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) …
Niti Aayog's strategy paper on Artificial Intelligence (AI), which outlines the opportunities offered by this rising technology and the challenges posed by it, calls for special attention from governments and policymakers. The endeavour of Artificial Intelligence is the development of intelligence in machines, either by feeding into them capability for specialised tasks or, increasingly, giving them the capability, such as sensors and large amounts of data, to learn on their own, called Machine Learning. The Niti Aayog paper presents the need to create a favourable AI ecosystem in India and identifies the sectors that need to be focused on, like agriculture, education, healthcare, infrastructure and transport. The economic rewards and social benefits will be great, as will be the disruptions caused by AI. The paper estimates that AI might account for about $1 trillion, or about 15% of GDP, by 2035.
The U.S. cotton market has remained stable since its spike in 2011, when China executed its cotton reserving and fiber hoarding plan. It is believed that U.S. cotton demand and price were artificially kept low because there are always worries that China would unexpectedly unleash its cotton stockpile, about half of the global storage. However, U.S. cotton price finally showed a revival in recent days. The ICE July cotton futures closed at 95.21 cents a pound on Tuesday, June 12, the highest level for a front-month future contract in the last 6 years. The revival could be attributed to multiple factors, with an emphasis on the worries about insufficient rain in the cotton-growing areas and the newly issued import quotas from China.
Questions like, "How did this shirt style perform five seasons ago?" "What color schemes are trending now?" "Is this trend close to being over?" and "What was our most popular piece in the last three runway collections?" AI is essentially drawing informed inspiration from past and current fashion trends, inside and outside of the brand, immediately -- a task that would take a human on a design teams days or weeks to achieve, and on a much less scientific level. Certain information scraped by the algorithms would be impossible for a human to acquire at all. For instance, the machine gathered Amazon and Zolando reviews for Tommy Hilfiger items to understand not just positive or negative reactions, but customers' views on items' fit, color options, price and quality.
Statistics and machine learning are two very closely related fields. In fact, the line between the two can be very fuzzy at times. Nevertheless, there are methods that clearly belong to the field of statistics that are not only useful, but invaluable when working on a machine learning project. It would be fair to say that statistical methods are required to effectively work through a machine learning predictive modeling project. In this post, you will discover specific examples of statistical methods that are useful and required at key steps in a predictive modeling problem.
The ultimate role of AI in marketing is to add value and purpose to both brands and customers. AI is a great tool that can augment the idea and capabilities of human marketers. According to the 2017 State of Inbound Report by HubSpot, generating traffic and leads and proving RoI, and lack of budget for marketing activities are the leading challenges that marketers face even today. Data overload from multiple sources, limited resources and time to implement activities, a crowded marketplace and increasing customer expectations make the job all the more difficult. Traditional marketing campaigns are far less efficient in gaining and retaining a customer.
Instead of merely selling copies of 1984, Amazon appears determined to help bring the dystopian classic's vision of widespread government surveillance to life. And Amazon employees are really not happy about it. In 2016, Amazon unveiled Rekognition, an AI-powered facial recognition software that scans videos or photos to detect people or objects. It can analyze a person's face to determine their emotions, identify 100 faces in a single photo, and track a person throughout a video even if they leave and reenter the field of view. In other words, it's a powerful surveillance tool, and government agencies and law enforcement are apparently two of Amazon's target customers.
TOPICS IN THIS INTERVIEW: 01:00 "Every corner of society will be transformed by artificial intelligence" 06:00 New industries emerging through A.I. 09:30 Machines making critical human-like decisions 12:50 Globalive pushing the emergence of A.I and Blockchain 18:40 Foreseeing the long-term outcome of Blockchain and smart technology
A little over a week after the fervor surrounding Google's involvement in the Department of Defense's Project Maven, an autonomous drone program, showed signs of abating, another machine learning controversy returned to the headlines: local law enforcement deploying Amazon's Rekognition, a computer vision service with facial recognition capabilities. In a letter addressed to Amazon CEO Jeff Bezos, 19 groups of shareholders expressed concerns that Rekognition's facial recognition capabilities will be misused in ways that "violate [the] civil and human rights" of "people of color, immigrants, and civil society organizations." And they said that it set the stage for sales of the software to foreign governments and authoritarian regimes. Amazon, for its part, said in a statement that it will "suspend … customer's right to use … services [like Rekognition]" if it determines those services are being "abused." It has so far declined, however, to define the bright-line rules that would trigger a suspension.
Members of the scientific and military communities from approximately 20 NATO countries came together to discuss leading developments in big data and artificial intelligence (AI) at the NATO Science and Technology Organization's specialists meeting, Big Data and Artificial Intelligence for Military Decision Making, from May 30 – June 01, 2018 in Bordeaux, France. Over the three day seminar, approximately 300 experts and researchers in data, artificial intelligence, modeling and simulation, and military operations discussed how technology, data and machine learning are increasingly influencing the trajectory of modern warfare – and the inherent risks and challenges involved. The meeting's purpose was to better inform technology acquisition, funding, and operational decisions within the NATO community. Specific topics focused on systems and operations, human-machine interface, security, and enabling technologies. Secretary of the Air Force for Operational Energy, Roberto Guerrero, led the senior leader panel on June 01.