Neuroscience & Technology
Since the time of the first Greek philosophers, intelligence has been a topic of investigation. In the mid 20th century, intelligence entered the field of scientific research. The term ‘artificial’ — which means both understanding intelligence as well as building intelligence — is of importance for this topic. Building intelligence involves the generation of intelligence and cognition by intelligent devices. Intelligent devices are machines that have internal computing capacity. Nowadays the amount and diversity of intelligent devices is growing exponentially. In 2015, there were a total of 10 billion internet-connected intelligence devices. In 2020, there will be a total amount of 34 billion devices, of which 10 billion are the traditional computer devices like smartphones, and 24 billion will be IoT (Internet of Things) devices (Greenough, 2016). Examples of intelligent devices are computers, cars, home applications, medical instruments, cameras and so on. Intelligence has been very popular the last decade and is expected to become more complex and important in the near future.
Due to the complex human brain, which is founded on a complex nervous system, humans are very well capable of interacting with their environment. This phenomenon is called Natural Intelligence (NI) (Weng et al., 2012). Humans have a well-developed understanding of speech, text and visual input and are able to anticipate on another person’s mood and feelings, which is called empathy. Now, imagine an Artificial Intelligence (A.I.) application that is able to act like the human brain — an application that can solve complicated tasks at any time. Such an application does not yet exist because up until now, little attention in the A.I. domain has been paid to neuroscientific knowledge. Also, the brain is a very complex organ to understand. D. Siegel says in an interview that we are just at the beginning of understanding the human brain and that we know not much about this organ (Dr. Dan Siegel — Recourses” 2016). A large number of neuroscientists are trying to figure out the processes of the brain. This lack of knowledge about neural processes is a result of the fact that neuroscientists research neuroscience in a ‘bottom-up’ way — meaning that their research starts with and gets stuck in trying to understand the tiniest particles of the brain (neurons). For this reason, it is hard to find neuroscientific principles that could be applied to A.I. For a better understanding of neuroscience, a more effective solution would be a combination of describing the brain in a ‘bottom-up’ way in combination with a ‘top-down’ way. The latter will enable researchers to better understand the general mechanism of neuroscientific processes. Recently, a study showed that 180 areas in the human cortex are well understood scientifically, whereas before this amount was only 83 (Glasser, 2016).
The general mechanisms that are involved in the area of neuroscience are strongly related to intelligence and cognition. These mechanisms can explain how higher forms of intelligence and cognition work. However, in order to truly understand a neuroscientific process, these explanations need to be complete. A theory of an intelligence or cognitive process that is based on information of only part of the entire explanation of a mechanistic process is not a complete theory of that process but merely an introduction to it.
With regard to understanding the general mechanisms, A.I. and cognitive neuroscience do not differ from other fields of science. The worlds of chemistry, astronomy and genetics also focus on the understanding of general mechanisms that explain certain processes. Still, as mentioned before, neuroscientific research studying intelligence and cognition has until today not been very interested in certain general mechanistic processes. Neuroscience generally focuses more on the physiology of the neurons — and thus on the small, individual parts and processes instead of the general mechanisms.
However, the 1990s faced the rise of cognitive neuroscience, which introduced more research into the mechanistic processes of N.I. (Ochsner et al., 2001). One of the most famous neuroscientists, who is also the founder of cognitive neuroscience, said: “At some point in the future, cognitive neuroscience will be able to describe the algorithms that drive structural neural elements into the physiological activity that results in perception, cognition and perhaps even consciousness. To reach this goal, the field has departed from the more limited aims of neuropsychology and basic neuroscience. Simple descriptions of clinical disorders are a beginning, as is understanding basic mechanisms of neural action. The future of the field, however, is in working toward a science that truly relates brain and cognition in a mechanistic way” (Gazzaniga, 2009). Algorithms can mimic the underlying basic processes of intelligence and cognition. These kinds of formulas, which are often used in computer science, have a lot in common with the algorithms that are used in A.I. In his quote, Gazzaniga thus indirectly stresses the close connection between the domains of cognitive neuroscience and A.I.
Human cognition research aims to investigate detailed brain structures and brain processes by neuroimaging. Theoretical and practical — computational — research on cognition considers how human cognitive processes are simulated in the brain, what the related underlying neural structures are and how these related structures behave relative to the founded neuro-images. All of these components should be studied thoroughly in order to fully understand human cognition. A combination of A.I. and cognitive neuroscience is therefore important to grasp human intelligence and cognition.
