Recently there has been an explosion of buzz words related to Artificial Intelligence. My goal with this article is to discuss the differences between Artificial Intelligence, Machine Learning and Cognitive Technology. I invite you to join in, it is very likely I am missing elements that you might be more familiar with. Artificial Intelligence (AI) is the hype at the moment. In most cases, the hype is related to a specific subclass of AI known as narrow AI.

After decades on every sci-fi fan's wish list, personal robots are on the cusp of entering our homes. Now it's time to put them to work. Everyone knows Pepper, the child-sized humanoid robot launched back in 2014 who was created to welcome visitors to SoftBank Mobile stores in Japan. Now Pepper has scored a few jobs in the US, from giving directions in a shopping mall in San Francisco to pouring beer at Oakland International Airport's Pyramid Taproom. The diminutive Pepper is not alone, not even at airports.

Google is adding functionality to allow Google Assistant to compete more directly with Amazon's Alexa, but what it really needs is to offer love and support to developers of smart home products. The company's failure to do this was visible on every stand where a smart home product was to be found at CES. They all worked with Amazon Alexa, but only a very tiny fraction worked with Google Assistant. Google's shopping functionality has involved linking the online ordering systems of retailers like Costco, PetSmart and Target with Google Home so it can offer a similar shopping experience to Amazon through the device. Measuring up to Amazon in this category is going to be tough because Amazon has one system through which millions of products are available globally, whereas Google will have to sign up lots of retailers in every locality where it aims to have this service available.

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms.

In terms of sales, the iPhone is clearly Apple's most important product. But a relatively unsung hero for the company is Siri, its voice-activated digital assistant. While Siri is primarily available on iPhones, it has also made its way to Macs, the Apple TV and automobiles via Apple's CarPlay platform. Taken together, these steps all mean that if you're a heavy Apple user, Siri is probably always at your beck and call. That's important for Apple as Silicon Valley's battle over voice-activated artificial intelligence apps continues heating up: Microsoft has Cortana, Google has Google Now, and Amazon has Alexa.

Computers are incredibly fast, accurate, and stupid. Human beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination.--Albert As I read the exciting work of Narula et al. (2) published in this issue of the Journal, I could not help but be transported back to the early days of my career at Mayo Clinic in Rochester, Minnesota. It is always enjoyable to take a journey down memory lane!

It's a 360 degree speaker with voice recognition powered by Amazon Alexa voice service. Amazon Echo Dot has the avabilityes to play music, give news, weather, sport results, traffic and much more. It can even order pizza from Domino's Pizza.

Artificial intelligence is not one thing, but many, spanning several schools of thought. In his book The Master Algorithm, Pedro Domingos calls them the tribes of AI. As the University of Washington computer scientist explains, each tribe fashions what would seem to be very different technology. Evolutionists, for example, believe they can build AI by recreating natural selection in the digital realm. Symbolists spend their time coding specific knowledge into machines, one rule at a time.

In this tutorial, we will learn about OpenCV tracking API that was introduced in OpenCV 3.0. We will learn how and when to use the 6 different trackers available in OpenCV 3.2 -- BOOSTING, MIL, KCF, TLD, MEDIANFLOW, and GOTURN. We will also learn the general theory behind modern tracking algorithms. This problem has been perfectly solved by my friend Boris Babenko as shown in this flawless real-time face tracker below! Jokes aside, the animation demonstrates what we want from an ideal object tracker -- speed, accuracy, and robustness to occlusion.

Tetiana is a mathematician turned data scientist currently working with NanoTechGalaxy on developing machine learning algorithms for medical image processing. She is also working on AI risk research as part of the Pareto Fellowship awarded by the Centre of Effective Altruism.