Amazon founder Jeff Bezos has'christened' the company's new wind farm by smashing a bottle of champagne while standing on top of a 300ft (90m) turbine. Footage of the stunt, which appears to have been recorded using a drone, gives a sweeping look of the new Texan wind farm which is currently Amazon's largest renewable energy project. The 53-year-old multi-billionaire flaunted the bottle-smashing video on Twitter - further shaping his new macho appearance which is a far cry from his nerdy look when he started Amazon in the mid-90s. Bezos' macho appearance is a far cry from his nerdy look when he started Amazon in the mid-90s. At the time, he was running it from a garage at a house he had rented in Seattle.
Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.
While governments are trying to handle the situation, how could technology innovations help prevent starvation and improve agriculture management in the future? For example, the drone can't fly "Beyond-Line-of-Sight" (BLOS), meaning it must not move away from the drone pilot more than 100 meters, except if its weight is under 2 kg (4,4 lb) or if you have a special exemption. As we map thousands of hectares thanks to solar energy to address conservation issues, we are directly progressing towards SDGs 7, 9 and 15 (cf.: We are currently raising funds and starting the drone marketing abroad, in Africa and Brazil.
Panasonic expects to launch its autonomous driving system for commercial vehicles by 2022, as it continues to turn its focus to advanced auto parts. Panasonic -- which is currently the battery cell supplier for Tesla's Model 3 -- has been pushing its range of driving-related products in an effort to catch up other suppliers such as Bosch and Continental AG. Panasonic announced 83.93 billion yen in profit for the quarter ended June 30, 2017, up from 71.81 billion a year prior. For the full year ended March 31, 2017, operating profit for the company stood at 276.8 billion yen, up from 230.3 billion yen a year prior.
When Chris Shelton, chief technology officer for electricity giant AES, takes the stage at a tech conference in San Francisco this week, he'll be the rare name in energy. According to Shelton, AES is exploring how AI can improve awareness, efficiency and maintenance of the company's grid systems and assets like solar farms and gas plants. Tech giants like GE and IBM are building prediction and maintenance systems for electricity, while many startups have emerged to tackle more niche energy issues like lowering the cost of selling solar panels or making office buildings more comfortable and efficient. Expect both new entrants to emerge with brand-new, focused applications, and for AI players like Google, GE and IBM to use data tools from the web and consumer internet for energy services.
We will explore some interesting side projects from Tesla (NASDAQ: TSLA), Walt Disney (NYSE: DIS), Alphabet (NASDAQ: GOOG)(NASDAQ: GOOGL), IBM (NYSE: IBM), and Amazon.com These ideas may grow into their breeches over time, but they look kind of crazy right now. More:Alphabet's Project Loon gets OK to use balloons to revive Puerto Rico cell service Created as a division of Google, and later of Alphabet, Calico wants to stop or slow human aging. Anders Bylund owns shares of Alphabet (A shares), Amazon, IBM, Tesla, and Walt Disney. The Motley Fool owns shares of and recommends Alphabet (A and C shares), Amazon, Tesla, and Walt Disney.
Teams from Tesla and Puerto Rico's energy sector will continue the talks early next week, Rosselló said. Oct. 6: Tesla can help fix Puerto Rico's ruined electrical grid, Elon Musk says Oct. 6: Elon Musk delays self-driving truck to focus on Model 3, Puerto Rico power Musk announced he was delaying the unveiling of Tesla's new semi-truck and diverting resources to its battery-producing Gigafactory in Nevada in part to "increase battery production for Puerto Rico and other affected areas." Diverting resources to fix Model 3 bottlenecks & increase battery production for Puerto Rico & other affected areas. They also spoke about using Tesla solar technology to rebuild Puerto Rico's power grid, including a pilot run in the island-municipality of Vieques, which still pulls its power from Puerto Rico.
While these meters give the consumer greater control and a better understanding of their energy usage, the data made available to them only scratches the surface of what is actually possible and what can have a real impact on changing consumer behaviour. See also: Connectivity concerns hindering IoT's potential for energy sector This means while they can provide the total cost of your energy usage, they miss the opportunity to provide enough detail to encourage long-term behavioural change in the home, that would ultimately lead to lower energy bills and increased efficiency. One step further, beyond smart meters, is the application of blockchain technology and the introduction of peer to peer energy trading, which is currently only being used in Germany, Australia, and Canada. Blockchain technology is bringing about new opportunities in many sectors but in the energy sector, combining machine learning methods with the transaction and authentication capabilities of blockchains means delivering cheaper and more sustainable energy to consumers is possible.
As a data scientist in this field, you'd help develop new ways to study massive amounts of data -- including interactive and semantic technologies and even machine learning. Just as developing oil fields required the study of vast amounts of data, installing and refining clean energy production facilities requires data about the natural environment and the needs of modern construction. Your keenly trained eyes can make sense of usage and breakage patterns, streamline production and transportation and ultimately deliver insights that deliver better and safer products to the world, faster. Beyond self-driving automobiles and the obvious shipping applications, the transportation industry is also eyeing ever-more-efficient ways to store and transport energy.
A system of insight is like your human nervous system: AI is the brain, IoT sensors are your senses, middleware is your skeletal system and streaming analytics complete the autonomous nervous system's function. The Vestas implementation captures terabytes of sensory input each and every day from its wind turbines to continuously train algorithms that continuously instruct turbines on how to react to wind and atmospheric conditions and optimize power production. Streaming analytics of trained algorithms provide this automatic intelligence in action for IoT systems and guide delivery drones to, say, avoid collision with one another. All automated IoT systems face this challenge, as the importance of a connected business nervous system makes for a big target.