AI and the Environment
Artificial Intelligence has always bounced around in the imagination of science fiction authors and enthusiast alike, popularized by shows such as Robocop and The Terminator. These sorts of AI are usually linked with robots and the justice system, which will no doubt change because of the technology but AI technology has a great influence on a number of industries from healthcare to finance. In fact, AI’s can be used to help the greater environmental issue we face today. As the earth’s temperature increases, it causes insects such as mosquitoes to increase whilst killing off animals that thrive in a cooler climate such as polar bears. Despite our best efforts preservation of animals that are going extinct have been dismal. Animals depend on clean water and healthy environments created by numerous species in order to survive, scientists only being able to describe and identify 1.5 million species versus the possible 50 million that actually exist.One could compare the situation to the global economic system wherein the 1% control the majority of the wealth in the world. But in this case, humans are >1% and are killing off the other 99% of the worlds animals and plants.
Further illustrating the idea, we understand that bees are going extinct due to the amount of pesticides used on crops and blood sucking parasites that only reproduce in bee colonies. Out of the 100 crop species that provide us with 90% of our food, 70% are pollinated by bees. Herbivores who depend on bees to pollinate plants will no doubt be caught up in the extinction. Even with the creation and introduction of robo-bees, the amount yielded would likely be a lot less than what bees are naturally able to produce, and the cost of making these robot bees as well as monitoring and controlling of the robots would be costly. That is in comparison to the free pollination we get now.
However, other machine learning systems can help the environment with species conservation is in relations to data. “In particular, the Cornell institute has been working with the Cornell Lab of Ornithology to combine the incredible zeal of birders with scientific observation. They’ve developed an app called eBird that allows ordinary citizens to submit data about the birds they observe around them, such as how many different species can be found in a given location. So far, Gomes says, they’ve had more than 300,000 volunteers submit more than 300 million observations, which amounts to more than 22 million hours of fieldwork.” The combination of data gathered from eBird and the lab’s own observational data about species distribution helps to predict where there will be changes in habitat for certain species and the paths along which birds will move during migration.
Companies such as Microsoft have also invested heavily into artificial intelligence for the betterment of the environment. Microsoft’s new program called AI for Earth “will offer access to cloud and AI computing resources, technology trainings and lighthouse projects – a $2 million commitment in this next fiscal year.” This project is particularly important because AI technology is usually out of reach to the public at large due to how expensive it is to create this technology. Additionally, Microsoft will provide training and educational opportunities for people and organizations in how to utilize these tools to achieve their individual goals. Three projects under this program is currently underway from land mapping to smart mosquito traps, Microsoft aims to innovate further to democratize AI and advance sustainability.
Similarly, the agriculture industry is also applying AI by using sensors and soil sampling to gather data of the soils moisture and nutrient levels across their fields. Once again, this use of data allows farmers to better care for their land, which prevents problems such as over farming. The usual method of crop rotation is a good method in ensuring that the soil gains proper nutrients but the ever changing environment because of pollution could change the needs of the fields.
Companies such as “Descartes and Orbital Labs are playing in the emerging market of geospatial analytics. These companies use machine learning to produce insights from satellite imagery and other data. This capability has proved exceedingly popular with hedge funds where images of store parking lots, for example, can be used to project out revenue numbers. But companies running the gamut from agriculture to logistics see the value in having an extra pair of trained eyes in the sky…..As Descartes’ internal data pipelines have been getting more and more robust, talk has slowly shifted to opening up the platform to a wider audience. Everyone in the space knows that the real money will be made through massive SaaS contracts and not through one-off consulting contracts.
The hope is that Descartes can open up all of its technology in an easy to use way such that customers can search across the globe”
Furthermore, if an individual has a fairly large piece of land which he personally farms on, it may be difficult to assess the area unless one were to hire a vast amount of human labour. These workers would then require training to be able to properly assess the crops which would involve quite a lot of time and money to complete not accounting the human error involved as one would unlikely be able to determine how well each and every individual is working. Hence, by using AI tech “Farmers today have access to software tools to assist in in-field scouting. From mobile apps to unmanned aerial vehicles, these tools collect data that can be used to assess crop health and monitor pest and disease conditions during the season.”
However, there are still challenges in regards to attaining this data. The Wall Street Journal stated that cell phone reception on farms tends to be bad and if not non-existent therefore posing a different obstacle towards the transference of data to a location where it can be analyzed. Forbes made in clear that a lack of standards, perceived opacity around data use and ownership, and the difficulty of gathering and sharing data has lead to a situation where AI algorithm developers in (Agriculture tech) Ag are still starved for data. Although, this drought of data is being tampered by products like the Climate Corporation’s FieldView Drive, John Deere’s JD Link, and Farmobile’s PUC whom are aiming to make the collection and transfer of equipment data easy and seamless.
Further complications in the industry of Ag is that some emerging companies in the field have tended to avoid the use of scientifically validated, statistically controlled field trials to quantify the benefits of their products. Instead, these companies have used “lean” methods to get to market quickly with a small subset of customers, following the usual style of building a tech startup by using avenues such as crowdfunding and the like. While the lean method has worked well in software, in agriculture, a grower/farmer simply can’t risk adopting a new technology across their whole farm that may not work or worse affect their crops and soil negatively. Before launching a product, major agricultural companies put their products through years of field trials to ensure consistent performance and clear benefit. Even with this testing, many growers will want to see new products perform well on a subset of their own acres before complete adoption. Thus, the pervasive “get to market fast” and “scale quickly” mentality may need to change into a more progressive and incremental product launch strategy.
A final hurdle that should be emphasised is the fact that competition for AI talent is fierce. A common complaint amongst the Ag-tech startup community is that it is often extremely hard to find AI talent, in light of competition with employers in the software, internet, and autonomous vehicle sectors. Further, even if a company is able to hire such talent they then face the challenge of trying to retain these individuals. For example, a machine learning specialist at one of the MGV portfolio was recently recruited away by a tech giant for over $700k in annual compensation. Not even including the fact that with the popularization of AI and blockchain technology within the financial sector the demand for talent at the moment, seems to exceed supply.
Moving on, contrary to popular belief AI is going further than simply transferring data within the Agriculture tech industry, companies like Abundant Robotics is a company building apple-picking robots that could eventually be adapted to harvest other fruits. It uses a machine similar to that of a vacuum in order to pick apples rather than using a claw or hand like machine due to the susceptibility of fruits to bruising. The chief executive and co-founder of the company said his company began working in the apple industry four years ago, with the thought to automate the cumbersome task of apple picking as “It’s very difficult to locate fruit that’s ready to be picked within a canopy and then retrieve it without turning it into apple sauce.”
All these uses of AI, can aid both humans and animals alike by properly using the available land to ensure that data collected from these areas, and environment monitoring allows the grower to continually use their land for many years to come without causing deficiency of soil nutrients. This could lead to less wildlife areas being cut down for the purposes of agriculture uses thus not affecting the lives of the animals that live in those habitats. It also safeguards those animals that are becoming extinct by keeping track of their population and migration habits. This could one day extend to helping fisherman’s ascertain where they may fish without accidentally catching marine animals such as dolphins or turtles in their nets as well as monitoring what fishes may be lower population wise that year,
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