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Category: robotics/AI – Page 1,702

“By contemplating the full spectrum of scenarios of the coming technological singularities, many can place their bets in favor of the Cybernetic Singularity which is the surest path to cybernetic immortality and engineered godhood as opposed to the AI Singularity when Homo sapiens is hastily retired as a senescent parent. This meta-system transition from the networked Global Brain to the Gaian Mind is all about evolution of our own individual minds; it’s all about our own Self-Transcendence.”-Alex M. Vikoulov, The Cybernetic Singularity: The Syntellect Emergence #CyberneticSingularity #SyntellectEmergence #CyberneticTheoryofMind #AlexMVikoulov ​#consciousness #phenomenology #evolution #cybernetics #SyntellectHypothesis #PhilosophyofMind #QuantumTheory #PhysicsofTime #PressRelease #NewBookRelease #AmazonKindle #AlexVikoulov #EcstadelicMediaGroup

Ecstadelic Media Group releases a new non-fiction book The Cybernetic Singularity: The Syntellect Emergence, The Cybernetic Theory of Mind series by Alex M. Vikoulov as a Kindle eBook (Press Release, San Francisco, CA, USA, January 102021 08.00 PM PST)

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Circa 2019 😃

The La Moto Volante from French company Lazareth demonstrated its first stable hover. NASA’s helicopter that will fly on Mars has passed its flight tests. And Boston Dynamics’ upgraded Handle robot is a champ at warehouse Tetris.

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Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using to learn more about how mental illness and other disorders affect the brain.

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The maker of a defunct cloud photo storage app that pivoted to selling facial recognition services has been ordered to delete user data and any algorithms trained on it, under the terms of an FTC settlement.

The regulator investigated complaints the Ever app — which gained earlier notoriety for using dark patterns to spam users’ contacts — had applied facial recognition to users’ photographs without properly informing them what it was doing with their selfies.

Under the proposed settlement, Ever must delete photos and videos of users who deactivated their accounts and also delete all face embeddings (i.e. data related to facial features which can be used for facial recognition purposes) that it derived from photos of users who did not give express consent to such a use.

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Two researchers at Duke University have recently devised a useful approach to examine how essential certain variables are for increasing the reliability/accuracy of predictive models. Their paper, published in Nature Machine Intelligence, could ultimately aid the development of more reliable and better performing machine-learning algorithms for a variety of applications.

“Most people pick a predictive machine-learning technique and examine which variables are important or relevant to its predictions afterwards,” Jiayun Dong, one of the researchers who carried out the study, told TechXplore. “What if there were two models that had similar performance but used wildly different variables? If that was the case, an analyst could make a mistake and think that one variable is important, when in fact, there is a different, equally good model for which a totally different set of variables is important.”

Dong and his colleague Cynthia Rudin introduced a method that researchers can use to examine the importance of variables for a variety of almost-optimal predictive models. This approach, which they refer to as “variable importance clouds,” could be used to gain a better understanding of machine-learning models before selecting the most promising to complete a given task.

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The process of systems integration (SI) functionally links together infrastructure, computing systems, and applications. SI can allow for economies of scale, streamlined manufacturing, and better efficiency and innovation through combined research and development.

New to the systems integration toolbox are the emergence of transformative technologies and, especially, the growing capability to integrate functions due to exponential advances in computing, data analytics, and material science. These new capabilities are already having a significant impact on creating our future destinies.

The systems integration process has served us well and will continue to do so. But it needs augmenting. We are on the cusp of scientific discovery that often combines the physical with the digital—the Techno-Fusion or merging of technologies. Like Techno-Fusion in music, Techno-Fusion in technologies is really a trend that experiments and transcends traditional ways of integration. Among many, there are five grouping areas that I consider good examples to highlight the changing paradigm. They are: Smart Cities and the Internet of Things (IoT); Artificial Intelligence (AI), Machine Learning (ML), Quantum and Super Computing, and Robotics; Augmented Reality (AR) and Virtual Reality Technologies (VR); Health, Medicine, and Life Sciences Technologies; and Advanced Imaging Science.

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