Indian Angel Network (IAN), Indian angel investor network, announced undisclosed investment in Gurgaon-based Staqu, an Artificial Intelligence (AI) focused research startup working in automated image understanding technology. The funding will be used to further build and democratize technology and strengthen the team. Staqu, founded in 2015, comprises of researchers and engineers as a part of its core team. Atul Rai, co-founder and CEO said, "We plan to invest this round to expand the computational strength of our VGrep Lab (AI research lab at Staqu) and fuel it with clusters of GPUs and other technical resources. Currently, we are applying our research to solve pressing problems in the e-commerce sector. In the near future, we are planning on use it to address issues across other market segments, too. Throughout the funding process, our ideas were constantly cross-questioned, which helped us set a clear vision and goals."

In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. However, both generation of new knowledge and population of already existing knowledge bases with new facts face several challenges. Most of the time knowledge bases have been manually built, resulting in a highly specialistic and time consuming activity. Nevertheless, sources of unstructured and semi-structured data are still growing at a much faster rate than structured ones, as such it could be desirable to exploit such a large non-structured sources to populate structured knowledge bases. In the Semantic Web, a major cornerstone of knowledge bases are ontologies and schemas that play a key role for providing common vocabularies and for describing and constructing the Web of Data.

By the end of this article, you'll understand how Numerai is using advances in cryptography like homomorphic encryption to allow for open participation in the problem of stock market efficiency. Over the last few years, machine learning algorithms solved big problems in computer vision. One such problem was getting an algorithm to learn how to recognize handwritten digits in the MNIST dataset. Everyone writes digits differently, so the problem was difficult for computers to grasp. When the dataset first became available in 1998, machine learning algorithms for computer vision were not very accurate.

That's just one of "five practical research problems" proposed by scientists at Google, OpenAI, Stanford and Berkeley in a paper called "Concrete Problems in AI Safety" (pdf). Others included "safe exploration" issues, or how to stop a curious cleaning robot from sticking a wet mop in an electrical socket, and "avoiding negative side effects" such as a robot breaking granny's vase when cleaning in a rush. The problems may seem a bit silly, when compared to an AI-induced doomsday, but Google researcher Chris Olah wrote, "These are all forward thinking, long-term research questions – minor issues today, but important to address for future systems." A particularly interesting portion of the paper was devoted to avoiding reward hacking, or how to stop AI from gaming its reward function. "Imagine that an agent discovers a buffer overflow in its reward function: it may then use this to get extremely high reward in an unintended way."

Data analytics firm Orbital Insight is partnering with the World Bank to test technology that could help measure global poverty using satellite imagery and artificial intelligence. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth. The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide.

AI is coming for your paintbrush too… A new iOS app, called Prisma, is using deep learning algorithms to turn smartphone photos into stylized artworks based on different artwork/graphical styles. Snap or choose your photo, select an'art filter' to be applied and then wait as the app works its algorithmic magic -- returning your stylized image in a matter of seconds, along with options to share it to your social networks. So if you've ever wanted your bedroom to resemble a rotoscope animation, or your selfie to have shades of manga, or your hopeless sketching skills not to hold back your yearning to create a web comic then Prisma is definitely the app for you… Prisma was launched only last week but has already garnered some 1.6 million downloads, CEO and co-founder Alexey Moiseenkov tells TechCrunch, on the phone from Moscow where the team is currently based. The key to this early growth is clearly the app's prominently placed social share function, which prompts users to post to Instagram as soon as they receive their processed shot. And just this week the Facebook-owned photo-sharing behemoth revealed it had more than doubled its monthly active users over the past two years -- reaching a whopping 500 million MAUs.

If you are not already, it's worth following the work of Steven Levy, a long-time technology journalist that has been covering Silicon Valley and the American technology sector for over 20 years (his work and criticism of Apple are particularly noteworthy). He now writes for Medium on its Backchannel site and recently published a long-form piece on Google and its attempts to integrate'machine learning' throughout all aspects of the company. The progress on artificial intelligence has been getting more attention recently, primarily because of comments from figures like Elon Musk and Stephen Hawking (some other great pieces on this broad area can be found here and here). But a lot of the work is taking place in massive companies like Google. Below are a few extracts from Levy's recent piece.

It's the stuff dreams are made of. A robot picks up your clothes and cleans your house for you – but at the same time – it could end up being a nightmare with the bot smashing-up your treasured belongings by mistake. Artificial Intelligence (AI) is rapidly expanding into video gaming, self-driving cars, healthcare and households, potentially transforming the world of science, medicine and technology -- and our lives. However scientists from Google Brain, Open AI, Stanford University and the University of California, Berkeley are predicting that the proliferation of AI will come at a price. In a paper titled, "Concrete Problems in AI Safety," the researchers examined the potential impacts AI has on poorly programed systems.

NOTE: This story has been revised from when it was originally published in October 2015 to reflect the latest information. Yesterday, news emerged that Google was using a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three original sources and has been updated over time, with notes where updates have happened.

Professor Russ Altman, left, and Professor Emeritus Yoav Shoham are members of a study group that will take a very long-term look at artificial intelligence. The two scientists were among the participants in a event looking at artificial intelligence's social benefits at Stanford on Thursday. As artificial intelligence emerges from science fiction to everyday life, the power to shape and direct this world-changing technology remains within society's reach. That overarching theme animated a crowd of more than 300 people at a Stanford event Thursday evening. The discussion was titled, "The Future of Artificial Intelligence: Emerging Topics and Societal Benefit."