The Digital Future

by Maseeh Syed Yazdani

ABSTRACT

Most of the world today is dependent on Artificial Intelligence whether they are aware of this or not. Artificial Intelligence (“AI”) has emerged as a discipline of computer science and ever since the start of the discipline a lot of remarkable research has been done in the field. We see several things around us that no one would have ever imagined could come into existence, from real life robots powered by artificial intelligence to self-driving cars. Just a few of the fields currently benefiting from breakthroughs in AI are healthcare, communications, agriculture, education, disease prevention, transport, (autonomous vehicles), space exploration, science, and entertainment. Biotechnology is another one of such upcoming and promising fields. It aims to provide environmental alternatives to the major environmental crisis of depleting resources which mainly involves the usage of organisms and living systems to develop or accumulate products. This paper will aim to analyse the intriguing cross-section of the two most developing and spellbinding fields of Biotechnology and Artificial Intelligence by analysing the advancement of Artificial Intelligence in the different branches of Biotechnology namely Animal Biotechnology, Medical Biotechnology, Industrial Biotechnology, Environmental Biotechnology and Plant Biotechnology. This paper would further indulge more in-depth into the various applications of Artificial Intelligence being used in the field of Industrial Biotechnological by focusing on the applications of ‘artificial neural networks’ in producing Biofuel.

INTRODUCTION

Is Artificial Intelligence really as impactful and evolved as its often perceived in today’s times? Artificial Intelligence has emerged as a discipline of computer science, John McCarthy one of the pioneers of the Artificial Intelligence discipline explained it as “making a machine behave in ways that would be called intelligent if a human were so behaving.”^[1]The discipline has evolved to become one in which a lot of people are doing ground-breaking research and due to this reason the applications of AI have become so vast that almost every person with access to internet has encountered some form of an AI assistant. From Siri to google assistant almost every smartphone has an AI assistant built in to make things easier for people using them, a study by creative strategies shows that 97% of mobile users are already using AI-powered voice assistants. Almost 90% of the world’s data was produced in the last 2 years alone^[2], and one can only imagine how much the use of technology and subsequently AI would increase in the upcoming years. However, AI may not be as beneficial as it appears to the common eye. I say this because on one hand we see jobs getting increased but on the other hand a study conducted by McKinsey&Company shows that AI powered robots could replace 30% of the workforce by 2030. We will have a plethora of AI powered technologies in almost every sector, laws on data protection and privacy will have to be amended to adapt to the racing discipline, implication of this discipline as AI will significantly alter the contours of economics, sociopolitical life, geopolitical competition, and conflict in the upcoming years.

Biotechnology as a discipline promises us a better and prosperous future. An easy way to find out what the discipline really means is by breaking the word ‘biotechnology’ itself. Simply put, biotechnology is the use of biological-based technology for industrial and other purposes.  The use of biotechnology dates as far back as 1919, approximately 50 years later, bacterial genes were used by researchers to perform the first successful recombinant-DNA experiment. The Norwegian University of Science and Technology defines biotechnology as,

“Technology that utilises biological systems, living organisms or parts of this to develop or create different products. Brewing and baking bread are examples of processes that fall within the concept of biotechnology (use of yeast (= living organism) to produce the desired product). Such traditional processes usually utilise the living organisms in their natural form (or further developed by breeding), while the more modern form of biotechnology will generally involve a more advanced modification of the biological system or organism.”^[3]

Since the discovery of the discipline in 1919, the field has reached a flabbergasting height in producing sophisticated medicines, devices used in medical care, biomaterials, and more. The discipline is further divided into five different fields of study, Animal Biotechnology, Medical Biotechnology, Industrial Biotechnology, Environmental Biotechnology and Plant Biotechnology.

APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Animal Biotechnology as the name suggests mainly deals with animals; Animal Genomics, Animal Cloning and Transgenic Animals are different sectors in the Animal Biotechnology field. Using science and engineering to change living organisms is Animal Biotechnology. The aim of the field is to make goods for particular agricultural uses, to enhance animals and to grow microorganisms.^[4] This division applies sub-atomic science techniques to hereditary change animals to enhance their medicinal or agricultural maintenance purposes. Different applications of the field includes research in advancement of human health care, advancement in animal healthcare, improvement of animal products and environmental benefits. The importance of ensuring animal production and health cannot be stressed on enough. Healthy animals lead to healthy people which further lead to a healthy environment. Artificial Intelligence models provide useful and additional insights in Artificial Animal Breeding, and this method of breeding gives the benefit of introducing selective genes into animals, which allows the development of animals with the most desirable characteristics.^[5] As support providers, complex tasks such as designing constructs for gene editing are taken care of by AI systems. This essentially means that Artificial Intelligence assists in understanding genome editing in a better manner and helps scientists in making educated choices with respect to the development of certain genes. Due to the expansive nature of AI in this field there are a lot of prevalent activities ongoing in the food biotech industry as well, specifically in the milk and meat industries.

