If you've ever seen a self-driving car in the wild, you might wonder about that spinning cylinder on top of it. It's a "lidar sensor," and it's what allows the car to navigate the world. By sending out pulses of infrared light and measuring the time it takes for them to bounce off objects, the sensor creates a "point cloud" that builds a 3D snapshot of the car's surroundings. Making sense of raw point-cloud data is difficult, and before the age of machine learning it traditionally required highly trained engineers to tediously specify which qualities they wanted to capture by hand. But in a new series of papers out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers show that they can use deep learning to automatically process point clouds for a wide range of 3D-imaging applications.

Prime Minister Narendra Modi on Sunday (October 20) advised against the demonisation of AI and similar technology, while saying that artificial intelligence has the potential to transform people's lives. He also assured listeners that AI will not replace all human jobs in the future. Speaking at the book launch of'Bridgital Nation,' written by Tata Sons chairman N Chandrasekaran and Roopa Purushothaman, chief economist and head of policy advocacy at the Tata Group, PM Modi was optimistic that the growing applications of artificial intelligence (AI) will not take away jobs from humans. Modi added that technology can act as a bridge between citizens and the government to meet the demand and delivery of governance services. "AI is a talent and force multiplier. The need is to build a bridge between AI and human intentions," – PM Narendra Modi.

Hackers are no slackers at innovation, and the list of their tricks is long and sophisticated. AI-generated deep fakes--fake images and videos--can be used in phishing campaigns; cryptojacking, using code to access and steal digital currencies; and attacks on public cloud providers to gain customers' data. And those are just a handful of ways cybercriminals are waging war against, well, everyone else. Attack vectors--the paths or means by which a hacker can gain access to a computer or network to deliver malicious outcomes--are extensive and changing. Things like the explosion of end-user devices accessing networks, as well as user interfaces growing as a result of advances in other technologies, are forcing organizations to continually improve their security posture.

This submission is in response to the search for ideas from the National Security Commission on Artificial Intelligence. It addresses item 3b -- the infrastructure needed to sustain leadership in artificial intelligence, and item 3d -- how data should be collected, stored, protected, and shared. If you are an analyst -- it does not matter what kind -- then like me, you eventually become begrudgingly resigned to spending most of your time preparing for, rather than actually conducting analysis. I sometimes share a dark joke with my fellow analysts. When asked how long it will take to provide an answer to a question, my response is, "About two days. But I'll see you in a month because it will take me 28 days to find, beg for access to, and clean the data I need to answer your question."

Markov chain Monte Carlo (MCMC) methods and neural networks are instrumental in tackling inferential and prediction problems. However, Bayesian inference based on joint use of MCMC methods and of neural networks is limited. This paper reviews the main challenges posed by neural networks to MCMC developments, including lack of parameter identifiability due to weight symmetries, prior specification effects, and consequently high computational cost and convergence failure. Population and manifold MCMC algorithms are combined to demonstrate these challenges via multilayer perceptron (MLP) examples and to develop case studies for assessing the capacity of approximate inference methods to uncover the posterior covariance of neural network parameters. Some of these challenges, such as high computational cost arising from the application of neural networks to big data and parameter identifiability arising from weight symmetries, stimulate research towards more scalable approximate MCMC methods or towards MCMC methods in reduced parameter spaces.

School of Information Technology, Deakin University, Geelong, Australia Robots are increasingly tested in public spaces, towards a f uture where urban environments are not only for humans but for autonomous syst ems. While robots are promising, for convenience and efficiency, there are challenges associated with building cities crowded with machines. This p aper provides an overview of the problems and some solutions, and calls for gr eater attention on this matter . Urban environments will increasingly be spaces for autonom ous systems, of which automated vehicles is only one popular type. Robot wheelchairs could be used in public as well other robot -transporters to help the elderly.

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Systems engineering processes coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. To this end, the objective of this paper is to develop and analyze a principal-agent model of a one-shot (single iteration), shallow (one level of hierarchy) systems engineering process. We assume that the systems engineer maximizes the expected utility of the system, while the subsystem engineers seek to maximize their expected utilities. Furthermore, the systems engineer is unable to monitor the effort of the subsystem engineer and may not have a complete information about their types or the complexity of the design task. However, the systems engineer can incentivize the subsystem engineers by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bi-level optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities.

In June, a video allegedly showing Datuk Seri Azmin Ali, the Malaysian minister of economic affairs, engaged in a sexual tryst with Muhammad Haziq Abdul Aziz, a deputy Malaysian minister's secretary, surfaced online. The video spread like wildfire, and subsequently threw the country's media into a frenzy. The video had real-world consequences, and Abdul Aziz, who in the eyes of the government had committed a crime, was quickly arrested. But, according to Malaysia's prime minister, the video was just one of countless other scarily-accurate deepfake videos that have been finding their way onto the internet in the last year. Deepfakes work by using something called a generative adversarial network (GAN), which is made up of two artificial intelligent processes that are pitted against each other – a generator and a discriminator.

ISLAMABAD: President Dr Arif Alvi has stressed upon strengthening of Pakistan-Japan cooperation in the field of artificial intelligence (AI) as Pakistan has huge potential in terms of more than half of its young population. During an interview with the Japanese News 24 (NTV), the president said he had launched the artificial intelligence training system initiative with target to produce about 100,000 AI experts within two years. Dr Alvi is in Japan on the invitation of the Japanese government to attend the enthronement ceremony of Emperor of Japan Naruhito. The president invited the Japanese software companies to invest in Pakistan and said it was possible to engage in the software development and artificial intelligence development. He said there were a lot of Japanese investment destinations in Pakistan.