"The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide." Those are the opening words on the website of Stanford University's Sustainability and Artificial Intelligence Lab, and its researchers have come up with an unusual -- and effective -- way to map and predict the distribution of poverty; their method combines high-resolution satellite imagery with machine learning. The researchers explain their methodology, which they call "cheap and scalable," in a video. The study, titled "Combining satellite imagery and machine learning to predict poverty," was published in the journal Science.

Identity management professionals have seen significant changes in the industry in recent years, and the pace of change is only accelerating. In the past, workforce Identity and Access Management (IAM) evolved to keep up with a growing need for access to workforce systems. Now Customer Identity and Access Management (CIAM) is critically important as businesses undergo digital transformation and engage with customers across multiple apps and channels. Even before the dust has settled with CIAM, the Internet of Things (IoT) is entering the scene with a need to manage and connect human and device identities. Just on the horizon is Artificial Intelligence (AI), which could be the next big area where IAM plays a significant role.

Is AI Stealing Your Job? New Chinese Cruise Missiles to Have'High Level of Artificial Intelligence'

Wang's department is said to have developed a large family of cruise missiles for the Chinese military. He added that China was a global leader in the artificial intelligence field. In March, China announces 7.6 percent defense budget rise, lowest in six years, reaching 146 billion.

After receiving widespread criticism for their Teen Talk Barbie that lamented, "Math class is tough," Mattel is stepping up their game by releasing Hello Barbie, full name Barbara Millicent Roberts, the first Barbie with artificial intelligence. Their goal is to create a toy that seems more lifelike because of its ability to carry on a conversation with kids. Whereas Teen Talk Barbie, and other previous talking Barbies, simply selected a phrase at random from a small database of possible phrases, Hello Barbie knows 8,000 lines of dialogue. Even more impressive, she selects certain phrases based on what kids are saying to her or asking her. How it works The secret is in Barbie's belt buckle which actually doubles as a button that can activate speech recognition software.

A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers said on Thursday. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 U.S. a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognises signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

Elon Musk has not been shy about his trepidations regarding the onset of artificial intelligence. The billionaire co-founder of PayPal pypl and CEO of Tesla tsla and SpaceX has often aired his misgivings about the technological advancement. He's even backed a non-profit research organization, Open AI, that aims to ensure the tech is developed ethically and safely. Now, in a video teaser shared exclusively with Fortune, Musk clarifies what he deems is the "biggest risk" that AI poses to humanity. The clip is a short segment from Lo and Behold, the latest film by German filmmaker Werner Herzog, due out this week. In it, Musk conjures a dystopian future usurped by profit-seeking AI warmongers.

Earlier this year, Adobe took the wraps off its new Adobe Marketing Cloud, touting new data science capabilities like Adobe Analytics' Segment IQ, which uses machine learning to help marketers gain deep insight into audience segments. On Wednesday, Adobe advanced Segment IQ another step with the release of Segment Comparison for Analysis Workspace. Segment Comparison for Analysis Workspace is the first in what Adobe promises will be a series of audience analysis and discovery tools within Segment IQ. It uses machine learning techniques to perform automated analysis on every metric and dimension to which you have access. Nate Smith, senior product marketing manager, Adobe Analytics, says this allows Segment Comparison to uncover the key characteristics of the audience segments that are driving your company's KPIs.

Humans are fairly sophisticated when it comes to understanding the complex meanings beneath the spoken or written word. For example, we can tell that a statement like, "My car had a flat. Brilliant!" is sarcastic, not actually brilliant. And with the help of machine learning, computers are beginning to get better at reading between the lines of our tweets, Facebook updates, and email messages, resulting in a new kind of analytics: sentiment analysis. Sentiment analysis, also known as opinion mining, seeks to determine the attitude of an individual or group regarding a particular topic or overall context – be it a judgment, evaluation, or emotional reaction – from text, video, or audio data.

In acoustic modelling Artificial Neural Networks can be used as an alternative approach to Hidden Markov Models for phoneme recognition. A pre-processed feature vector is fed into the input layer of a neural network. The goal is to correctly match different phones to phonems, which can then be further processed in the language model. The dynamic nature of speech is an impairing factor when using artificial neural networks for phonem recognition. Traditional neural networks require the phones to be perfectly aligned in time to allow for flawless allocation.