The video discusses the various approaches to self-driving cars, with particular focus on self-driving cars that transport humans. It is noted that it is difficult to predict when each approach will become widespread, but that all of them are likely to have an impact in the near future.

Mark Normand discusses the inevitability of self-driving cars, and how truckers will need to adapt.

Chris Urmson, the head of Google's self-driving car project, discusses the challenges and opportunities of developing self-driving cars. He notes that costs must be reduced to make self-driving cars economically viable, and that human factors such as vigilance and complacency must be taken into account. He also discusses the importance of active safety systems.

Chris Urmson discusses the various efforts being made to create self-driving cars, including at Aurora, Google, CMU, and DARPA. He argues that there will be individual metrics to measure safety outside of fatalities and that ultimately the goal is to make roads safer while reducing the cost of mobility.

Chris Urmson, head of Google's Self-Driving Car project, discusses the challenges of developing self-driving cars and how they will become more advanced over time. He also discusses the importance of having a good perception forecasting capability to anticipate future traffic patterns.

Chris Urmson discusses the potential benefits of self-driving cars, and the need for careful communication of the technology's capabilities. He believes that the technology will diverge from the path needed to achieve truly self-driving cars, and that the economics of gradual increasing from level 2 will not be cost-effective in the long term.

The video discusses the importance of the Defense Advanced Research Projects Agency's (Darpa) 2000 challenges, which led to the development of self-driving vehicles. The author argues that if this technology fails, it will be the biggest technology failure in human history.

Dmitri Dolgov discusses the progress Waymo has made in developing self-driving cars, including their success in driving 10 routes without any human intervention. He also talks about the importance of believing that the technology is feasible and discusses some of the lessons he learned while working on the project.

Dmitri Dolgov discusses how self-driving cars are becoming more and more reliant on data-driven development, and how various models of machine learning are used to fuse sensory data together to make accurate predictions. He also discusses how to inject inductive bias into a self-driving system in order to encourage predictability and human-like decision-making.

Dmitri Dolgov discusses how Waymo and other self-driving car companies approach decision making and planning. He notes that the technology and principles that apply to self-driving cars across all domains continue to carry over between domains.

Dmitri Dolgov discusses the potential for self-driving cars to lead to more user-friendly car interfaces and delightful experiences for riders. He also discusses the potential for ride-sharing companies to improve the customer experience by removing any friction or complexity.

Dmitri Dolgov discusses how Waymo's self-driving cars are built and operated, and how Alphabet's ML infrastructure helps the process. He notes that while self-driving cars are still in the early stages of development, they are making great strides in terms of data collection and analysis.

Dmitri Dolgov discusses Waymo's approach to self-driving cars, focusing on the company's focus on safety, privacy, and comfort for passengers. He argues that humans must "flirt" with the rules of the road in order to be efficient and safe, and that Waymo wants to create a driver archetype that is both safe and efficient.

Dmitri Dolgov discusses Waymo's plans for self-driving cars and the future of the technology. He discusses the importance of customer service and the role that humans will play in the cars' operation. Waymo plans to open its self-driving service to the public in Phoenix on October 8th.

Dmitri Dolgov discusses how Waymo has learned from previous self-driving car projects and incorporated user feedback into its system. He also discusses why it is important for a self-driving car to arrive reliably at the destination.

Dmitri Dolgov discusses his time working on the self-driving car project at Waymo, the challenges and lessons learned during that time.

Dmitri Dolgov of Waymo discusses the company's journey from developing self-driving cars to having a public commercial service available to users. He talks about the fifth generation of hardware that is on the new vehicle and how it is qualitatively different from the previous generations.

Dmitri Dolgov discusses his experience riding in Waymo's self-driving cars, which he found to be more assertive and efficient than autonomous vehicles typically are. He also discusses how Waymo's trucks differ from autonomous trucking and how the company's fundamental challenges in this area carry over to other aspects of driver training and trucking.

In this video, Dmitri Dolgov discusses how Waymo's self-driving cars are performing and how they are able to avoid collisions. He also discusses the trolley problem, which is a difficult philosophical construct that highlights the difficult ethical decisions that humans must make. He says that although humans are not particularly good at making these decisions, the algorithm must still make them.

