Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
The autonomous vehicle industry is in the process of rerouting. Early AV leaders said fully autonomous cars would hit the mass market by 2020 or 2021--Elon Musk even promised a self-driving Tesla by 2017. But with the end of the decade in sight, two things are certain: The autonomous future remains a long way off, and AV-makers are going to have to change their plan for how to get there. In this presentation, we show you what this new path looks like and lay out the step-by-step changes we'll see on the way to full autonomy. We make the case that AV developers' early shortcomings have ushered in a new era of collaboration and realism.
This is an updated version of a story that initially appeared in Interglobix Magazine, the publication for data centers, connectivity and lifestyle. The road to the self-driving car of the future is paved with hardware and data centers. Autonomous vehicles promise to be one of the transformational technologies of the 21st century, with the potential to remake much of our urban and economic landscape. But many questions remain about how the connected car of 2019 will evolve to meet the vision for the autonomous vehicles of the future, and tough issues to be resolved on multiple fronts – including technology, regulation and infrastructure. The long-term vision is to create networks of connected vehicles that "talk" to one another using vehicle-to-vehicle (V2V) communications over low-latency wireless connections, which can also allow vehicle-to-infrastructure (V2I) that enable robot cars to connect with traffic lights and parking meters.
When it comes to the automotive industry and the role of artificial intelligence within it, your mind will begin to conjure up exciting thoughts. Whether images of self driving vehicles or driver monitoring, AI can impact and develop our experience when behind the wheel. We will be taking a look at how artificial intelligence is being used within cars, the current developments taking place and the future landscape of artificial intelligence within the automotive industry. Artificial Intelligence, AI for short, has an array of different meanings. Artificial intelligence for many is most commonly seen as a technique that enables computers to mimic human behavior.
Artificial intelligence has been rapidly gaining attention for this decade. In the current trends, artificial intelligence mainly means machine learning techniques including neural networks, i.e., deep learning, and probabilistic generative models, i.e., Bayesian models. Robotics is clearly the representative of the targets of the application of artificial intelligence. Robots need to deal with uncertainty in the real world and learn knowledge from the daily environment including human users. Machine learning methods enable robots adapt to the real-world environment by dealing with uncertainty in a statistical manner.
Seemingly, one of the most controversial things about Tesla cars is its Autopilot feature, a driver-assist feature that helps drivers navigate and pilot their vehicle. Oddly, while news of exciting Autopilot features comes out regularly, general information about exactly what Autopilot is, what the options are, and what it can and cannot do seem to be few and far between. I have tried to collect and answer the biggest questions about Autopilot below to help prospective buyers know what the system is and is not, as well as to inform journalists about the system in case they find themselves trying to cover a news story regarding the system. When the next questionable news story comes out, please feel free to link this article for anyone wondering about the system. Please note that all of the below information refers to Tesla vehicles containing Autopilot 2.0 hardware or higher in them (vehicles built since October of 2016). Although, the majority of the information will apply to all Tesla vehicles that are Autopilot enabled.
Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.
In this paper, we introduce an extension of our presented cognitive - based emotion model  and , where we enhance o ur knowledge - based emotion unit of the architecture by embedding a fuzzy rule - based system to it. The model utilizes the cognitive parameters dependency and their corresponding weights to regulate the robot's behavior and fuse their behavior data to achiev e the final decision in their interaction with the environment. Using this fuzzy system, our previous model can simulate linguistic parameters for better controlling and generating understandable and flexible behaviors in the robots. We implement our model on an assistive healthcare robot, named Robot Nurse Assistant (RNA) and test it with human subjects. Our model records all the emotion states and essential information based on its predefined rules and learning system. Our results show that our robot inte racts with patients in a reasonable, faithful way in special conditions which are defined by rules. This work has the potential to provide better on - demand service for clinical ex-p e rts to monitor the patients' emotion states and help them make better decis ions accordingly .
PLEASE NOTE THE NEW ADDRESS OF MORSE BARNES-BROWN & PENDLETON at 480 Totten Pond Road. Artificial intelligence technologies are threatening to take over many decision-making tasks humans perform at work and in personal life. AI systems are already making critical decisions in areas previously thought to be the exclusive domain of humans: driving cars, reviewing job applications, underwriting loans, and even endeavoring to create patentable innovation and recommending sentencing in the criminal justice system. What does this rapid and seemingly unstoppable development in artificial intelligence mean for the legal profession? In his talk, Joe Barkai will provide an overview of key AI technologies.
We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components. We explain how some of the existing ideas on runtime monitors for perception systems can be seen as a specific instance of logical scaffolds. Furthermore, we describe how logical scaffolds may be useful for improving AI programs beyond perception systems, to include general prediction systems and agent behavior models. Keywords: AI · Autonomous systems · Formal methods. 1 Introduction Recent progress in AI has led to possible deployment in a wide variety of important domains. This includes safety-critical cyberphysical systems such as automobiles  and airplanes , but also decision making systems in diverse domains including legal  and military applications .
Japan is known as a leader in the field of robotics and is also a dominant player in artificial intelligence (AI). In fact, after Canada, Japan was the second nation worldwide to adopt a national AI strategy. By 2020, the objective is to increase the number of AI experts from a few thousand to 250,000 per year. AI startups in the Japanese ecosystem are raising millions of dollars in various industries. To provide an overview, they are mainly promoting productivity solutions like ABEJA and LeapMind; Fintech with Moneytree for intelligent online banking and Alpaca for online trading.