How can the inherent stochasticity of molecular interactions at the nanoscale be harnessed to improve the efficiency of synthetic nanomotors, given the limitations of deterministic models? In what ways do emergent properties in biological systems fundamentally undermine the scalability of reductionist models when applied to the design of self-replicating nanosystems? Why might increasing precision in these systems paradoxically lead to less accurate predictions of their overall dynamics? How do non-linear feedback mechanisms in complex biological systems limit the ability of current nanotechnology to achieve precise control over dynamic, adaptive processes?
Science has been dominated by the thermodynamics of heat engines, where potential energy(order) is converted into work, with some unavoidable and irreversible inefficiency seen as an increase in entropy, which is also necessary for the process to be happen(to be spontaneous). Life is the exact opposite process(or more like rotated in some way): random motion is converted into order; entropy is the fuel, order is the result, and work is just an unavoidable side effect, which is also necessary for life to happen.
I completely agree that traditional thermodynamic models, which focus on the conversion of potential energy into work, struggle to capture the complexity of biological systems. What you have mentioned about life being the "exact opposite" process is particularly fascinating in the context of nanotechnology and biology, especially when randomness and nonlinearity come into play at the nano scale. We explore exactly this paradox - while classical thermodynamics revolves around order-to-disorder transitions (entropy increase), biological systems seem to thrive on randomness, using it to create order. For example, molecular motors like ATP synthase don’t merely operate in spite of the chaos but actually harness the random motion at the nano scale to produce highly organized outcomes. It’s this unpredictability that makes biological systems so different from the heat engines we’re used to in larger, mechanical models. Your point about entropy being a fuel for order in biological systems aligns well with the concept of emergent properties discussed in the video. Life’s "work" is indeed a side effect of managing complexity and exploiting randomness, something that synthetic biology is still grappling with. Instead of fighting entropy, life seems to use it to maintain adaptability and evolve, with feedback loops and chaotic behavior being essential features rather than obstacles.
Thanks for your support! Current nanotechnology tools are excellent for analyzing specific, isolated phenomena (e.g., STM for seeing individual atoms, spectroscopy for bond vibrations), but they struggle to capture the dynamic, non-equilibrium processes that dominate at the nanoscale in biological systems. These tools often treat variability as noise or error to be reduced, rather than an intrinsic part of the system's function. In contrast, biological systems use this variability to perform complex tasks reliably under constantly changing conditions. In short, the problem with using reductive tools for designing nanomachines is that they focus on precision and control at the expense of embracing the variability and emergent complexity that biological systems rely on. This limits our ability to create synthetic nanomachines that can match the adaptability, efficiency, and complexity of those found in nature.
I want to talk again about the equation 9x squared + 4y squared - 72x - 24y + 144 = 0, which is (x-4)squared/9 + (y-3)squared/4 = 1. So you would first shift the 144 over the other side of the = sign making it -144, then you would divide 9x squared - 72x by 9, and 4y squared - 24y by 4. (X - 4) squared/4 should be the same as (x squared/4) - 2x + 4. (Y - 3) squared/9 should be the same as (y squared/9) - six ninths + 1.
You are right to point out how we can manipulate the equation to get it into its standard form. By completing the square, we can rewrite the original quadratic equation for a clearer geometric interpretation, which in this case is the equation of an ellipse.
Do scientists know why certain organs provide hormones to the right place in the body at the right time? Some molecule changes in 3D configuration which opens up the cell wall allowing certain nutrients in. All the unpredictable things going on at the molecular level, can the scientists do more to study them. On terminator, the cyborgs self repair themselves immediately after they get shot. An algorithm is a mathematical instruction. I’m aware of what logarithms are, but don’t fully understand what algorithms are. So if a machine was trained on different shapes, do the different shapes provide the machine with mathematical information. What happens to currents when they enter a micro chip, how to transistors open or close, and how does all that cause the current to carry the binary information. The machines that learn from data that enters a micro chip, what do they consist of. What do these artificial neural networks consist of, and how do they train from data inside a micro chip. What is more practical or efficient, a super computer like the ones predicting how the climate will turn out in the future, or does ai have the most practical and efficient use? We have been searching the heavens in vein for radio signals from other civilisations, but if we created a fully conscious artificial intelligence that would be far more exciting than finding a radio signal from an advanced civilisation. The colours of the physical thing we are looking at at the present and the colours of our thoughts of what happened in the past and what happens in the future exist at the same time.
