Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Deep in the Mojave Desert, 60 miles from the city of Barstow, is the Slash X Ranch Cafe, a former ranch where dirt bike riders and ATV adventurers can drink beer and eat burgers with fellow daredevils speeding across the desert. Displayed on a wall alongside trucker caps and taxidermy is a plaque that memorializes the 2004 DARPA Grand Challenge, a 142-mile race whose starting point was at Slash X Ranch Cafe. It was the first race in the world without human drivers. Instead, it featured the fever-dream inventions -- robotic motorcycles, monster Humvees -- of a handful of software engineers who were hellbent on creating fully autonomous vehicles and winning the million-dollar prize offered by the Defense Department's Defense Advanced Research Projects Agency.
Beyond trendy names like Tesla and Alphabet chasing self-driving cars, a host of auto brands and other tech heavyweights are also investing in autonomous R&D. Private companies working in auto tech are attracting record levels of deals and funding, with autonomous driving startups leading the charge. Along with early-stage startups, VCs, and other investors, large corporations are also angling to get a slice of the self-driving pie. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. Using CB Insights' investment, acquisition, and partnership data, we identified over 40 companies developing road-going self-driving vehicles. They are a diverse group of players, ranging from automotive industry stalwarts to leading technology brands and telecommunications companies. This list is organized alphabetically and focuses on larger corporate players in the space (as opposed to earlier-stage startups). Companies working on industrial autonomous vehicles were not included in this analysis. A few of the companies or brands listed below belong to the same parent organization but are detailed separately if they are operating distinct autonomous development programs. Some companies are grouped together by key partnerships or alliances. Given the complex web of relationships between these players, other collaborations are also noted in each profile. This is not intended to be an exhaustive list of corporations working on autonomous vehicle technology. This brief was originally published on 9/25/2015 and featured 25 select corporations. It was updated and expanded on 5/17/2017, 9/4/2018, and 8/28/2019. Over the last decade, Amazon has spent billions of dollars working on finding ever-better solutions to the last-mile problem in delivery. It's built its own fleet of cargo jets, explored delivery by drone in the form of "Prime Air," and more. More recently, an increasing percentage of that investment has been directed toward autonomous vehicle technology. In February 2019, Amazon invested in Aurora Innovation, an autonomous tech startup run by former executives from two other firms with strong ties to self-driving technology: Google and Tesla. "Autonomous technology has the potential to help make the jobs of our employees and partners safer and more productive, whether it's in a fulfillment center or on the road, and we're excited about the possibilities." The Aurora investment isn't the only autonomous technology play that Amazon is pursuing. In January 2019, the company introduced the Amazon Scout, a six-wheeled electric-powered delivery robot.
On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.
Jurisdictions might be on-the-hook for their self-driving car laws that allow autonomous cars and for which might get into mishaps or crashes. Florida just passed a law that widens the door for self-driving driverless cars to roam their public roadways and do so without any human back-up driver involved. Some see dangers afoot, others see progress and excitement. Ron DeSantis, governor of Florida, declared that by approving the new bill it showed that "Florida officially has an open-door policy to autonomous vehicle companies." There are now 29 states that have various driverless laws on their books, per the National Conference of State Legislatures: Alabama, Arkansas, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Michigan, Mississippi, Nebraska, New York, Nevada, North Carolina, North Dakota, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, Vermont, Washington, and Wisconsin, plus Washington, D.C. Here's a question that some politicians and regulators are silently grappling with, albeit some think that they have the unarguably "right" answer and thusly have no need to lose sleep over the matter: Should states, counties, cities and townships be eagerly courting self-driving autonomous cars onto their public roadways, or should those jurisdictions be neutral about inviting them into their locales, or should they be highly questioning and require "proof until proven safe" before letting even one such autonomous car onto their turf?
San Diego-based startup TuSimple said its self-driving trucks will begin hauling mail between USPS facilities in Phoenix and Dallas to see how the nascent technology might improve delivery times and costs. A safety driver will sit behind the wheel to intervene if necessary and an engineer will ride in the passenger seat. If successful, it would mark an achievement for the autonomous driving industry and a possible solution to the driver shortage and regulatory constraints faced by freight haulers across the country. The pilot program involves five round trips, each totaling more than 2,100 miles (3,380 km) or around 45 hours of driving. It is unclear whether self-driving mail delivery will continue after the two-week pilot.
One of the most active areas of research and development at the moment is in the push towards self driving cars and autonomous transportation. Should self driving cars be realized it will free up time and reduce stress levels on the daily commute. If perfected it may also be safer than cars being driven by humans. Electric powered autonomous shuttles replacing gas guzzling cars could also have a significant, positive impact on the environment. It is no wonder, therefore that many of the leading players in the autonomous sector, as well as many major car manufacturers, are investing significant amounts of time and expense into developing smart self driving and autonomous solutions. The following list is organized alphabetically and will look at 33 of the leading companies who are currently pioneering self driving car technology. Since 2016 there has been much interest surrounding Project Titan. This is the name given to Apple's development program for self driving cars. However the drive for autonomous cars did not get off to a smooth start. Early setbacks, such as a rumoured hiring freeze following the departure of project head Steve Zadesky hit the program. There was also a rumours of uncertainty within Apple regarding the vision of the project. Since then Apple has tasked Bob Mansfield, a noted hardware executive, with leading their charge towards developing self driving cars. The subsequent appointment of Dan Dodge, the founder and former CEO of QNX indicates that Project Titan now has a firm strategy. This means that Apple is focusing the majority of its efforts on the development of autonomous or self driving cars. Apple documents released in April 2017 revealed that the company was working towards creating an "automated system". To help further their quest towards achieving self driving cars Apple also revealed that they had conducted a recruitment drive. This has seen the company hire some former Waymo and NASA's robotic engineering experts amongst others. The most notable of these recruits was Jaime Waydo, a senior engineer who had worked at NASA's Jet Propulsion Laboratory as well as on various Waymo projects. This recruitment drive has served to kick start Project Titan. Following the new recruits arrival vehicular patent activity connected to Apple has noticeably increased. Since 2018 Apple has been fleshing out its autonomous cars fleet. In the summer of 2018 the company officially had 66 vehicles registered with the California DMV most of which were also operating on public roads around the state. This means that Apple has the third largest fleet of self driving cars in California, behind only Waymo and GM Cruise.
This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.