In a recent article on Autonomous Intelligent Systems (AIS) [1], Ajit Joakar described various features and characteristics of such systems, including associated technologies and research areas, building blocks and core elements, critical factors for success, and cross-cutting enablers. He introduces AIS as an "emerging interdisciplinary field that deals with situations where humans interact with AI systems that are autonomous." From this, we see immediately the synergistic interaction between the intelligent system and its human users. While full autonomy suggests that the system can operate without human interaction, it is useful to leave open the opportunity (even the essential necessity) for human intervention, to provide mid-course corrections that keep the AIS on the right (and ethical) course. Another detailed description of AIS comes from a 2014 report on Autonomous Manufacturing [2].

Driving in a dynamic environment that consists of other actors is inherently a risky task as each actor influences the driving decision and may significantly limit the number of choices in terms of navigation and safety plan. The risk encountered by the Ego actor depends on the driving scenario and the uncertainty associated with predicting the future trajectories of the other actors in the driving scenario. However, not all objects pose a similar risk. Depending on the object's type, trajectory, position, and the associated uncertainty with these quantities; some objects pose a much higher risk than others. The higher the risk associated with an actor, the more attention must be directed towards that actor in terms of resources and safety planning. In this paper, we propose a novel risk metric to calculate the importance of each actor in the world and demonstrate its usefulness through a case study.

Drivers working for Amazon Delivery Service Partners (DSPs) are increasingly under constant surveillance for safe driving, monitored by artificial intelligence which awards them a score and generates voice reminders for safe driving. That score is used to award bonuses, promotions and more. Drivers who spoke to Vice's Motherboard complained the tech is too sensitive, often wrong and making their jobs miserable -- and not to mention, taking money out of their paycheck. But Amazon spokeswoman Alexandra Miller told Threatpost if the choice is between a few unhappy drivers and improved safety, it's an easy call -- safety wins. Earlier this year, Amazon rolled out the pilot program for its DSPs using video surveillance and AI tech from Netradyne.

The explosion of interest in Artificial Intelligence (AI) and Machine Learning has triggered the design and development of all sorts of autonomous systems, including digital marketing & ad campaigns, self-driving cars, drones, self-healing (autonomic) systems, autonomous manufacturing, deep space exploration probes, and more. In all of these systems, safety and risk mitigation are paramount. So, what is an autonomous system? A report on "Autonomous Manufacturing" offers these definitions and features: We apply these concepts to a broader set of autonomous systems, borrowing liberally from the concept of a self-driving car: the self-driving organization, self-driving enterprise, and self-driving city (e.g., Smart Cities). We specifically examine five defining characteristics of the self-driving enterprise, their analytics implications, and their related organizational positioning.

In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition. Nowadays there exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multi-target objective functions of energy saving, safe driving, headway distance control and comfort driving, as well as the resolvability of the networks that automatic driving relied on and the high-performance chips like GPU on the complex driving environments. According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL(Super Deep Learning) for optimal multi-targetcontrol based on knowledge acquisition. We will present in this paper the optimal multi-target control by combining the fuzzy relationship of each multi-target objective function and the implementation of excellent drivers' knowledge acquired by machine learning. Theoretically, the impact of this method will exceed that of the fuzzy control method used in automatic train.

The explosion of interest in Artificial Intelligence (AI), Machine Learning (ML), and Machine Intelligence (MI) has triggered the design and development of all sorts of autonomous systems, including digital marketing & ad campaigns, self-driving cars, drones, self-healing (autonomic) systems, autonomous manufacturing, deep space exploration probes, and more. In all of these systems, safety and risk mitigation are paramount. So, what is an autonomous system? A report on "Autonomous Manufacturing" offers these definitions and features: We apply these concepts to a broader set of autonomous systems, borrowing liberally from the concept of a self-driving car: the self-driving organization, self-driving enterprise, and self-driving city (e.g., Smart Cities). We specifically examine five defining characteristics of the self-driving enterprise, their analytics implications, and their related organizational positioning.

Ben Dickson is a software engineer and the founder of TechTalks. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.

Ben Dickson is a software engineer and the founder of TechTalks. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015.