Hello my name is Andrew!)

Introduction

Artificial intelligence and machine learning technologies are emerging quickly in every strata of people’s life on our planet.
Artificial intelligence can be used to create machines that perform tasks more efficiently than humans, allowing them to work in stressful environments twenty-four hours a day all year long with no lunch breaks, sick days or paid vacations.
The advanced technologies are nowadays being used to fight against the effects of global warming. Climate change is the biggest challenge facing the planet. The majority of people believe that it is necessary to step up the commitment to protecting the planet’s future. Here, the input of artificial intelligence and machine learning is great. The more people utilize these modern technologies, the better chance there is to stall or even reverse the climate change trajectory.

My present report is based on a number of articles dealing with the study of climate. I was particularly interested in the article entitled «Tackling Climate Change With Machine Learning», written by some of the leading proponents of artificial intelligence.
There are also some articles about the application of artificial intelligence and drones in the agricultural industry, on which I make an attempt to give a brief review.

CLIMATE  STUDY
Machines can analyze the flood of data that is generated every day from sensors, gauges and monitors quickly and automatically. The data about the changing conditions of the world’s land surface enables to form a very accurate picture of how the world is changing. This knowledge from climate scientists can be shared with decision-makers, so they know how to respond to the impact of climate changes - severe weather such as hurricanes, rising sea levels and higher temperatures.

IBM’s GREEN HORIZON PROJECT
Artificial intelligence and deep learning can help climate researchers and innovators test out their theories and solutions about how to reduce air pollution, for example. This matter is dealt with by the Green Horizon Project from IBM that analyses environmental data and predicts pollution as well as tests ‘what-if’ scenarios that involve pollution-reducing tactics.
IBM created the Project with the hope that they could help cities become more efficient, one day. Their aspirations became a reality in China. Between 2012 and 2017 , IBM’s Green Horizon Project helped the city ofd Beijing decrease their average smog levels by 35%.

GREEN INITIATIVES
The easier we make green initiatives for each person, the higher the adoption rate and more progress we make to save the environment. Artificial intelligence and machine learning innovations can help create products and services that make it easier to take care of our planet. There are several consumer-AI devices such as smart thermostats (which could save up to 15% on cooling annually for each household) and irrigation systems (which could save up to 8,800 gallons of water per home per year) that help conserve resources. Everyone doing their part over time will add up.

BETTER WEATHER EVENT PREDICTIONS
The damage to human lives and property can be reduced if there are earlier warning signs of a catastrophic event. There has been significant progress in using machine-learning algorithms that were trained on data  from other extreme weather events to identify tropical cyclones and atmospheric rivers. The earlier warning that governments and citizens can get about severe weather, the better they are able to respond and protect themselves. Machines are also being deployed to assess the strengths of models that are used to investigate climate change by reviewing the dozens of them that are in use and extracting intelligence from them. They also predict how long a storm will last and its severity. Since the machine can’t tell you ‘how’ it arrived at its prediction or decisions, most climate professionals don’t feel comfortable relying on only what the machines suggest will happen, but use machine insight along with their own professional analysis to complement one another.

Climate change is a complex problem that cannot be solved with a swift flick of a magic wand.

The global threat of climate change is the scariest problem that mankind has ever faced.

Some recent reports and predictions about the future of our climate suggest that human hardships are right around the corner. The studies by researchers point to a scaring possibility of water levels rising 25 meters by 2050.  In March of 2012, the World Water Assessment Program predicted that by 2025, 1.8 billion people on earth will be living in regions with absolute water scarcity.

According to NASA, the main cause of climate change is the rising amount of greenhouse gases in our atmosphere. In 1830, humans began engaging in activities that released greenhouse gases, contributing to the rising temperatures that we are feeling today.

Obviously, we should be limiting all the activities, such as using electricity, coal, oil and cease the production of beef, let alone our daily oil-guzzling automobiles and airplanes.

Solving any problem takes time. With climate change, it took scientists about 40 years to gain any sort of understanding of the problem. Now, mankind has to find and implement some solutions how to combat climate change relatively fast. That’s where AI could help.

To date, there are two different approaches to AI : rules-based and learning-based.
Rules-based AI are coded algorithms of if-then statements that are basically meant to solve simple problems. A rules-based AI could be useful in helping scientists compile data, saving  humans a lot of time in manual labor. But a rules-based AI  has no memory capabilities. It can provide a solution to a problem that’s defined by a human.

That’s why learning-based AI was created.

Learning-based AI is more advanced than rules-based AI because it diagnoses problems by interacting with the problem. Basically, learning-based AI has the capacity for memory, whereas rules-based AI does not.

Here’s an example : let’s say you asked a rules-based AI for a shirt. That AI would find you a shirt in the right size and color, but only if you told it your size and preferences. If you asked a learning AI for a shirt, it would assess all of the previous shirt purchases you’ve made over the past year, then find you the perfect shirt for the current season.
When it comes to helping solve climate change, a learning-based AI could essentially do more than just crunch CO2 emission numbers. A learning-based AI could actually record those numbers, study causes and solutions, and then recommend the best solution - in theory.

