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) …
Leave it to the folks at Google to devise AI capable of predicting which machine learning models will produce the best results. In a newly-published paper ("Off-Policy Evaluation via Off-Policy Classification") and blog post, a team of Google AI researchers propose what they call "off-policy classification," or OPC, which evaluates the performance of AI-driven agents by treating evaluation as a classification problem. The team notes that their approach -- a variant of reinforcement learning, which employs rewards to drive software policies toward goals -- works with image inputs and scales to tasks including vision-based robotic grasping. "Fully off-policy reinforcement learning is a variant in which an agent learns entirely from older data, which is appealing because it enables model iteration without requiring a physical robot," writes Robotics at Google software engineer Alexa Irpan. "With fully off-policy RL, one can train several models on the same fixed dataset collected by previous agents, then select the best one."
Despite advances in manufacturing that will move the bulk of product production away from mega-factories in developing countries back to Europe and America, logistics will always play a big role in a world of global trade. The industry employs millions of people worldwide, and even robots will struggle to make a big impact in the short-term. That is because logistics is inherently a complicated industry, with hundreds and thousands of variables, complex system, interconnectivity problems and standards varying from country to country, even port to port. If you have ever tracked a parcel through a delivery service, you know that at present we only have information when a package transit from one system to another, from one data point to another. We know very little about what happens to shipments in transit.
To describe the future of work, Richard Baldwin is developing a new lexicon. The professor of international economics at the Graduate Institute in Geneva warns that we are unprepared for the ways in which new technology is changing the nature of globalization. Baldwin's new book, The Globotics Upheaval: Globalization, Robotics, and the Future of Work, is a natural follow-up to his 2016 book, The Great Convergence. Three years ago, he explained how a third wave of globalization--a collapse in the cost of the movement of people thanks to technology--would be the most disruptive, because it hits workers in the service sector. Baldwin's new book, published earlier this year, breaks down what this disruption will entail.
They have reached the point where they learn by themselves and make their own decisions. The consequences can be downright freaky. There are machines that dream, read words in people's brains, and evolve themselves into art masters. The darker skills are enough to make anyone wear an anti-AI device, which is being developed. Some AI systems show signs of mental illness and prejudice, while others are too dangerous for release to the public. In 2019, a video was released on YouTube.
Here are 5 significant artificial intelligence trends to look forward to that will affect myriad industries on an international scale led by giant tech companies that are now investing huge sums in artificial intelligence research. Last year, implementations of AI rose significantly in so many platforms, tools and applications around the world, impacting healthcare, education and other industries as more and more people are opting for e-solutions based on AI and machine learning. Then there's the automotive industry with self-driving cars, the agricultural sector opting for intelligent robots to tackle the sowing as well as insecticide spraying on crops; the list goes on. As tech industry giants, including Google, Facebook and Amazon, invest billions now in AI and machine learning research, let's explore how 2019 is unfolding on this front. Major chip manufacturers including Intel, Nvidia, AMD and ARM aim to produce AI-powered chips to speed up the operations of applications that run on AI.
But the idea of AI -- of machines that can sense, classify, learn, reason, predict, and interact -- has been around for decades. Today, the combination of massive and available datasets, inexpensive parallel computing, and advances in algorithms has made it possible for machines to function in ways that were previously unthinkable.1 While the more obvious examples such as robotics, driverless cars, and intelligent agents such as Siri and Alexa tend to dominate the news, artificial intelligence has much wider implications. Gartner predicts that "by 2020, algorithms will positively alter the behavior of billions of global workers."2 Markets & Markets expects the AI market to reach $5.05B by 2020.3 This report lays out the current state of AI for business, describes primary and emerging use cases, and states the risks, opportunities, and organizational considerations that businesses are facing. It concludes with recommendations for companies thinking about applying AI to their own organizations and a look at some of the business, legal, and technical trends that are likely to shape the future. Executive Summary 1 What is Artificial Intelligence? 2 Use Cases for Artificial Intelligence 8 Implications and Recommendations 13 A Look at the Future 17 End Notes 19 Methodology 22 Acknowledgements 23 About Us 24 TABLE OF CONTENTS 3. www.altimetergroup.com
Chatbots today pop up at websites in smartphone apps; the same technology helps robots, smart speakers, and other machines operate in a more human-like way. The idea of conversing with a computer is nothing new. As far back as the 1960s, a natural language processing program named Eliza matched typed remarks with scripted responses. The software identified key words and responded with phrases that made it seem as though the computer was responding conversationally. Since then, such conversational interfaces--also known as virtual agents--have advanced remarkably due to greater processing power, cloud computing, and ongoing improvements in artificial intelligence (AI) and machine learning.
It's one thing to bring conversational bots and robotic process automation (RPA) into business applications. But the strongest opportunity for innovative companies is in combining these capabilities. I discovered how some market leaders have already found this exponential power during one session at the recent SAPPHIRE NOW and ASUG Annual Conference. "It's the convergence of these technologies that provide the opportunity for a better experience, as well as the ability to apply machine learning technologies to create an ever-learning enterprise, applying knowledge and skills acquired from that machine learning technology to create the Intelligent Enterprise," said Shawn Brodersen, global vice president and SAP CTO at HCL Technologies Ltd. One pharmaceutical company improved workforce engagement with intelligent RPA and conversational artificial intelligence (AI).
Since its founding in 1910, Japanese company Hitachi has been at the forefront of innovation with a philosophy to contribute to society through "the development of superior, original technology and products." Today, Hitachi is a multinational conglomerate that offers operational products and services as well as IT-related digital technologies such as artificial intelligence and big data analysis. Its artificial intelligence and machine learning technologies are impacting not only their own services and products but how other industries such as healthcare, shipping, finance operate. Announced in 2015, H is Hitachi's solution for a generalized artificial intelligence technology that can be applied to many applications rather than just built for a specific application. H supports a wide range of applications and can generate hypotheses from the data itself and select the best options given to it by humans.
LAUSANNE: Since the earliest days of virtual chess and solitaire, video games have been a playing field for developing artificial intelligence (AI). Each victory of machine against human has helped make algorithms smarter and more efficient. But in order to tackle real world problems – such as automating complex tasks including driving and negotiation – these algorithms must navigate more complex environments than board games, and learn teamwork. Teaching AI how to work and interact with other players to succeed had been an insurmountable task – until now. In a new study, researchers detailed a way to train AI algorithms to reach human levels of performance in a popular 3D multiplayer game – a modified version of Quake III Arena in Capture the Flag mode.