If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The moral implications as we hand over decision making processes off to machines are astounding, the automation of killing people in certain military drones is quickly becoming a reality, and the displacement of jobs for the future is also a concrete wall that we're speeding towards and there are no brakes. In her Ted Talk from 2016, she makes the case for human morals being more important than ever in a world where we are handing over more and more decision making processes to algorithms and automation we don't entirely understand. For example, let's say you show a program one hundred pictures of dogs, all kinds of dogs; it'll analyze every picture and start to learn the features of a dog. Leave a comment below and I'll see you on the next Part of Is Artificial Intelligence Good, bad, or neither?
Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue? Recent research results from applying machine learning to diagnosis are impressive (see "An AI Ophthalmologist Shows How Machine Learning May Transform Medicine"). Your chips are already driving some cars: all Tesla vehicles now use Nvidia's Drive PX 2 computer to power the Autopilot feature that automates highway driving.
Many human social exchanges and coordinated activities critically involve dialogue interactions. Hence, we need to develop natural humanlike dialogue processing mechanisms for future robots if they are to interact with humans in natural ways. In this article we discuss the challenges of designing such flexible dialogue-based robotic systems. We report results from data we collected in human interaction experiments in the context of a search task and show how we can use these results to build more flexible robotic architectures that are starting to address the challenges of task-based humanlike natural language dialogues on robots.