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Ever since the beginning programming and computers were invented they have had a language; this language is called code, made up of 1s and 0s and they build a computer’s functions, allowing them to accomplish small tasks. From these 1s and 0s, we have learned how to create complex algorithms that can solve larger problems. In the field of robotics, OpenAI has created a robot that can write and translate coherently and intelligently enough to submit to the “US SAT and get a good score,” (VIncent 2019).  This Program takes information from the internet to answer prompts. This is an incredible feat in the world of robotics that will open up doors of opportunities for future research and improvements to other machines and programs, like chatbots.

On the other hand, humans influence and are psychologically influenced by the language we speak. It is a dynamic process unlike that of the robot, who is given a set of algorithms to follow.  According to Boroditskey people who grow speaking languages that have more words to express the different colors in the spectrum of color that we see can also detect differences in colors that speakers of a different language would not (2012). Likewise in countries that have gendered terms tied to a person have different cultural norms and behaviors towards the two distinct groups of people. For example, people may associate softness and fragileness to the female personality and toughness to the male, therefore those inferences dictate how a person would tell either one to do something. To a male one might say, “Eat the whole plate! You’re a strong growing boy,” whereas to a female one might say, “awww sweetheart, don’t worry about finishing that your poor stomach might not be able to take it all.” This example is a bit exaggerated but the point is clear. This differs from the way the people of Juchitán  Behave. Their native culture does not reference gender, only the difference between humans and animals. This has made way for a third gender, muxes, to be common and accepted among the people of this culture (Olita 2017).

Even more scary is the way the savings trends favor those who speak “futureless” languages. What a futureless language is, is a language that does not differentiate between the present and the future. For example, in English we say “it will rain tomorrow,” which differs from expressing that it is raining right now. In Futureless languages when talking about rain in the future it would sound like, “tomorrow it rains,” with no verbal tense at all. This allows us to think that the present is separate from the future, hence making it seem distant and separate and therefore harder to save. Conversely in a futureless language, the present and future are one and the same which makes it easier to feel good about saving money, knowing that it is okay to save the money for a later time (Chen 2012).

Consequently, researchers as old as the soviet union have been researching language and how it affects human behavior. Lev Vigotsky had a passion for this topic and believed that language is our consciousness and the space between two people communicating is where the mind is found. This part of the cognition has not only been extensively researched by him but by modern psychologists such as Alan Baddely. This man made aided by some colleagues has created one of the best tools out there for understanding the mind and understanding consciousness at this point is, the working memory model. They have done extensive research, trying to come up with a way to understand what memory is, how it is used in the context of the environment and the new problems that appear (2012). The largest part of the research was done on what he coins the phonological loop, which is what construct in the brain is used to communicate, taking information from the ear and connecting it to the sound that comes out of our mouths. This shows just how important language is in understanding our minds and our consciousness. It is how one communicates and one of the most complicated extensions of the human mind.

This, however, is not the only piece that contributes to working memory. There is also what Baddeley calls the visuospatial sketch pad which is where information is gathered and organized about the space around us. In addition, this model has what is called the episodic buffer which combines the phonological loop and the visuospatial sketch pad and analyses it to react to the world. This space, the episodic buffer is what consciousness is believed to be. It is the process by which all other processes come together to create what we perceive (Baddeley 2012).

Furthermore, throughout history, humans have always likened their minds to the most complicated machine of the time in attempts to best understand it. We have found that over the years that our brains are like none of these things. And like these things our brains are not like modern computers either. Yet psychologists have an extremely hard time trying to get rid of that way of thinking. It is almost impossible to talk about the brain without talking about memory long and short term or representation systems. Unlike computers, humans do not have buffers, symbol encoders, or decoders yet we cannot give this up. Our brains are just a bunch of cells organized into a pattern and many differing patterns at that. Every human has a different pattern. The human brain will never and has never reflected the computers that we use in the modern days (Stock 2016).

To make an artificial agent have a human-like- consciousness scientists would have to make a machine with at least 86 billion points that were all connected to act like neurons (Stock 2016). This is daunting enough as the highest number of connections that we have been able to have all at the same time hasn’t been near this number (Kassan 2006). Beyond that, there would need to be the development of this mechanical “brain” because it takes years and a whole human culture to make a person what they are and help the brain organize and make connections to the world around it. The point of the brain and why it evolved the way it did, is to survive within the environment that the body is living in (Class Discussion). Without this, the artificial construct could not emulate humanness. Humans are not born with data already within it like in a computer program. Humans come with very few initial capabilities and grow into our skills with time (Stock 2016).

As of now, Cognitive Science is going in the wrong direction if it is attempting to make a human-like consciousness. We are continuing to make linear algorithms and this way of programming allows only for linear robotics that can perform one set task extremely well but nothing more than that. These programs and robots depend greatly on their human creators to supply them with the capacity and the information to do its job. In the case of the robot that OpenAI has been creating. The fear is not that the machine is smarter than the human that is controlling it because in its way it is. It can compile information and write better than most high schoolers but that is all it can do; without a human, it would have no purpose. The real fear is that this machine would fall into the wrong hands. The robot could easily be fed bad information and perpetuate conflict in the virtual world. It could just as easily, given the right information, write an essay on saving the environment as it could promote the nazi party (Vincent 2019). In and of itself this robot has no capacity to reason or understand ethics the same way that a human does.

As far as language goes, it is important but it is not the only aspect of the mind that expresses consciousness. Making an algorithm to replicate our syntax, grammar and words do not imply that the machine doing this work is conscious or capable of anything more than what it was programmed to do. We have amazing machines. Machines that can learn, write, see and perform the tasks that they were made to do. But we are not yet at a point where these machines could work without us. They can still be turned off and cannot turn themselves back on at will. The singularity is nowhere close to being able to happen, and it may never be.

The explanation in cognitive science there are many questions unanswered and there will always be. As Cognitive Science gets closer to an answer a million more questions get generated, ever-broadening the field as it is known.  For this field to continue there will have to be more integration of the biological, and social sciences with robotic and psychological aspects of cognitive science. There is so much that is being overlooked in Cognitive Science by focusing just on artificial intelligence in the way that it has been. Philosophizing about the mind can only get us so far. New research must combine all of what we know to be true and dispose of what will not benefit the field or humanity as a whole. The misconceptions mentioned above are inhibiting the right questions to be asked. Without the right questions, we will never find the true answer to our questions about consciousness.

References

Baddeley, A. (2012). Working memory: Theories, Models, and Controversies. The Annual Review of Psychology. 63:1-29. doi: 10.1146/annurev-psych-120710-100422

Boroditsky, L. (2012) How the language we speak shapes the way we think. The Cambridge Handbook of Psycholinguistics, Chapter 31 pg. 615-632

Chen, K. (2013). How the language you speak can save you money. TED Conferences.

Kassan, P. (2006). A.I. gone awry: the futile quest for artificial intelligence. Skeptic, Vol 12 (Issue 2), pg. 30-37

Olita, I. (2017). Third Gender: An Entrancing Look at Mexico’s Muxes. National Geographic. Retrieved from: https://www.youtube.com/watch?v=S1ZvDRxZlb0&feature=emb_title

Stock,G. (2016). The Empty Brain. Aeon Media group.

Vincent, J. (2019).  OpenAI’s new multi-talented AI writes, translates and slanders. The Verge, Retrieved from https://www.theverge.com/2019/2/14/18224704/ai-machine-learning-language-models-read-write-openai-gpt2