last September, when it revealed that it's using one of the 2016 Lincoln MKZs that are popular among self-driving software and component companies because a third-party is selling them ready to roll for autonomous conversion.

At dinner recently, a guest held us all entranced as he described his current work: a post-doc at a prestigious London university, he has been working for nearly two decades in artificial intelligence (AI), specializing in trying to teach computers how to teach other computers. While much of his work is simply too esoteric to explain here (that's my code for "it went right over my head"), what was very obvious to me was the extent to which things have advanced since we first met - as he was just setting out upon his journey in this field - and how rapidly theoretical advances are becoming practical innovations which then, in turn, move out into the mainstream. Problems he and his peers were wrangling with only a few years ago now seem like ancient history, he said, and while "the future is always infinitely far away, tomorrow seems closer than ever." If any of us at the table had had any doubts before that we're on the verge of tremendous social change as a result of automation and smart technology - and I don't believe anyone did have such doubts (as one would have to have had one's head thoroughly buried in the sand not to be aware of the whirlwind approaching us), they would have been thoroughly dispelled by the end of our companion's passionate and impressive address. But, of course, how to react to the automation revolution is immeasurably more difficult than simply to assert that it's coming...

Due to recent major technological developments, Artificial Intelligence is evolving at an exponential rate. It is even considered to be the technology of the future. But the truth is, it is already present in our everyday lives: fraud detection, purchase predictions on e-commerce sites, videos games and virtual personal assistants like our favorite one, Julie. In case you still don't fully understand what it entails or want to know more about AI, here is a list of 10 Artificial Intelligence experts and influencers who specialized in Artificial Intelligence and related fields and who share their knowledge on the subject area or just keep you up to date with the latest developments. CEO of Thilium, an Influencer Marketing Agency and International branding expert, Tamara McCleary also specializes in Business experiences in IoT, Machine Learning, Blockchain, Wearables, FinTech and Artificial Intelligence, to name a few.

Dennis Bonilla, executive dean of information systems and technology at the University of Phoenix, told NewsOne that the transformational technology reaches into classrooms and impacts how students learn. For example, in flipped classrooms, teachers assign students homework that utilizes artificial intelligence technology. The software can send the instructor a detailed analysis of students' comprehension of the assignment. That can enable the teacher to prepare more effectively for interactive learning the next day in the classroom. With that data in hand, the teacher can begin her lesson with information that large swaths of students struggled to understand.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.