I think one way is to observe a particle in superposition – that’s going to be random by default. There’s even an app called “Universe Splitter” on iOS that allows you to perform a measurement on a particle in superposition and based on its outcome do a particular action which is guaranteed to be random.
I am not exactly sure how does the Rule 30 Cellular Automata is random – I mean, sure, the outcome looks random, but how do you choose a subset of that result set? Isn’t the decision of choosing going to be determined? Isn’t it the same as using a random number generator with a seed? Or is that actually the point, that’s what a random number generator uses under the hood?
The unpredictability of certain elements is also a strength and a weakness. By being able to infer from these models we can begin to create truly random systems. This randomness can be used to perform many different things. We can rely upon their unpredictability. There are numerous use cases for this.
Thank you, for the first time I understand the language of computing from a new perspective. Subverted my understanding of computing and computer languages. People do a lot of things with computers, such as predicting the weather. It’s hard to imagine what the world would be like without weather forecasting.
In contemplating the utilization of irreducible systems in nature for human purposes, I believe we must first recognize the fundamental principle of computational irreducibility. Irreducibility implies that the only way to determine the behavior of a system is to simulate or observe it in its entirety, as there are no shortcuts or simplifications that can accurately predict its evolution over time.
The unpredictability of weather storms serves as a quintessential example of such irreducibility. Harnessing these systems for human benefit involves embracing their intrinsic complexity rather than seeking to completely control or predict them. Here’s how I would approach this concept: Data-Driven Understanding: Instead of attempting to predict weather storms with absolute precision, we can focus on collecting vast amounts of data from various sources such as satellites, sensors, and weather stations. Advanced computational methods can then be employed to mine insights from this data, allowing us to develop a more nuanced understanding of storm patterns, their triggers, and potential outcomes. Risk Management: While we may not be able to predict individual storm occurrences with certainty, we can use probabilistic models to assess the likelihood of specific weather events and their potential impact on human activities. This information can be valuable for strategic planning, disaster preparedness, and resource allocation. Adaptive Systems: By acknowledging the inherent unpredictability of weather systems, we can design adaptive infrastructure and technologies. For instance, buildings and transportation systems in storm-prone areas could be constructed with flexible materials and modular designs that can withstand a wide range of weather conditions. Pattern Recognition: Nature’s irreducibility often emerges from complex underlying patterns. Machine learning algorithms, inspired by these patterns, can be employed to improve weather forecasts, identifying trends and tendencies that might escape human observation.
Inspiration for Innovation: The intricacies of irreducible systems can inspire novel technological solutions. For instance, biomimicry could lead to inventions that mimic natural strategies for handling unpredictability, such as self-stabilizing structures or adaptable materials. Simulation and Visualization: Advanced simulations, driven by powerful computational systems, can replicate the behavior of irreducible systems. It enables us to explore “what-if” scenarios, aiding decision-making processes in fields ranging from urban planning to agriculture.
In essence, my perspective on harnessing irreducible systems aligns with the philosophy that these systems are not obstacles to overcome but rather fountains of knowledge to tap into. By embracing their complexity, we can derive valuable insights and develop innovative approaches that enhance our ability to adapt, innovate, and thrive in an unpredictable world.
This phenomenon of computational irreducibility it’s very important and fundamental.
One of the best things is that now we know there are some processes and programs that can’t be predicted. So before knowing this phenomenon we would have wasted a lot of time trying to predict certain things or simplify and reduce some complicated process so that we can work out consequences. But now we know that there is this principle of a reducibility and some things just cannot be predicted by conventional simplification we do not let our ego come in the way, our brain is no different than the program.
It is very similar to Godels incompleteness theorem there’s some theorems that can’t be proved from the axioms and our conventional understanding would tell us that if something is true there must be a way to prove it.
Obviously it can be used to simulate some complicated and irreducible processes like what’s going to happen in the market or what’s going to happen in our body by some given drug.
The applications are limitless, but the only thing that sucks is that there is no way for us to predict the rules that would give us this phenomenon.
We can just check the rules by running them and see that a specific computation which is irreducible can be utilised to model this particular system.