Power Play: This Software Takes The Guesswork Out Of Energy Demand
By Bruce Watson – Predicting power demand used to be a simple science: People use more power during certain times — like the morning, when they cook breakfast and turn on their lights — and less during others, like when they hit the sack. Relying on predictable sources of electricity — like gas- and coal-fired power plants — utilities were able to balance supply and demand with some fairly straightforward math based on historical records and other data.
But the steady rise of renewable energy made the power landscape infinitely more complicated. On the supply side, changes in wind or cloud cover can sharply shift the amount of power available. Demand has also become harder to nail down as more consumers manage their power use with smart thermostats and appliances like connected ACs.
At the same time, market forces demand better power forecasts. Power plants and fuel are expensive, and they don’t want to operate or buy more equipment than they may need. “In some countries, regulators are asking power generators to guarantee the quality of their forecasts,” says Olivier Cognet, CEO of Swiss-based startup Predictive Layer.
“It’s no longer possible to say ‘We’ll sell you 20 turbines and see what they produce.’ It’s ‘We’ll produce x amount of energy by noon, y amount of energy in two hours and z energy in one month.” more>
Posted in Business, Economic development, Economy, Energy, History, Nature, Technology
Tagged Big data, Business improvement, GE, Machine learning, Productivity, Renewable energy, Technology
By Gregory C. Allen – Every type of animal, whether insect, fish, bird, or mammal, has a suite of sensors (eyes, ears, noses), tools for moving and interacting with its environment (arms, beaks, wings, fins), and a high-speed data processing and decision-making center (brains).
Humans do not yet know how to replicate all the technologies and capabilities of nature, but that these capabilities exist in nature proves they are indeed possible.
Humans do not know what the ultimate technological performance limit for autonomous robotics is. But it can be no lower than the very high level of performance that nature has proven possible with the pigeon, the goose, the monkey, the mouse, or the dolphin.
The United States is far from the only country interested in these capabilities. In 2015, Russian scientists celebrated their development of a robotic “cockroach,” which they said would be an ideal platform for secretly recording conversations and taking photographs. One can easily imagine such a cockroach being outfitted with venom and an injector needle, making it an ideal platform for covert assassination as well. more> https://goo.gl/Wd1Ecv
I Machine, You Human: How AI Is Helping GE Build A Powerhouse Of Knowledge
By Tomas Kellner – Every fall, GE Global Research holds a scientific gathering called the Whitney Symposium highlighting the latest scientific trends. Last year the two-day event explored industrial applications of artificial intelligence. We sat down with Mark Grabb and Achalesh Pandey, two GE scientists looking for ways to apply AI to jet engines, medical scanners and other machines.
“We are starting to see significant performance increases from the combination of deep learning and reinforcement learning, where you have a human in the loop correcting the system,” Grabb said. “Once you build a smooth user experience and get the system going, people don’t even know they are correcting the AI along the way.”
At GE, we are writing software like Predix, which is the cloud-based operating system for machines that allows us to connect them to the Industrial Internet. But we also have a tremendous number of domain experts. There’s a lot of physics and domain knowledge that’s required to build good analytics and machine learning models. We have actually built AI systems that help data scientists more quickly and more effectively capture the domain knowledge across all the people inside GE building these models. So AI comes in even in the developing of analytics. more> https://goo.gl/OMZ9TS
Posted in Broadband, Business, Economy, Education, Science, Technology
Tagged Business improvement, GE, Industrial economy, Internet, Machine learning, Manufacturing, Technology
Basin and Range, Author: John McPhee.
Descartes’ Error, Author: Antonio Damasio.
By Ben Medlock – Things took a wrong turn at the beginning of modern AI, back in the 1950s. Computer scientists decided to try to imitate conscious reasoning by building logical systems based on symbols. The method involves associating real-world entities with digital codes to create virtual models of the environment, which could then be projected back onto the world itself.
In later decades, as computing power grew, researchers switched to using statistics to extract patterns from massive quantities of data. These methods are often referred to as ‘machine learning’. Rather than trying to encode high-level knowledge and logical reasoning, machine learning employs a bottom-up approach in which algorithms discern relationships by repeating tasks, such as classifying the visual objects in images or transcribing recorded speech into text.
But algorithms are a long way from being able to think like us. The biggest distinction lies in our evolved biology, and how that biology processes information. Humans are made up of trillions of eukaryotic cells, which first appeared in the fossil record around 2.5 billion years ago. A human cell is a remarkable piece of networked machinery that has about the same number of components as a modern jumbo jet – all of which arose out of a longstanding, embedded encounter with the natural world.
We only have the world as it is revealed to us, which is rooted in our evolved, embodied needs as an organism. Nature ‘has built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it’,
In other words, we think with our whole body, not just with the brain. more> https://goo.gl/oBgkRF
Posted in Book review, Economic development, Education, History, Leadership, Nature, Science, Technology
Tagged Algorithm, Brain, Machine learning, Productivity, Symbolic logic, Technology