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Category: robotics/AI – Page 1,734

Elon Musk’s Neuralink showcases working implanted brain computer and promises future health benefits.

Elon Musk company Neuralink has been researching how directly interfacing with the brain could be used as therapy for chronic and debilitating medical conditions, as well as exploring how technological augmentation could expand and develop the capabilities of the human brain.

Longevity. Technology: Neuralink have been decidedly cagey about their progress, despite having $158m, in funding, $100m of which comes from Musk himself [1]. Tonight’s live broadcast featured misbehaving pigs (I’m looking at you here, Gertrude!) and a glimpse of the future of robotic surgery, but Elon Musk continued to operate at his self-proclaimed “speed of thought” pushing the boundaries between brains and technology.

Prior to today’s update, the last real news was in July last year, when they announced they were developing a “sewing machine-like” device that could implant incredibly thin (4 to 6 μm) threads in the brain. The company also demonstrated a system that read information from a lab rat via 1500 electrodes and revealed they planned to start experiments with humans in 2020 [2].

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We are fascinated by machines that can control cars, compose symphonies, or defeat people at chess, Go, or Jeopardy! While more progress is being made all the time in artificial intelligence (AI), some scientists and philosophers warn of the dangers of an uncontrollable superintelligent AI. Using theoretical calculations, an international team of researchers, including scientists from the Center for Humans and Machines at the Max Planck Institute for Human Development, shows that it would not be possible to control a superintelligent AI. Their study is published in the Journal of Artificial Intelligence Research.

Suppose in the not-too-distant future that a research team builds an AI system with intelligence superior to that of humans, so it can learn independently. Connected to the Internet, the AI would have access to all of humanity’s data. It could replace existing programs and take control of all machines globally.

Would this produce a utopia or a dystopia? Would the AI cure cancer, bring about world peace, and prevent a climate disaster? Or would it destroy humanity and take over the Earth?

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“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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