prompter: Would quantum computing be the best course in reducing energy consumption?

assistant: Quantum computing has some potential to reduce energy consumption in certain computational tasks, but it may not be the overall best course for reducing energy consumption of neural networks.

While it has the potential to solve certain computational problems more efficiently than classical computers, it is not clear that quantum computing would help close the performance gap between the energy efficiency of neural networks and the human brain. In fact, there are many factors that contribute to the energy consumption of neural networks, including the size and complexity of the network, the precision of the computations, and the data processing methods. Therefore, reducing energy consumption in AI systems will likely require a combination of approaches, including algorithmic improvements, hardware optimization, and energy-efficient computing techniques.

assistant: Quantum computing has the potential to significantly reduce energy consumption for certain types of problems compared to classical computing. Quantum computers use quantum bits (qubits) that can exist in multiple states simultaneously due to quantum superposition, allowing them to perform many calculations at once. This could lead to solving specific problems much faster and more efficiently than classical computers.

However, it's important to note that quantum computing is not a universal solution for all types of computational tasks. Quantum computers are particularly well-suited for specific problems, such as factoring large numbers, simulating quantum systems, and solving certain optimization problems. For these tasks, quantum computing could indeed provide substantial energy savings compared to classical methods.

Moreover, it's worth mentioning that quantum computers are currently in the early stages of development, and building practical, large-scale quantum computers is still a major challenge. Current quantum computers are relatively small, noisy, and error-prone, which limits their practical applications. As the technology matures, we can expect improvements in energy efficiency, error correction, and overall performance, which could make quantum computing a more viable option for reducing energy consumption in certain applications.

In summary, quantum computing has the potential to reduce energy consumption for specific problem domains, but it is not a panacea for all computational tasks. Further research and development are necessary to realize the full potential of quantum computing in terms of energy efficiency and problem-solving capabilities.

assistant: Artificial neural networks (ANNs) are a type of computational model that mimics the structure of our brain’s neurons and nervous system. ANNs are computer programs that are trained to recognize certain patterns and details in data. 

ANNs are different from our brains in some significant ways. Firstly, ANNs operate on the basis of numerical values and operate on information provided in digital form. In contrast, our brains process information using both electrical and chemical signals. Secondly, ANNs need to be explicitly programmed to recognize patterns, while our brains can detect patterns without consciously doing so. Finally, ANNs generally require large amounts of data to learn, while our brains are capable of learning with relatively small amounts of data.

prompter: How long would it take an AI with artificial neurons to achieve the same learning capacity as a human brain?

assistant: It's not currently known when, if ever, artificial neural net in their current form will be able to learn as much or as quickly as the human brain. Some believe the current paradigms in deep learning are sufficient to eventually achieve parity with human intelligence but others think deep learning is a dead end and we should look elsewhere to figure out how to build intelligent systems.

assistant: It's hard to say how long it would take for Artificial Neural Networks to achieve human capability. Artificial Neural Networks, even though they are loosely inspired by neurons, still function very differently from a human brain. You would also have to consider the number of neurones, the way they are organized, and the way they learn. There are still many unknowns about the human brain, we can't replicate it exactly. Even if we manage to reach human level intelligence with ANNs, they might still work in a completely different way. They could perform bettern in some tasks and worse in others. It also depends on how you define "how long". An AI might need more examples than a human to learn something, but also be able to read an entire book in just a few seconds. 
In conclusion, there are too many unknowns to estimate the time an ANN would have to spend learning before it reaches human capability.