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) …
In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do here. We'll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable approaches; they can be grouped into generative/discriminative, parametric/non-parametric, supervised/unsupervised, and so on. However, from our experience, this isn't always the most practical way to group algorithms.
Artificial intelligence (AI), machine learning and cognitive analytics are having a tremendous impact in areas ranging from medical diagnostics to self-driving cars. AI systems are highly dependent on enormous volumes of data--both at rest in repositories and in motion in real time--to learn from experience, make connections and arrive at critical business decisions. Usage of AI is also expected to expand significantly in the not-so-distant future. As a result, having the right storage to support the massive amounts of data required for AI workloads is an important consideration for an increasing number of organizations. Availability: When a business leader uses AI for critical tasks such as understanding how best to run their manufacturing process or to optimize their supply chain, they cannot afford to risk any loss of availability in the supporting storage system.
Artificial Intelligence is beginning to have transformative effects on consumers, enterprises, and governments around the world. The impacts are contributing by automating repetitive task, creating efficiencies, ubiquitously improving user experience, and creating ways for humans to improve our cognition. Furthermore, by 2020, the AI market is projected to reach $70 billion, driven by increasing computational power and improving approaches/applications with machine, deep learning, natural language processing and robotics and many a number of other technologies. To gain a better understanding of the perception of AI in the US, PwC surveyed 2,500 consumers and business decision makers. The objective is to better understand their attitudes towards artificial intelligence, and the future implications on business and society.
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.
A few years ago, when my son was barely three, he confused me for a slow hard drive. As I was explaining a new concept to him, I stumbled. While I searched my brain for the right words, he looked up at me: "Mama, it's loading." We are surely on a path to faster downloads. We just need to make sure we are loading the right stuff.
This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn't necessary to have prior knowledge of them to gain value from this series. Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic. The rate of acceleration is already astounding.
Excited about using AI to improve your organization's operations? I want to warn you about bias and how it can appear in those types of projects, share some illustrative examples, and translate the latest academic research on "algorithmic bias." What we call things shapes our understanding of them. That's why I try to avoid the hype-driven term "artificial intelligence." Most projects called that are more usefully described as "machine learning."
When people think of robots, they often think of machines equipped with artificial intelligence capabilities that will, at some point, have the power to rise up and overtake humans as they learn and grow. Sci-fi narrative aside, what many people don't realize is that robots have been around for a very long time, and often not in the way that they think. There are different types of robotics technologies, some of which are the moving robots that Amazon uses to staff their distribution centers or are the helpers currently roaming the aisles of some Target and Lowe's stores. However, physical robots deployed in the blue collar workforce are not the only ones enabling businesses. There are other, much less conspicuous robots that many organizations are already using to exponentially increase staff productivity in white collar roles.
Imagine an artificially-intelligent smartphone so clever that when we point the camera at a beautiful scene, it will guide us to the best spot to snap a picture, ensuring the lighting, composition, and colors are all perfect. It's a feature that in theory is not too far away. Don't worry, this nightmare scenario is nonsense, and a great example of how artificial intelligence and its benefits are still misunderstood. At a recent event in London, Huawei gathered together experts on AI and human behavior to put our minds at rest about how the technology will help free us from mundane decisions, and actually encourage creativity further. Huawei has a vested interest in making us understand the benefits of AI.
Artificial intelligence already decides who you are. Your reputation online influences whether people date you, hire you, buy from you, rent to you, loan you money, admit you to their school and much more. Many of these decisions involve subjective and highly personal calculations. It will impact lives directly and in deeply personal ways. New AI will determine your reputation and other AI algorithms will learn about you from that information and make decisions about you.