Medical Biotechnology has a very different application compared to that of Animal Biotechnology, even though their aim is the advancement and improvement of the human lifestyle. The use of biotechnology techniques to manufacture medical products that can be used for the diagnosis, counteractive action, and management of illness is defined as medical biotechnology.^[6] The medical biotechnology industry has been at the peak of advances in technology. Pioneering work in genetic testing, experimental drug therapies and artificial tissue development are some of the new fields of medical biotechnological progress. In drug discovery, Artificial Intelligence and Machine Learning are used frequently. Organisations are following a drug discovery structure-based approach, using Machine Learning to identify small molecules that could provide therapeutic and clinical benefit depending on known target configurations. In diagnosing diseases, machine learning is commonly used as it utilises the true outcome to enhance diagnostic tests, in other words, the more diagnostic tests are performed, the more reliable outcomes can be obtained. AI also aims to reduce the preparation process for radiation therapy, contributing in minimising time and enhancing healthcare delivery.^[7] Electronic Health Records is another area where Artificial Intelligence has a prominent impact, evidence-based healthcare and support systems for clinical decision-making built on the machine learning platform are capable of making an EHR system more efficient and can help doctors make educated clinical choices related to the needs and clinical background of a patient. Through AI and advanced digitalisation, clinical records can also be effectively supervised.^[8] The vast volume of data can be packed aside, organised and received for improving clinical care fruitfully. AI applications are also used in techniques such as genetic modification, radiology, regenerative medicine, drug administration, etc.

The next fields of Plant Biotechnology and Environmental Biotechnology can be classified under the broader definition of Agricultural Biotechnology. The collection of scientific techniques used to develop plants, livestock and microorganisms is agricultural biotechnology. Scientists have developed solutions to improve agricultural productivity on the basis of their understanding of the DNA. Biotechnology increases the ability of breeders to make changes in crops and livestock, starting with the ability to recognise genes that can impart benefits on certain crops and the capacity to operate very efficiently with those characteristics.^[9] An element of agricultural biotechnology that has been prominently established recently is plant biotechnology. The desired characteristics are exported from a specific plant species to form an entirely different category, in terms of taste, colour of plants, rate of growth, volume of produce and resistance to diseases and parasites, these transgenic crops exhibit positive attributes. Therefore, the use of cultured cells and genetic modification methods to generate transgenic plants that demonstrate unique or enhanced desirable properties is known as plant biotechnology. Environmental Biotechnology on the other hand has more of a disease removal role as it deals with the removal of industrial discharge waste, the processing of sewer water and the prevention of plant and insect outbreaks by using biological agents such as bacteria, viruses, fungi, etc. In agriculture, artificial intelligence has brought about an agricultural revolution. This innovation has secured crop yields from numerous factors, such as climate change, population development, problems with jobs and food security concerns. AI technology saves water, pesticides and herbicides from excess usage, preserves soil fertility, also leads to the productive use of human resources and raises efficiency and enhances quality. Artificial intelligence and machine learning techniques are currently being used by biotechnology companies to develop and configure autonomous robots that perform critical agricultural tasks such as harvesting crops at a much quicker speed than individuals. In order to process and analyse the data obtained by the automatons, vision and deep learning calculations are used. These are primarily useful for cultivation and ground well-being monitoring. AI equations help to follow and predict multiple environmental changes, such as climate changes that affect the yield of the harvest.^[10]

The current use and implementation of biotechnology for the sustainable processing and manufacture of chemicals, products and fuels from renewable sources by utilising living cells and their enzymes, is industrial biotechnology, also referred to as white biotechnology. Enzymes and microorganisms or plant/animal cells are used in biotechnological processing to manufacture goods in a wide variety of industrial fields, including drugs, medicines, nutrition & feed, disinfectants, pulp & paper and fabrics. In industrial biotechnology, interest has been created primarily because this area is associated with significantly lower energy usage, greenhouse emissions and waste production, and can allow the paradigm shift from fossil fuel-based to bio-based processing of value-added chemical products.^[11] The business economy is the key driving force behind the growth and deployment of industrial biotechnology, as biotechnology offers highly efficient processes with lower capital and operational expense. Moreover, political and social demands for sustainable and environmentally friendly industrial production systems, combined with the scarcity of oil resources and the increasing world demand for natural resources and electricity, will move this development further.^[12] Machines are studied by the web of things, machine learning, and artificial intelligence, predicting outages, optimising machinery for efficient production and improved product quality. ^[13] With the necessary molecule design, computer-aided models and Artificial Intelligence are coming up. The strains are cultivated by robotics and machine learning and the degree to which the appropriate molecule has been reached is tested. Biofuel is one of the major products that the field of Industrial Biotechnology produces and recently, Artificial Intelligence has played a major role in its production through the introduction of Artificial Neural Networks.

