This article was originally published on the Motley Fool. Flying a plane with solar energy alone may seem like a far-fetched idea, but it's now a reality. In 2016, a prototype plane built by Solar Impulse with a single pilot flew around the world on nothing but solar energy, a first for humanity. A number of companies are trying to take that test flight from prototype to commercial viability. A realistic commercial application for solar-powered aircraft could be drones flying high above Earth where energy requirements to maintain altitude are lower.
Panasonic may be best known for consumer electronics, but it has started moving into high-tech urban design in recent years. The company is now building "smart city" infrastructure near Denver, Colorado, with the goal of turning the area into a "smart city" by 2026. The initiative is part of a larger Panasonic program Panasonic called CityNow. Although the definition of a "smart city" varies depending on who you ask, the term typically describes a metro area that prioritizes the use of technology in its infrastructure. On a 400-acre swath of empty land near the Denver Airport, the company has installed free WiFi, LED street lights, pollution sensors, a solar-powered microgrid, and security cameras.
The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.
Each new year provides the opportunity for reflection upon how far we have come and how far we still have to go, on both a personal and societal level. It was also the year that it took you at least a few minutes to realise the customer agent answering your queries in that little chat-box wasn't human, when you picked up a VR headset from your local toyshop for the price of a pizza, when you found yourself in far too many political arguments around the water-cooler, and when you began seriously questioning whether a computer might someday take your job -- maybe for the second time that year. We will see continuing tensions within and between countries, as 20th century nationalist sentiments push resentfully against 21st century supranational integration. There will be moments when it feels like only technology can save us, followed by events which remind us of how perilous our inventions can be when we still barely understand them. The following is not investment or professional advice of any kind, and is intended only to promote discussion and reflection on some of the rising trends and ideas of our time.
I highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, I describe a modeling framework that is based on five fundamental dimensions and that is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where I identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and look-ahead policies. There is the familiar array of decisions: discrete actions, continuous controls, and vector-valued (and possibly integer) decisions. The tools for these problems are drawn from computer science, engineering, applied math, and operations research.
Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.
At the intersection of machine learning and energy consumption stands an incredibly powerful force with the potential to transform the way we globally produce and consume energy. So powerful in fact, that the concept of merging machine learning and renewable resources has been named the "energy internet" by economic theorist and author Jeremy Rifkin or "digital efficiency" by Intel and GE. Going green with machine learning solutions can drastically improve the way we consume energy, in terms of lower operational costs, more efficient production, better use of natural resources and lower environmental impacts. Last year, Google, with the help of its U.K.-based subsidiary DeepMind, reduced the amount of energy used to cool its data centers by 40%. By introducing machine learning to compensate for the nonlinear interactions between equipment and environment, and using the unique architecture and environment of each data center, this decrease saves Google millions of dollars each year.
Researchers recently uncovered Kepler-90i, a sizzling-hot eighth planet that orbits Kepler-90 once every 14.4 days. To find it, they used a unique method: machine learning. Machine learning is a type of computer science that gives machines (computers) the ability to go beyond strict programming and learn on their own. In this case, computers learned to identify planets by finding instances in the Kepler data where the telescope recorded signals from exoplanets beyond our solar system. "Just as we expected, there are exciting discoveries lurking in our archived Kepler data, waiting for the right tool or technology to unearth them," said Paul Hertz, director of NASA's Astrophysics Division in Washington, in a news release.
New research has posited that artificial intelligence will increasingly automate operations for the wind and solar industries, boosting their efficiencies in areas such as decision making and planning, condition monitoring, robotics, and inspections. The new position paper published this week by DNV GL -- international accredited registrar and classification society headquartered near Oslo -- entitled Making Renewables Smarter: The benefits, risks, and future of artificial intelligence in solar and wind, outlines the advances being made in robotics, inspections, supply chain, and the way we work and showcases a variety of opportunities for the solar and wind industries to embrace artificial intelligence (AI) applications to improve their efficiency. "The use of artificial intelligence in industries continues at an impressive rate -- in manufacturing, engineering, healthcare, transportation, finance, telecommunications, services, and energy," the authors of the report explain. "Artificial intelligence's ability to use machine learning to analyse historical and new data, make predictions, control physical operations, and make decisions at increasingly higher levels is having an immense impact." The report explores ways in which AI applications like machine learning can impact the efficiency levels of areas involved in the wind and solar industries such as decision making and planning, condition monitoring, robotics, inspections, certifications and supply chain optimization, as well as the way technical work is carried out.
The newly-discovered Kepler-90i – a hot, rocky planet that orbits its star once every 14.4 days – was found using machine learning from Google Forget self-driving cars and computers that can beat humans at chess, artificial intelligence is helping astronomers make huge steps towards solving some of the Universe's biggest mysteries. For the first time, artificial intelligence has been used to discover two new exoplanets. One of the discoveries, made by Nasa's Kepler mission, brings the Kepler-90 solar system to a total of 8 planets - the first solar system found with the same number as our own. The majority of exoplanets are discovered using what is called the transit method. Telescopes are pointed at stars, studying them over long periods of time, which means they can look out for tiny dips in the brightness as a planet passes in front.