In all, a few lines of R or Python code will suffice for a piece of machine intelligence and there's a plethora of resources and tutorials online to train your quasi-neural networks, like all sorts of deepfake networks, manipulating image-video-audio-text, with zero knowledge of the world, as Generative Adversarial Networks, BigGAN, CycleGAN, StyleGAN, GauGAN, Artbreeder, DeOldify, etc. They create and modify faces, landscapes, universal images, etc., with zero understanding what it is all about.

Artificial intelligence (AI) has proven capable of doing many things all by itself, but it is still true that humans play a critical role. There is a lot of work that is done behind the scenes to enable AI to be the incredible tool it is. When a business begins to make the transition into an AI-driven organization, leadership must keep this in mind.

Enterprises know they want to do machine learning, but they also know they can't afford to think too long or too hard about it. They need to act, and they have specific business problems that they want to solve. And they know instinctively and anecdotally from the experience of the hyperscalers and the HPC centers of the world that machine learning techniques can be utterly transformative in augmenting existing applications, replacing hand-coded applications, or creating whole new classes of applications that were not possible before. They also have to decide if they want to run their AI workloads on-premise or on any one of a number of clouds where a lot of the software for creating models and training them are available as a service. And let's acknowledge that a lot of those models were created by the public cloud giants for internal workloads long before they were peddled as a service.

In all, a few lines of R or Python code will suffice for a piece of machine intelligence and there's a plethora of resources and tutorials online to train your quasi-neural networks, like all sorts of deepfake networks, manipulating image-video-audio-text, with zero knowledge of the world, as Generative Adversarial Networks, BigGAN, CycleGAN, StyleGAN, GauGAN, Artbreeder, DeOldify, etc. They create and modify faces, landscapes, universal images, etc., with zero understanding what it is all about.

Consumer expectations are higher than ever as a new generation of shoppers look to shop for experiences rather than commodities. They expect instant and highly-tailored (pun intended?) To be forward-looking, brands and retailers are turning to startups in image recognition and machine learning to know, at a very deep level, what each consumer's current context and personal preferences are and how they evolve. But while brands and retailers are sitting on enormous amounts of data, only a handful are actually leveraging it to its full potential. To provide hyper-personalization in real time, a brand needs a deep understanding of its products and customer data.

Two years ago, Google unveiled its Tensor Processing Units or TPUs -- specialized chips that live in the company's data centers and make light work of AI tasks. Now, the company is moving its AI expertise down from the cloud, and has taken the wraps off its new Edge TPU; a tiny AI accelerator that will carry out machine learning jobs in IoT devices. The Edge TPU is designed to do what's known as "inference." This is the part of machine learning where an algorithm actually carries out the task it was trained to do; like, for example, recognizing an object in a picture. Google's server-based TPUs are optimized for the training part of this process, while these new Edge TPUs will do the inference. These new chips are destined to be used in enterprise jobs, not your next smartphone.

Commodity artificial intelligence-as-a-Service (AI-aaS) offerings are popping up everywhere. Just as you can whip out a credit card and spin up a virtual data center in Amazon, Microsoft, or Google's cloud, you can now call on previously trained machine learning clusters to handle your AI chores. Using an API, you can upload a photo library to Google Cloud Vision or Amazon Rekognition to have the program scan it for objects, faces, logos, or terms of service violations in seconds, for fractions of a penny per image. Any business can now deploy the same technology used by the Google Photos app and Amazon Prime Photos to automatically categorize and label smartphone snaps based on the people, objects, and landmarks inside them. Real estate companies use image recognition to allow prospective home buyers to search for houses whose appearance pleases them. Car companies like Kia use AI to customize marketing campaigns based on the photos people post to social media.

I wanted to talk to Moore about some of the AI basics -- like how the School of Computer Science defines artificial intelligence. That may seem simplistic, but the term is used so broadly that I think it's worth taking the time to make sure we all know what we're talking about when we talk about AI. So, our conversation started with a definition, it moved to CMU's AI stack, which I'll explain in a minute and which could help CIOs wrap their heads around this sprawling term. Moore: We've tried to make it pretty focused. You're building an artificial intelligence system if you're building a system which does two things: It must understand the world and it must make smart decisions based on what it's understanding.

AI has become one of the great, meaningless buzzwords of our time. In this video, the Chief Data Scientist of Dun and Bradstreet explains AI in clear business terms. Intel, arguably the biggest ingredient brand ever, wants to be known as the processing brains behind artificial intelligence as well as enabling technologies such as the cloud and Internet of Things. Think Intel Inside for artificial intelligence. Intel is transitioning from being a company one step removed the CXOs driving digital transformation to a vendor directly involved with prototyping the future with customers.