MIT’s new robotic insects, inspired by bees, achieve longer flights and enhanced precision, paving the way for autonomous pollination tasks.
Tomorrow at 1PM PT / 4PM ET, we Premiere a new episode of Robots In Space, and this is about bots, including the latest on Phoenix from Sanctuary AI, the impact of cognitive automation on jobs, the Economic Singularity, plus our proprietary Event Horizon Indicator.
Discover how robotics and AI are reshaping our economic landscape in this eye-opening analysis. As an engineer, I break down the latest developments in humanoid robots, particularly Sanctuary AI’s breakthrough in hydraulic robotics and robot dexterity. Learn about my proprietary Event Horizon Indicator that tracks our progression toward the Economic Singularity through labor force participation and unemployment trends. From warehouse robotics to manufacturing automation, understand how the robot workforce is transforming industries and what this means for the future of work. Whether you’re interested in AI economics or concerned about tech unemployment, this video provides crucial insights into the ongoing robot revolution and its impact on our economy.
Dan dennet real patterns.
Daniel C. Dennett, Real Patterns, The Journal of Philosophy, Vol. 88, No. 1 (Jan., 1991), pp. 27–51.
Dan dennett on patterns and ontology.
I want to look at what Dennett has to say about patterns because 1) I introduced the term in my previous discussion, In Search of Dennett’s Free-Floating Rationales [1], and 2) it is interesting for what it says about his philosophy generally.
You’ll recall that, in that earlier discussion, I pointed out talk of “free-floating rationales” (FFRs) was authorized by the presence of a certain state of affairs, a certain pattern of relationships among, in Dennett’s particular example, an adult bird, (vulnerable) chicks, and a predator. Does postulating talk of FFRs add anything to the pattern? Does it make anything more predictable? No. Those FFRs are entirely redundant upon the pattern that authorizes them. By Occam’s Razor, they’re unnecessary.
Reservoir computing (RC) is a powerful machine learning module designed to handle tasks involving time-based or sequential data, such as tracking patterns over time or analyzing sequences. It is widely used in areas such as finance, robotics, speech recognition, weather forecasting, natural language processing, and predicting complex nonlinear dynamical systems. What sets RC apart is its efficiency―it delivers powerful results with much lower training costs compared to other methods.
RC uses a fixed, randomly connected network layer, known as the reservoir, to turn input data into a more complex representation. A readout layer then analyzes this representation to find patterns and connections in the data. Unlike traditional neural networks, which require extensive training across multiple network layers, RC only trains the readout layer, typically through a simple linear regression process. This drastically reduces the amount of computation needed, making RC fast and computationally efficient.
Inspired by how the brain works, RC uses a fixed network structure but learns the outputs in an adaptable way. It is especially good at predicting complex systems and can even be used on physical devices (called physical RC) for energy-efficient, high-performance computing. Nevertheless, can it be optimized further?
Dan dennet real patterns.
As I’m waiting for the tests and results from my oncologist, my employer has decided to put me on a medical leave of absence as they say they can’t accommodate me any longer. As a result, I only get limited pay. Please help me so that I can pay some bills so that I can keep a roof over my head and some food in the fridge. Please reach out if you have any questions.
Microbial motility as an amazingly simple biosignature to detect life on Mars and elsewhere (and dangerous pathogenic bacteria on Earth)
Posted on BigThink, Link through SearchforLifeintheUniverse.
Insects exhibit impressive agility and responsiveness even when faced with low-light conditions. The secret lies in their compound eyes, which are capable of detecting motion with incredible speed and sensitivity.
Now, researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a camera that mimics this feat to achieve ultra-high-speed imaging.
Continue reading “Tiny camera shoots sharp 9,120 frames per second in ultra-low light” »
As an embryo grows, there is a continuous stream of communication between cells to form tissues and organs. Cells need to read numerous cues from their environment, and these may be chemical or mechanical in nature. However, these alone cannot explain collective cell migration, and a large body of evidence suggests that movement may also happen in response to embryonic electrical fields. How and where these fields are established within embryos was unclear until now.
“We have characterized an endogenous bioelectric current pattern, which resembles an electric field during development, and demonstrated that this current can guide migration of a cell population known as the neural crest,” highlights Dr. Elias H. Barriga, the corresponding author who led the study published in Nature Materials.
Initially, Dr. Barriga and his team began research on the neural crest at the former Gulbenkian Institute of Science (IGC) in Oeiras, Portugal before continuing research in Dresden, establishing a group at the Cluster of Excellence Physics of Life.
