Artificial intelligence (AI) is thriving as a result of large investments, and big enterprises with significant environmental footprints can use it to make their operations more sustainable. It has become an important area to tackle most environmental sustainability issues such as biodiversity, energy, transportation and water management.
What exactly is artificial intelligence (AI)?
The ability of any machine or computer to mimic human capabilities such as learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, and solving problems is referred to as artificial intelligence, or AI. The machine can perform complex functions such as driving a car by combining these various capabilities.
According to a World Economic Forum report, AI refers to computer systems that “can sense their environment, think, learn, and act in response to what they sense and their programmed objectives”.
Importantly, to address most of the current global and regional environmental issues, artificial intelligence is employed in areas like biodiversity, water, energy and transportation, although many of such fields have penetrated each and every category and keeps on evolving. There has been practical implementation of AI in transportation and biodiversity in many developed countries, these include but not limited to collecting e-waste via advanced routing plan, safeguarding the ocean from being polluted, using powered AI autonomous garbage collection trucks and wildlife conservation for enhanced biodiversity.
Positive Impacts of Artificial Intelligence on the Environment
PwC emphasized that artificial intelligence has helped improve the ecological environment through the following areas.
1. Autonomous and connected electric vehicles
AI-guided autonomous vehicles (AVs) will enable a transition to mobility on-demand over the coming years and decades. Substantial greenhouse gas reductions for urban transport can be unlocked through route and traffic optimization, eco-driving algorithms, programmed “platooning” of cars to traffic, and autonomous ride-sharing services. Electric AV fleets will be critical to deliver real gains.
2. Distributed energy grids
AI can enhance the predictability of demand and supply for renewables across a distributed grid, improve energy storage, efficiency and load management, assist in the integration and reliability of renewables and enable dynamic pricing and trading, creating market incentives.
3. Smart agriculture and food systems
AI-augmented agriculture involves automated data collection, decision-making and corrective actions via robotics to allow early detection of crop diseases and issues, to provide timed nutrition to livestock, and generally to optimize agricultural inputs and returns based on supply and demand. This promises to increase the resource efficiency of the agriculture industry, lowering the use of water, fertilizers and pesticides which cause damage to important ecosystems, and increase resilience to climate extremes.
4. Next generation weather and climate prediction
A new field of “Climate Informatics” is blossoming that uses AI to fundamentally transform weather forecasting and improve our understanding of the effects of climate change. This field traditionally requires high performance energy-intensive computing, but deep-learning networks can allow computers to run much faster and incorporate more complexity of the ‘real-world’ system into the calculations.
In just over a decade, computational power and advances in AI will enable home computers to have as much power as today’s supercomputers, lowering the cost of research, boosting scientific productivity and accelerating discoveries. AI techniques may also help correct biases in models, extract the most relevant data to avoid data degradation, predict extreme events and be used for impacts modelling.
5. Smart disaster response
AI can analyze simulations and real-time data (including social media data) of weather events and disasters in a region to seek out vulnerabilities and enhance disaster preparation, provide early warning, and prioritize response through coordination of emergency information capabilities. Deep reinforcement learning may one day be integrated into disaster simulations to determine optimal response strategies, similar to the way AI is currently being used to identify the best move in games like AlphaGo.
Risks of Artificial Intelligence
While AI enables us to better manage the impacts of climate change and protect the environment in addition to transforming the fields of business, finance, health care, medicine, law, education and more, it is not without risks. Some prominent individuals such as the late physicist Stephen Hawking and Tesla CEO Elon Musk have warned of the existential dangers of uncontrolled artificial intelligence.
Elon Musk has compared AI to “summoning demons”, and the late physicist Stephen Hawking has also warned that AI “could bring about the extinction of the human race”. Swedish philosopher Nick Bostrom (Nick Bostrom) is slightly more optimistic, predicting that artificial intelligence will be “the last invention that mankind needs to create.”
The World Economic Forum report identified six categories of AI risk:
- Performance. The black box conclusions of AI may not be understandable to humans and thus it may be impossible to determine if they are accurate or desirable. Deep learning could be risky for applications such as early warning systems for natural disasters where more certainty is needed.
- Security. AI could potentially be hacked, enabling bad actors to interfere with energy, transportation, early warning or other crucial systems.
- Control risks. Since AI systems interact autonomously, they can produce unpredictable outcomes. For example, two systems came up with a language of their own that humans couldn’t understand.
- Economic risks. Companies that are slower to adopt AI may suffer economic consequences as their AI-based competition advances. We are already seeing how brick and mortar stores are closing as the economy becomes increasingly digitized.
- Social risk. AI is resulting in more automation, which will eliminate jobs in almost every field. Autonomous weapon systems could also hasten and exacerbate global conflicts.
- Ethical risks. Since AI uses inferred assumptions about groups and communities in making decisions, it could lead to increased bias. The collection of data also raises privacy issues.
To deal with these risks, the World Economic Forum states that government and industry “must ensure the safety, explainability, transparency and validity of AI application.” More interaction among public and private entities, technologists, policy-makers and even philosophers, and more investments in research are needed to avert the potential risks of artificial intelligence—and to realize its potential benefits to the environment and humanity.