When you think about artificial intelligence (AI), robots, androids and other futuristic technologies may come to mind. And while some of the most successful tech companies like Facebook, Amazon and Google have built their success on industry-changing applications of artificial intelligence, the concept is still fairly new to content marketing teams. In the most basic sense "artificial intelligence" broadly refers to the processes and technologies that are created to teach machines to perform intelligent tasks. For content marketers, "intelligent tasks" typically refer to algorithms designed to process data. This goes far beyond automation, which is where the majority of marketing technology supports marketing teams today.

There is an ongoing race among semiconductor companies, including the established market heavyweights and startups alike, to define the hardware platform that will run compute-intensive deep learning algorithms quickly and efficiently. Until now, NVIDIA has dominated the deep learning market with its graphics processor unit (GPU) chips, which bring massive parallelization, however field programmable gate arrays (FPGAs) and digital signal processors (DSPs) are starting to catch up. Deep learning is largely characterized by deep neural networks (DNNs) and convolutional neural networks (CNNs), which can become massively complex. Google's cat recognition neural network back had 1 billion connections using 16,000 processors. GPUs are known to achieve the best speed and throughput, around 100x faster compared to an FPGA, while FPGAs are known to have better power efficiency, around 50x better compared to a GPU.

Everyone is talking about artificial intelligence (AI) and machine learning these days. This is not just of strategic relevance for companies the likes of Google, Apple, Amazon, Facebook or Salesforce.com. AI is now a term that all companies should be familiarizing themselves with (if they're not already) because it will have a profound impact on their business in the near future. We have already witnessed vehicles operating autonomously and a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks, which has all given rise to a flurry of people claiming that the AI revolution is upon us. What is Driving This Next Wave of Change?

Google's artificial intelligence can play the ancient game of Go better than any human. It can identify faces, recognize spoken words, and pull answers to your questions from the web. But the promise is that this same kind of technology will soon handle far more serious work than playing games and feeding smartphone apps. One day, it could help care for the human body. Demonstrating this promise, Google researchers have worked with doctors to develop an AI that can automatically identify diabetic retinopathy, a leading cause blindness among adults. Using deep learning--the same breed of AI that identifies faces, animals, and objects in pictures uploaded to Google's online services--the system detects the condition by examining retinal photos.

This article was posted by Adrian Rosebrock on Pyimagesearch. Adrian is an entrepreneur and Ph.D who has launched two successful image search engines, ID My Pill and Chic Engine. This list is by no means exhaustive, it's simply a list of libraries that he has used in his computer vision career and found particular useful at one time or another. The goal of this blog post is to introduce you to these libraries. He encourages you to read up on each them individually to determine which one will work best for you in your particular situation.

Machine learning is being used by companies such as Netflix, Facebook, and Spotify for automated data analysis. The results are then used to create recommendations based on past consumption habits. This approach is based on algorithms that learn and adapt to the usage data and patterns that emerge, not the hard-coded rules used in traditional analytics. Netflix in particular has successfully leveraged analytics for years, and their strategy serves as an example of how machine learning can help you gain a competitive advantage. Big Data analytics, being based on manual processes to search for patterns in data, has a major flaw--humans.

We all know that alcohol and tweeting is not always a good combination. Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea. Today, these guys show how they've trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society.

Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted "an electronic scanner-computer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films."3

Regardless of industry, the companies that win in the digital era are those that take the shortest paths to the best results. That means getting the right information in the right hands at the right time. These realities are why more organizations are turning to cognitive solutions. Our market report, "The cognitive advantage: Insights from early adopters on driving business value," reveals that early adopters employ cognitive computing for competitive differentiation. In fact, 65 percent say that cognitive adoption is very important to their strategy and success, and more than half regard cognitive computing as a must-have to remain competitive.

But what exactly is machine learning? And how is it different to what we call artificial intelligence and the term deep learning frequently used today? Artificial intelligence is the general term for a series of methods, tools and technologies that imitate human cognition, including recognition, learning, planning, reasoning and the ability to solve problems. One of these methods is machine learning, aimed at making the computer capable of performing these cognitive skills. Machine learning is based on data and expert knowledge, out of which computers extract significant patterns and transfer these patterns to other data in order to generate predictions and recommendations.