An insight into AI #1 – What is Artificial Intelligence?
When doing some research to write this article, I decided to start with a broad google search on “Artificial Intelligence pros and cons”. The main arguments coming out from my preliminary unscientific research seem to be based on a profoundly wrong understanding of what Artificial Intelligence is currently. How it is used and what is the potential of this technology.
Another striking element is the repetitiveness of the arguments, most popular websites showcasing pros and cons are handling similar cases. It is somewhat disappointing to see that such technology is studied thoroughly in the scientific community. Still, its mainstream understanding is based on only a couple of sources. Thus, in this article, I will try to bring new elements based on current research.
Although before we go deeper into our discussion, we need to cover one essential aspect of the debate. What is artificial intelligence? "Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these technologies, computers can be trained to accomplish specific tasks recognising patterns in the data.”
Thus the potential for AI in almost limitless as long as we learn how to programme it. One of the very often repeated arguments against AI is its potential to replace humans in menial tasks. We can all agreed that saying it will take away jobs but also create some is too easy. Indeed, jobs are taken away; and jobs are created. Although it will most probably not affect the same people.
Although we must also consider that artificial intelligence will also take essential tasks. In personalised medicine and X-ray readings. Such application should not be seen as stripping medical professionals away from their job but rather as allowing them to pursue further research. As you might have noticed, we are quite a fund of innovation at Next Wave Network. Artificial Intelligence has the potential to allow room for better and faster innovation. Research is based on the studies of data. Technologies allowing us to study these data, find patterns and irregularities fasters will significantly advance our understanding of our surrounding.
Let us look at an example: AI in Agriculture
Agriculture is one of our civilisation's pillars. It thus stands to reason that our methods for agriculture have continued to evolve to this day and will surely continue in the future. For better or for worse in some instances, most notably in the excessive use of pesticides and chemical degrading our soil, our understanding of best practice in agriculture is still evolving.
Our over usage of certain types of chemicals has a dramatic effect on the environment. Although we cannot completely stop from one day to another fertilisers and pesticides without threatening food security and our economy. Looking at the Sustainable Development Goals number 1 (no poverty) and 2 (zero hunger); it would be foolish to disrupt our food production system without endangering more impoverished populations. However, looking at number 3 (good health and wellbeing) and 13 (climate action), we cannot pursue it either without endangering the environment and biodiversity. Thus lies the dichotomy of agriculture.
But then how can we use AI to find a better solution?
Farms are generating an incredible amount of measurable data, from the quality of water to the erosion of the soil, quality of the production, amounts of chemicals used, the quantity of biodiversity present, health hazards… you name it! Yet, apart from specific studies, we do not have enough resource to continually check all these data. Thus, we are unable to find a proper solution.
Based on deep-learning algorithms, AI can process data to monitor crop and soil health. Being able to oversee real-time data evolution, patterns and irregularities in soils allows for taking the most appropriate decision. Thus, AI can undertake extensive research that no human can do in a limited time frame and on a massive scale.
This is far from being the extent of how AI can support us, even in the field of agriculture; this example barely scratched the surface of the possibilities for evolution. AI has the potential to accelerate our green transition. This article is merely an introduction to the debate surrounding AI and sustainability. We will soon come back with more articles on the matter, and we aim to cover several fields where AI can be applied to support the green transition. We hope that this article can spark a debate amongst our readers!
Stay safe, live well, live green!
Some Reading Material:
(This one is pretty crazy and cool, next article?)
Jean, N, Burke, M, Xie, M, Davis, W, M, Lobell, D, B, Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 19 Aug 2016: Vol. 353, Issue 6301, pp. 790-794. DOI: 10.1126/science.aaf7894
Bolukbasi, T., Chang, Kai-Wei, Zou, J, Saligrama, V, Kalai, A. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.
Armstrong, L., Gandhi, N., Taechatanasat, P. and Diepeveen, D. (2020) Advances in artificial intelligence (AI) for more effective decision making in agriculture. In: Burleigh Dodds Series in Agricultural Science. Burleigh Dodds Science Publishing, Cambridge, UK.
Smith, M. Getting value from artificial intelligence in agriculture. Animal Production Science 60(1) 46-54.
Bolandnazar, E, Rohani, A & Taki, A. (2020) Energy consumption forecasting in agriculture by artificial intelligence and mathematical models, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42:13, 1618-1632, DOI: 10.1080/15567036.2019.1604872
Acemoglu, D, Restrepo, P. Artificial Intelligence, Automation and Work.
SAS Website, www.sas.com.