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.
This set of FAQs offers information about the founding of the MIT Stephen A. Schwarzman College of Computing, announced today, and its implications for the MIT community and beyond. Q: What is MIT announcing today that's new? A: Today MIT is announcing a $1 billion commitment to address the global opportunities and challenges presented by the ubiquity of computing -- across industries and academic disciplines -- and by the rise of artificial intelligence. At the heart of this endeavor will be the new MIT Stephen A. Schwarzman College of Computing, made possible by a foundational $350 million gift from Stephen Schwarzman, the chairman, CEO, and co-founder of Blackstone, a leading global asset manager. An additional $300 million has been secured for the College through other fundraising.
People have been dreaming about Artificial Intelligence for hundreds, if not thousands of years. Well, it's starting to feel like the future is actually here, and AI can be seen almost everyone nowadays. So how should you feel about it? Here are 42 facts about the past, present and future of artificial intelligence to help you decide for yourself. In Ancient Greek mythology, the blacksmith god Hephaestus was believed to have built what were essentially robots. His "automatons," as they were called, were crafted from metal and designed to perform different tasks for him or other gods.
Electric drones booked through smartphones pick people up from office rooftops, shortening travel time by hours, reducing the need for parking and clearing smog from the air. This vision of the future is driving the government's "flying car" project. Major carrier All Nippon Airways, electronics company NEC Corp. and more than a dozen other companies and academic experts hope to have a road map for the plan ready by the year's end. "This is such a totally new sector Japan has a good chance for not falling behind," said Fumiaki Ebihara, the government official in charge of the project. For now, nobody believes people are going to be zipping around in flying cars any time soon.
Chinese companies are "aggressively investing" in artificial intelligence (AI) applications and show more thirst for talent, a joint study by Massachusetts Institute of Technology (MIT) and Boston Consulting Group (BCG) shows, at a time when the race for AI superiority is in the spotlight around the world. The conclusion – based on a survey of over 3,000 participants in 126 countries and 300 executives from China – shines a light on China's ambitions in AI, which is seen as a major driver of the new economy, and the perceived competitive threat the country poses to other big economies. "China's rapid rise in AI has been a wake-up call for nations, industries and corporate executives globally," says the report, which was released on Tuesday and titled Artificial Intelligence in Business Gets Real. "Indeed, many recent national programmes to advance the development of AI refer to China as a competitive threat." Betting big on the core technology behind an array of cutting-edge applications from autonomous driving to facial recognition, China's State Council last July laid out a three-step road map to AI supremacy.
The joint availability of computational power and huge datasets has considerably changed the landscape of Artificial Intelligence. In many fields, applications (self-driving cars, cybersecurity, e-health…) that seemed out of reach in the past are now closer to becoming a reality. Recent advances in Machine Learning, the key component of AI, show the growing maturity of algorithms that are now able to handle an increasing number of new tasks. However, simple adversarial attacks can still easily defeat a learning algorithm and the potentially massive deployment of AI tools in various environments raises many new concerns. Additionally to scalability and versatility of algorithms, awareness of drifting or fake data, privacy, interpretability, accountability are now all features that a learning and decision system should take into account.
Autonomous vehicles should be able to generate accurate probabilistic predictions for uncertain behavior of other road users. Moreover, reactive predictions are necessary in highly interactive driving scenarios to answer "what if I take this action in the future" for autonomous vehicles. There is no existing unified framework to homogenize the problem formulation, representation simplification, and evaluation metric for various prediction methods, such as probabilistic graphical models (PGM), neural networks (NN) and inverse reinforcement learning (IRL). In this paper, we formulate a probabilistic reaction prediction problem, and reveal the relationship between reaction and situation prediction problems. We employ prototype trajectories with designated motion patterns other than "intention" to homogenize the representation so that probabilities corresponding to each trajectory generated by different methods can be evaluated. We also discuss the reasons why "intention" is not suitable to serve as a motion indicator in highly interactive scenarios. We propose to use Brier score as the baseline metric for evaluation. In order to reveal the fatality of the consequences when the predictions are adopted by decision-making and planning, we propose a fatality-aware metric, which is a weighted Brier score based on the criticality of the trajectory pairs of the interacting entities. Conservatism and non-defensiveness are defined from the weighted Brier score to indicate the consequences caused by inaccurate predictions. Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp. The results are evaluated by the baseline and proposed metrics to construct a mini benchmark. Analysis on the properties of each method is also provided by comparing the baseline and proposed metric scores.
Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicle prepare better in the future. Online Transfer Learning will help transferring prior knowledge to a new task and also keep the knowledge updated as the task evolves. This paper presents the different methods of transfer learning, online transfer learning and organic computing that could be adapted to the domain of autonomous vehicles.
If you had your very own home robot, what would you want it to do, exactly? Yeah, me too, but that kind of robot is a long, long ways off. Consider Jibo, essentially a dancing Amazon Alexa. And Kuri, a miniaturized R2-D2 that roams around your house taking pictures. If that doesn't sound particularly impressive to you, well, the market felt the same way.
This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.
Bruce Newsome reviews the recently published book: "Strategy, Evolution, and War: From Apes to Artificial Intelligence," authored by Kenneth Payne and published by Georgetown University Press. Artificial intelligence (AI) has been explicit in the practices and policies of defence since at least the 1970s, at least in high-capacity countries, given the exponential growth in the power of electronic computing per unit cost. It was already specified in training and forecasting simulations, decision-making aids, targeting aids, robotics, adaptive navigation systems (as in the Tomahawk Cruise Missile), and ballistic missile defence. Any child with a video game could experience AI. AI raced up Western governmental priorities in the 2000s by application to countering terrorism; in 2009, the US escalated its cyber capabilities and authorities, partly on the promise of AI; in 2014, the Russians seemed to know first what the defenders of Ukraine were doing, in part because of integration of AI; and in 2016, Western governments consensually blamed Russia for unprecedented interference in American and other elections, partly aided by AI.