Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 1,361

Penn State agricultural engineers have developed, for the first time, a prototype “end-effector” capable of deftly removing unwanted apples from trees—the first step toward robotic, green-fruit thinning.

The development is important, according to Long He, assistant professor of agricultural and , because manual thinning is a labor-intensive task, and the shrinking labor force in apple production makes manual thinning economically infeasible. His research group in the College of Agricultural Sciences conducted a new study that led to the end-effector.

The apple crop is a high-value agricultural commodity in the U.S., with an annual total production of nearly 10 billion pounds and valued at nearly $3 billion, according to He, who is a leader in agricultural robotics research, previously developing automated components for mushroom picking and apple tree pruning. Green-fruit thinning—the process of discarding excess fruitlets in , mainly to increase the remaining fruit size and quality—is one of the most important aspects of apple production.

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Human brains process loads of information. When wine aficionados taste a new wine, neural networks in their brains process an array of data from each sip. Synapses in their neurons fire, weighing the importance of each bit of data—acidity, fruitiness, bitterness—before passing it along to the next layer of neurons in the network. As information flows, the brain parses out the type of wine.

Scientists want artificial intelligence (AI) systems to be sophisticated data connoisseurs too, and so they design computer versions of neural networks to process and analyze information. AI is catching up to the human brain in many tasks, but usually consumes a lot more energy to do the same things. Our brains make these calculations while consuming an estimated average of 20 watts of power. An AI system can use thousands of times that. This hardware can also lag, making AI slower, less efficient and less effective than our brains. A large field of AI research is looking for less energy-intensive alternatives.

Now, in a study published in the journal Physical Review Applied, scientists at the National Institute of Standards and Technology (NIST) and their collaborators have developed a new type of hardware for AI that could use less energy and operate more quickly—and it has already passed a virtual wine-tasting test.

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So, I think I uncovered a treasure. The Killing Star by Charles Pellegrino and George Zebrowski was originally published 1995 and it paints a dark and seemingly plausible depiction of humanity’s potential future. This book is about several things genetic engineering and cloning, it’s about the destructive power of fanaticism, It’s about the over confidence and hubris of humanity, and that gets really hammered home in this book with all it’s references to the titanic, which has for a very long time been thought of as one of the greatest symbols of human hubris, it’s about AI, and when it goes to far, it’s about our over dependence on technology, it’s about humanity’s indefinite survival outside of earth, and most importantly, it’s about the devastating annihilation of the vast majority of the human race.

Join Dune Club!
https://twitter.com/DanikaXIX/status/1540394079069999106

Music: https://www.youtube.com/watch?v=63UR4xLiUNo.

Cover art: https://www.artstation.com/artwork/L3YP2w.

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Three-Body Playlist: https://youtube.com/playlist?list=PLRXGGVBzHLUfIzEhovpQJ2ENiNvJoOD2A

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A newborn giraffe or foal must learn to walk on its legs as fast as possible to avoid predators. Animals are born with muscle coordination networks located in their spinal cord. However, learning the precise coordination of leg muscles and tendons takes some time. Initially, baby animals rely heavily on hard-wired spinal cord reflexes. While somewhat more basic, motor control reflexes help the animal to avoid falling and hurting themselves during their first walking attempts. The following, more advanced and precise muscle control must be practiced, until eventually the nervous system is well adapted to the young animal’s leg muscles and tendons. No more uncontrolled stumbling—the young animal can now keep up with the adults.

Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart conducted a research study to find out how learn to walk and learn from stumbling. They built a four-legged, dog-sized robot, that helped them figure out the details.

“As engineers and roboticists, we sought the answer by building a robot that features reflexes just like an animal and learns from mistakes,” says Felix Ruppert, a former doctoral student in the Dynamic Locomotion research group at MPI-IS. “If an animal stumbles, is that a mistake? Not if it happens once. But if it stumbles frequently, it gives us a measure of how well the robot walks.”

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Summary: A new artificial neural network aced a wine tasting test and promises a less energy-hungry version of artificial intelligence, researchers report.

Source: NIST

Human brains process loads of information. When wine aficionados taste a new wine, neural networks in their brains process an array of data from each sip. Synapses in their neurons fire, weighing the importance of each bit of data — acidity, fruitiness, bitterness — before passing it along to the next layer of neurons in the network. As information flows, the brain parses out the type of wine.

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How do you teach an autonomous drone to fly itself? Practice, practice, practice. Now Microsoft is offering a way to put a drone’s control software through its paces millions of times before the first takeoff.

The cloud-based simulation platform, Project AirSim, is being made available in limited preview starting today, in conjunction with this week’s Farnborough International Airshow in Britain.

“Project AirSim is a critical tool that lets us bridge the world of bits and the world of atoms, and it shows the power of the industrial metaverse — the virtual worlds where businesses will build, test and hone solutions, and then bring them into the real world,” Gurdeep Pall, Microsoft corporate vice president for business incubations in technology and research, said today in a blog posting.

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H+DAO Interview:

“Transhumanism is a philosophy, worldview, and a movement,” Dr. Natasha Vita-More states in the book “Transhumanism: What is it?” Essentially, it’s the idea of being able to move beyond being human, and finding solutions to living longer, healthier lives.

Natasha holds a Ph.D. from the University of Plymouth. As a long-term figure in the transhumanist movement, she spends much of her time speaking and lecturing around the world. Her areas of expertise include topics such as trans-humanity and human evolution, artificial intelligence, and what it means to be human in an AI-driven world.

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A machine-learning algorithm that includes a quantum circuit generates realistic handwritten digits and performs better than its classical counterpart.

Machine learning allows computers to recognize complex patterns such as faces and also to create new and realistic-looking examples of such patterns. Working toward improving these techniques, researchers have now given the first clear demonstration of a quantum algorithm performing well when generating these realistic examples, in this case, creating authentic-looking handwritten digits [1]. The researchers see the result as an important step toward building quantum devices able to go beyond the capabilities of classical machine learning.

The most common use of neural networks is classification—recognizing handwritten letters, for example. But researchers increasingly aim to use algorithms on more creative tasks such as generating new and realistic artworks, pieces of music, or human faces. These so-called generative neural networks can also be used in automated editing of photos—to remove unwanted details, such as rain.

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