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) …
The latest market analysis report on the Deep Learning market performs industry diagnostic as a way to accumulate valuable data into the business environment of the Deep Learning market for the forecast period 2019 – 2026. The subject matter experts behind the research have collected vital statistics on the market share, size and growth as a way to help stakeholders, business owners and field marketing personnel identify the areas to reduce costs, improve sales, explore new opportunities and streamline their processes. Unbiased perspective on intangible aspects such as key challenges, threats, new entrants as well as strengths and weaknesses of the prominent vendors too are discussed in this market intelligence report. By offering expert assistance, it would be able to assist humans in extending their capabilities. Organizations are using deep learning networks to get valuable insights from huge amount of data.
If you have seen one of the many schematic charts full of logos illustrating the autonomous vehicle ecosystem, you would be forgiven for being confused. Most, like the one linked to in the above paragraph, dive deep into the layers of technology involved in enabling cars to drive themselves. It provides a nice summary for people in the industry (with good eyesight). To the layperson, however, this can add to the confusion about how autonomous vehicles work. Also, it is important to note that the majority of the companies and the technologies represented only have to do with the vehicles.
As we can see from the hardening religious impulse in our current authoritarian, autocratic moment, the traditionalist defense of "civilization" ultimately rests upon the complicated, combined influence of the major Abrahamic revealed religions (Judaism, Christianity, and Islam). The Abrahamic faiths – based as they are on divine revelation, sacred texts and prophetic moments – require a creator-centric moral order that exists outside of time and space. In the last millennium, Thomist natural law – the philosophical thread of law and logic running through many of our core assumptions about "the West" – has extended and refined, but in no way refuted, these central premises of the Abrahamic religions. In The Creation Project, my argument has been that the axis of conflict in the coming decades will be a civilizational battle between two irreconcilable, non-liberal (i.e., non-Enlightenment) regimes and worldviews – backward-looking, creator-centered natural law and forward-looking creation-centered complexity science. In prior posts, I have specifically focused on Princeton professor Robby George's views on natural law because his ideas distill nearly everything about the foundational beliefs of western civilization that complexity science calls into question, and that require root-and-branch reassessment.
In response to the serious threat that AI-enabled bots and deepfakes pose for election integrity, the California government has pushed forward progressive pieces of legislation that have influenced federal and international efforts. Passed in 2018, the "Bots Disclosure Act" makes it unlawful to use a bot to influence a commercial transaction or a vote in an election without disclosure in California. This includes bots deployed by companies in other states and countries, which requires those companies to either develop bespoke standards for Californian residents or harmonize their strategies across jurisdictions to maintain efficiency. At the federal level, the "Bots Disclosure and Accountability Act" includes many of the same strategies proposed in California. The California "Anti-Deepfakes Bill" seeks to mitigate the spread and impact of malicious political deepfakes before an election and the federal "Deepfakes Accountability Act" seeks to do the same.
Automation is coming, pant the breathless pundits warning of A.I.-induced job loss. Ratcheting up the fear meter, presidential candidate Andrew Yang recently sounded the alarm for unprecedented employment gutting -- not just among blue-collar professions, but white-collar jobs, too. Meanwhile, renowned studies paint a gloomy picture, one in which rapid A. advances kneecap our middle-class dreams, sapping the hopes of young people who are left to wonder: Will there be a job for me when I graduate? And yet, the on-the-ground reality doesn't fit these sour prognostications. If anything, it offers good news for workers.
For many people, the general idea of artificial intelligence still conveys a dark, robot-master future. Yet simply add the word "conversational" in front of it, and suddenly it's a whole lot friendlier. Maybe that's why conversational AI is this year's "everywhere topic." It's a consensus pick in every technology forecast; a keynote subject at business, marketing, and IT conferences; a core capability claimed by software vendors of all sizes and specializations; and the destination of millions in venture funding. As evidenced by this year's CES conference, it's also reaching ever further into the connected home, vehicle, and workplace, at lightning speed.
This is a practical introduction for using iOS 6 to create universal apps. If you have prior experience programming in an object-oriented language and are eager to start building universal apps for iPad and iPhone (including the iPod touch), then this is the book for you! Using the latest version of iOS (iOS 6) along with the latest version of Xcode (Xcode 4.5), this book is a practical introduction rather than just a catalog of components. Full-color and packed with groundbreaking, innovative designs, this book teaches you how to create eye-catching, unique apps. It teaches you the various aspects of iOS development, beginning with getting started with iOS 6, getting Up to Speed with Xcode, and learning the tools and Objective-C.
If customer experience is the center of digital transformation, customer relationship management (CRM) must be central to managing that experience. But mentioning the term "CRM" in your meeting room often leads to groans of disgust rather than coos of excitement. Indeed, most companies have a love/hate relationship with their customer management software. It allows them to keep in touch with the people keeping them in business. But in many cases, it's sluggish, time-sucking, and confusing--not words you'd like to describe the tech most central to your company's success.
Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionising supply chain management in the process. Machine learning algorithms and the models they're based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon's Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyses 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions.