Self-driving cars, or autonomous vehicles, have long been earmarked as the next generation mode of transport. To enable the autonomous navigation of such vehicles in different environments, many different technologies relating to signal processing, image processing, artificial intelligence deep learning, edge computing, and IoT, need to be implemented. One of the largest concerns around the popularization of autonomous vehicles is that of safety and reliability. In order to ensure a safe driving experience for the user, it is essential that an autonomous vehicle accurately, effectively, and efficiently monitors and distinguishes its surroundings as well as potential threats to passenger safety. To this end, autonomous vehicles employ high-tech sensors, such as Light Detection and Ranging (LiDaR), radar, and RGB cameras that produce large amounts of data as RGB images and 3D measurement points, known as a "point cloud."

During the difficult years since the start of the ongoing COVID-19 pandemic, the need for efficient artificial intelligence models, tools, and applications has been more evident than ever. Machine learning and data science, not to mention the huge amount of data they produce, form a clear new source of valuable information. New and innovative approaches are required to tackle the new research challenges faced in this area. In this framework, artificial intelligence is crucial and thus may be described as one of the most important research areas of our time. Since this view is applicable to the research community, it also faces huge challenges from the perspective of data management and involves emerging disciplines in information processing and related tools and applications.

On Thursday June 23, the National Security Institute (NSI), in partnership with the Federalist Society, hosted the 2022 National Security Symposium entitled, "Next Generation National Security." The first panel of the Symposium considered the various threats and potential benefits that digital currencies may pose, as well as discussed whether and how the U.S. might develop policies on digital assets that both protect and encourage freedom and payment security while maintaining our safety from bad actors, and thwarting the schemes and initiatives of bad actors. The panel on Cryptocurrency and National Security featured Gus Coldebella, Partner, True Ventures; William Hughes, Senior Counsel and Director of Global Regulatory Matters, Consensys Software; Dr. Oonagh McDonald, Senior Adviser, Crito Capital, and; Hon. Eric Kadel, Partner, Sullivan & Cromwell LLP, served as the moderator. The second panel titled, " Artificial Intelligence: Implications for National Security," addressed the national security ramifications of the scaling artificial intelligence (AI) developments.

Tumor malignancy identification plays an essential role in clinical management of cancer. Currently, biopsy is the gold standard for malignancy identification in most tumor cases, it is, however, invasive that can cause great discomfort to patients, and potentially increase the risk of distant metastases due to the complex sampling process. With the development of molecular imaging probes, non-invasive medical imaging approaches, such as magnetic resonance imaging (MRI), fluorescence imaging (FI), computed tomography, and ultrasound, etc., have been used for non-invasive tumor diagnosis. Nevertheless, majority of imaging strategies are often dependent on imaging probes that lack specificity for identification of tumor malignancy. Considering that necrotic cell death and overexpressed potassium ion (K) channels are major hallmarks of malignant tumors, but not for benign ones, the extracellular K concentration is significantly elevated in the malignant tumor microenvironment compared with that of benign tissue. Based on this, a new research led by Prof. Daishun Ling from Shanghai Jiao Tong University reported a K -sensitive dual-mode imaging probe (KDMN) to realize real-time tumor imaging while identifying the malignancy.

We develop reinforcement learning techniques to enable interaction across multiple agents including AIs and humans, with potential applications from AI-assisted design to autonomous driving. Methodological contexts of the research include deep reinforcement learning, inverse reinforcement learning, hierarchical reinforcement learning as well as multi-agent and multi-objective reinforcement learning. FCAI is working on a new paradigm of AI-assisted design that aims to cooperate with designers by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We use generative user models to reason about designers' goals, reasoning, and capabilities. In this call, FCAI is looking for a postdoctoral scholar or research fellow to join our effort to develop AI-assisted design. Suitable backgrounds include deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modelling, and user modelling. Computational rationality is an emerging integrative theory of intelligence in humans and machines (1) with applications in human-computer interaction, cooperative AI, and robotics. The theory assumes that observable human behavior is generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself (2).

Entrepreneur Elon Musk, one of the masterminds behind projects such as Tesla and SpaceX, announced his next venture, namely Neuralink. The company aims to merge humans with electronics, creating what Musk calls the neural lace. It is a device that injected into the jugular vein would reach the brain and then unfold into a network of electrical connections connected directly to human neurons. The idea is to develop enhanced brain-computer interfaces to increase the extent to which the biological brain can interact and communicate with external computers. The neural lace will go down to the level of brain neurons: it will be a mesh that will be able to connect directly to brain matter and then connect with a computer. That human being will be a cyborg.

The classification of brain tumors – and thus the choice of optimal treatment options – can become more accurate and precise through the use of artificial intelligence in combination with physiological imaging. This is the result of an extensive study conducted by the Karl Landsteiner University for Health Sciences (KL Krems). Multiclass machine learning methods were used to analyze and classify brain tumors using physiological data from magnetic resonance imaging. The results were then compared with classifications made by human experts. Artificial intelligence was found to be superior in the areas of accuracy, precision and misclassification, among others, while professionals performed better in sensitivity and specificity. Brain tumors can be easily detected by magnetic resonance imaging (MRI), but their exact classification is difficult in this way.

"Enabling humans to effectively join the team of AI agents calls for both the humans and AI agents to share their understanding and representation of their shared worlds. Such shared understanding requires formal representations of concepts to support transparency during bi-directional communications between team members." Our research highlights that a unified conceptual space is required for meaningful teaming between biological and artificial agents. Our approach represents a shared conceptual space, enabling the development of interdependent understanding between agents of non-homogeneous physical and cognitive abilities. See our latest research, "Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming" here: https://lnkd.in/d-4AE_Ua

The day is coming--some say has already arrived--when artificial intelligence starts to invent things that its human creators could not. But our laws are lagging behind this technology, UNSW experts say. It's not surprising these days to see new inventions that either incorporate or have benefitted from artificial intelligence (AI) in some way, but what about inventions dreamt up by AI--do we award a patent to a machine? This is the quandary facing lawmakers around the world with a live test case in the works that its supporters say is the first true example of an AI system named as the sole inventor. In commentary published in the journal Nature, two leading academics from UNSW Sydney examine the implications of patents being awarded to an AI entity.

The Vice-Chancellor of the Kwame Nkrumah University of Science and Technology (KNUST), Professor Mrs. Rita Akosua Dickson, has said the University remains committed to advancing research in Artificial Intelligence (AI). This she said is part of efforts to ensure that the country does not get left behind in the application of AI for national socio-economic development. "We at the KNUST are providing the enabling research environment to our cherished scientists to lead in scientific discoveries, harness innovation and foster scientific collaborations," the Vice-Chancellor said. "This is because Ghana and the sub-Saharan Region cannot be left out of the positive outlook that the application of AI is projected to make on global development and national socio-economic transformation. Mrs. Dickson, who was addressing a workshop in Kumasi on Friday, May 6, 2022, on the theme: "The Role of Responsible AI in Promoting the Sustainable Development Agenda in the sub-Region", said the global market contribution of AI as of 2019, according to the Grand View Research, was about US $39.9 billion.