Nature inspired computing draws on the principles of emergence, self-organization and complex systems. It aims to develop new techniques, algorithms and computational applications by getting ideas by observing how nature behaves to solve complex problem. Research on NIC has opened new branches such as evolutionary computation, neural networks, artificial immune systems. Robotics researchers, inspired by nature, have developed robotic salamander, water strider robot, mechanical cockroaches, self-configuring robots, and so on. The nature-inspired computing group creates and applies algorithms based on natural phenomena such as the human brain, evolution and swarms of insects.

Elon Musk's recent announcement of an upcoming Tesla Bot -- complete with a human form, "human-level hands" and a characteristically optimistic delivery date -- has garnered a healthy serving of criticism for good reason. Among other capabilities, Musk says, the robot will eventually be capable of running errands such as going to the grocery store alone. Boston Dynamics, which has developed the most advanced humanoid robot ever created, has spent more than a decade working on its Atlas platform. While progress has been impressive, with Atlas running, jumping and even dancing in front of tens of millions of YouTube viewers, the company is quick to acknowledge that the robot is a long way from performing complex tasks autonomously. One of the best examples of evolutionary robotics potential -- and unfulfilled promise -- goes as far back as 2010 to a study published in the PLOS Biology journal.

AI-designed (C-shaped) organisms push loose stem cells (white) into piles as they move through their environment. To persist, life must reproduce. Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses. Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction--and applied their discovery to create the first-ever, self-replicating living robots. The same team that built the first living robots ("Xenobots," assembled from frog cells--reported in 2020) has discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, find single cells, gather hundreds of them together, and assemble "baby" Xenobots inside their Pac-Man-shaped "mouth"--that, a few days later, become new Xenobots that look and move just like themselves.

Elon Musk's recent announcement of an upcoming Tesla Bot -- complete with a human form, "human-level hands" and a characteristically optimistic delivery date -- has garnered a healthy serving of criticism for good reason. Among other capabilities, Musk says, the robot will eventually be capable of running errands such as going to the grocery store alone. Boston Dynamics, which has developed the most advanced humanoid robot ever created, has spent more than a decade working on its Atlas platform. While progress has been impressive, with Atlas running, jumping and even dancing in front of tens of millions of YouTube viewers, the company is quick to acknowledge that the robot is a long way from performing complex tasks autonomously. One of the best examples of evolutionary robotics potential -- and unfulfilled promise -- goes as far back as 2010 to a study published in the PLOS Biology journal.

Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction -- and applied their discovery to create the first-ever, self-replicating living robots. The same team that built the first living robots ("Xenobots," assembled from frog cells -- reported in 2020) has discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, find single cells, gather hundreds of them together, and assemble "baby" Xenobots inside their Pac-Man-shaped "mouth" -- that, a few days later, become new Xenobots that look and move just like themselves. And then these new Xenobots can go out, find cells, and build copies of themselves. "With the right design -- they will spontaneously self-replicate," says Joshua Bongard, Ph.D., a computer scientist and robotics expert at the University of Vermont who co-led the new research. The results of the new research were published November 29, 2021, in the Proceedings of the National Academy of Sciences.

Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses. Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction--and applied their discovery to create the first-ever, self-replicating living robots. The same team that built the first living robots ("Xenobots," assembled from frog cells--reported in 2020) has discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, find single cells, gather hundreds of them together, and assemble "baby" Xenobots inside their Pac-Man-shaped "mouth"--that, a few days later, become new Xenobots that look and move just like themselves. And then these new Xenobots can go out, find cells, and build copies of themselves. "With the right design--they will spontaneously self-replicate," says Joshua Bongard, Ph.D., a computer scientist and robotics expert at the University of Vermont who co-led the new research.

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.

Computational Intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligence behavior in complex and uncertain environments. The main objective of CI is to realize a new approach for analyzing and creating flexible information processing of humans such as sensing, understanding, learning, recognizing, and thinking. It plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Some of the most successful AI systems are based on CI. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions.

This article was published as a part of the Data Science Blogathon. There are multiple ways that one can take to either minimize or maximize any function so that the optimal value can be found out. You can find several optimisation solutions on the internet but in the end, no one solution is the best for all. Everyone has its own advantage and disadvantages. The one that we are going to discuss here is the PSO or the Particle Swarm Optimization.

Federated learning is a method that stores only learnt models on a server in order to protect data privacy. This approach does not collect data on the server but instead collects data from scattered clients directly. Due to the fact that federated learning clients frequently have limited transmission bandwidth, communication between servers and clients should be streamlined to maximize performance. As a result, researchers have created the FedPSO algorithm, which combines the particle swarm optimization technique with federated learning to boost network communication performance. We will attempt to cover certain aspects of this system and comprehend the proposed system in this post.