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) …
I have come to the personal conclusion that while all artists are not chess players, all chess players are artists. Originally called Chaturanga, the game was set on an 8x8 Ashtāpada board and shared two key fundamental features that still distinguish the game today. Different pieces subject to different rules of movement and the presence of a single king piece whose fate determines the outcome. But it was not until the 15th century, with the introduction of the queen piece and the popularization of various other rules, that we saw the game develop into the form we know today. The emergence of international chess competition in the late 19th century meant that the game took on a new geopolitical importance.
In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised. Various scenarios exist where a user's preferences over objectives (also known as the utility function) are unknown or difficult to specify. In such scenarios, a set of optimal policies must be learned. However, settings where the expected utility must be maximised have been largely overlooked by the multi-objective reinforcement learning community and, as a consequence, a set of optimal solutions has yet to be defined. In this paper we address this challenge by proposing first-order stochastic dominance as a criterion to build solution sets to maximise expected utility. We also propose a new dominance criterion, known as expected scalarised returns (ESR) dominance, that extends first-order stochastic dominance to allow a set of optimal policies to be learned in practice. We then define a new solution concept called the ESR set, which is a set of policies that are ESR dominant. Finally, we define a new multi-objective distributional tabular reinforcement learning (MOT-DRL) algorithm to learn the ESR set in a multi-objective multi-armed bandit setting.
With schools reopening worldwide, Google has worked hard to ensure the big market gains it made in 2020 can be sustained and strengthened as students return to physical rather than virtual classrooms. With user numbers of its digital learning platform, Google Classroom, up to 150 million from 40 million just a year before, it announced a new "road map" for the platform in early 2021. "As more teachers use Classroom as their'hub' of learning during the pandemic, many schools are treating it as their learning management system (LMS)," wrote Classroom's program manager. "While we didn't set out to create an LMS, Classroom is committed to meeting the evolving needs of schools." The road map for Classroom as a school LMS was just one plan laid out at its annual Learning with Google conference, which also included the launch of 40 new Chromebook laptop models alongside feature upgrades across its educational products.
Like a bolt from the blue, the Navy has a new modernization priority -- Project Overmatch, a campaign to accelerate delivery of artificial intelligence, machine learning and tools needed to allow the fleet to disperse forces, mass fires, integrate unmanned ships and, in the view of service leaders, maintain maritime dominance in the future. The project aims to begin delivering the Naval Operational Architecture (NOA), a lackluster name for a breathtaking effort whose results will determine nothing less than the service's future ability to establish and sustain sea control by integrating network infrastructure, data and analytic tools to provide decision-advantage in a fight. "Beyond recapitalizing our undersea nuclear deterrent, there is no higher developmental priority in the U.S. Navy," Chief of Naval Operations Adm. Mike Gilday wrote in Oct. 1, 2020, memo to Rear Adm. Douglas Small establishing Project Overmatch. "Your goal is to enable a Navy that swarms the sea, delivering synchronized lethal and nonlethal effects from near and far, every axis and every domain." Small, who in addition to heading Project Overmatch is head of Naval Information Warfare Systems Command, was further tasked by the CNO "to develop the networks, infrastructure, data architecture, tools, and analytics that support the operational and developmental environment that will enable our sustained maritime dominance." The two-star admiral says he has committed the memo to memory and, for good measure, carries a copy at all times.
You're probably reading this on a browser built by Apple or Google. If you're on a smartphone, it's almost certain those two companies built the operating system. You probably arrived from a link posted on Apple News, Google News or a social media site like Facebook. And when this page loaded, it, like many others on the Internet, connected to one of Amazon's ubiquitous data centers. Amazon, Apple, Facebook and Google -- known as the Big 4 -- now dominate many facets of our lives. But they didn't get there alone. They acquired hundreds of companies over decades to propel them to become some of the most powerful tech behemoths in the world.
Google's grip on the web has never been stronger. Its Chrome web browser has almost 70 percent of the market and its search engine a whopping 92 percent share. This story originally appeared on WIRED UK. But Google's dominance is being challenged. Regulators are questioning its monopoly position and claim the company has used anticompetitive tactics to strengthen its dominance.
Boosting techniques and neural networks are particularly effective machine learning methods for insurance pricing. Often in practice, there are nevertheless endless debates about the choice of the right loss function to be used to train the machine learning model, as well as about the appropriate metric to assess the performances of competing models. Also, the sum of fitted values can depart from the observed totals to a large extent and this often confuses actuarial analysts. The lack of balance inherent to training models by minimizing deviance outside the familiar GLM with canonical link setting has been empirically documented in W\"uthrich (2019, 2020) who attributes it to the early stopping rule in gradient descent methods for model fitting. The present paper aims to further study this phenomenon when learning proceeds by minimizing Tweedie deviance. It is shown that minimizing deviance involves a trade-off between the integral of weighted differences of lower partial moments and the bias measured on a specific scale. Autocalibration is then proposed as a remedy. This new method to correct for bias adds an extra local GLM step to the analysis. Theoretically, it is shown that it implements the autocalibration concept in pure premium calculation and ensures that balance also holds on a local scale, not only at portfolio level as with existing bias-correction techniques. The convex order appears to be the natural tool to compare competing models, putting a new light on the diagnostic graphs and associated metrics proposed by Denuit et al. (2019).
The National Security Commission on Artificial Intelligence today released its report today with dozens of recommendations for President Joe Biden, Congress, and business and government leaders. China, the group said, represents the first challenge to U.S. technological dominance that threatens economic and military power for the first time since the end of World War II. The commissioners call for a $40 billion investment to expand and democratize AI research and development a "modest down payment for future breakthroughs", and encourage an attitude toward investment in innovation from policymakers akin that which led to building the interstate highway system in the 1950s. The report recommends several changes that could shape business, tech, and national security. For example, amid a global shortage of semiconductors, the report calls for the United States to stay "two generations ahead" of China in semiconductor manufacturing and suggests a hefty tax credit for semiconductor manufacturers.
Over the past year, we have seen the acceleration of technology and science despite the devastation that COVID has had on businesses and many core industries. Artificial intelligence (AI) and machine learning (ML) have pushed science faster than ever before with the goal of a vaccine that is already rolling out across the nation. With a handful of additional companies already in late stage testing of a vaccine, we may see a return to normalcy in 2021, or rather a new-normal. Reflecting on the past year, we've seen major advancements in AI and ML and how these technologies impact core business planning and operations. Last year, commercial airlines were simply using machine learning tech to predict what passengers would order for lunch, this year ML is featured in the boardroom daily to better understand how to restart business, maximize services, reduce risk in a continuously volatile travel market, and more.
Most of the world has not yet experienced the benefits of a 5G network, but the geopolitical race for the next big thing in telecommunications technology is already heating up. For companies and governments, the stakes couldn't be higher. The first to develop and patent 6G will be the biggest winners in what some call the next industrial revolution. Though still at least a decade away from becoming reality, 6G -- which could be up to 100 times faster than the peak speed of 5G -- could deliver the kind of technology that's long been the stuff of science fiction, from real-time holograms to flying taxis and internet-connected human bodies and brains. The scrum for 6G is already intensifying even as it remains a theoretical proposition, and underscores how geopolitics is fueling technological rivalries, particularly between the U.S. and China.