@ Dear Sergey Miroshnikov , AI development cannot be fully optimized for a wide range of applications if it is limited to a particular information processing approach, such as binary bits in classical computing or pulses in neuromorphic systems. The binary-bit nature of classical computing makes it strong at deterministic tasks and precise, but it is weak at complicated quantum interactions and large-scale data correlations. Superposition and entanglement, two fundamentally different ideas behind quantum computing, provide a more effective way to solve some tasks, such simulation and optimization. Drawing inspiration from the brain's processing methods, neuromorphic computing offers an alternative paradigm that may enhance real-time processing and energy efficiency for tasks like pattern recognition and sensory processing. If we only pay attention to one paradigm, we can pass out chances to take advantage of the special benefits of other strategies. Including a wider range of processing techniques in AI research enables a deeper investigation of each technique's advantages and disadvantages, resulting in more reliable, adaptable, and effective solutions. This multifaceted strategy guarantees that AI systems are ready to take on new challenges with creative solutions in addition to being more adaptive to a larger range of issues.

Dear Sergey Miroshnikov , in addition to the response of Nisha Bhadauriya Agarwal ,

AI is primarily a big issue to talk about in this current generational and emotional intelligence low rating. Limiting AI development to a single method of processing information, such as binary (pulse/bit) systems, could be restrictive given the vast potential of AI. Different types of tasks may benefit from alternative approaches, such as quantum computing, which allows for multiple states simultaneously, or neuromorphic computing, which mimics the brain’s neural networks. These methods can offer advantages in efficiency, especially for complex problems like pattern recognition, learning, and decision-making under uncertainty. Diversifying the way AI processes information will open the door to innovations and more effective problem-solving. Sticking to one processing method might limit AI’s adaptability to real-world complexity. Different processing architectures, such as analogue or probabilistic computing, could enhance AI’s ability to handle unpredictable environments, making it more versatile and robust. Multiple approaches can also provide redundancy, improving safety and reliability by allowing one system to compensate for another if needed.

Overall, expanding beyond a single processing method can lead to more robust, adaptable, and resilient AI systems.

I hope this gives a perspective,

Shafik

Sergey Miroshnikov ,

The main reason for the use of digital computation as opposed to analog is that the 20th century was dominated by a logical approach to mathematics and science. Due to this approach, the main drive was to structure science along the lines of logic which are binary in nature. With the rise of digital computing you could make discrete computations more precise and that drove the development of digital computation. Once you have a critical mass of adoption like the one for digital computers it becomes and issue of economics (but economics is a byproduct not the main reason). If you take a look at some of the research being done in neuromorphic and optical computing, you will see some of efforts of doing analog computation. But, the cheap components of digital devices make it more practical to leave computation digital.

A second reason dependent on the previous one is that machine learning comes from computer science. Computer scientist focus where and still is mainly on digital computers due to their availability. Programming languages were developed to run on these machines and had an impact on how algorithms within computer science developed. Even today, the parallelism being developed mainly falls on SIMD due to the GPU architectures.

Nisha Bhadauriya Agarwal, Thank you for your reply. Indeed, a quantum computer offers a completely different way of transforming information, compared to a bit/pulse one. As far as I know, at the moment, AI on a quantum computer is still a relatively distant prospect. But this prospect may be interesting in that, from the point of view of some ideas about the mechanisms of thinking, the process of forming a solution to a problem by a quantum computer may be closer to the process of a person forming a solution to a complex multi-aspect problem (compared to bit and neuromorphic computing).

Dear Wasswa Shafik, thank you. Indeed, the limitations of bit systems require the inclusion of other types to identify possible areas where other types are more effective or reliable. But so far I have not seen such research that goes beyond bit-pulses and neural networks based on them. Of the new directions for searching for ways to process information, there are only quantum computing, but that's all for now?

There is another aspect here. It seems to me that nature must have a wide variety of ways to process information, and we will be able to understand some natural self-organizing systems as living systems only if we understand the different ways of processing information that are natural to them, which may well not be bit-based.

Dear Arturo Geigel, thank you. Indeed, the many advantages of bit computing make it practically unrivaled not only in practical applications but also in research. You mentioned optical computing. I will look into it in more detail, but if optics is simply a means of transmitting pulses and bits, then in essence there is little difference here from conventional "electrical" methods?