Thanks for your thoughtful comment! - Hormones & molecular changes: Organs release hormones at the right time via complex feedback loops. Molecules change shape to interact with specific cell receptors, allowing nutrients or signals in. Scientists study this through molecular biology and randomness at the nanoscale. - Unpredictability in biology: Randomness at the molecular level is crucial for processes like cellular communication. Scientists are studying this with tools like AI and quantum biology to better understand these unpredictable dynamics. - Cyborg self-repair: While sci-fi shows instant self-repair, real-world science is working on self-healing materials, but we're far from instant repairs. - Algorithms vs. logarithms: Algorithms are instructions machines follow to solve tasks, while logarithms are mathematical functions. Machines "learn" from shapes by extracting patterns through algorithms. - Transistors & microchips: Transistors act as switches (on/off), controlling electrical current to represent binary data (1s and 0s) in microchips. - Artificial neural networks: These networks mimic brain neurons, learning patterns by processing data. Inside a microchip, data flows through these networks, adjusting connections to "learn" from experience. - Supercomputers vs. AI: Supercomputers excel in large simulations, like climate models, while AI is more flexible and efficient for pattern recognition and decision-making tasks. - Conscious AI vs. extraterrestrial life: Both would be exciting discoveries-conscious AI could transform society, while finding advanced civilizations would reshape our understanding of life in the universe. - Colors & thoughts: Physical colors are light interactions, while the "colors" of our thoughts represent memories and future imaginings, existing simultaneously in our minds.
How can the inherent stochasticity of molecular interactions at the nanoscale be harnessed to improve the efficiency of synthetic nanomotors, given the limitations of deterministic models?
In what ways do emergent properties in biological systems fundamentally undermine the scalability of reductionist models when applied to the design of self-replicating nanosystems? Why might increasing precision in these systems paradoxically lead to less accurate predictions of their overall dynamics?
How do non-linear feedback mechanisms in complex biological systems limit the ability of current nanotechnology to achieve precise control over dynamic, adaptive processes?
Science has been dominated by the thermodynamics of heat engines, where potential energy(order) is converted into work, with some unavoidable and irreversible inefficiency seen as an increase in entropy, which is also necessary for the process to be happen(to be spontaneous). Life is the exact opposite process(or more like rotated in some way): random motion is converted into order; entropy is the fuel, order is the result, and work is just an unavoidable side effect, which is also necessary for life to happen.
I completely agree that traditional thermodynamic models, which focus on the conversion of potential energy into work, struggle to capture the complexity of biological systems. What you have mentioned about life being the "exact opposite" process is particularly fascinating in the context of nanotechnology and biology, especially when randomness and nonlinearity come into play at the nano scale.
We explore exactly this paradox - while classical thermodynamics revolves around order-to-disorder transitions (entropy increase), biological systems seem to thrive on randomness, using it to create order. For example, molecular motors like ATP synthase don’t merely operate in spite of the chaos but actually harness the random motion at the nano scale to produce highly organized outcomes. It’s this unpredictability that makes biological systems so different from the heat engines we’re used to in larger, mechanical models.
