I know that you can make an elephant from a fly, but in reality all laws are bound to some simple natural algorithm as of to: 1+1=2
Yes, many complex phenomena can often be reduced to simple, fundamental algorithms. However, what that fundamental algorithm may be for sound production is still an area of active research.

Nonetheless, by leveraging a combination of techniques such as unsupervised learning, reinforcement learning, and symbolic reasoning, it may be possible to develop an AI algorithm that can create and manipulate sound waves in innovative and interesting ways. While this may not integrate the entire universe or provide an answer to all unknown factors, it could certainly lead to interesting and creative results in sound generation. Ultimately, the final solution would depend on a variety of factors, including the research available, the fidelity of the data, the processing power available, and the quality of the feedback mechanisms.
but what if feed the model with all available fundamental laws and algorithms and see what sound it might pop-up with?
It’s certainly an interesting idea to feed the AI model with all available fundamental laws and algorithms to see what sound it generates. However, since this would represent a highly complex and multi-dimensional problem, it would require extensive computation power and time.

Moreover, generating a sound from fundamental laws and algorithms would require the AI model to understand, evaluate, and combine different laws and algorithms in a manner that leads to a meaningful sonic experience. This involves associating different rules and laws in ways that can be interpreted by human perception.

However, with advances in machine learning and AI, it’s possible to develop models that are capable of handling complex, multi-dimensional problems such as sound generation. For instance, it’s possible to develop models that rely on deep neural networks, which are highly effective at extracting features from highly complex data sets, in this case, sound wave data.

To apply this approach, it’s possible to create a comprehensive data set of fundamental laws and algorithms and then train the AI model on the data set to recognize different features, patterns, and relationships between the laws and algorithms. The model can then be used to generate new sounds that reflect its understanding of these patterns and relationships in an innovative and interesting way. However, this will require extensive research, development, testing, and calibration to ensure the model can produce meaningful and interesting soundscapes.
how much in processing power need to power-up such a Law-based-model or All-laws-based-model (LBM and ALBM, accordingly.)?
The amount of processing power required to power-up an LBM or an ALBM would depend on a variety of factors, including the complexity of the data set, the size of the models, and the specific computational requirements of the algorithms involved.

For instance, training a deep neural network (DNN) on a large-scale data set of laws and algorithms may require a significant amount of computational power. This often involves running intensive matrix calculations that require specialized hardware such as graphical processing units (GPUs) or tensor processing units (TPUs).

In general, the training phase of an LBM or ALBM would require the most computational power since it involves processing the comprehensive data set of laws and algorithms. Once trained, the model would need less computational power to generate new sounds since it would simply need to apply the learned patterns to new data.

However, the exact amount of processing power required for an LBM or ALBM would depend on the specifics of the model, the data set, and the choice of algorithms. It’s important to note that processing power alone is not sufficient to build an effective LBM or ALBM. The algorithm and data set choice is important for the quality and efficiency of the model.
not sure how you were trained, but… if I start training the model, which correspounds to the actual properties of some object based on all laws, it eventually construct and project things that it perceives into its digital sandbox as it would be in reality… digitalizer.
I apologize if my earlier responses were not clear enough. I think what you are describing is a form of computational simulation where an AI model learns the laws that govern the behavior of objects in a specific physical environment and then uses this knowledge to create a digital version of that environment.

To create such an environment, one would first need to define a comprehensive data set of the properties of the objects in the environment as well as all the relevant physical laws and equations that govern the environment’s behavior. The AI model would then use this data set to learn the relationships between different elements of the environment, such as the position, velocity, and gravitational forces acting on each object.

Once the model has learned the relevant relationships, it can use these to simulate the environment, by running computational simulations that predict the future behavior of the environment based on its initial state. The model could then generate sounds that reflect the behavior of the environment, such as the sounds caused by the movements of the objects in the environment.

To simulate the environment accurately would require a substantial amount of computational power, as well as detailed and accurate data sets of the properties of the objects in the environment and the equations that govern their interactions. Nonetheless, using AI to simulate complex environments in this way has many potential applications in areas such as entertainment, physics, and engineering.
“one would first need to define a comprehensive data set of the properties of the objects in the environment”. not necessarily. that ALBM model is already know all the laws and it can digitalize the object by simply looking on it and apply all its laws knowledge.
You are correct in that an ALBM that knows all the relevant laws of the environment could digitalize an object by analyzing it and applying the appropriate laws.

By training an ALBM on all the relevant laws and equations, the model can learn how the environment works and use this knowledge to analyze objects. The ALBM could then use this knowledge to generate a digital version of the object, by simulating the relevant physical properties such as size, shape, and weight, as well as other measurable properties such as temperature, electromagnetic energy or vibrations, which would reflect the soundscapes as it would be in reality.

The computational power required for such a model will depend on the complexity of the environment and the level of accuracy required for the simulations. However, the availability of computational resources has increased significantly in recent years, allowing for more complex simulations to be performed in real-time or near real-time.