ARTIFICIAL NEURAL NETWORKS AND ITS IMPACT ON INDUSTRIAL BIOTECHNOLOGY

Artificial Neural Network (“ANN”) is a human nervous system-inspired computational methodology that enables example-based inference from data samples representing a physical phenomenon or a subsequent decision. Neurons are usually aggregated into layers, on their inputs, different layers will execute various transformations. Signals shift from the very first layer (the input layer), to the very last layer (the output layer), probably many times after going between the layers. A set of simulated neurons consists of an artificial neural network. Each neuron is a node that, through ties that conform to biological axon-synapse-dendrite connections, is linked to other nodes. Any relation has a weight that decides the impact of the effect of one node on another.^[14] A special characteristic of ANN is that they are able to create observational correlations between independent and dependent variables and derive nuanced details from descriptive data sets and conceptual knowledge.^[15] For unusual or serious cases, implementation of ANNs is not possible, where data is inadequate for the model to be educated. ANNs do not encourage quantitative data to be supplemented by the inclusion of individual expertise (expert opinion). In the late nineteenth and early twentieth centuries, some of the historic studies for the ANN sector took place. This mostly consisted of intersectional physics, psychology, and neurophysiology work. This pioneering study stressed general learning theory, perception, conditioning, etc., and did not include particular neuron operation mathematical models.^[16] The field of neural networks has been revitalised by these recent advances. A lot of articles have been written over the past two decades and a lot of various forms of ANNs have been studied. Neural networks have been applied in numerous fields, including aerospace, automobile, finance etc. ANNs are effective tools to model complex environments as they can forecast how ecosystems adapt to changes in environmental influences (e.g., nutrient inputs).^[17] In particular, ANNs can be used to find interactions between variables, which helps to explain the role of the ecosystem.

A range of ANN applications include a promising setup for biofuel contributions that are classified as biomass-derived green fuels that can be used in gasoline engines or for other forms of generation of electricity. The goal of the use of biofuels is to eliminate external reliance on petroleum (partial or complete substitution of fossil fuels), to mitigate the effects on the atmosphere, and to improve agricultural output. The two biofuels (ethanol and biodiesel) have typically gained international interest and have thus expanded their production in contrast to previous years.  The case study presented by Alex Oliveira Barradas Filho and Isabelle Moraes Amorim Viegas in their paper on Artificial Neural Networks enabled us to understand, in practice, the procedures to be carried out in the process of predicting the physical-chemical characteristics of biodiesel such as oxidative stability, data pre-processing, ANN configuration and training, and further, how to calculate and interpret the evaluation criteria was also discussed. Although a regression approach was used for practical development, this work also clarified the classifiers and procedures for both model building and evaluation.^[18] Compared to the official methods, the ANNs proved to be a promising instrument in the production of more reliable and cost-effective alternative methods to regulate and track the quality of biofuels. Furthermore, optimising the chemical reaction needed for biodiesel production, called transesterification, is an expensive and time-consuming process requiring costly reactants and laboratory equipment. In this case, the ANN model has been developed to simulate the production of biodiesel by trans esterifying the olive oil used for frying. Antonio J. Yuste and M. Pilar Dorado through set of experimental data obtained from multiple tests also concluded that ANNs can be used as a more cost-effective technique to forecast the biodiesel yield from used olive oil.^[19]

ANALYSIS OF THE CAPTIVATING CROSS-SECTION

Artificial Intelligence has opened the door to many exciting opportunities for Biotechnology. The intersection of the two fields has provided us, as a community a lot to look forward to. Artificial intelligence and machine learning (“ML”) technologies are playing an increasingly important role in the biotech sector and are therefore, driving new collaborations between the technology and healthcare sectors as discussed previously. It is precisely because they have such far-reaching opportunities that AI and biotech are witnessing rapid growth. The influence that AI has had on Biotechnology is nothing but positive as it has assisted in laying down a foundation on which new applications and techniques can be developed that would further benefit our community. The usage of AI in procedures such as drug trials and radiation therapy just goes on to prove the valuable and advantageous impact that the cross-section of AI and Biotechnology is bringing to our community. The technologies posed by this junction, if effective, will revolutionise broad fields of public health, with the drug rehabilitation regime being just the beginning. Furthermore, we all know that there are several disadvantages such as environmental pollution and depletion of natural resources that are occurring because of the excess usage of petrol and diesel, and even though biofuel can act as a supplement for these resources, its production is extremely costly. Through the introduction of ANN, a cost-effective model for the production of biofuel can be achieved which will not only help in taking a step towards environmental sustainability but could also be implemented on a large scale due to its cost-effective nature. Many new implications have been brought into biotechnology just because of its intersection with Artificial Intelligence, and I believe that there are many more positive implications of this field that we are yet to see. In order to solve public health and environmental issues, these two frontier disciplines must be harmoniously merged. It’s unquestionable that AI has a lot more to offer, and one must sit back and watch as this exhilarating and stimulating intersection presents us with new and beneficial solutions to environmental and healthcare problems.

