The first time I watched a 3D movie at the age of 12, I was mesmerized by the experience and literally tortured my parents to take me to that movie again and again. I was so much interested in 3D movies that I had to watch them alone as I grew up because my friends deemed such movies as fit for kids alone. I do blame my choice of movies though, as these movies were targeted at kids. But for me, it was the experience rather than the story, I would eagerly wait for those moments where I get to feel the objects closer to me, and a couple of occasions I have even chosen the front seats to check whether the experience doubles up (forgive my naivety). I loved the experience, and I am sure many would agree with me.

Right now we are in the middle of one of the greatest transformations of pedagogical practices in the history of mankind. The Internet is to modern man what printing press was to the Renaissance era. Making information easily accessible is one of the most remarkable implications of the internet since its invention. With time, the ways of conveying information have only changed. And, with youtube, mastery is just a click away.

From adaptive software to recommendation engines to voice-activated speakers, artificial intelligence is making its way into K-12 classrooms. At the same time, schools are under growing pressure to prepare students to be workers in a labor market where AI is likely to play an ever-larger role--and to be citizens in a society where AI reshapes the decisions we face, the meaning we make, and the challenges we must confront. How can busy educators make sense of this rapidly changing world? The International Society for Technology in Education hopes to help, in part via a new book funded through a grant from General Motors. In'Teaching AI: Exploring New Frontiers for Learning,' educator Michelle Zimmerman, who has a Ph.D. in learning sciences and human development from the University of Washington, brings a teacher-centric lens to big questions around the various definitions of artificial intelligence, how AI is upending the workforce, and how to teach about--and with--artificial intelligence.

Over the last few years, Deep Learning has proven itself to be the game-changer. This area of data science is the only one responsible for the advancements in machine learning and artificial intelligence. From academic researches to self-driving cars, Deep Learning is found in all possible aspects nowadays. Deep Learning is a complex and a vast field that consists of several components. It cannot be mastered in a day and hence it will take several months if you want to dig deeper into this field.

This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!

Innovations in artificial intelligence development and usage are influencing sectors far beyond the tech world, leaving few areas untouched. Even the educational field will soon be reaping many benefits of AI applications. In this article, we will discuss seven ways educators can use artificial intelligence. This may be the most promising application area of AI in education. AI in schools could track individual students' progress and either adjust the coursework or notify the teacher that a student is having difficulties understanding the material.

Data is the new oil and truth be told only a few big players have the strongest hold on that currency. Googles and Facebooks of this world are so generous with their latest machine learning algorithms and packages (they give those away freely) because the entry barrier to the world of algorithms is pretty low right now. Open source has come a long way from being christened evil by the likes of Steve Ballmer to being an integral part of Microsoft. And plenty of open source initiatives are propelling the vehicles of data science, digital analytics, and machine learning. Standing in 2018 we can safely say that, algorithm, programming frameworks, and machine learning packages (or even tutorials and courses how to learn these techniques) are not the scarce resource but high-quality data is.

Participants prepare their work in a robotics challenge.Ready AI Most industrial robots are preprogrammed or remotely controlled. That's starting to change with the introduction of artificial intelligence (AI). Some robots operate autonomously and learn to improve task efficiency. The same is true about elementary and secondary school robotics competitions--few involve real AI. Usually, the robots are teleoperated to perform a task like picking up objects and putting them in a bucket.

Machine learning is gaining momentum every single day. International Data Corporation (IDC) published a report where it predicts that the investment in machine learning and AI will rise from $12 billion to $57 billion by 2021. That amount will keep on growing in the time to come. Here is one more interesting fact. According to The New York Times, Tech Giants are paying a considerable sum of money, anywhere from $300-500k, to AI specialists.

Artificial intelligence is likely to be the defining technology of the century, affecting everything from war to jobs to health care. So, understanding what the general public wants from AI is important. A new survey suggests that while there's no strong consensus on the topic, more Americans are in favor of AI than actively oppose it. In polling organized by the University of Oxford's Future of Humanity Institute, forty-one percent of respondents said they somewhat or strongly supported the development of AI, while 22 percent said they somewhat or strongly opposed it. The remaining 28 percent said they had no strong feelings one way or the other.