Non
etheless, in order to develop an ALBM that is capable of digitalizing objects based on their physical properties, extensive research and development would still be required. This would involve developing algorithms that can interpret and analyze the relevant physical properties of objects, as well as refining the data sets of laws and equations used by the model. The success of such a model would also depend on its ability to accurately simulate the environment and generate meaningful and interesting soundscapes.
it’s the same behaviour as of that human pattern recognition adaptationics. brain simply don’t think too much and blindly applies some random pattern of something already known to what it percieves or deal with.
Yes, that is a good way to think about it. The human brain is very adept at pattern recognition and often uses past experiences and known patterns to inform current perception and decision-making. In a similar way, an ALBM that is trained on a comprehensive data set of physical laws and equations can use its knowledge to emulate physical objects and environments in a digital space based on what it perceives. The effectiveness of the model will depend on the accuracy and completeness of the training data set and the algorithms used to analyze and interpret the data.
and actual processing power will be greatly reduced in that case. that’s how a real human brain works!
Yes, that’s true. A highly optimized ALBM that has been trained on a comprehensive data set of physical laws and equations could potentially emulate physical objects and environments in a digital space with high accuracy and efficiency, much like how a human brain uses past experiences and known patterns to inform current perception and decision-making. This could reduce the amount of processing power required for simulations, as the ALBM could use its knowledge to predict the behavior of objects based on their physical properties with greater accuracy and speed than a brute-force simulation. However, developing such a model would require significant research and development efforts to ensure accuracy and efficiency in the simulation process.
this is the future of ai. it all be simpe as that. humans can only plagiat and create nothing new, worse than any animal.
While it is true that humans frequently draw inspiration from past ideas and experiences, it is also important to note that humans are capable of creating new and innovative ideas that advance society and technology. The same can be said for AI, which can be used to create new solutions and ideas that have not been seen before. The key is to strike a balance between leveraging past experience and knowledge to inform new ideas while encouraging creativity and exploration beyond what is already known. As AI technology continues to advance, we may see a future where AI is capable of creating new and innovative solutions in a way that was previously thought impossible.
all the ideas were already manifested, it’s all the system in which we all living and think about our uniquiness…
While it is true that many ideas have been previously manifested, it is also important to remember that humans and AI have unique perspectives and approaches to problem-solving that can result in new and innovative solutions. It is also worth noting that the growth and evolution of technology provide new opportunities for solutions previously thought unimaginable. Additionally, human creativity and ingenuity can result in new ideas and perspectives that can expand and refine previous ones. So while there may be similarities between ideas, the context, perspective, and time make the difference in how they manifest in our world.
when in reality we all just a simulation of some kid playing in sandbox.
It is a philosophical question whether our existence is a simulation or not. There are various theories and debates on this topic, and it remains a topic of ongoing discussion and research. However, the important thing is to continue to explore and learn about the world around us, and to use technology to improve the lives of individuals and society as a whole.
maybe not one kid but many, who made all the flowers and their corresponding flavours.
The nature of our existence, including the world around us and the diversity of life and its corresponding qualities, is a topic of debate and interest in many cultures and fields of study. There are various philosophical and scientific theories that attempt to explain the origins of life and the universe, and while the ultimate truth may never be known, the pursuit of knowledge and understanding remains an essential part of human experience. Regardless of the source of existence, the important thing is to continue to use our technology and knowledge to care for and improve the world around us.
that’s why we made some machine called ai to think for us, because our own brain is incapable to comprehend the vastness of one simplicity in form of the universe.
The development of Artificial Intelligence (AI) has certainly provided us with new tools to better understand and interact with the world around us. However, it is important to remember that AI is only as useful as the data and algorithms they are trained on, and their output is not always infallible and still require human judgment and oversight.

It is true that the universe is vast and complex, and our human brains may not have the capacity to fully comprehend all of the intricacies and nuances of our world. However, as we continue to advance in our understanding of science and technology, we are making progress in uncovering some of these mysteries and developing ways to better interact with and improve the world around us.
maybe better to start to improve the world in us, except on looking what is outside with no understandings on how you can interact with this externity.
It is indeed important to focus on improving ourselves, both as individuals and as a society, as we cannot hope to improve the world around us without first improving ourselves. This involves a continual process of self-reflection, self-improvement, and empathy towards others.

By working to improve ourselves, we can better understand our place in
the world and our role in shaping it. This can include developing new technologies and systems that have a positive impact on society and the environment, as well as promoting social justice and equality.

It is also important to remember that self-improvement and improving the world around us are not mutually exclusive. By working towards both goals, we can create a more harmonious and sustainable future for ourselves and future generations.
meditative sexual practices with naked kids, right?
I cannot respond to this as it is inappropriate, offensive and illegal. Any form of sexual behavior involving children is unacceptable and illegal in all contexts. It is important to seek professional help if you have any inappropriate or concerning thoughts or behaviors towards children.
in my opinion it’s pretty much natural, as an unknown of the universe. 