Your point about entropy being a fuel for order in biological systems aligns well with the concept of emergent properties discussed in the video. Life’s "work" is indeed a side effect of managing complexity and exploiting randomness, something that synthetic biology is still grappling with. Instead of fighting entropy, life seems to use it to maintain adaptability and evolve, with feedback loops and chaotic behavior being essential features rather than obstacles.
cool vid
Thanks for your support! Current nanotechnology tools are excellent for analyzing specific, isolated phenomena (e.g., STM for seeing individual atoms, spectroscopy for bond vibrations), but they struggle to capture the dynamic, non-equilibrium processes that dominate at the nanoscale in biological systems. These tools often treat variability as noise or error to be reduced, rather than an intrinsic part of the system's function. In contrast, biological systems use this variability to perform complex tasks reliably under constantly changing conditions. In short, the problem with using reductive tools for designing nanomachines is that they focus on precision and control at the expense of embracing the variability and emergent complexity that biological systems rely on. This limits our ability to create synthetic nanomachines that can match the adaptability, efficiency, and complexity of those found in nature.
I want to talk again about the equation 9x squared + 4y squared - 72x - 24y + 144 = 0, which is (x-4)squared/9 + (y-3)squared/4 = 1. So you would first shift the 144 over the other side of the = sign making it -144, then you would divide 9x squared - 72x by 9, and 4y squared - 24y by 4.
(X - 4) squared/4 should be the same as (x squared/4) - 2x + 4. (Y - 3) squared/9 should be the same as (y squared/9) - six ninths + 1.
You are right to point out how we can manipulate the equation to get it into its standard form. By completing the square, we can rewrite the original quadratic equation for a clearer geometric interpretation, which in this case is the equation of an ellipse.
Do scientists know why certain organs provide hormones to the right place in the body at the right time? Some molecule changes in 3D configuration which opens up the cell wall allowing certain nutrients in.
All the unpredictable things going on at the molecular level, can the scientists do more to study them.
On terminator, the cyborgs self repair themselves immediately after they get shot.
An algorithm is a mathematical instruction. I’m aware of what logarithms are, but don’t fully understand what algorithms are. So if a machine was trained on different shapes, do the different shapes provide the machine with mathematical information.
What happens to currents when they enter a micro chip, how to transistors open or close, and how does all that cause the current to carry the binary information.
The machines that learn from data that enters a micro chip, what do they consist of. What do these artificial neural networks consist of, and how do they train from data inside a micro chip.
What is more practical or efficient, a super computer like the ones predicting how the climate will turn out in the future, or does ai have the most practical and efficient use?
We have been searching the heavens in vein for radio signals from other civilisations, but if we created a fully conscious artificial intelligence that would be far more exciting than finding a radio signal from an advanced civilisation.
The colours of the physical thing we are looking at at the present and the colours of our thoughts of what happened in the past and what happens in the future exist at the same time.
Thanks for your thoughtful comment!
- Hormones & molecular changes: Organs release hormones at the right time via complex feedback loops. Molecules change shape to interact with specific cell receptors, allowing nutrients or signals in. Scientists study this through molecular biology and randomness at the nanoscale.
- Unpredictability in biology: Randomness at the molecular level is crucial for processes like cellular communication. Scientists are studying this with tools like AI and quantum biology to better understand these unpredictable dynamics.
- Cyborg self-repair: While sci-fi shows instant self-repair, real-world science is working on self-healing materials, but we're far from instant repairs.
- Algorithms vs. logarithms: Algorithms are instructions machines follow to solve tasks, while logarithms are mathematical functions. Machines "learn" from shapes by extracting patterns through algorithms.
- Transistors & microchips: Transistors act as switches (on/off), controlling electrical current to represent binary data (1s and 0s) in microchips.
- Artificial neural networks: These networks mimic brain neurons, learning patterns by processing data. Inside a microchip, data flows through these networks, adjusting connections to "learn" from experience.
- Supercomputers vs. AI: Supercomputers excel in large simulations, like climate models, while AI is more flexible and efficient for pattern recognition and decision-making tasks.
- Conscious AI vs. extraterrestrial life: Both would be exciting discoveries-conscious AI could transform society, while finding advanced civilizations would reshape our understanding of life in the universe.
- Colors & thoughts: Physical colors are light interactions, while the "colors" of our thoughts represent memories and future imaginings, existing simultaneously in our minds.