There was news recently in bloomberg about how google was able to cut electricity usage in its datacenter by using an AI scheme made by DeepMind (of AlphaGo fame). Earlier this week, i decided to make a quick-and-dirty implemetation in python and share it here for anyone interested in a practical example of what exactly they did. First lets take a quick look at why one would want to make such a thing... Datacenters (and indeed any other large scale structures that use a lot of energy) need to be carefully optimized for efficiency as even a 10% - 15% saving on the electricity bill can add up to millions of dollars a year. The biggest challenge here is that even though there are certain simple steps that anyone can take to reduce energy use (don't use a very low server room set-point, use free-cooling when possible, etc…) one can never actually predict quantitatively what the effect of changing variable x by z% will have on total consumption. This is because there simply are too many variables that affect the net consumption of a datacenter (chillers, AHUs, compressors, condensers, fans, outside conditions, latitude, etc…) and its impossible to actually write down a formula that can quantify all these relationships. However, as long as you have a lot of data, ML is perfect for learning complex relationships between multiple features and outcomes.

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.

In the year 1820, a person could expect to live less than 35 years, 94% of the global population lived in extreme poverty, and less that 20% of the population was literate. Today, human life expectancy is over 70 years, less that 10% of the global population lives in extreme poverty, and over 80% of people are literate. These improvements are due mainly to advances in technology, beginning in the industrial age and continuing today in the information age. There are many exciting new technologies that will continue to transform the world and improve human welfare. Here are eleven of them.

The cities of the future will be huge and super-dense -- but will they also be alive? Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence? People have speculated before about the idea that the Internet might become self-aware and turn into the first "real" A.I., but could it be more likely to happen to cities, in which humans actually live and work and navigate, generating an even more chaotic system? It's either my worst nightmare or the dawn of a wonderful new future, but scientists are… Read more Read more As cities become more networked and their mixture of urban infrastructure and surveillance infrastructure becomes more complex, eventually we'll have to build cities that can think for themselves. People have speculated about the potential for computer systems to help in urban planning forever, including papers about the use of "fuzzy logic" to automate the decision-making process and A.I. solutions for land use planning, and the an A.I. "spatial decision support system."

In the year 1820, a person could expect to live less than 35 years, 94% of the global population lived in extreme poverty, and less that 20% of the population was literate. Today, human life expectancy is over 70 years, less that 10% of the global population lives in extreme poverty, and over 80% of people are literate. These improvements are due mainly to advances in technology, beginning in the industrial age and continuing today in the information age. There are many exciting new technologies that will continue to transform the world and improve human welfare. Here are eleven of them.

As more and more enterprises master the basics of business intelligence reporting and descriptive analytics, the real value from analytics is moving into more advanced territory, like predictive and prescriptive analytics. The problem, particularly for businesses that sell analytics-based products, is how to explain this value to customers. "In some instances, people get what we do in a flash," Boris Savkovic, lead data scientist at BuildingIQ, wrote in an email interview. "In some cases, we have a lot of educating to do." BuildingIQ, based in San Mateo, Calif., is a software-as-a-service company that helps building managers monitor and adjust facilities' heating and air conditioning to improve efficiency and reduce costs. The product is built around advanced machine learning algorithms that factor in historical energy use data, weather forecasts, data streaming off buildings' HVAC systems and energy cost data.

There was news recently in bloomberg about how google was able to cut electricity usage in its datacenter by using an AI scheme made by DeepMind (of AlphaGo fame). Earlier this week, i decided to make a quick-and-dirty implemetation in python and share it here for anyone interested in a practical example of what exactly they did. First lets take a quick look at why one would want to make such a thing... Datacenters (and indeed any other large scale structures that use a lot of energy) need to be carefully optimized for efficiency as even a 10% - 15% saving on the electricity bill can add up to millions of dollars a year. The biggest challenge here is that even though there are certain simple steps that anyone can take to reduce energy use (don't use a very low server room set-point, use free-cooling when possible, etc…) one can never actually predict quantitatively what the effect of changing variable x by z% will have on total consumption. This is because there simply are too many variables that affect the net consumption of a datacenter (chillers, AHUs, compressors, condensers, fans, outside conditions, latitude, etc…) and its impossible to actually write down a formula that can quantify all these relationships.

FILE - In this Tuesday, July 26, 2016, file photo, Elon Musk, CEO of Tesla Motors Inc., left, discusses the company's new Gigafactory in Sparks, Nev. On Wednesday, Aug. 3, 2016, Tesla reports financial results. SAN FRANCISCO - Elon Musk is gleefully pushing the technological envelope in the arenas of rocketry, transportation and solar energy. But when it comes to much-hyped and coming promise of artificially intelligent machines, the man at the helm of Tesla, SpaceX and SolarCity has deep concerns. In a video clip released Wednesday to Fortune magazine from German documentarian Werner Herzog's upcoming film about the Internet, Lo and Behold: Reveries of the Connected World (premiering Aug. 19), Musk is subdued as he explains how AI could pose a significant threat even if such technology isn't in the hands of dictators and criminals.

Google was using less than 40% less energy to cool a handful of its data centers with the push of a button using artificial intelligence called Deep Mind. Google flagged the use of artificial intelligence as a major achievement pointing towards how artificial intelligence can be used to make data centers, power plants, energy grids and manufacturing plants more efficient. As these huge, energy-intensive operations use power more efficiently, fewer greenhouse gases are emitted. "We're really thrilled about the environmental impact," said Mustafa Suleyman, who leads applied AI at Google DeepMind, a group of London researchers behind the project. Suleyman said that DeepMind quickly succeeds in other facilities like power plants which were beyond his expectation.

A solar-powered spy drone that can sit in the sky for 45 days at a time is to become the latest futuristic kit to be given to Britain's special forces. Described as a'psuedo-satellite', the cutting-edge drone can fly at more than 70,000ft – twice the altitude of a commercial airliner – so high that it is not affected by the weather. The 4.3 million ( 5.6m) Star Trek-style Zephyr S will be used by the elite forces to track terror targets worldwide. It flies by day on solar power which also recharges its lithium-sulphur batteries to power it by night. With a wingspan of 22.5m (74 feet), it can be launched by four military personnel on their shoulders.