Dmitri Dolgov discusses Waymo's origins and its mission to build self-driving cars. He shares the story of the Stanford victory lap, a metaphor for the project's successes.

Dmitri Dolgov discusses the challenges of autonomous driving and how Waymo is working to overcome them. He explains that Waymo is particularly focused on safety around vulnerable road users, such as pedestrians and cyclists, and emphasizes the importance of performance across all senses in order to be successful. Dolgov also discusses a recent incident in which a woman tripped and fell into the path of a Waymo car, illustrating the importance of strong reactions in autonomous driving.

Dmitri Dolgov discusses his admiration for Google's self-driving car project and how it has changed his perspective on life. Dolgov says that he's a huge fan of both Waymo and technology in general, and he looks forward to seeing what the future holds for self-driving cars and technology in general.

Wojciech Zaremba discusses the challenges of creating self-driving cars, and how much interaction is necessary to achieve a compelling experience for the user. He also discusses the importance of touch in human-robot interactions.

George Hotz discusses his paper on self-driving cars, which he believes will be looked back as the "canonical paper" of the deep learning era. He describes how self-driving cars will require a transition from human-driven cars to vehicles that are driven by neural networks. He explains that monitoring is already adaptive and that the "driver monitoring policy" is adjusted based on the situation. He predicts that self-driving cars will be available by the 2020s.

Sebastian Thrun discusses his work on deep learning and self-driving cars, as well as his experience founding and running companies such as GM Cruise and Uber. He notes that self-driving cars are likely to have a large impact on society, and argues that everyone should be able to build self-driving cars.

Sebastian Thrun discusses the challenges and opportunities associated with autonomous vehicles and related fields, such as machine learning. He describes his experiences working on the DARPA Grand Challenge and the Stanford autonomous car project. He also discusses the importance of team spirit and the importance of developing robust systems.

The video discusses how Waymo plans to develop self-driving cars, and how the company is facing unique physical and perceptual challenges. The long-term goal is to expand self-driving technology to more diverse types of vehicles and routes.

Boris Sofman, a professor of engineering at the University of California-Berkeley, discusses the potential implications of self-driving cars on society. He notes that while it is difficult to drive, self-driving cars offer a life-and-death situation that brings value to millions of people. He also discusses the potential for society to embrace the benefits of self-driving cars.

The YouTube video "Boris Sofman: Waymo, Cozmo, Self-Driving Cars, and the Future of Robotics | Lex Fridman Podcast #241" discusses the significance of Waymo, Cozmo, and self-driving cars, and how they are changing the future of robotics. Waymo has found a way to make money as a safety driver, and the long tail of computer vision is still very difficult. Tesla is helping to push self-driving cars forward by having humans interact with the robots.

Boris Sofman discusses Waymo's self-driving cars and how they are different from other self-driving cars on the market. Sofman also discusses the importance of combining different sensors to achieve a better understanding of the surrounding environment.

Boris Sofman, a robotics expert, discusses how the development of self-driving cars will lead to more characters in the physical world that are more forgiving of human error. He also discusses how intelligent these characters need to be in order to form fulfilling relationships.

Boris Sofman, a professor at the MIT Sloan School of Management, discusses the future of robotics and self-driving cars. He predicts that automation will create new jobs while taking away others, and warns of the potential consequences of increased efficiency. He also discusses the benefits of increased efficiency, and how it will benefit the environment and safety.

Boris Sofman, a research scientist at Google, discusses the company's work on self-driving cars and the future of robotics. Sofman notes that while there are still many challenges to overcome, the potential for autonomous vehicles is great. He also discusses the importance of randomness in the development of autonomous systems.

Boris Sofman, a robotics expert, discusses the challenges of developing and releasing autonomous cars, and how society will deal with the potential for accidents. He also discusses Cosmo, a self-driving dog, and how the public perceives the risks and benefits of autonomous technology.

Boris Sofman, a robotics expert, discusses how difficult it is to create a popular and successful self-driving car company like Waymo. He also recommends caution when trying to replicate the success of Cosmo, as it requires a unique convergence of skills and experiences.

