Computer science doesn’t need computers. Theoretically, we can talk about many systems. That are still impossible or extremely hard to create. In those cases. computer science doesn’t need computers. We can think theoretically.
About computers without physical infrastructure. Or. Those computers require physical structures to maintain the data-processing structures.
Those structures have the shape of multiple internal quantum fields. The touching points of those fields would be the quantum dots. And that allows this kind of network computing. Sometimes people think that the Multivac. Fictional supercomputer from Isaac Asimov's novel could be the multiple internal quantum fields. The idea is that. Multiple internal vacuums or bubbles could form a quantum computer. But those ideas remain in the imagination.
Theoretically, we can create things like interstellar spaceships. Turning theory into practical solutions. That is something more difficult.
Than just drawing formulas on paper. So does computer science need computers? We could make everything that a computer does with paper and pencil. We can store our writings and drawings in data storage called books.
So does data science exist without computers? The question is similar to the next.
Does astronomy exist without telescopes?
Telescopes collect data. And a researcher computes data. The data that the researcher uses doesn’t come only from telescopes. That data comes from multiple sources. Also. Historical cases and sources. Particle accelerators and other tools, like chemical analysis, are part of astronomy. Another thing is that astronomers require mathematics. So, can astronomy exist without telescopes? Cosmologists can work without ever looking through any telescopes.
There must be something.
That collects data. That cosmologists process. But then to computing. Researchers make many new ideas, like quantum computers. Those systems have one problem. Being trusted. The information that the system produces. It must be confirmed. The system will not know whether the data that it uses is right or wrong.
There is. An interesting detail in computer science. The system doesn’t create information or knowledge. It just processes information that it collects from multiple sources. Then that system creates an entirely new data mixture.
Data systems. They are tools that increase data mass. Modern computers are more effective than computers were in the 1950s. But the problem is. The data mass that they handle. It is much larger than it was in the 1950s. This data accumulation creates a need.
For more and more effective computers. Moore's law is the historical observation that the number of transistors on a microchip doubles roughly every two years while the cost of computers decreases. The problem with Moore’s law is this. The number of transistors that manufacturers can put on microchips cannot grow eternally.
This means. That computers require more and more microchips.
Moore’s law claim. The computer’s price decreases. But what if Moore’s law expands into microchip-level structures? Moore’s law is two-stage. The first stage is that the number of transistors on microchips doubles every two years. The second stage is that it makes computers cheaper. This can be true. New processors make older computers cheaper. The new computers are more expensive. AI creates a need to make new types of computers. Those computers need more power. So they need more microchips. Another thing. That. Moore’s law doesn’t apply. It is the network-based solutions. PHP (Hypertext Preprocessor) outsources computing to the computer centers.
This means that the local workstations don’t require as much power. But. Because. PHP runs code on servers. It delivers answers to workstations. That increases the need for more power in servers and data centers. PHP allows cloud-based architecture creation. In those cases, multiple regular computers form a network-based computer. The problem is the same as in computers or data centers. There are new applications. That require more computer power. And that increases the need to improve the power of those data systems. That happens by connecting more processors into that network.
When researchers create new computers.
Existing computers. Or their resources are not used.
They could be used. And increasing memory capacity. It makes it possible to write code that wastes memory. Lack of precision. In code makes some modern programs vulnerable. Another thing is that. AI makes. Possibility. And the illusion. Of. The ability to create customized tools for every office. This means. Outsourcing human work. Too easily to AI. AI is not complete yet. And it might not ever be at the level we are. We can create AI that mimics humans. But those reactions are programmed into its algorithms. That makes AI so interesting and frightening.
When we think about the future of computing.
We must realize that directly networked human brains are the most effective computers. BCI systems and implanted microchips make it possible to create such a system. There, humans form the data systems without individuals. Those systems melt human minds into one entire mind.
Technological singularity among humans. And humans and computers. It is one possible way. To develop data systems for the future. But another thing that can turn reality. It is the retrocausal network.
Retrocausality means.
The ability to send information from the future to the past. This means. If. Retrocausal networks can be made. That means. The solution comes before the input is given. And that is one of the most exciting things in data science. Retrocausality is one of the most interesting and frightening phenomena that we can imagine. Information that comes from the future is hard to confirm. The information exists from the point at which it starts to exist. This means.
That we cannot be sure. That the information we see is retrocausal. Or. Is it generated for some other reason? But those things are philosophical questions. And that brings other questions.
To the minds of people. What are the limits in data science? If we connect our brains straight to computers. We can make data systems effective. But otherwise, we open our minds. Our brains. To computers. And that makes it possible for hackers to read our minds. The problem is that when we use BCI systems. Our minds don’t make a difference.
Between reality and a computer’s virtual reality. In BCI, the system interacts with brains. That allows hackers to input data into our brains. If. They can hack the computer. The microchip implant makes it possible to see what happens in human brains. That makes it possible.
To remotely control robots. That gives freedom to handicapped people. But the same tools. They can be dangerous in the wrong hands. They can destroy individualism and diversity. And many other things. Diversity guarantees. That there are lots of routes. That we can select. Lack of diversity Means that we have only one route. And if that route is wrong. We are in trouble. We have no other choices. That. We can select. If. We choose the wrong way. Even. If the solution looks fine. Later, that can turn into a route. To destruction. We cannot see the future. We must realize this in modern technology.
Computers and microchips play a vital role. Internet is the platform that shares information. This information contains data. That involves virtual information.
But these systems can also use that data to control physical items like drones. The robot is the link. Between. The virtual and physical worlds.
This means that AI can make the system. That inputs information into human brains. That can turn humans into physical tools for the computers. As well as. AI it. Turns robots into physical tools for computers. But humans are much more than robots. We have imagination.
The ability to create synthetic memories. And the ability to handle abstractions like physical things. The ability to interconnect information from multiple sources. It makes it possible to create things that seem real. But today, those things are imagination.
https://www.quantamagazine.org/does-computer-science-need-computers-20260828/