CONCLUSION

Through this paper we have seen the different opportunities and applications that Artificial Intelligence has presented specifically in the field of biotechnology. It is safe to say that Artificial intelligence is evolving exponentially, and the applications for the same are endless. From the assistant that we carry around in our phones to autonomous vehicles, there seems to be no field that Artificial Intelligence has failed to have an impact on. AI is important for any intellectual mission in today’s time, modern techniques for artificial intelligence are widespread and too abundant to mention here. I believe that Artificial Intelligence’s ultimate research objective is to develop technology that enables computers and machines to work in an intelligent way and the introduction of Artificial Neural Networks enables AI to mimic the human nervous system to an extent, expanding the boundaries of Artificial Intelligence, and opening the applications of AI to even more interesting opportunities.

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^[1] John McCarthy et al., A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, 27 AI Magazine (2006).

^[2] IBM Market Cloud.

^[3] What is Biotechnology?, Norwegian University of Science and Technology, https://www.ntnu.edu.

^[4] ANIMAL BIOTECHNOLOGY, North Carolina Association for Biomedical Research, https://www.aboutbioscience.org/topics/animal-biotechnology.

^[5] Prabhu Prasad, Role of artificial intelligence in biotechnology, Eleyon Publishers, 2020, at 6-10.

^[6] Phuc V. Pham, Chapter 19 – Medical Biotechnology: Techniques and Applications, (Debmalya Barh &Vasco Azevedo eds., Omics Technologies and Bio-Engineering, Academic Press, 2018), at 449-469.

^[7] K. M. Giacomini et al., The pharmacogenetics research network: from SNP discovery to clinical drug response, Clinical Pharmacology & Therapeutics, 81(3), 2017, at 328-345.

^[8] B. Mesko, The role of artificial intelligence in precision medicine, Expert Review of Precision Medicine and Drug Development, 2:5, 2017, at 239-241.

^[9] What is Agricultural Biotechnology?, PBS and ABSP II, Cornell University (2004).

^[10] Tanha Talaviya et al., Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides, 4 Artificial Intelligence in Agriculture, 2020, at 58-73.

^[11] Ram Singh, Industrial Biotechnology: An Overview in Advances in Industrial Biotechnology (Ram Sarup Singh et al., eds., I. K. International Publishing House Pvt. Ltd., New Delhi, Banglore, India, 2014).

^[12] Wim Soetaert & Erick Vandamme, The impact of industrial biotechnology, 1 Biotechnology Journal, 2006, at 756-769.

^[13] F. Collado-Mesa et al., The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program, 15(12) Journal of the American College of Radiology, 2017, at 1753-1757.

^[14]  Artificial intelligence, Addison-Wesley Pub. Co., (3rd ed. 1992).

^[15] Rehan Sadiq et al., Empirical Models to Predict Disinfection By-Products in Drinking Water: An Updated Review. Reference Module in Earth Systems and Environmental Sciences, (2018).  doi:10.1016/b978-0-12-409548-9.11193-5.

^[16]  Sovan Lek & Y.S. Park, Artificial Neural Networks. Encyclopedia of Ecology, 2008, at 237–245. doi:10.1016/b978-008045405-4.00173-7.

^[17] H.-Y. Kang et al., Artificial Neural Network Modeling of Phytoplankton Blooms and its Application to Sampling Sites within the Same Estuary, Treatise on Estuarine and Coastal Science, 2011, at 161–172. doi:10.1016/b978-0-12-374711-2.00908-6.

^[18] Alex Barradas Filho & Isabelle Viegas, Applications of Artificial Neural Networks in Biofuels, 10.5772/intechopen.70691 (2018).

^[19] Antonio J. Yuste & M. Pilar Dorado, A Neural Network Approach to Simulate Biodiesel Production from Waste Olive Oil, 20(1) Energy & Fuels, 2006, at 399–402. doi:10.1021/ef050226t.