Revolutionizing Technology With Neuroscience Research: A Hypothetical Approach
By Chelsea Zou
From birth, we are born with a system located inside our skulls capable of information processing, retention, and interpretation. Although essential to human survival and reasoning, these fundamental processes are not exactly what differentiates us as a unique and powerful species. In the field of neuroscience, the seemingly unclear concept of consciousness is often viewed as a philosophical taboo, rendering it rather futile in the technical aspects of applied science. These days however, such a rapidly advancing world of technology further perpetuates the need to better understand the inner workings of the mind and its experience. Artificial intelligence has already been expanding to lengths never reached, yet with such fast progress in the technological fields, actual understanding on the biological side of science is comparatively slow. The overarching goal is for biological understanding to keep up with technological progress so both can work relative to one another and advance in the most effective ways possible.
As of now, creativity seems to be a distinguishing characteristic reserved only for human capability. How close are we to be able to create machines capable of intellectual insights and creative processes? Essentially, machines can potentially come up with innovative ways to solve specific problems that humans would have otherwise overlooked. These outcomes can possibly revolutionize our problem solving efficiency and assist in major insights, unveiling scientific revelations in critical fields such as physics or biology to help us better understand this complex universe we live in.
Human thought processes involve a series of convoluted networks, interconnected signals, and complicated causal relationships. Despite its intricate operations, our brains work at speeds insignificant compared to those of typical computers. Below is a chart comparing the differences between the human brain and a typical computer back in 2008.
Artificial intelligence with the spotlight in speed and accuracy, integrated with the simulated “creative” processes of the human brain can transcend scientific discovery. As we are probably aware of, great insights do not come easy. They are often times characterized by sporadic moments and are products of stochastic revelations occurring within the mind, sprouting from a collection of preliminary knowledge, facts, and information.
Scientists like Albert Einstein and Charles Darwin have contributed their entire lives to their achievements, allowing us to understand concepts like special relativity and natural selection. Their extraordinary intelligence is hard to come by, and these individuals have dedicated years of unprecedented effort to form theories that have undoubtedly contributed to major advancements in our civilization.
What if there was a way to catalyze these leaps in scientific understanding? After all, aren’t these breakthroughs merely chemical reactions occurring within the brain? Although complicated, it is not entirely unfeasible to configure a digital imitation of the process. Hypothetically, machines capable of creative insight can facilitate these intellectual insights in a way that may someday revolutionize our understanding of the world.
How can we begin? Currently, the impact of machine learning is dramatically expanding and taking a great toll in the tech-world. In our modernizing society, the majority of our daily lives are heavily influenced by machine learning algorithms. How does a quick glance at our iPhones give it the ability to unlock? How can a medical app predict specific diseases based on several symptoms and images provided? How are we able to have meaningful two hours conversations with Siri? Face recognition, predictive image diagnosis, and deep talks with inanimate bots are all related to a specific branch of machine learning called deep learning. In this field lies the subcategory of artificial neural networks (ANNs), which is a loose analogy of the neural networks found in our brains. For the sake of simplicity, the general foundation of ANNs simulate specific correlations of the way the brain analyzes and uses information.
Today, machines are already capable of learning on their own without human interaction. After all, if digital systems have the ability to independently accumulate knowledge, how large is the gap between autonomous learning, and autonomous thinking?
Unfortunately, simulated insights and creativity is no simple task. Attempting to recreate this complex level of cognition in artificial intelligence will require technologists to consider a different approach. Technology, although progressing rapidly, is still not enough to imitate creativity in machines without support from a biological counterpart. This is due to the fact that very little is known about the exact causes of creativity in the human brain, and the complexity of the biological brain stems beyond mechanistic analogies. The brain manifests itself in intricate layers, making it difficult for scientists to “single out” and pinpoint specific parts of the brain to attribute it to creative processes.
An interdisciplinary approach would be the greatest adaptation, however that methodology remains hazy and leaves scientists in a muddle of conflicting discrepancies. Furthermore, the brain is constantly evolving at the face of new information and stimuli. This makes it hard for neuroscientists to ascertain precise conclusions about neural activity.
However, what scientists can do is try to improve the understanding of how the brain works relative to each of its parts, its essential overlap of cause and effect. Researching which specific structures are at work when an individual is fulfilling his or her creative tendencies, and later analyzing the relationships between the working structures can yield a more “structured” basis of information. This provides scientists with a more solid and circumstantial foundation to build upon, as these studies involve more coherent questions rather than meta philosophical topics such as the formation of consciousness and creativity.
The goal is to eventually form the most comprehensible profile of the brain, refining our understanding as the indications reveal. As we learn more, evidence builds and our accuracy improves. There may not be an ultimate conclusion, but there will be impactful hypotheses and predictions that will later assist us in multiple areas of technology and healthcare. Although this approach does not directly seek answers to the formation of creativity and consciousness, we are likely to find useful insights to help add essential pieces to the puzzle. Fortunately, there are many sectors of neuroscience research already attempting to analyze inter-relational cognitive processes. What we need more of, however, is increased emphasis on collaboration between AI researchers and neuroscientists, a specific and integrated approach bridging together the vast realms of science and technology.
By researching the specific structures of cognition and its relationships, an opportunity is paved to map out the enigma of creativity from a biological standpoint. Deconstructing the layers of brain processes can allow us to further grasp the fundamental aspects of a higher-level cognitive process to later reconstruct it in artificial intelligence. And although it sounds somewhat ironic, a robot may someday improve the understanding between our human experience and our physical reality. Science fiction may no longer be just fiction. The future starts soon.
