The Consciousness of AI
AI and Neuroscience?
In my experience, one of the biggest opportunities for AI in the field of neuroscience is doing more biologically plausible research and creating a visual perception of the unique features of human cognition. That will lead us in understanding and knowing so much more about the human brain which still is a mystery to a huge extent. This also eventually will lead us to understand mental illnesses, triggers to mental illnesses and the best way to recover from them.
Brain, AI, and neurons!
Biologically-inspired artificial neural networks and computational neuroscience approaches that attempt to elucidate brain networks are gradually getting closer to experimental neuroscience. However this would definitely require a lot more research and more conversations and collaborations between neuroscientists and AI researchers. It’s very fascinating how both can learn from each other and develop even further.
How can neuroscience benefit from AI?
As we all know, brains are far too complex for us to understand at present. I read a book called “The Psychopath Inside” by James Fallon, where he explains the brain in terms of a 3*3 rubik’s cube (it’s still so impossibly difficult to understand and visualize without prior knowledge). This is where AI jumps in according to me, and can be employed in a number of ways. Using AI we can produce new tools or applications to come up with connections or general theoretical principles. This will help us understand the complex machine that our brain is.
Also, on a more logical level AI can help us visualise the different patterns in a brain and try to find the underlying reason for the difference in patterns (as well as analyse the effects). For example; certain types of overlapping in the brain cause people to lose memory for a short duration of time — this can be visualised and studied using Recurrent Neural Networks (RNNs).
We can also visualise using AI driven tools, what part of the brain is getting affected to understand more about different mental illnesses which also might co-occur, diseases like depression, PTSD, OCD, schizophrenia, etc., by analysing what parts of the brain are more active and which ones are not.
Also, the modern neuroscience tools such as multi-electrode arrays, real-time imaging, Functional magnetic resonance imaging (fMRI), etc., produce massive amounts of dataset which depend on AI to be analysed and dealt with.
How can AI benefit from neuroscience?
Neuroscience has always been a key inspiration behind the history and development of artificial intelligence and of course there is still so much to learn from it. Neuroscience has always been an inspiration to make the network more intelligent and human-like. There are two key points to it: One is that the Artificial neural networks try to replicate or imitate human intelligence. Now with ideas comes a structure in which you would envelope your idea, that’s the second key point here; artificial networks also tend to mimic the brain structure. Transformations made using AI are breaking the internet nowadays. We hear how reliable and accurate AI is getting, however it still has a lot to learn from our brain/neuroscience.
As AI networks are inspired by the structure of a human brain, so are the neurons in the neural networks. The neural networks in AI have very similar characteris when compared to the biological neurons in a human brain. The basic working mechanism is that when one neuron cell gets activated, it generates a spike and sends signals to other neurons. In the artificial neural networks, which also have interconnected neurons, when a neuron receives an input, it gets activated and passes on the information to the other neurons. It works very similar to our brain, in a simple example — we keep getting better at the task we perform often in real life. In a similar way we train the AI on a lot of data. The artificial neural network has X number of connections and Each connection on the neural network is associated with a weight . Weight decides how much influence the input will have on the output. Biases, which are constant, are an additional input into the next layer that will always have the value of 1. During the training process, we tune the weight from node x to node y as required. However, there’s a lot to learn about the plasticity and the malleability of the human brain and try to implement them into AI networks. It is an ongoing research to make AI more human-like and more versatile. For example: As a human, I can walk even with my eyes closed. But for the AI model to do the same task in a new environment (‘walking’ with ‘eyes closed’), will probably not work well.
AI if used correctly and cautiously is very promising both is understanding the complex mental health diseases and even transforming mental healthcare.
AI can be very beneficial in predicting and classifying mental health disorders. It can also help subgroup them based on mood disorders, anxiety disorder, or if they are co-occurring (as for example depression which is an anxiety disorder can occur with a mood disorder) etc. Also, AI can potentially achieve high accuracy, as we keep developing more robust and advance AI techniques, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual’s unique characteristics.
Obviously, this needs to be done with a lot of caution and a lot of research is required for the same. I also believe the amalgamation of AI into identifying mental health illnesses could also reduce the stigma surrounding it, as it is still an issue that’s refrained upon in a lot of parts of the world. When we analyse it using advanced neuroscience tools and using AI, it might be sought after as a more acceptable illness, or something closer to a physical diagnosis.
I believe AI has huge potential in research, healthcare (mental and physical), and basically everything. When implemented with caution, ethics, transparency and explainability — it will do wonders!
