BEIJING: China is eyeing the use of a high level of artificial intelligence and automation for its next generation of cruise missiles, a senior designer was quoted as saying on Friday. "We plan to adopt a'plug and play' approach in the development of new cruise missiles, which will enable our military commanders to tailor-make missiles in accordance with combat conditions," Wang Changqing of the China Aerospace and Industry Corp told the state-run China Daily newspaper. "Moreover, our future cruise missiles will have a very high level of artificial intelligence and automation," Wang added. "They will allow commanders to control them in real time manner, or to use a fire-and-forget mode, or even to add more tasks to in-flight missiles." China is already a global leader in the field of using artificial intelligence in missiles, Wang added, without elaborating.

BEIJING: China is eyeing the use of a high level of artificial intelligence and automation for its next generation of cruise missiles, a senior designer was quoted as saying on Friday. "We plan to adopt a'plug and play' approach in the development of new cruise missiles, which will enable our military commanders to tailor-make missiles in accordance with combat conditions," Wang Changqing of the China Aerospace and Industry Corp told the state-run China Daily newspaper. "Moreover, our future cruise missiles will have a very high level of artificial intelligence and automation," Wang added. "They will allow commanders to control them in real time manner, or to use a fire-and-forget mode, or even to add more tasks to in-flight missiles." China is already a global leader in the field of using artificial intelligence in missiles, Wang added, without elaborating.

You may recall that Google snapped up Nest back in January 2014 for US 3.2 billion. For much of the time since then, Nest has languished, a view bolstered by the recent departure of its visionary founder and CEO Tony Fadell. But with the recent release of the Nest outdoor camera, it appears Google has an ace up its sleeve. Leveraging its expertise in artificial intelligence and machine learning, Google is on the verge of creating security cameras capable of advanced scene recognition, a feat that could enable them to corner the entire market for surveillance cameras. Imagine a camera that could detect on its own when a robber had entered the house or when a falling branch had cracked a window, or that an elderly loved one had fallen down, or even that a baby had climbed out of its crib and was heading towards the stairs.

Bill Gates recently declared artificial intelligence "the holy grail of computer science." The industry has made massive strides in recent years and there are even more exciting things ahead.

Startup Civil Maps has a plan to get human-piloted cars to hasten their own demise. Autonomous vehicles like those being tested by Google, Uber, and major automakers rely on 3-D maps that record the position of curbstones and traffic lights with high accuracy. The maps are usually created by driving around in vehicles outfitted with expensive sensors. Civil Maps wants to use consumer cars as a low-cost mapping workforce instead, taking advantage of the sensors being added to premium models for advanced cruise control and crash avoidance. Those cheaper sensors can't match those in a dedicated mapping vehicle.

By combining satellite data and sophisticated machine learning, researchers have developed a technique to estimate household consumption and income. Such data is particularly difficult to obtain in poorer countries, yet it is critical for informing research and policy, and for efforts including resource allocation and targeted intervention in these developing nations. The African continent provides a particularly striking example of limited insights into economic wellbeing. According to World Bank data from 2000 to 2010, 39 out of 59 African countries conducted less than two surveys substantial enough to result in poverty measures. Surveys are costly, infrequent, and cannot always reach countries or regions within countries, for instance, due to armed conflict.

Scientists at Stanford University have found a new method in predicting poverty through the use of machine learning and satellite images. The technique could make it easier for organizations to know where across the world their aid is needed most. Also, this could help governments develop a better policy to prevent or fight poverty. Using three data sources namely daytime images, night light images, and survey data, scientists built an algorithm to predict how wealthy or poor an area is. The results of the study have been published in the journal Science. "The idea is that if we train our models right, they help us predict poverty in areas where we don't have the surveys, which will help out aid orgs that are working on this issue," explained Neal Jean, co-author of the study and a doctoral candidate at Stanford.

Kaggle is best known for running machine learning competitions. These competitions have helped classify whales in the oceans and galaxies in the sky; they've helped diagnose diabetic retinopathy and predict ad clicks. You can now instantly share and publish data through Kaggle. This creates a home for your dataset and a place for our community to explore it. Your data immediately becomes available in Kaggle Kernels, meaning that all analysis and insights are shared alongside the dataset.

You can't ignore - Technologies like Cloud computing, Internet of Things, Big Data, Artificial Intelligence and Machine Learning are creating a new kind of labor system; called Digital Labor With cognitive platforms available now, it is feasible for the machine to learn and understand the meaning of what's going around. There is no doubt that this is another industrial revolution. Many tasks we perform today will be taken up by Digital Labor. Machine Learning can significantly transform IT services, but have some objectives before you hit the road -as Machine Learning is not some magic you apply and get benefits out of it. Machine learning techniques can complement your current monitoring tools, providing root-cause analysis and real-time detection of any anomaly.

Organizations often conduct door-to-door surveys to identify people living in poverty, but the downside is that these surveys are often time-consuming and expensive. Indeed, locating impoverished environments is still a challenging process for researchers, and the availability of accurate information is still lacking. Now, in a new study, scientists from Stanford University propose a more reliable method to map poverty in areas previously void of data -- by combining satellite images and making use of machine learning. Led by Stanford computer science doctoral student Neal Jean, researchers sought to determine whether the combination of high-satellite imagery and machine learning -- the science of designing algorithms that learn from data -- could predict estimates of areas where impoverished people lived. Specifically, they extracted information about poverty from these satellite images, and built upon previous machine learning algorithms to detect impoverished areas across five countries in Africa.