Prof Stephen Hawking, one of Britain's pre-eminent scientists, has said that efforts to create thinking machines pose a threat to our very existence. He told the BBC:"The development of full artificial intelligence could spell the end of the human race." His warning came in response to a question about a revamp of the technology he uses to communicate, which involves a basic form of AI. The theoretical physicist, who has the motor neurone disease amyotrophic lateral sclerosis (ALS), is using a new system developed by Intel to speak. Machine learning experts from the British company Swiftkey were also involved in its creation.

Machine learning is based on old artificial intelligence concepts. It was first defined in 1959 as the ability... This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners.

Okay, automatic, artificial intelligence cargo inspection isn't actually a thing that's happening right now, but research at University College London has proven that it's a viable solution to a very real problem. A team at the school's Department of Computer Science successfully trained a convolutional neural network to spot automobiles in X-ray images of shipping containers. The neural network was startlingly accurate -- correctly identifying cars 100-percent of the time with very few false alarms. The system even spotted cars in images that were challenging for human observers, finding the vehicles that were intentionally obscured by other objects. It wasn't a revolutionary study, to be sure, but the project is a great example of how deep learning image recognition will be used to make our lives easier in the future.

Microsoft CEO Satya Nadella, shown here at the company's shareholder meeting in December 2015. Satya Nadella is a believer in the vast promise of artificial intelligence. But the Microsoft CEO says humans and machines need to work together to solve the world's great societal challenges, including issues of diversity and inequality. And responsibility is in the hands of the designers. "Ultimately, it's not going to be about human vs. machine," Nadella wrote in a piece on Slate later reposted to LinkedIn, the professional networking service Microsoft is buying.

But where do you start? Which library do you use? There are just so many! This list is by no means exhaustive, it's simply a list of libraries that I've used in my computer vision career and found particular useful at one time or another. Some of these libraries I use more than others -- specifically, Keras, mxnet, and sklearn-theano.

A graph is a representation of a set of objects where some pairs of objects are connected by links. The interconnected objects are represented by mathematical abstractions called vertices (also called nodes orpoints), and the links that connect some pairs of vertices are called edges (also called arcs or lines). Typically, a graph is depicted in diagrammatic form as a set of dots for the vertices, joined by lines or curves for the edges. Graphs are one of the objects of study in discrete mathematics. The edges may be directed or undirected.

It was at Carnegie Mellon where Dr. Hon began seriously building the foundation for his later work in machine-human interaction. His PhD supervisor, Turing Award winner Raj Reddy, was a former student of John McCarthy, the computer scientist who coined the term'artificial intelligence' in 1956 and is widely known as the father of AI. This connection would have richly benefited Dr. Hon, except that an'AI Winter' was happening from 1986 to 1992 when he was completing his doctorate degree. Recalling the climate of this period, "government agencies, universities and even companies slowed down or stopped funding to the field. It was only until the first decade of the 2000's that AI got so hot," said Dr. Hon. Dr. Hon credits the power of improved hardware and software and Big Data for heating up the AI scene.

In the field of artificial intelligence, the phrase deep learning applies to software that improves its model of reality with experience. Consider, for example, a project developed at Google in 2012, in which a neural network running on 16,000 computer processors, browsing through 10 million YouTube videos, began on its own to identify and seek out one of the most popular YouTube genres: cat videos. The then director of that project, Andrew Ng, went on to become the founding chief scientist at Baidu Research, an innovation center run by the giant Web services company Baidu. The parent company owns the largest search engine in China, along with Chinese-language browsers, online encyclopedias, social networks, and other Web-based services. According to the company, Baidu responds to more than 6 billion search requests from more than 138 countries every day. Because search engines and advertising placement platforms (such as Baidu's Phoenix Nest) depend on artificial intelligence (AI) to satisfy vague or ambiguous requests, the company -- along with Google, Microsoft, and other providers of internet guidance -- has a natural interest in machine learning.

Daniel Schwartzkopff, commercial director and DataProphet co-founder, says the start-up is looking towards the European and North American markets for further expansion. Yellowwoods Capital Holdings, a member of the European-based global investment and private equity focused Yellowwoods Group, has acquired a significant interest in DataProphet, where the local start-up will act as the advanced analytics partner for the group. The group's local investments include Hollard Insurance, Clientele and Nandos, among others. While the actual value of the investment into the start-up remains under wraps, DataProphet commercial director and co-founder, Daniel Schwartzkopff, says the business has hit multimillion-dollar status. "As a private fund, our investor partners would prefer us not to discuss the amount. It can be disclosed, however, that DataProphet was priced at a multimillion-dollar valuation," he explains.

After fifty years of soaring crop yields thanks to fertilizers, pest control, and irrigation, that growth is bottoming out. We solved the food shortfall in the 20th century, but we need to do it again in this century. The UN says crop production must rise 70% by 2050 to meet demand. Startups see cheap sensors and artificial intelligence as the solution. Clever algorithms are processing a deluge of high-resolution data enabling real-time monitoring of crops and their environment for the first time.