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Category: robotics/AI – Page 1,215

Quick update/ reminder for Neuralink Event.

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Neura Pod is a series covering topics related to Neuralink, Inc. Topics such as brain-machine interfaces, brain injuries, and artificial intelligence will be explored. Host Ryan Tanaka synthesizes informationopinions, and conducts interviews to easily learn about Neuralink and its future.

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Please consider supporting by joining the channel above, or sharing my other company website with retirees: https://www.reterns.com/. Opinions are my own. Neura Pod receives no compensation from Neuralink and has no affiliation to the company.

#Neuralink #ElonMusk #Tesla

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Cornell University researchers have created an interface that allows users to handwrite and sketch within computer code – a challenge to conventional coding, which typically relies on typing.

The pen-based interface, called Notate, lets users of computational, digital notebooks open drawing canvases and handwrite diagrams within lines of traditional, digitized computer code.

Powered by a deep learning model, the interface bridges handwritten and textual programming contexts: notation in the handwritten diagram can reference textual code and vice versa. For instance, Notate recognizes handwritten programming symbols, like “n”, and then links them up to their typewritten equivalents.

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Dr. Svitlana Volkova, Ph.D. (https://www.pnnl.gov/people/svitlana-volkova) is Chief Scientist, Decision Intelligence and Analytics, National Security Directorate, Pacific Northwest National Laboratory (PNNL), which is one of the United States Department of Energy national laboratories, managed by the Department of Energy’s (DOE) Office of Science.

Dr. Volkova is a recognized leader in the field of computational social science and computational linguistics and her scientific contributions and publication profile cover a range of topics on applied machine learning, deep learning, natural language processing, and social media analytics.

Dr. Volkova’s research focuses on understanding, predicting, and explaining human behavior, interactions, and real-world events from open-source social data and her approaches help advance effective decision making and reasoning about extreme volumes of dynamic, multilingual, multimodal, and diverse real-world unstructured data.

Dr. Volkova has a Ph.D. in Computer Science from Johns Hopkins University, Center for Language and Speech Processing, and an M.S. in Computer Science, from Kansas State University.

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This superintelligent AI is quite astounding learning similarly to a human even. What I am wanting someday is from labor to digital commerce like bitcoin to even stock markets to everything could essentially automated. Also with the neuralink we could essentially have similar intelligence as the superintelligence allowing for humans to attain a superintelligent level of abilities. I think with DNA computers could be better than essentially for implants or essentially downloading information onto human DNA computers or even brain downloads from simple impulses from devices could give binary code files for abilities or making the superintelligence abilities a simple download rather than other forms of technology.

OpenAI has always focused on artificial intelligence (AI) and machine learning advances that benefit humanity. Recently, the company successfully trained a bot to play Minecraft using more than 70,000 hours of gameplay videos. The achievement is far more than just a bot playing a game. It marks a giant stride forward in advanced machine learning using observation and imitation.

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Year 2018 o.o! This could be the first step toward avatars and as well as medical sciences finding a way to treat a human being better essentially with more precision. Also this means we really are wetware computers that can be coded and controlled much like robots are which can lead to our own level of superintelligence in the future by having more abilities with downloaded information.

Cannot be used to help you avoid snack food.

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Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

The metaverse skyrocketed into our collective awareness during the height of the pandemic, when people longed for better ways to connect with each other than video calls. Gaming’s hot growth during the pandemic also pushed it forward. But the metaverse became so trendy that it now faces a backlash, and folks aren’t talking about it as much.

Yet technologies that will power the metaverse are speeding ahead. One of those technologies is generative AI, which uses deep learning neural networks to produce creative concept art and other ideas based on simple text prompts.

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Scientists from the Dutch Institute for Fundamental Energy Research (DIFFER) have created a database of 31,618 molecules that could potentially be used in future redox-flow batteries. These batteries hold great promise for energy storage. Among other things, the researchers used artificial intelligence and supercomputers to identify the molecules’ properties. Today, they publish their findings in the journal Scientific Data.

In recent years, chemists have designed hundreds of molecules that could potentially be useful in flow batteries for energy storage. It would be wonderful, researchers from DIFFER in Eindhoven (the Netherlands) imagined, if the properties of these molecules were quickly and easily accessible in a database. The problem, however, is that for many molecules the properties are not known. Examples of molecular properties are redox potential and water solubility. Those are important since they are related to the power generation capability and energy density of redox flow batteries.

To find out the still-unknown properties of molecules, the researchers performed four steps. First, they used a and smart algorithms to create thousands of virtual variants of two types of molecules. These molecule families, the quinones and aza aromatics, are good at reversibly accepting and donating electrons. That is important for batteries. The researchers fed the computer with backbone structures of 24 quinones and 28 aza-aromatics plus five different chemically relevant side groups. From that, the computer created 31,618 different molecules.

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