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How artificial intelligence can tackle climate change

Climate change is the biggest challenge facing the planet.

Seeing a chance to help the cause, some of the biggest names in AI and machine learning—a discipline within the field—recently published a paper called “Tackling Climate Change with Machine Learning.” The paper, which was discussed at a workshop during a major AI conference in June, was a “call to arms” to bring researchers together, said David Rolnick, a University of Pennsylvania postdoctoral student and one of the authors.

“It's surprising how many problems machine learning can meaningfully contribute to,” says Rolnick, who also helped organize the June workshop.

A beautiful polar bear is carefully touching the sea surface in order to cross a melt pond in the high Arctic Ocean, which is strongly influenced by climate change.

The paper offers up 13 areas where machine learning can be deployed, including energy production, CO2 removal, education, solar geoengineering, and finance.

Within these fields, the possibilities include more energy-efficient buildings, creating new low-carbon materials, better monitoring of deforestation, and greener transportation.

This push builds on the work already done by climate informatics, a discipline created in 2011 that sits at the intersection of data science and climate science.

Climate informatics covers a range of topics: from improving prediction of extreme events such as hurricanes, paleoclimatology, like reconstructing past climate conditions using data collected from things like ice cores, climate downscaling, or using large-scale models to predict weather on a hyper-local level, and the socio-economic impacts of weather and climate.

AI can also unlock new insights from the massive amounts of complex climate simulations generated by the field of climate modeling, which has come a long way since the first system was created at Princeton in the 1960s.

Of the dozens of models that have since come into existence, all look at data regarding atmosphere, oceans, land, cryosphere, or ice.

But, even with agreement on basic scientific assumptions, Claire Monteleoni, a computer science professor at the University of Colorado, Boulder and a co-founder of climate informatics, points out that while the models generally agree in the short term, differences emerge when it comes to long-term forecasts.

“They don't even agree on how precipitation will change in the future.” Gallery: Before-and-after pictures show how climate change is destroying the Earth (Business Insider) One project Monteleoni worked on uses machine learning algorithms to combine the predictions of the approximately 30 climate models used by the Intergovernmental Panel on Climate Change.

Better predictions can help officials make informed climate policy, allow governments to prepare for change, and potentially uncover areas that could reverse some effects of climate change.

To make it more realistic for more people, researchers from Montreal Institute for Learning Algorithms (MILA), Microsoft, and ConscientAI Labs used GANs, a type of AI, to simulate what homes are likely to look like after being damaged by rising sea levels and more intense storms.

“Our goal is not to convince people climate change is real, it’s to get people who do believe it is real to do more about that,” said Victor Schmidt, a co-author of the paper and Ph.D.

Future plans include releasing an app to show individuals what their neighborhoods and homes might look like in the future with different climate change outcomes.

But the app will need more data, and Schmidt said they eventually want to let people upload photos of floods and forest fires to improve the algorithm.

Carbon Tracker is an independent financial think-tank working toward the UN 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.

grant from Google is expanding the nonprofit’s satellite imagery efforts to include gas-powered plants’ emissions and get a better sense of where air pollution is coming from.

“And we don’t have to ask permission.” AI can automate the analysis of images of power plants to get regular updates on emissions.

It also introduces new ways to measure a plant’s impact, by crunching numbers of nearby infrastructure and electricity use.

Carbon Tracker will now crunch emissions for 4,000 to 5,000 power plants, getting much more information than currently available, and make it public.

In the future, if a carbon tax passes, remote sensing Carbon Tracker’s could help put a price on emissions and pinpoint those responsible for it.

How Machine Learning Can Help Your Business Fight Climate Change

Machine learning (ML) is touted as a technology on the verge of changing how we plan and optimize not only our businesses, but also our lives.

The onset of the climate crisis leads us to ask questions about how we can use this technology to help fight ― and eventually prevent ― overall climate change over the next few decades.