It is currently impossible for an A.I. device to copy completely the functions of the human brain, like N.I. and cognition. However, the algorithms that are used in the field of A.I. can be beneficial for understanding the general cognitive mechanisms of the brain. In order to understand the general cognitive processes of human brain, neuroscientific knowledge should be applied to A.I. algorithms. This collaboration between the two disciplines science and technology will be crucial for the personal development of both disciplines. This development will have great impact on our daily lives, considering the effect that the evolution of technology has always had on the way people think, decide and act. In other words, the combination between neuroscience (scientific knowledge) and A.I. (smart technology) will determine how human beings will work, buy, learn and communicate. Taking into account and adapting to the fusion of the two disciplines will enable us to maximize the benefits that it provides us. In summary, the world as we know it is swiftly changing. Until today, A.I. has mainly examined the brain from a small scale perspective — the neuroscience –, but should now turn its attention towards the bigger scale — cognitive neuroscience.
There are two main reasons why the combination between neuroscience and A.I. are interesting and promising for current and future research and projects. First of all, the techniques and experimental methods that are available at the moment can be used to investigate in detail how the brain works. With these methods, the knowledge about neuroscience will increase. As a result, the field of A.I. will improve. Also, the field of A.I. will improve as a result of the significantly growing computational power. These two exponentially growing features — neuroscientific knowledge and computational power — will enable researchers to investigate large scale brain simulations that are used to build greater and smarter artificial intelligence. However, the complex features of the brain require intensive multidisciplinary expertise.
The focus of neuroscience is on how neurons work and how they react in different situations. The focus of cognitive neuroscience is on how cognitive processes work in the human brain. A.I. has two different points of focus. First of all, A.I. focuses on how to help the neuroscience to investigate the cognitive processes in the brain. Through the use of A.I. algorithms, neuroscientific researchers are able to investigate the relation of different brain regions without looking for the function of every little particle between those brain regions. Second, A.I. is used for the optimization of already exi sting processes and for building new processes. With the help of A.I., daily life processes are becoming easier. Ordinary daily tasks that, for example, took 5–10 hours ten years ago, are now completed within an hour and will probably happen automatically in the near future.
In addition, A.I. will play a significant role regarding the use of data in a sustainable, decentralized way. In the near future, people will own their own data. Each user of upcoming platforms will likely accumulate data by interacting on the platform and control this data in their personal data silo. They will be enabled to use their data to optimize their personal user flow on the platform. In other words, these data silos allow for a more balanced interplay between supply and demand. Furthermore, it will be impossible for third parties to interact with users’ data silos without approval. Users therefore have the control to sell, show or share their (unique!) data. Such unique data allows for the implementation of more human-like, neuroscientific based algorithms. For example, by applying tokenization and algorithmic dynamics, the gaia platform (www.moveforgaia.io) will be able to quantify the social aspects that arise when using the gaia platform. In each user’s data silo, which is encrypted by a private key that is owned exclusively by that user, all experiences, interactions, communications and other activities are collected and saved quantitatively. This represents great value for third parties, but only if they are allowed access. The platform will become a network that facilitates user-empowered monetization of created data. Companies such as Facebook have proven that the data that is collected when using a platform can be extremely powerful and valuable.
In conclusion, the domain of artificial intelligence is growing due to the exponentially growing body of neuroscientific knowledge. The field of neuroscience will continue to grow exponentially due to research on artificial intelligence. This interplay between neuroscience and artificial intelligence is expected to continue to exist for the next few years. Where this will guide us, who knows, but it will be an impressive expedition!
References
Dr. Dan Siegel — Recources — Video Clips. (2016) Drdansiegel.com. Retrieved 29 September 2016. Gazzaniga M. S. (2009) “The Cognitive Neurosciences”, MIT Press, Cambridge, Mass, USA.
Glasser, M., Coalson, T., Robinson, E., Hacker, C., Harwell, J., & Yacoub, E. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178.
Greenough, J. (2016). How the ‘Internet of Things’ will impact consumers, businesses, and governments in 2016 and beyond. Business Insider. Retrieved 29 September 2016
Ochsner, K. & Lieberman, M. (2001). The emergence of social cognitive neuroscience. American Psychologist, 56(9), 717–734.
Weng, J. (2015). Brain as an Emergent Finite Automaton: A Theory and Three Theorems. International Journal Of Intelligence Science, 05(02), 112–131.