SILVIATERRA
Forests are important for our climate. The carbon dioxide that’s emitted by many human activities is absorbed by trees.
This is why SilviaTerra was brought to life. Powered by the funds and technology of Microsoft, SilviaTerra uses AI and satellite imaging to predict the sizes, species, and health of forest trees. It is very important because the advanced technology helps to save countless hours of manual fieldwork. In addition, there is a possibility to help trees grow bigger, stronger and healthier, so they can continue to assist our climate.

GOOGLE and DeepMind ARE TO CONTRIBUTE INTO SOLUTION OF THE PROBLEM
Google together with a company called DeepMind developed an AI that would teach how to use only the bare minimum amount of energy necessary to cool Google’s data centers.
As a result, Google was able to cut the amount of energy they use to cool their data centers by 35%. DeepMind’s co-founder, Mustafa Suleyman, said that their AI algorithms are general enough and they could be used for other energy-saving applications in the future.

GENERATIVE ADVERSARIAL NETWORK (GAN) and its predictions.
GAN is a network that generates statistics or information. It is very important because automation can save time and resources to solve a problem.
Intellectuals of Cornell University used GANs to create an AI to train itself to produce images that portray geographical locations before and after extreme weather events. The visuals produced by this AI could help scientists predict the impacts of certain climate changes, helping humans prioritize the combative efforts.

AIRLITIX
Airlitix is an AI and machine-learning software that is currently being used in drones. It was originally developed to automate greenhouse management processes, but it could quite easily be used to manage the health of national forests. Airlitix has the capacity to not only collect temperature, humidity and carbon dioxide data, but the AI can also analyze soil and crop health to ensure that plants are disease-free and are growing in an optimal ecosystem. The Airlitix software installed on  the drones could plant trees, release plant nutrients or even deter forest arsonists.

BETTER CLIMATE PREDICTIONS
In 2011, a new discipline, called climate informatics, was created. It sits at the intersection of data climate sciences. Climate informatics covers a range of topics from improving prediction of extreme events such as hurricanes, for example, to the socio-economic impacts of weather and climate.
Claire Monteleoni, a computer science professor at the University of Colorado and a co-founder of climate informatics, worked on a project which used machine learning algorithms to combine the predictions of the approximately 30 climate models employed by the Intergovernmental Panel on Climate Change. Better predictions can help officials make informed climate policy, allow governments to prepare for a change and potentially uncover areas that could reverse some effects of climate change.

Some homeowners have already experienced the effects of a changing environment.  To make it more realistic  for more people, researchers from Montreal Institute for Learning Algorithms  (MILA), Microsoft, and Conscient AI Labs used GANs, a type of AI, to simulate what homes are likely to look like after being damaged by rising sea levels and intense storms.
The researchers’ goal is not to convince people that climate change is real, but to make them do more about it. So, they want to let people upload photos of floods and forest fires to improve the algorithm, as the app will need more data.

MEASURING WHERE CARBON COMING FROM
CARBON TRACKER is an independent financial company working toward the goal of preventing new coal plants from being built by 2020. By monitoring coal plant emissions with satellite imagery, Carbon Tracker can use the data it gathers to convince the finance industry that carbon plants aren’t profitable.
AI can automate the analyses of images of power plants to get regular updates on emissions.
Machine learning is going to help a lot in this field, the researchers are sure.

Below there is a description of some companies utilizing AI in different ways to address climate change.

DHL and IBM
Transportation accounts for 23 percent of global greenhouse-gas emissions. That’s why DHL and IBM has teamed up to use artificial intelligence to improve DHL’s global logistics operations. This is significant, as between 1970 and 2004, there was a 120 percent increase in emissions  from the transportation and logistics sector. There is here plenty of room for positive contributions from AI.

GOOGLE
Google’s UK-based DeepMind laboratory applies its knowledge of neural networks and machine learning to more efficient energy consumption  and energy distribution based on predictability models by utilizing artificial intelligence recommendations.

MICROSOFT and LONG LIVE the KINGS
Since the 1980s, salmon populations have declined by up to 80 percent in some natural fisheries around the world. The World Wildlife Fund suggests climate change has a significant impact on salmon survival rates. But by using artificial intelligence with a Microsoft grant, Seattle-based conservation organization Long Live the Kings has been able to compile large data sets to answer questions around salmon -population disappearance.

50 REEFS
Extremely sensitive to acidity, temperature and toxins, coral reefs  are deteriorating around the world as a result of climate change and greenhouse gas emissions. 50 Reefs, an initiative run by The Ocean Agency, combines advanced imaging technology  with AI to gather and analyze images of shallow-water reefs at scale, and within seconds. Using deep learning, the AI is able to recognize different types of corals based on their colors, textures, giving scientists a powerful array of information to track the effects of climate change on coral populations around the world and make more informed decisions on how to ensure their survival.