The increase in ways in which we can measure data-generating systems ― whether they are electrical power and delivery systems, the many forms of transportation systems, or the materials and systems used in construction of buildings ―allows us to identify underlying consistencies or patterns and use them to understand how we can optimize utilization and construction of the component systems.

Attacking this problem head-on will not only allow businesses to realize potential cost savings within their logistics spend, but may also lead to a larger overall environment net positive impact.

However, there’s a more general problem hidden within TSP known as the vehicle routing problem (VRP).1 VRP is a framework we can use to describe and formulate many product sourcing and distribution problems that present themselves in the real world.

While there are many ways to solve the TSP problem, and many algorithms can provide some form of provable guarantees, we can utilize methods from ML to push these methods even further and to make them amenable to the stochastic nature of the changing world of customer demand and product distributions.

It accounts for about one quarter of the GHG generated today.3 As industries and societies strive to move toward low(er)-carbon sources of electricity, it’s important to understand that there are many ways in which ML can be applied to the generation, transportation and consumption of electricity within many facets of both business and residential systems.

Current materials research is leveraging solvers from the ML space to help identify how to structure future materials to store this energy.5 There are less-obvious ways in which we can utilize ML methods to identify potentially useful energy-related advances.

Artificial Intelligence and the Future of Work and Business

One of the most prominent themes in science fiction is robots taking over.

But while this may seem a fantasy, job security and business viability are genuine fears affecting individuals and organizations across a wide spectrum.

In today’s blog post, we cut to the chase, avoiding complex terms and telling you the nitty-gritty of what this technology is and what the future holds.

As our world becomes smarter and more connected, machines are also beginning to sense, learn, react, and adapt to real-life situations, creating amazing interactions between people and computers.

The two core concepts of AI are: Machine Learning (ML) ML is a computational method that enables machines to think and act specific functions without being explicitly directed to do.

Deep Learning This is a branch of ML that uses neural network models to process and make sense of large amounts of data by speeding up processes like speech recognition.

AI can help organizations and experts to sift through large volumes of data and notice trends and patterns that help in better and faster decision-making.

Moreover, as AI brings new capabilities to businesses, many new jobs will be created in the process as well.  What businesses need is to engage employees at the earliest stages of AI development so that they can use it to boost their own skills and become better at their jobs.

Here are 10 ways AI could help fight climate change

MIT Technology Review by Karen Hao June 20, 2019 Some of the biggest names in AI research have laid outa road mapsuggesting how machine learning can helpsave our planet and humanity from imminent peril.

Also stay updated on MIT Technology Review initiatives and events?YesNo The report’s compilation was led by David Rolnick, a postdoctoral fellow at the University of Pennsylvania, and advised by several high-profile figures, including Andrew Ng, the cofounder of Google Brain and a leading AI entrepreneur and educator;

If we’re going to rely on more renewable energy sources, utilities will needbetter ways of predictinghow much energy is needed, in real time and over the long term.

This could, for example, help createsolar fuels, which can store energy from sunlight, or identify more efficient carbon dioxide absorbents or structural materials that take a lot less carbon to create.

Shipping goods around the world is acomplexand often highly inefficient process that involves the interplay of different shipment sizes, different types of transportation, and a changing web of origins and destinations.

Intelligent control systems candramatically reduce a building’s energy consumptionby taking weather forecasts, building occupancy, and other environmental conditions into account to adjust the heating, cooling, ventilation, and lighting needs in an indoor space.

In the same way that machine learning can optimize shipping routes, it can also minimize inefficiencies and carbon emissions in the supply chains of the food, fashion, and consumer goods industries.

Robots run on machine-learning software couldhelp farmersmanage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.

Satellite imagery and computer vision can automatically analyze the loss of tree cover at a much greater scale, and sensors on the ground,combined with algorithmsfor detecting chainsaw sounds, can help local law enforcement stop illegal activity.

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Video 3/3 Apologies for the poor sound :(

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