'I'm an accidental environmentalist,' said Richard Hardiman, who runs a project called WASTESHARK. He says that while walking at his local harbour one day he stopped to watch two men struggle to scoop litter out of the sea using a pool net. Their inefficiency bothered Hardiman, and he set about trying to solve the problem. It was only when he delved deeper into the issue that he realised how damaging marine litter, and plastic in particular, can be, he says. 'I started exploring where this trash goes – ocean gyres (circular currents), junk gyres, and they're just full of plastic.

If data is the new oil, then more refining capacity is needed. Many customers are leveraging the data to be part of the core competitive advantage. However, refining that data to become valuable, actionable information is difficult. Data collection and preparation take an inordinate amount of manual labor (80% of data scientist's time). Difficult to guarantee success before the analysis is actually done.

Scientists at the Columbia University have discovered a totally new way to study earthquakes. They picked out different types of earthquakes from three years using machine learning algorithms. According to them, these machine learning methods pick out very subtle differences in the raw data that we're just learning to interpret. Scientists particularly identified earthquake recordings at The Geysers in California, one of the world's oldest and largest geothermal fields. They assembled a catalog of 46,000 earthquake recordings, each represented as energy waves in a seismogram. They then mapped changes in the waves' frequency through time, which they plotted as a spectrogram--a kind of musical roadmap of the waves' changing pitches, were they to be converted to sound.

Just over an hour into Tuesday's California Public Utilities Commission public meeting on the future of self-driving taxis, the machines took over. "Please pardon the interruption," a kindly robotic voice said, cutting into a government official's prepared remarks. "Your conference contains less than three participants at this time. If you would like to continue, press star 1 now, or your conference will be terminated." In fact, there were three commissioners and two administrative judges sitting on the auditorium's dais.

Big data is usually associated with Facebook, not farming. But as Ian MacGregor writes, harnessing data from agriculture, mining, forestry and other primary industries could be the next big economic opportunity in Canada -- if we do it right. This is part of a series of excerpts from essays commissioned by the Centre for International Governance Innovation. He used the data he collected to create what he called the "great map"-- and in the process unlocked the commercial potential of North America. Big data is as important to Canada in the 21st century as Thompson's topographical data was in the 19th century.

In a new study in Science Advances, researchers at Columbia University show that machine learning algorithms could pick out different types of earthquakes from three years of earthquake recordings at The Geysers in California, one of the world's oldest and largest geothermal reservoirs. The repeating patterns of earthquakes appear to match the seasonal rise and fall of water-injection flows into the hot rocks below, suggesting a link to the mechanical processes that cause rocks to slip or crack, triggering an earthquake. "It's a totally new way of studying earthquakes," said study coauthor Benjamin Holtzman, a geophysicist at Columbia's Lamont-Doherty Earth Observatory. "These machine learning methods pick out very subtle differences in the raw data that we're just learning to interpret." The approach is novel in several ways.

In short, there is a global demand for clean, cheap, reliable energy – and artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. Enabling the growth of low-carbon, green electricity is an AI application with a potentially huge long-term impact. Enabling the growth of low-carbon, green electricity is an AI application with a potentially huge long-term impact Renewable forms of electricity are emerging as the successors to traditional coal and gas-fired power plants. A key problem with renewable electricit y, however, is its inconsistency. A cloudy day or a string of calm, windless afternoons will cut generation and can create power shortfalls. Conversely, too much energy can be generated; in March this year, for example, a sunny, windy Portugal produced more renewable electricity than it consumed.

The global energy industry is facing fundamental shifts in the way it generates, sells and distributes power. The pressure is on to cut carbon emissions and, as a result, methods must be found to manage the increasing gigawatts of unpredictable, weather-dependent renewable energy flowing on to power grids. The cost of electricity is also a concern, not just for consumers, but for governments keen to keep their voters happy. In short, there is a global demand for clean, cheap, reliable energy – and artificial intelligence (AI) is increasingly being used to help meet this need. Enabling the growth of low-carbon, green electricity is an AI application with a potentially huge long-term impact.

This article will cover a brief introduction to these topics and show how to implement them, using Google Colaboratory to do automated machine learning on the cloud in Python. Originally, all computing was done on a mainframe. You logged in via a terminal, and connected to a central machine where users simultaneously shared a single large computer. Then, along came microprocessors and the personal computer revolution and everyone got their own machine. Laptops and desktops work fine for routine tasks, but with the recent increase in size of datasets and computing power needed to run machine learning models, taking advantage of cloud resources is a necessity for data science.

In the 21st century the world still faces many geographical challenges including climate change, disease outbreaks, natural disasters and a growing scarcity of vital resources such as water, food and land. Overcoming these problems is dependent on our ability to chart these issues and analyse them spatially. This comes at a time when we're increasingly able to produce millions of data points from connected devices – the internet of things (IoT) – such as mobiles, drones, satellites, vehicles and social media, combined with more affordable, powerful cloud computing and machine-learning. Technologists realise the potential for smart mapping has never been greater. "If you think about it, there isn't an area that isn't touched by location, from responses to hurricanes and typhoons, wars, international health scares or utility outages," explains Stuart Bonthrone, managing director of Esri UK, a world leader in mapping and spatial analytics software.