One of the biggest problems in solving poverty worldwide is the scarcity of reliable data in developing countries. Researchers have sought to address this by combining satellite imagery with artificial intelligence to identify impoverished areas from space, the BBC reports. A team from Stanford University trained a computer system and surveyed information in five African countries. Researchers Neal Jean, Marshall Burke, and their colleagues say this method could go a long way in tracking and targeting poverty in specific countries. Burke, an assistant professor of Earth system science at Stanford, says, "The World Bank, which keeps the poverty data, has for a long time considered anyone who is poor to be someone who lives on below 1 a day."

If you thought that eBay was the place you went to just to sell off that laserdisc collection you haven't watched for ten years, it turns out you're only part right. Far from tagging itself as something catchy like'the biggest yard sale on the Internet' (go ahead, eBay, you can use it), it now decides that it really wants to join the cool kids and get into this whole machine learning stuff. It has acquired a company called SalesPredict (you'll never guess what they do!) for the catchy figure of'an undisclosed sum'. So why is eBay doing this? Well, according to venturebeat it is to improve their artificial intelligence, machine learning and data science efforts.

If you thought that eBay was the place you went to just to sell off that laserdisc collection you haven't watched for ten years, it turns out you're only part right. Far from tagging itself as something catchy like'the biggest yard sale on the Internet' (go ahead, eBay, you can use it), it now decides that it really wants to join the cool kids and get into this whole machine learning stuff. It has acquired a company called SalesPredict (you'll never guess what they do!) for the catchy figure of'an undisclosed sum'. So why is eBay doing this? Well, according to venturebeat it is to improve their artificial intelligence, machine learning and data science efforts.

You can't offend a bot. Most of them don't have enough of a personality to be offended. Not only that, few of the bots available today are smart enough to debate with anyone. There are plenty of bot development tools, an immense amount of venture capital, and a massive number of bots. But no one wants to speak to a dumbot.

In the not too distant future the smartphone of a farmer in India will not only track the weather forecasts but perhaps advise him on the next best action to take if the weather turns inclement. It might even go a step ahead and help him make a decision on locking up the best future price for his produce. A smart fishing app will learn from the past performance of fishing trips on the high seas to guide Indian fishermen on improving their catch while an intelligent learning app will coach Indian students to cope better for competitive examinations. A clever tax collection app will help the government detect sophisticated methods of tax evasion while a subsidy app will better target benefits to those who need them the most, helping plug leakages. All of these have a chance of becoming a reality in the next decade, given the recent advances in artificial intelligence if only we in India wake up and get our act right.

In 2009, Intel gave up on developing Larrabee, a homegrown discrete GPU, targeted at PC gaming systems. Now, some analysts are questioning whether Intel needs its own high-performance GPU, a driving force behind the fast-growing gaming, virtual reality, and artificial intelligence markets. GPUs aren't a big presence at the ongoing Intel Developer Forum, which is centered around the chip maker's A.I. and VR strategy. Intel is highlighting its CPUs and FPGAs (field programmable gate arrays) for those categories, and the lack of a GPU to chase the hot markets could be a hole in the company's product line. Intel has never been a leader in graphics, and it hasn't aggressively competed in the area, like Nvidia and AMD have.

Machine learning is a top priority, but there's also space for the system to be applied in the field of planning and scheduling -- perhaps not the most … Mojtaba Arvin is a computer programmer who's fascinated by artificial intelligence . Right now, he spends his time as an information technology student .

I've written a few times recently about various projects that are helping develop robots capable of learning by watching how a task is performed. The machines typically observe something like a YouTube video in their attempt to pick up the skill, but the tasks have generally been quite manual in nature, such as the correct handling of kitchen utensils. A recent study from researchers at MIT set out to test whether machines could also use a similar method for picking up something so intuitively human. The researchers were hoping to train robots to be able to instinctively predict how an encounter with a human might unfold. It's the kind of intuition that we develop subconsciously as a result of our lifetime of experience.

Have we learned nothing from Tay, the Microsoft chatbot that spewed foul racist garbage after only a few hours of interacting with trolls on Twitter? Sure, Reddit models a colloquial tone, as Sophie Kleeman at Gizmodo points out, and its many communities discuss a wide range of subjects, but it is also frequently the boneyard where all grace and decency go to die. Will OpenAI's learning systems absorb strategies for choosing careers and college majors, or only gain expertise in nihilistic lulz and platform-specific acronyms? At least, a success from the researchers on this front would break new ground: They'd have created the only brain ever to get smarter by reading Reddit.

Artificial intelligence has been tasked with many projects in its brief history, but assisting in the latest United Nations projects aimed at ending world poverty, could represent the noblest goals yet posed to the artificial thinking of machine learning. It may also be the most challenging as its measurements and assessments would be applied to the entire world. Ending world poverty by 2030 is the most ambitious of 17 global missions, planned by the United Nations in 2015. These Sustainable Development Goals will require substantial amounts of data and data analysis. Marshall Burke of Stanford University's Earth System Science explained to Quartz the need for some measure to gauge the success of the project. "How are we going to know if we've eliminated poverty if we don't collect data?