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) …
Artificial intelligence, for all its mind-boggling potential, is a double-edged sword. Sure, AI might save lives through early cancer or heart disease detection. In cybersecurity, though, even AI companies worry that the bad guys will use artificial intelligence to launch more potent attacks. Little wonder, then, that computer security has become an AI hot spot. Venture capital firms are throwing money into "machine learning" startups.
And yet according to 313 executives recently surveyed by Forbes Insights--63% of whom were in the C-Suite--almost all (95%) believe that AI will play an important role in their responsibilities in the near-future. The majority of CEOs today are not drivers of AI adoption--that responsibility falls on C-level technology leaders who need to build a strong business case and show results that encourage a deeper dive into change. With that firmly in mind, Forbes Insights and Intel have taken their combined experience covering and developing technology to produce this introductory guide to AI adoption, from buy-in and deployment to building a corporate culture around data. Consider the below three steps your beginner's guide to AI. It's important to see beyond the swirl of hype and expectations around AI technologies and view them for what they really are--massive accelerators of processes and insights and profound amplifiers of human capability.
Developers know a whole lot about the machine learning (ML) systems that they produce and manage, that is a given. But, there's a demand for non-developers to have a higher level understanding of the kinds of systems. Expert systems and artificial neural networks would be the classical two important classes. With the advancements in computing functionality, softwares capacities, algorithm complexity and analytical algorithm could be said to have combined both of them. This article is a summary of the three different types of systems.
When Benjamin Franklin said, "An ounce of prevention is worth a pound of cure," he was talking about fire safety. Nevertheless, the axiom works just as well when taken literally. In fact, Franklin's advice anticipated hundreds of years of healthcare best practices. Spotting and preventing medical problems early on is far cheaper and more efficient than catching them late. The problem for overworked physicians is that issues are not always easy for human eyes to detect.
New Delhi: Artificial Intelligence (AI) may be a hot topic in tech circles, but its real benefits will be served when it has been truly democratized, making it available for each individuals. While we are not so distant from a personal AI butler, tech firms are already making efforts in that direction. The first and critical aspect of the rollout is deepening the AI integration to the core of mobile phones. From Samsung Electronics Co., LG Electronics Inc., Huawei Technologies Co. Ltd to Xiaomi Inc., a number of old and new smartphone players are leveraging Artificial Intelligence in their newer phones in one way or another. And then, we have companies like Google (Alphabet Inc.) and Microsoft Corp., which are incorporating AI in their software to deliver smarter products like Google Photos and Windows OS.
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Machine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
Far from being the stuff of science-fiction, artificial intelligence, or AI, is becoming an increasingly common sight in today's world. Combining the latest powerful software with top-of-the-range hardware, AI tools are being used to transform many areas of everyday life, from healthcare to traffic problems. But what is AI, and how is it being used today? Here is our guide to everything you need to know, and some of the most innovative and interesting use cases around today. For years, it was thought that computers would never be more powerful than the human brain, but as development has accelerated in modern times, this has proven to be not the case.
Welcome to the club if you are still behind the artificial intelligence curve. This is the last chapter of my AI series, and I hope it has shed a humble light upon the linchpin of the Fourth Industrial Revolution (4IR). Included below are links to previous installments. You do not want to miss the mini-documentary in part 3. Keep the following quotes in mind as I prognosticate today on AI jobs for the near-term. "I have all the tools and gadgets. I tell my son, who is a producer.
At the Artificial Intelligence Conference in New York, Kathryn Hume pointed me to Ellen Ullman's excellent book, Life in Code: A Personal History of Technology. In Part 3 of her book "Life, Artificial," Ullman talks about artificial intelligence, robotics, and the desire to create artificial life. What these views of human sentience have in common, and why they fail to describe us, is a certain disdain for the body: the utter lack of a body in early AI and in later formulations like Kurzweil's (the lonely cortex, scanned and downloaded, a brain-in-a-jar); and the disregard for this body, this mammalian flesh, in robotics and ALife [Artificial Life]. By connecting the poverty of AI with its denial of the body, Ullman follows an important thread in feminist theory: our thinking needs to be connected to bodies, to physical human process, to blood and meat. The male-dominated Western tradition is all about abstraction, for which Plato is the poster child.
In the 1970s, when Microsoft and Apple were founded, programming was an art only a limited group of dedicated enthusiasts actually knew how to perform properly. CPUs were rather slow, personal computers had a very limited amount of memory, and monitors were lo-res. To create something decent, a programmer had to fight against actual hardware limitations. In order to win in this war, programmers had to be both trained and talented in computer science, a science that was at that time mostly about algorithms and data structures. The first three volumes of the famous book The Art of Computer Programming by Donald Knuth, a Stanford University professor and a Turing Award recipient, were published in 1968–1973.