As this new century begins and a new generation of engineers is thrown into the world to solve its problems, it is important to prioritize which innovations in science require the most attention. Fortunately in 2008, the National Academy of Engineering listed the fourteen most pressing challenges that we, as engineers, will face in this coming century [1]. The challenges of the previous century were similar in some aspects of environmental and nuclear science, but what really defines this era is the progression of bioengineering and computer engineering. One of the challenges that combines these two fields is the creation of supercomputers by studying the human brain. This network of teamwork is important to realize as a young engineer because so the majority of projects, inventions, and studies require several fields of engineering. As expected, a whole category of ethics issues come into play from limitations on how to study the brain to the forced specificity that researchers must work with in order to continue researching in their field of expertise. All of these considerations are important for young engineers to realize if they were to pursue studying such a delicate and complex object like the brain in a legal and overall safe manner. This research on reverse-engineering of the brain to learn more about it and to improve AI is essential to increasing our knowledge of how the human mind works, and in turn use these computer innovations to treat common brain disorders.
To begin with this complicated process of attempting to understand how the brain works by studying its complete adult form and implementing this information to create better AI, scientists started at the microscopic level. The use of scanning electron microscopes and dye- bearing antibodies was essential to observing neural activity at the cellular level. This helped explain how neurons interconnect and the numerous connections that just one neuron can achieve with its neighbors. Researchers also studied EEGs to understand how larger portions of the brain worked. Scientists were successful enough with their research that they began to create miniature postsynaptic potentials. Thus, they essentially created a small interconnected web of brain cells that could spark neurons to send signals to one another. Thus, a self-operating neural circuit called Blue Brain was created [2].
Despite the exciting breakthrough, there are still limitations to the research done on the brain. First off, only a small network of cells can be created and tracked with a computer. Hundreds of petabytes would be needed in order to simulate the numerous synapses in a real human brain. Additionally, the energy cost to run the model would reach billions of dollars. Although that technology seems decades away, in reality it will only take approximately less than ten years for computers to upgrade enough to be compatible with the requirements of a full brain model circuit [3]. Another limitation to building a full-model brain is that the circuit still relies solely on individual interaction of neurons. There is no conscience created or train of thought formed from the model. Scientists lack the information to build a brain as a whole and interdependent organ, and the bottom line is that the synthetic brain is not alive. Additionally, scientists are stronger discouraged to study human brains in any way besides observing them through machinery. Still, the information used to create the circuit is a step closer to the improvements in AI that will occur.
Our brains can be thought of as brilliantly efficient computers. The expedition of forming model circuits is equally important for finding out about the brain as it is for using this information to make better computers [4]. Our human biology ironically is the least known field of science, but if we can reciprocate even a portion of its characteristics, computers will perform exponentially better. For example, most computers can only complete three or four tasks at the exact same time. The brain, on the other hand, can perform hundreds of functions and still not “lag” while operating. This is due to the trillions of interactions on synapses that exist efficiently in the brain. Furthermore, the brain’s neurons act on repeating patterns or multiple signals that alter the intensity of certain signals. Apart from that, the brain has feedback mechanisms that help it to correct itself. While these mechanisms are not yet implemented on computers, the Blue Brain model is the start to the supercomputers that our generation will create in the next couple of years by studying the human brain. Moreover, almost every single field of engineering will become more complicated, have more data, and therefore require more complex computers to keep up with the innovations in science. Supercomputers will be a very positive influence on society that can only be achieved by the study of the human mind.
The creation of AI from studying the brain can lead to a reciprocal effect. In other words, studying the brain can help create artificial intelligence chips which in turn could be used to help treat brain disorders. Many senses, sight and hearing for example, have already been fixed in disabled people using computer technology. The next goal for scientists is to build a chip which could be used as a memory center where memory neurons have been destroyed. This chip will circumvent the damaged memory neurons and make new paths in order for the person to be able to form new memories. Diseases such as dementia or Alzheimer’s would be cured just by placing a few computerized neural cells in the specified location of the brain [5]. Eventually, the procedure would use much less resources, ultimately being much more cost effective. Overall, creating computers to work like brains would not only revolutionize computer technology, but also it would provide treatments to brain disorders that current treatment procedures cannot solve as effectively.
