To me, the San Francisco streets seemed deserted. To my self-driving car, they were full of hazards. In mid-March, just as the coronavirus outbreak started to change the world as we knew it, I took a ride in an autonomous vehicle through the narrow and winding, topsy-turvy streets of downtown San Francisco -- from the hairpin turns of Lombard Street to the steep hills surrounding Coit Tower and the famed Embarcadero waterfront. Even with tens of thousands of workers staying put as the first work-from-home orders hit, in the back of a Toyota Highlander piloted by autonomous vehicle start-up Zoox, I started to become hyper-aware of the circus of hazards robocars encounter on a daily basis. There was a cyclist or skateboarder in the blind spot.
In 2004, the U.S. Department of Defense issued a challenge: $1 million to the first team of engineers to develop an autonomous vehicle to race across the Mojave Desert. Though the prize went unclaimed, the challenge publicized an idea that once belonged to science fiction -- the driverless car. It caught the attention of Google co-founders Sergey Brin and Larry Page, who convened a team of engineers to buy cars from dealership lots and retrofit them with off-the-shelf sensors. But making the cars drive on their own wasn't a simple task. At the time, the technology was new, leaving designers for Google's Self-Driving Car Project without a lot of direction.
In early 2009, the Google Self-Driving Car Project was born, evolving over the years into what we now know as Waymo. Not long after the project started, Google founders Sergey Brin and Larry Page challenged engineers to drive autonomously without human intervention or disengagements along ten challenging 100-mile routes in Google's home state of California. Yes, that was long before state governments started granting licenses for driverless car testing on public roads. The first state to issue such a license was Nevada in May 2012, with California doing the same only in September 2014. So how did Google get away with testing fully-autonomous cars on public roads in California in 2009?
Four years ago, mathematician Vlad Voroninski saw an opportunity to remove some of the bottlenecks in the development of autonomous vehicle technology thanks to breakthroughs in deep learning. Now, Helm.ai, the startup he co-founded in 2016 with Tudor Achim, is coming out of stealth with an announcement that it has raised $13 million in a seed round that includes investment from A.Capital Ventures, Amplo, Binnacle Partners, Sound Ventures, Fontinalis Partners and SV Angel. More than a dozen angel investors also participated, including Berggruen Holdings founder Nicolas Berggruen, Quora co-founders Charlie Cheever and Adam D'Angelo, professional NBA player Kevin Durant, Gen. David Petraeus, Matician co-founder and CEO Navneet Dalal, Quiet Capital managing partner Lee Linden and Robinhood co-founder Vladimir Tenev, among others. Helm.ai will put the $13 million in seed funding toward advanced engineering and R&D and hiring more employees, as well as locking in and fulfilling deals with customers. Helm.ai is focused solely on the software.
Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).
Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relations among them. Finally, we discuss the current gaps and suggest future research directions.
Well-designed technologies that offer high levels of human control and high levels of computer automation can increase human performance, leading to wider adoption. The Human-Centered Artificial Intelligence (HCAI) framework clarifies how to (1) design for high levels of human control and high levels of computer automation so as to increase human performance, (2) understand the situations in which full human control or full computer control are necessary, and (3) avoid the dangers of excessive human control or excessive computer control. The methods of HCAI are more likely to produce designs that are Reliable, Safe & Trustworthy (RST). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.
The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.
Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity. However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence. Some even see AI as dehumanizing, dystopian, and a threat to humanity. As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...
This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Tantalizingly close and yet still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood. You've heard the consumer hype, but what gets less ink are the transformative changes that autonomous vehicles will bring -- in some cases already are bringing -- to the enterprise. Affecting sectors as disparate as shipping and logistics, energy, agriculture, transportation, construction, and infrastructure -- to name just a few -- it's hard to overstate the impact of the diverse and versatile set of technologies lumped into the decidedly broad category of'autonomous vehicles'. This guide will help you sort the hype from the business reality and tell you all you need to know about the autonomous vehicle revolution on the ground, in the air, and even at sea. In 1939, General Motors predicted we'd have an autonomous vehicle highway system up and running by the dawn of the 1960s. As with a lot of autonomous vehicle hype, that prediction was a tad premature, but it demonstrates the long history of autonomous vehicle development.