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Incorporating nanoparticles into a porous hydrogel to propel an aquabot with minimal voltage

A team of researchers from Korea University, Ajou University and Hanyang University, all in the Republic of Korea, has created a tiny aquabot propelled by fins made of a porous hydrogel imbued with nanoparticles. In their paper published in the journal Science Robotics, the group describes how the hydrogel works to power a tiny boat and reveals how much voltage was required.

Scientists and engineers have been working for several years to build tiny, soft robots for use in and have found that hydrogels are quite suitable for the task. Unfortunately, such materials also have undesirable characteristics, most notably, poor electro-connectivity. In this new effort, the researchers took a new approach to making hydrogels more amenable for use with electricity as a —adding conductive nanoparticles.

The work involved adding a small number of nanoparticles to a part of a porous hydrogel which they then used as a wrinkled nanomembrane electrode (WNE) . Adding the nanoparticles allowed the hydrogel to conduct electricity in a reliable way. Testing showed the actuator could be powered with as little as 3 volts of electricity. The researchers then fashioned two of the actuators into finlike shapes and attached them to a tiny plastic body. Electronics added to the body controlled the electricity sent to the fins. The resulting robot had a water bug appearance, floating on the surface of the water in a tank.

AI Helped Design a Clear Window Coating That Can Cool Buildings Without Using Energy

Demand is growing for effective new technologies to cool buildings, as climate change intensifies summer heat. Now, scientists have just designed a transparent window coating that could lower the temperature inside buildings, without expending a single watt of energy. They did this with the help of advanced computing technology and artificial intelligence. The researchers report the details today (November 2) in the journal ACS Energy Letters.

Cooling accounts for about 15% of global energy consumption, according to estimates from previous research studies. That demand could be lowered with a window coating that could block the sun’s ultraviolet and near-infrared light. These are parts of the solar spectrum that are not visible to humans, but they typically pass through glass to heat an enclosed room.

Energy use could be even further reduced if the coating radiates heat from the window’s surface at a wavelength that passes through the atmosphere into outer space. However, it’s difficult to design materials that can meet these criteria simultaneously and at the same time can also transmit visible light, This is required so they don’t interfere with the view. Eungkyu Lee, Tengfei Luo, and colleagues set out to design a “transparent radiative cooler” (TRC) that could do just that.

Why Continual Learning is the key towards Machine Intelligence

The last decade has marked a profound change in how we perceive and talk about Artificial Intelligence. The concept of learning, once confined in the corner of AI, has now become so important some people came up with the new term “Machine Intelligence”[1][2][3] as to make clear the fundamental role of Machine Learning in it and further depart form older symbolic approaches.

Recent Deep Learning (DL) techniques have literally swept away previous AI approaches and have shown how beautiful, end-to-end differentiable functions can be learned to solve incredibly complex tasks involving high-level perception abilities.

Yet, since DL techniques have been proven shining only with a large number of labeled examples, the research community has now shifted his attention towards Unsupervised and Reinforcement Learning, both aiming to solve equivalently complex tasks but without (or less as possible) explicit supervision.

This New AI is a Game Changer!

One small step for a machine… one giant leap for the singularity.

This AI actually improved a key algorithm that makes it run even faster.


In this video I discuss new Deepmind’s AlphaTensor algorithm and why this work is so important for all the fields of Engineering!

Deepmind’s paper “Discovering faster matrix multiplication algorithms with reinforcement learning”:
https://www.nature.com/articles/s41586-022-05172-4

My Gear:

H+ Academy Roundtable Features Dr. Michael Rose

Dr. Michael Rose is an evolutionary biologist and authority in gerontology. His many years of research and keen insight establish unique methods to frame the problems of aging. Michael made scientific history with experiments manipulating the life spans of fruit flies. As a pragmatist, Michael sees beyond today’s quick fixes to examine what could be the most important changes in the longevity industry to slow down and stop aging. His view is that genomics in conjunction with machine learning is the future of longevity.

Google Just Shut Down It’s Artificial Intelligence After It Revealed This

Thumbnail Inspiration:
https://www.youtube.com/c/DigitalEngine/videos.

Credit:
https://bit.ly/3ggrNND

Many people are scared of artificial intelligence or AI, and it is not hard to see why! The.
advances made in that field of technology are mind-boggling, to say the least! One such scary.
outcome of AI is Google’s AI, which, before it was switched off, ominously revealed one thing.
billions of people have spent a lifetime trying to discover; the purpose of life! What did Google’s.
AI say the purpose of life is? Can AI truly become smarter than us? What does AI becoming.
more intelligent than humans mean? In this video, we dive deep into Google’s Artificial.
Intelligence and what it revealed was the purpose of life before being switched off!

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Robots That Write Their Own Code

A common approach used to control robots is to program them with code to detect objects, sequencing commands to move actuators, and feedback loops to specify how the robot should perform a task. While these programs can be expressive, re-programming policies for each new task can be time consuming, and requires domain expertise.

What if when given instructions from people, robots could autonomously write their own code to interact with the world? It turns out that the latest generation of language models, such as PaLM, are capable of complex reasoning and have also been trained on millions of lines of code. Given natural language instructions, current language models are highly proficient at writing not only generic code but, as we’ve discovered, code that can control robot actions as well. When provided with several example instructions (formatted as comments) paired with corresponding code (via in-context learning), language models can take in new instructions and autonomously generate new code that re-composes API calls, synthesizes new functions, and expresses feedback loops to assemble new behaviors at runtime.

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