In the effort to progress, humans have made their lives easier through their inventions. Electricity, cars, and machinery all make daily lives and jobs simpler and easier to carry out. However, as we are now finally discovering, this progress is also harming not only the health of the planet (carbon emissions), but our own health as well (particulate matter). It’s not just the environment close to home (e.g., cities) that is influenced, habitats and species are also being affected by our progress.
The rise of ever more powerful computers is resulting in an investigation and utilisation of artificial intelligence or AI. AI does not have one specific definition; in simple terms it can be described as a computer being able to think like a human, adapting and learning with every scenario it encounters.
Our homes require a lot of energy to function the way we need. Whether it’s for heat or for the electrical appliances we use, almost everything we do uses energy. Dounis (2010) carried out a review on how artificial intelligence could help reduce this energy requirement
The conservation of endangered species is a prominent issue in ecology that is keying into the idea of using technology to improve species locating and identification of individuals. As demonstrated by Gonzales and company (2016), this is being done using Unmanned Aerial Vehicles and artificial intelligence, which can be programmed to identify individuals using specific shapes, colours, or sizes—to name but a few. The algorithms used can them keep count of these individuals to give researchers a better understanding of population size and health (e.g., age diversity).
Villa and cohort (2009) use a type of probabilistic modelling to identify the provision and ultimately the usage of ecosystem services. They do this using ARIES (ARtificial Intelligence for Ecosystem Services), which helps to identify, understand, and quantify environmental assets so that their value to users can be identified according to needs and priorities. Ultimately, this will help decision and policy making to ensure an ecosystem’s true value to all who use it is not lost.
Even organisms as small as insects are being identified using artificial intelligence. Fedor and company (2009) used artificial neural networks or ANN in conjunction with other statistical tools to identify Thysanoptera by using the unique dimensions of the whole insect. It is intended that this form of programme can be used in pest control.
It’s not just species that move that are getting the AI treatment. Liu (1993) developed the ECOLECON model (ECOLogical-ECONomic) to enhance forestry species conservation. The model is able to predict population dynamics and the chance of species extinction. It doesn’t stop there— the model, which can generate artificial forests, is also able to predict the future structure of the landscape as well as economic value of timber (based on current tax).
Similar to the ideas put forward by Dounis (2010), Ramos and Co (2008) look into Ambient Intelligence (AmI). The next step in AI, AmI is a way to make our lives simpler in many ways such as giving us access to knowledge—Amazon Alexa or Google Home are just some of the examples. Devices such as these can also help our environmental footprint. Allowing us to turn the light off during a film when ordinarily we’d have been too lazy to do so. AmI is set to get even better by being able to predict things such as what we may want to watch based on our mood or the visitors we have.
AI may have some unnerved, and we may need to be cautious of just how much we rely on it, but in the long run it could help us put right the multitude of mistakes we make. Relying on AI too much to solve these issues could result in bigger, more disastrous mistakes for the planet (and ourselves). Getting the balance right between easy lives and laziness or carelessness is going to be the next task. In the meantime, there is nothing wrong with enjoying the convenience of “Hey Google, dim the lights”.
Do you think that using AI to fix our problems is actually going to encourage us to make the same mistakes again, or is it helping us see our role in the problem?
Dounis, A.I., 2010. Artificial intelligence for energy conservation in buildings. Advances in Building Energy Research, 4(1), pp.267-299.
Fedor, P., Vaňhara, J., Havel, J., Malenovský, I. and Spellerberg, I., 2009. Artificial intelligence in pest insect monitoring. Systematic Entomology, 34(2), pp.398-400.
Gonzalez, L., Montes, G., Puig, E., Johnson, S., Mengersen, K. and Gaston, K., 2016. Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors, 16(1), p.97.
Liu, J., 1993. ECOLECON: An ECOLogical-ECONomic model for species conservation in complex forest landscapes. Ecological modelling, 70(1-2), pp.63-87.
Ramos, C., Augusto, J.C. and Shapiro, D., 2008. Ambient intelligence—the next step for artificial intelligence. IEEE Intelligent Systems, 23(2), pp.15-18.
Villa, F., Ceroni, M., Bagstad, K., Johnson, G. and Krivov, S., 2009, September. ARIES (Artificial Intelligence for Ecosystem Services): A new tool for ecosystem services assessment, planning, and valuation. In 11Th annual BIOECON conference on economic instruments to enhance the conservation and sustainable use of biodiversity, conference proceedings. Venice, italy.
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