A team of computer scientists has used theoretical calculations to argue that algorithms could not control a super-intelligent AI. Their study addresses what Oxford philosopher Nick Bostrom calls the control problem: how do we ensure super-intelligence machines act in our interests? The researchers conceived of a theoretical containment algorithm that would resolve this problem by simulating the AI's behavior, and halting the program if its actions became harmful. If you break the problem down to basic rules from theoretical computer science, it turns out that an algorithm that would command an AI not to destroy the world could inadvertently halt its own operations. If this happened, you would not know whether the containment algorithm is still analyzing the threat, or whether it has stopped to contain the harmful AI.
For the past 4 years, the Trump Administration has been committed to strengthening American leadership in artificial intelligence (AI). After recognizing the strategic importance of AI to the Nation's future economy and security, the Trump Administration issued the first ever national AI strategy, committed to doubling AI research investment, established the first-ever national AI research institutes, released the world's first AI regulatory guidance, forged new international AI alliances, and established guidance for Federal use of AI. Building upon this critical foundation, today the White House Office of Science and Technology Policy (OSTP) established the National Artificial Intelligence Initiative Office, further accelerating our efforts to ensure America's leadership in this critical field for years to come. The Office is charged with overseeing and implementing the United States national AI strategy and will serve as the central hub for Federal coordination and collaboration in AI research and policymaking across the government, as well as with private sector, academia, and other stakeholders. The National AI Initiative Office is established in accordance with the recently passed National Artificial Intelligence Initiative Act of 2020.
According to the National Oceanic and Atmospheric Administration (NOAA), more than 80% of the ocean "remains unmapped, unobserved, and unexplored" – despite constituting more than 70% of the planet's surface. Now, a pair of Navy veterans are looking to change that with a line of autonomous robot vehicles that will plunge the ocean's depths in search of big data for the company's clients. "The company really started when Joe [Wolfel] and I first got together, which was back in 2004," said Judson Kauffman, who shares the CEO role with Wolfel, in an interview with Datanami. "We met in [Navy] SEAL training together, and ended up being assigned the same unit, and then went into combat together and became very close friends. There, they developed the idea for Terradepth, which "stemmed from some knowledge that we gained in the Navy" – really, Kauffman said, "just of how ignorant humanity is of what's underwater, what's in the sea." "It was shocking to learn how little we know, how little the U.S. Navy knew," he continued – and the more they dug into the issue after their time in the Navy, the more surprised they were.
Artificial Intelligence (AI) has come a long way over the past few years in simulating human intelligence. Today, AI is the lifeblood of almost every organisation cutting across sectors including, retail, financial, healthcare, among others. Here's an updated list of 10 best intro books on artificial intelligence geared towards AI enthusiasts. About: Mathematics and statistics are the backbone of artificial intelligence. This book is perfect for understanding the basics and the mathematics behind AI.
The field of artificial intelligence (AI) has created computers that can drive cars, synthesize chemical compounds, fold proteins and detect high-energy particles at a superhuman level. However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation. Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.
The first wave of artificial intelligence (AI) has already replaced humans for repetitive physical tasks like inspecting equipment, manufacturing goods, repairing things, and crunching numbers. That shift started way back with the Industrial Revolution. This gave rise to our current Thinking Economy, where employment and wages are more tied to workers' abilities to process, analyze and interpret information to make decisions and solve problems … Just like the industrial revolution automated physical tasks by decreasing the value of human strength and increasing the value of human cognition, AI is now reshaping the landscape and ushering in a Feeling Economy. What characterizes this emerging economy? Consider, for example, the role of a financial analyst, which seems pretty quantitative and thinking-oriented.
Almost 40% of IT leaders have adopted artificial intelligence (AI) or machine learning (ML). According to a report by RobertHalf, another 33% said that they plan to use AI within the next three years, and 19% anticipate using it within the next five years. These five tools can help keep your company connected with customers in 2021. It provides continuous, personalized, messaging-based experiences. Amplify uses next-generation chatbots that create a wide range of conversational engagement, from text-only to media-rich.
Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that a single step size (learning rate) is used for all input variables. Extensions to gradient descent like AdaGrad and RMSProp update the algorithm to use a separate step size for each input variable but may result in a step size that rapidly decreases to very small values. The Adaptive Movement Estimation algorithm, or Adam for short, is an extension to gradient descent and a natural successor to techniques like AdaGrad and RMSProp that automatically adapts a learning rate for each input variable for the objective function and further smooths the search process by using an exponentially decreasing moving average of the gradient to make updates to variables. In this tutorial, you will discover how to develop gradient descent with Adam optimization algorithm from scratch.
Trustable data is defined as data that comes from reliable sources and used according to its intended and delivered in the appropriate formats and time frames for the specific users. Trustable data helps in effective decision making. The properties mentioned in the definition makes data trustworthy for effective decision making. Trustable data are good only if they meet certain basic requirements. Most AI and Machine learning algorithms require their data aligned in a very specific way.
The edge is an end point where data is generated through some type of interface, device or sensor. Keep in mind that the technology is nothing new. But in light of the rapid innovations in a myriad of categories, the edge has become a major growth business. "The edge brings the intelligence as close as possible to the data source and the point of action," said Teresa Tung, who is the Managing Director at Accenture Labs. "This is important because while centralized cloud computing makes it easier and cheaper to process data at scale, there are times when it doesn't make sense to send data off to the cloud for processing."