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) …
Global Artificial Intelligence (AI) in Automotive Market has valued 566.80 Mn in 2016 and is estimated to reach US$ 10,600.3 Global Artificial Intelligence (AI) in Automotive Market is segmented by technology, offering, process, application, and geography. By technology, Global Artificial Intelligence (AI) in the automotive market is divided into Computer Vision, Machine Learning, Context Awareness, natural language processing. Based on the offering, Artificial Intelligence (AI) in Automotive Market is categorized hardware and software. By process, the market is fragmented into Data Mining, Signal Recognition, and Image Recognition.
Google's latest smartphone demonstrates how artificial intelligence and software can enhance a camera's capabilities, one of the most important selling points of any mobile device. The Pixel 4, the latest entrant in a phone line defined by its cameras, touts an upgraded ability to zoom in when shooting photos as its biggest upgrade. But the Alphabet Inc. company isn't going about it the way that Samsung Electronics Co., Huawei Technologies Co. or Apple Inc. have done -- instead of adding multiple cameras with complicated optics, Google has opted for a single extra lens that relies on AI and processing to fill in the quality gap. In place of the usual spec barrage, Google prefers to talk about a "software-defined camera," Isaac Reynolds, product manager on the company's Pixel team, said in an interview. The device should be judged by the end-product, he argued, which Google claims is a 3x digital zoom that matches the quality of optical zoom from multi-lens arrays.
"We are probably one of the last generations of homo sapiens." Those were the opening words of acclaimed historian and best-selling author Professor Yuval Harari, who spoke at the World Economic Forum Annual Meeting in Davos, Switzerland, where politicians, thought leaders and executives from the world's leading companies congregate to discuss solutions to global challenges. What comes after us, Harari said, are entities that are more different from us than we were from our predecessors, the Neanderthals. However, those species will not be the outcome of the organic evolution of human genes, Harari explained, but the outcome of humans learning to engineer bodies, brains and minds. "This will be the main product of the economy of the 21st century."
Tank warfare isn't traditionally easy to predict. In July 1943, for instance, German military planners believed that their advance on the Russian city of Kursk would be over in ten days. In fact, that attempt lasted nearly two months and ultimately failed. Even the 2003 Battle of Baghdad, in which U.S. forces had air superiority, took a week. The U.S. Army has launched a new effort, dubbed Project Quarterback, to accelerate tank warfare by synchronizing battlefield data with the aid of artificial Intelligence.
We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. Representing connected data is possible using a graph data structure regularly used in Computer Science. In this article, we will provide an introduction to the assorted types of connected data, what they represent, and the challenges we can solve.
Reinforcement learning (RL) practitioners have produced a number of excellent tutorials. Most, however, describe RL in terms of mathematical equations and abstract diagrams. We like to think of the field from a different perspective. RL itself is inspired by how animals learn, so why not translate the underlying RL machinery back into the natural phenomena they're designed to mimic? Humans learn best through stories.
Armed violence is on the rise and we don't know how to stop it1. Since 2011, conflicts worldwide have killed up to 100,000 people a year, three-quarters of whom were in Afghanistan, Iraq and Syria. The rate of major wars has decreased over the past few decades. But the number of civil conflicts has doubled since the 1960s, and terrorist attacks have become more frequent in the past ten years. The nature of conflict is changing.
Greater scrutiny on these databases has spurred some progress. ImageNet, an online image database, recently said it would remove 600,000 pictures of people from its system after an art project showed the severity of the bias wired into its artificial intelligence. Artist Trevor Paglen and AI researcher Kate Crawford showed how the system could generate derogatory results when people uploaded photos of themselves. A woman might be called a "slut," for example, and an African American user could be labeled a "wrongdoer" or with a racial epithet.
Some years ago, at Gramener, we built a customer churn modeling solution for one of the largest global telecom operators. The machine learning solution predicted which of their customers would leave, one month before they stopped usage. In test pilots, the solution helped reduce customer churn by more than 56 percent compared to the earlier process. We were amazed at the impressive results and stellar accuracy. But the celebrations were a bit premature, for the solution was never used.
This headline may seem a bit odd to you. Since data science has a huge impact on today's businesses, the demand for DS experts is growing. At the moment I'm writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it's important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions. To help you out, our data-obsessed CV Compiler team analyzed some vacancies and defined the data science employment trends of 2019.