Human and Machine Intelligence Essay
I have been tasked with examining the root similarities and differences between human and machine intelligence. This is no small feat considering the copious amounts of scientific and philosophical information attributed to each, including fact, speculation, conviction, or otherwise. Defining intelligence alone has been historically subjected to great debate; add to that the hypotheticals of artificial intelligence and a whole new scale of complications become apparent. Through the course of this essay Team C will analyze and discuss a handful of key variables pertaining to biological (human or human-like) and mechanical intelligences, including their presumed natures and components, etc., and in the case of machines current achievements, ambitions, and the direction of development. With greater understanding of the elements of each, Team C can begin to compare and contrast the two with as much precision as possible, given the level of uncertainty.
Artificial Intelligence (AI) has been a difficult goal to reach for scientists. Major milestones have been crossed on the road to true AI. According to Anthony Tongen, “In 1999, a group of scientists from Emory University and Georgia Tech made a calculator (called the “leechulator”) with neurons taken from leeches” (Tongen, 2003). This adaptation of biological components and mechanical material strikes major ground in the collective effort to reach true melding of flesh and machine. By 2010 Swedish researchers at University of Gothenburg are crossing new ground by genetically modifying cells to act in a fashion similar to electronic circuits (Science Daily, 2010). In a period of 11 years the amount of progress is not extraordinary but it has been steady. With the advances made in 2010, the scientific community should be expecting new doors and uses to come up in the field of Biomechanical engineering. Ultimately the goal is to reach a point where machines the size of molecules can be created, nanotechnology, the foundation has been laid with the ability to create biological components with functionality of a microcomputer. Even the most recent of achievements is nowhere to being at a useful level of concept. Ideally we could have minicomputers floating in our blood ready to get an early jump on infection or cancer cells. This will be a reality one day but now we are still in the infant stages.
Functionality of the human brain in terms of AI is often compared to computer software. In the article The Fall of Machines, Michio Kaku talks about how scientist believe the human brain is the “most complicated system ever created” (Kaku, 2008). Kaku also discusses site and hearing of a robot, and the robots may see and hear better than a human, however, the robot does not know what it is seeing or hearing. Scientists refer to the two problems of not understanding what is being seen or heard as twin problems. Robots to not contain common sense like humans (Kaku, 2008). When considering our human brain functionality, research suggests the left side of the brain controls language, logic, critical thinking, numbers, and reasoning and the right side controls the expressive and creative tasks (Cherry, n.a.). Humans have created robots with the ability to use left brain functionality better than a human’s left brain. Creating a robot with the right brain functionality is not possible (Stevens, 2011). How is it possible to create the ability to express emotions, read emotions, common sense, or have intuition? Facial recognition is also one of the right brain abilities; however, computer software has been designed to use a facial recognition feature. One of the most popular social network sites, Facebook, uses a facial recognition feature on photos posted on the site. The functionality allows users to tag friends in photos by simply selecting “yes” after the system recognizes the face as one of the user’s friends.
The gap between human thinking and machine thinking is vast, for a number of reasons. Computers process information in a similar way to the human brain, by opening and closing transistors to relay data and communicate complicated calculations that essentially break down into 1s or 0s, but on a massive scale. The human brain has a similar process in which neural synapses fire signals between each other to relay information from and to all parts of the body. The difference, according to an article by Douglas Fox is the chaos that exists in the human brain; the synapses of the human brain fail to fire 30 to 90 percent of the time which is a condition that some scientists feel is responsible for human creativity and abstract thought (Douglas, 2009). Because of this, in order to produce a machine that thinks in terms of what we as humans consider thinking, the computer must be able to process information through noise. This allows considerably less perfection but a greater capacity for random signal transmissions, or what guides the creativity and inspiration of the human brain. The machine would also have to adapt and learn much in the same way that the human brain generates new neural pathways through development. A thinking machine would have to process equations the way the brain does by initializing numerous different neurons and making them compete to provide the correct answer, a process that depends more on probability than the single path of calculation that a modern computer takes to solve an equation (Douglas, 2009). These changes to a modern computer system would cripple it in terms of precision, but would bring machine thought worlds closer to human thought, which itself is anything but precise.
To recap Team C’s Human and Machine Intelligence essay, many advances have been made in the field of organically and mechanically engineered artificial intelligences, as well as in the study of the human mind. It’s plain to see that the two intelligences while still not perfectly understood are at the moment fundamentally different. With time and given the focus of scientific efforts the two may one day coalesce or at least closely mimic each other, but presently machines are inclined to perform primarily logical tasks, while the human mind is capable of both logic and creativity. The different intelligences seem to process information in a similar, binary or binary-like fashion, but machines can’t yet duplicate the biological mind’s ability to entertain abstract thought. Alternately humans can’t perform intense, focused computations with the speed and precision that an artificial intelligence can. Scientists are bridging these gaps in both directions by devising bio-computers from organically derived building blocks and nano-machines that will coexist with living tissue. Someday perhaps the two intelligences will merge, be indistinguishable, or similar to the point that the distinction will be negligible. For now there are still drastic differences between the two, but the future looks bright for both human and machine intelligences.

References
Cherry, K. (n.a.). Left Brain vs Right Brain. Retrieved from http://psychology.about.com/od/cognitivepsychology/a/left-brain-right-brain.htm
Kaku, M. (2008, April 05). The Fall of Machines. Financial Times, 18. Retrieved from http://search.proquest.com/docview/248343876?accountid=35812
Douglas, F. (2009, October). Thinking machine. Discover , pp. 58-75. Retrieved from https://ehis.ebscohost.com/eds/detail?vid=2&hid=1&sid=984db307-5459-4c23-98ea-428d01bb26b0%40sessionmgr14&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=f5h&AN=44189117
Science Daily. (2010, December 14). Biological Computer: Genetically Modified Cells Communicate like Electronic Circuits. Retrieved from http://www.sciencedaily.com/releases/2010/12/101214122850.htm
Stevens, B. (2011, February 21). Left Brain vs Right Brain – Are You Becoming Obsolete? Retrieved from http://unlimitedfutures.org/left-brain-brain-obsolete
Tongen, A. (2003, November 07). Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Retrieved from http://cbhd.org/content/will-biological-computers-enable-artificially-intelligent-machines-become-persons