Boris Sofman discusses how Waymo's self-driving cars and Cozmo, its physical robot, are powered by computation and artificial intelligence, and how the latter allows for more complex and immersive experiences. He also discusses how the company's first product, drive, functions as a video game engine for the physical world, and how its second product, cosmo, expands that engine to create a character and animation and interaction engine.

Boris Sofman, a robotics expert, discusses the differences between self-driving cars and trucks, and how simulation can be used to help improve autonomous driving. He also discusses the challenges of testing autonomous driving in the real world, and how validation helps ensure safety.

Boris Sofman, a professor of computer science at the University of Illinois, discusses the differences between Waymo and Tesla's approaches to self-driving cars and the future of robotics. He discusses the importance of the touring test, which is used to assess a driver's general intelligence, and argues that Waymo is likely to win the race to level four because of its mastery of the average case.

Boris Sofman discusses how Waymo, Cozmo, and self-driving cars are enabling the future of robotics, and how the first checkpoint in the journey to autonomous trucking is not that far off. He goes on to discuss how the learnings of partnering with large fleets will drive the roadmap for autonomous trucking.

In this talk, Boris Sofman discusses how Waymo, Cozmo, and self-driving cars differ from traditional vehicles, and how developers must approach quality assurance in these new technologies. He also discusses how traditional structural testing methods may not be applicable to driverless car development, and how developers must be creative in order to address potential issues.

Boris Sofman discusses how Waymo, Cozmo, and self-driving cars will change the future of robotics, and how being thoughtful about the application and market can help overcome obstacles.

The video discusses how Waymo, a self-driving car company, is focusing on the long-haul freeway stretch of it where that's where a majority of the value is captured. Boris Sofman, a professor at the University of California, Berkeley, discusses how the level of automation (level four) affects what can be automated and the different levels of automation. He also talks about trucking and how, like with self-driving cars, every route segment (or dot) that is added increases the value of the whole network.

Boris Sofman, a robotics expert and futurist, discusses how the current AI and robotics landscape compares to the past, how self-driving cars are changing the future, and how companies like Anki can succeed despite the current market conditions.

John Carmack believes that self-driving cars could be a major step towards artificial general intelligence (AGI), and that AGI does not need to be embodied in the physical world to be successful. He believes that virtual environments can provide much of the same value as real-world environments, and that restricting AGI to operating in the real world would be a major handicap.

In this excerpt, John Carmack talks about the potential for self-driving cars to save lives, the progress that has been made in AI and machine learning, and the importance of nuclear energy. He argues that the potential for self-driving cars to save lives is underestimated and that the technology will continue to progress. He also argues that nuclear energy is an important part of the future and that it is often misunderstood.

Jim Keller discusses the future of computing, AI, life, and consciousness. He believes that the self-driving car will be much safer than a human driver, but that there will be a few accidents that will be eliminated by the car. He also notes that Tesla is pursuing a variety of different AI-related projects, some of which are more promising than others.

Rodney Brooks discusses how his experience working on self-driving cars has led him to rethink the limits of computer vision. He mentions that the number of motor vehicle accidents is high, and points out that self-driving cars will not be able to completely eliminate accidents, but could make them much less frequent.

Anca Dragan explains that there are already relatively successful systems in place that use level two autonomy or semi-autonomous driving. She notes that from a technical perspective, adding something such as taking control and doing its thing can be difficult, but if the thing is trustworthy and the person is engaged, it can go to what is known as "off policy." She also points out that from a human factors perspective, the assumption that a person functions the same when they are the driver versus when they are an observer is questionable. However, she notes that from a practical perspective, if the system is designed correctly, the human can be energized and better at observing the situation.

Melanie Mitchell discusses the future of AI, including the concept of edge cases and the long tail problem. She explains that current self-driving car vision systems have problems with obstacles and that current policies are designed to be exceptionally responsive to anything that could potentially be an obstacle. She also notes that Tesla's approach to autonomous driving is based largely on vision only, while other companies are using a combination of different sensors to improve accuracy.

Kyle Vogt discusses the opportunities and challenges for autonomous driving, focusing on the benefits for ride-sharing and delivery. He believes that the technology has reached a baseline, but that reliability, consistency, and safety needs to be improved before monetization can happen.