Cyber operations that impact the physical world rely on attacks against industrial control systems. These are the operational systems that control production lines, electrical plants, and critical infrastructure. Industrial systems' distinct structures, proprietary communication protocols, and blend of operational technology and information technology make attacking such systems a tall-order for cyber operations, yet machine learning could alter the nature of offensive operations. Machine learning may change cyber operations against industrial systems in three ways. First, modeling the industrial process using machine learning may decrease the number of failed attacks by advanced actors.

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He also shares his experience about using robotics for K-12 education. Jivko Sinapov received his Ph.D. in Computer Science and Human-Computer Interaction from Iowa State University (ISU). While working toward his Ph.D. at ISU's Developmental Robotics Lab, he developed novel methods for behavioral object exploration and multi-modal perception. He went on to be a clinical assistant professor with the Texas Institute for Discovery, Education, and Science at UT Austin and a postdoctoral associate working with Peter Stone at the Artificial Intelligence lab. Sinapov's research interests include developmental robotics, computational perception, autonomous manipulation, and human-robot interaction.

The coronavirus pandemic is pushing more jobs to automation faster, but that doesn't mean more humans need be out of work. A McKinsey and Company study estimates half of American job tasks will be automated in five years, so diverse populations are needed to program and run artificial intelligence. "If you only have certain types of people and certain types of populations in forming that translation of the human mind in machine learning, you're only going to get a portion of what you need," said Darcy Renfro, chief workforce and economic development officer with Maricopa Community Colleges. Renfro and other Valley education leaders spoke in the live webinar "Future of Workforce," hosted by the Greater Phoenix Economic Council on Thursday. She also said advanced degrees are not always needed to program the computers and machines that think for us.

This article philosophically analyzes online exam supervision technologies, which have been thrust into the public spotlight due to campus lockdowns during the COVID-19 pandemic and the growing demand for online courses. Online exam proctoring technologies purport to provide effective oversight of students sitting online exams, using artificial intelligence (AI) systems and human invigilators to supplement and review those systems. Such technologies have alarmed some students who see them as `Big Brother-like', yet some universities defend their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This article philosophically analyzes these technologies, focusing on the ethical concepts of academic integrity, fairness, non-maleficence, transparency, privacy, respect for autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics and all are relevant to the education context. The essay provides ethical considerations that educational institutions will need to carefully review before electing to deploy and govern specific online proctoring technologies.

Class Scheduling is a highly constrained task. Educational institutes spend a lot of resources, in the form of time and manual computation, to find a satisficing schedule that fulfills all the requirements. A satisficing class schedule accommodates all the students to all their desired courses at convenient timing. The scheduler also needs to take into account the availability of course teachers on the given slots. With the added limitation of available classrooms, the number of solutions satisfying all constraints in this huge search-space, further decreases. This paper proposes an efficient system to generate class schedules that can fulfill every possible need of a typical university. Though it is primarily a fixed-credit scheduler, it can be adjusted for open-credit systems as well. The model is designed in MiniZinc and solved using various off-the-shelf solvers. The proposed scheduling system can find a balanced schedule for a moderate-sized educational institute in less than a minute.

Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregation results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA, which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. BiLA is flexible to accommodate any generating distribution of labels by the exact computation of its posterior distribution. We also derive the convergence bound of the proposed optimizer. We compare BiLA with the state of the art based on minimax entropy, neural networks and expectation maximization algorithms, on synthetic and real-world data sets. Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1.5 percent points for synthetic and real-world datasets, respectively.

So much has been made of Artificial Intelligence (AI) potential to replace humans that its introduction … Even if that is learning, it is machine learning.

India has many milestones to achieve if it is to catch up with China, concluded a study published by the Observer Research Foundation, that offered a comparative analysis between the two in terms of higher education reforms for the development of talent in artificial intelligence (AI) and its research. The study, which compared various parameters including AI development plans and strategies of the two countries, their automation readiness index, talent retention, and research output, found China and India to have a very evident difference in their approach to bring reforms in higher education for AI. "Countries' strength in AI will potentially impact their position in the power structure in international politics in the long term," said Dr Romi Jain, author of the study and a Postdoctoral Research Fellow at the University of British Columbia. "From the geopolitical perspective, acquisition of robust AI capabilities could even shape the contours and outcomes of Sino-Indian conflict, competition and rivalry." As both countries introduce AI degree programmes in higher education, the results are too premature to be judged.

New research suggests combining educational topics and research disciplines to help researchers breathe life into lifelike intelligent robots. The comment piece suggests that teaching materials science, mechanical engineering, computer science, biology and chemistry as a combined discipline could help students develop the skills they need to create lifelike artificially intelligent (AI) robots as researchers. Known as Physical AI, these robots would be designed to look and behave like humans or other animals while possessing intellectual capabilities normally associated with biological organisms. These robots could in future help humans at work and in daily living, performing tasks that are dangerous for humans, and assisting in medicine, caregiving, security, building and industry. Although machines and biological beings exist separately, the intelligence capabilities of the two have not yet been combined.