Nowadays, advanced technologies have come to every field of our life. No wonder that agriculture, being both a major industry and foundation of the economy, is using artificial intelligence in new amazing ways to modernize almost everything about the farming process.

Artificial intelligence is being applied to agricultural data in order to make farming much more efficient. The predictions made by AI prompt the farmers where a seed will grow best, what soil conditions are likely to be, etc.
Automation and robotics help farmers find more efficient ways to protect their crops from weeds.
In this respect the «SEE and SPRAY» robot is an excellent example of using the power of artificial intelligence and computer vision.
The robot has been developed by the «BLUE RIVER TECHNOLOGY» firm , which is highly skilled and dedicated to rapidly advancing the implementation of machine learning in agriculture.
Precision spraying can reduce herbicide expenditures by 90 percent.

Who’s Picking Your Food ?
That’s a question put by a scientist  in an article about some brilliant new technologies applied in the modern agricultural industry. A better question, the scientist writes, would be «What’s picking your food ?» There are companies which are already producing robotic harvesting equipment.
So, HARVEST CROO ROBOTICS has developed a robot to help strawberry farmers pick and pack their crops.
Harvest technologies use sophisticated directed movements to pick precisely.
Harvest robotics is saving humans from one of the most repetitive and difficult jobs in the economy.
Harvest Croo Robotics claims that its robot can harvest 8 acres in a single day and replace 30 human laborers.
We know that artificial intelligence excels at image processing - computers can now ’see’ almost as well as we can. So by deploying mobile technologies with AI and computer vision built in, farmers can find weeds and eradicate them, instead of blanket spraying an entire crop. That makes the food cleaner, and it saves enormous amounts of money. It’s just another example of real new technologies  that are having a dramatic impact on yields and everything else.
Farmers are quickly adopting new high-tech ways of protecting plants against weeds and various kinds of pests.

YIELD BOOSTING ALGORITHM
When we talk about machine learning and artificial intelligence, we often talk about algorithms. The mathematical models behind computer science are the fundamental basis for how we deal with big data to make decisions.
Companies are now quickly developing agricultural yield boosting algorithms that can show farmers what’s going to be best for a crop. Despite some concerns about the difficulty of doing this type of analysis in nature, farmers and others have been able to make quite a lot of headway in maximizing crop yield, simply by applying the algorithms and intelligent generators that we’ve built to help computers imitate our own cognitive abilities.

DRONES AND COMPUTERS IN AGRICULTURE
The presence of drones or unmanned aerial vehicles in agriculture reportedly dates back to the 1980s  for crop dusting in Japan.
Drones are outfitted with precision sensors in order to run the fields and get the data that’s needed. These new technologies in agriculture can look for stunted crops, signs of pest or weed damage, dryness and many other variables that are part of the difficulty of farming in general. With all of these data in hand, farmers can enhance their production models and map out their strategies.
Today, farmers are using AI and aerial technology to monitor crop health.
The market for drones in agriculture is projected to reach $480 million by 2027.

SkySQUIRREL TECHNOLOGIES Inc. is one of the companies bringing drone technology to vineyards. The company aims to help users improve their crop yield and reduce costs. Users pre-program the drone’s route and the device will use computer vision to record images which will be used for analysis. Once the drone completes its route, users can transfer a USB drive from the drone to a computer and upload the captured data to a cloud drive.
SkySquirrel uses algorithms to integrate and analyze the captured images and data to provide a detailed report on the health of the vineyard, specifically the condition of grapevine leaves. Since grapevine leaves are often indicative of grapevine diseases (such as molds and bacteria), the leaves are good for understanding the health of the plants and their fruit as a whole.
The company claims that its technology can scan 50 acres in 24 minutes and provides data analysis with 95 percent accuracy.

PREDICTIVE ANALYTICS
aWHERE, a Colorado based company, uses machine learning algorithms in connection with satellites to predict weather, analyze crop sustainability and evaluate farms for the presence of diseases and pests.
Daily weather predictions are based on the needs of each client. Types of clients include farmers, crop consultants and researchers.
The company claims that it provides its users with access to agronomic data on a daily basis. Data sources include temperature, wind speed and solar radiation.
AI-driven technologies are called to improve efficiency in agriculture.
Software applications can inform users exactly where fertilizer is needed and can reduce the amount of fertilizer used by nearly 40 percent.


CONCLUSION
In my report I touched upon a very important issue facing mankind - global warming and its impact on people’s life.
The urge to combat climate change and protect the planet’s future was recently voiced at the United Nations.
The application of AI in various spheres of our life is great and is currently accelerating. AI is getting increasingly advanced. It is now used in drones, a sophisticated technology, playing key-problem-solving roles in a variety of sectors, including defense, security, construction and agriculture, a major industry and foundation of the economy.