Brains exist to solve problems. They help us process information and find the best solution for these problems. Initially, this problem was survival, but brains have since enabled us to use our environment to our advantage in developing efficient ways to achieve our goals. There is very little doubt about brains being conscious. Consciousness, in most theories, exists within or connected to the brain in some way. But when you look into the fundamental elements that make our brains, there is no source of consciousness to be found. Instead, neurons are firing electrical signals in patterns that eventually become complex enough to create our thoughts. Mathematician Gottfried Wilhelm Leibniz explained this using the windmill thought experiment, saying that if we designed a machine to think and feel, and then entered the machine as if it were a mill we would find only pieces working together, with no clear source of perception (Leibniz, 1714/1898). Perception would only be observed when looking at the machine as a whole. So if consciousness can be observed as an emergent property born out of extremely complex electrical circuitry, then what makes computers unconscious? Why are they seen as tools for us to use instead of minds designed to carry out specific tasks? The answer lies in their contemporary lack of complexity when compared to the human brain. Computers have the potential for consciousness, but in their current state, they are not complex enough and have not been designed in the specific fashions necessary for an observable emergent consciousness.
Computers as they exist now are far behind our brains when it comes to ability and affordances. Because of this, it is very hard to see them as conscious beings. After all, they do not really act like living things, except in their continuous need for fuel. But one may argue that they do not seem like living beings because they were not designed in the same way that every other living being was. Unlike other living things, computers were designed with goals beyond survival in mind. Instead of evolution creating living beings to exist and survive within the world of nature, humans have created computers to be used within the world of humanity. So when approaching the topic of consciousness within computers, it is important to take into account the vast differences between the conception of our consciousness and the intention behind the creation of computers. We are dealing with an entity unlike anything before, an entity that we alone are responsible for. So how does the difference in conception justify computers having consciousness? It doesn’t, but it does explain why consciousness in computers may look very different compared to ours, which brings us closer to finding out what true consciousness in computers may look like.
Artificial intelligence is the next step computers have made towards our perceived definition of consciousness. The complexity of AI and the massive amount of computing power needed for it reveal similarities between our thought processes and the patterns that make up AI. Artificial Neural Networks are computing systems designed in ways that imitate human thought processes (IBM Cloud Education, 2020). What’s more, is that these neural networks truly work. They can be trained, and “learn” in ways similar to the ways we do, with positive reinforcement being carried out through numerical representations instead of more biological methods like the dopamine releases within our brains. So are these neural networks conscious? No, they are no more conscious than a biological neural network existing entirely on its own and functioning towards one goal. If you substituted our current AI for a hypothetical organic, biological intelligence and gave it the same function and goals of AI it would not be considered conscious either. So this is as close to consciousness as our modern computers get. But that does not mean they will never achieve consciousness. In fact, scientists today have developed an algorithm that could potentially accurately simulate the mind of a human. (Jordan, Jakob et al, 2018) The only obstacle is computing power. Currently, the simulation would only be able to run at 10% capacity, which already maxes out the power of the supercomputers running it. “This is because the act of connecting neurons — crucial for every activity that happens in the brain — requires more power than today’s hardware has” (Tristan Greene, 2018).
So can computers be conscious? Absolutely. Their potential to represent and calculate any given phenomenon dates way back to the Universal Turing Machine, and simulating the human brain, which contains our version of consciousness, is an issue of resources, not ability. Computers can solve problems and make decisions better than we can, but because of their status as a tool for humans to use, they have not been designed in ways that may promote consciousness. In other words, the potential is there, but the pieces of the puzzle have not been correctly arranged to create consciousness. But with the rise of artificial intelligence and growing computing power, we could realistically see conscious machines in the near or distant future.
Bibliography (some great reads on consciousness in here)
Blackmore, S. J., & Troscianko, E. T. (2018). Consciousness: An Introduction (3rd ed). Oxford. University Press.
Greene, Tristan. “There’s an Algorithm to Simulate Our Brains. Too Bad No Computer Can Run It.” TNW | Artificial-Intelligence, March 22, 2018.https://thenextweb.com/news/theres-an-algorithm-to-simulate-our-brains-too-bad-No-computer-can-run-it.
IBM Cloud Education. “What Are Neural Networks?,” August 17, 2020. https://www.ibm.com/cloud/learn/neural-networks.
Jordan, Jakob, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, and Susanne Kunkel. “Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.” Frontiers in Neuroinformatics 12 (2018): 2. https://doi.org/10.3389/fninf.2018.00002.
Leibniz, G. W. (1714/1898). Monadology and other philosophical writings [La Monadologie]. Trans. R. Latta. Oxford: Oxford University Press.