While researching may help to cure more diseases in the future, it is important to avoid creating problems in the process of researching in an attempt to understand the brain more fully. An ethic issue that deals with this is treating patients responsibly if any research is done on them [6]. Machines cannot figure out everything about a person and how their mind works. Many mechanisms in the brain depend on action reaction pairs that deal with strong stimuli, which usually entail some type of pain or detriment to the body. Finding these reactions to strong stimuli and how the brain fixes them would greatly improve the making of AI to mimic the mind, but every engineer in this field must remember his responsibility to the subject or patient. The experimenter cannot and is not allowed to let his desire for information cloud his better judgment on how to treat his subject, even if the person consents to the consequences. Apart from the patient and researcher bond, there are many legal manners that restrict the extent to which experiments can be completed. For now, researchers will have to rely mostly on mechanical ways to study the brain through imaging and microscopes in order to find the information needed to improve AI and get closer to their goal of creating supercomputers. Furthermore, if an engineer does hit upon an unrelated finding in the course of his or her research, that engineer must remain in their field of expertise. It is unethical for any scientist to pursue a subject without the proper experience and knowledge that another engineer in that certain field would [7]. The final product resulting from such an experiment could be unsafe and potentially illegal if public safety is threatened. This separates the bioengineers from the computer engineers in reverse-engineering research because a computer engineer is not allowed to work with the brain portion of the experiment and the bioengineer cannot tinker with the AI portion of the research. This is a positive aspect of ethical restrictions because it highlights the interconnectedness and efficiency of engineering for younger engineers. It is more productive for many engineers to focus on specific topics and work together than for many engineers to have a general idea about all the fields of science.
While a topic such as reverse-engineering may be interesting to a freshman in college, one may question whether researching such a topic and its ethics is worthwhile or not. I believe that writing about these specific real-world topic helps develop a young engineer. First, it helps that person realize the interconnectedness that all engineers have with each other. Every one of us has a goal to improve our lifestyles and almost every issue takes into account several fields of engineering. For example, studies on reverse-engineering and how it can help us create better AI require several fields of science to work together: computer, mechanical, and bioengineers. Along with that, these writing assignments are not focused on some obscure and unknown research experiments. On the contrary, reverse-engineering of the brain and artificial intelligent are extremely popular topics at the moment along with many of the other issues concerning the environment, materials science, and other technologies. Finally, these writing assignments help us to physically and professionally learn how to place our research and opinions together in a cohesive manner so that when we will have to publish an article in the future, we will have the necessary skills to do so. You never know if you might discover something worthy enough to be published in the near future.
The impact that reverse-brain engineering would have on supercomputers would diffuse to other fields of study where large quantities of information must be stored or programmed, increasing the output and decreasing the time it takes for research to be completed. Artificial intelligence would also develop to the point where it could be reentered into the brain to cure a multitude of brain disorders. Eventually, AI will progress to the point where mechanical animals will be made from scratch to control their movements and actually learn from their surroundings [3]. The implications of researching the brain and implementing its information to computers are limitless, however the means by doing so are not. Ethical issues such as patient care must be considered and respected. Also, A researcher cannot stray from his or her area of expertise in order to ensure that the product is handled only by engineers that specialize in that product’s specific engineering type. This exemplifies the fact that many engineers must work together in order to efficiently reach a goal. Additionally, young engineers reap many benefits from studying the effects of such collaborations and from learning about the multitude of modern engineering problems that we face, such as reverse-engineering the brain to create better AI. Engineers have solved many problems in the past and now it is our generation’s turn to learn from the best and further sharpen the skills and knowledge of the past in a constant drive towards our hopes and dreams of what our lifestyles will become in the future.