We researched the necessary and desirable/nice-to-have skills the top companies look for when hiring engineers for Machine Learning, Data Science, Big Data etc. Our motive is to keep this list updated so it's relevant.

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.

They can still have trouble understanding simple commands to play music or look up directions, though, especially in noisy places. Rather than focusing on cleaning up the audio signal that captures your voice, Israeli startup VocalZoom thinks it might be possible to make all kinds of speech-recognition applications work a lot better by using a tiny, low-power laser that measures the itty-bitty vibrations of your skin when you speak. The company, which has raised about 12.5 million in venture funding thus far, is building a sensor with a small laser that it says will initially be built into headsets and helmets; there, it will be used alongside existing speech-recognition technologies that rely on microphones in order to reduce overall misunderstandings. VocalZoom founder and CEO Tal Bakish thinks it will first be used for things like motorcycle helmets or headsets worn by warehouse workers--you might use it to ask for directions while riding your Harley, for instance. A Chinese speech-recognition company called iFlytek plans to have a prototype headset ready at the end of August.

Years ago, I was working on a project that needed to be adaptive. Essentially, the software needed to learn and get better at a frequently repeated task over time. I'd read about neural networks and some early success people had achieved with them, so I decided to try it out myself. That marked the beginning of a life-long fascination with AI. AI is a really big deal.

Everyone wants to keep ahead of the technology curve, whether it's being'in-the-know' or as a vital part of your business, to allow for effective future planning or perhaps to start building systems and products based on that technology. So how do you know what that next big thing will be? Were people betting on touch interfaces in 2002? Not me, I was in a Macromedia Flash phase and couldn't possibly imagine anything beating a vector shape moving across the screen at 12 frames per second. I am not that same guy anymore.

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.

Katherine Noyes reports in InfoWorld, "Twitter has made no secret of its interest in machine learning in recent years, and on Monday the company put its money where its mouth is once again by purchasing London startup Magic Pony Technology, which has focused on visual processing. 'Magic Pony's technology -- based on research by the team to create algorithms that can understand the features of imagery -- will be used to enhance our strength in live [streaming] and video and opens up a whole lot of exciting creative possibilities for Twitter,' Twitter cofounder and CEO Jack Dorsey wrote in a blog post announcing the news. The startup's team includes 11 Ph.Ds with expertise across computer vision, machine learning, high-performance computing, and computational neuroscience, Dorsey said. They'll join Twitter's Cortex group, made up of engineers, data scientists, and machine-learning researchers."

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.