We are a data analytics company using computer vision and robotics to automate data capture. Our core technology enables our own high-performance flying robots to operate fully autonomously in indoor environments with centimeter accuracy. Our first product uses these flying robots to automate inventory counting in warehouses.

Quadrotors are capable of doing some incredible stunts, like flying through narrow windows and thrown hoops. Usually, when we talk about quadrotors doing stuff like this, we have to point out that there are lots of very complicated and expensive sensors and computers positioned around the room doing all of the hard work, and the quadrotor itself is just following orders. Vijay Kumar's lab at the University of Pennsylvania is often responsible for some of the most spectacular quadrotor stunts, but their latest research is some of the most amazing yet: They've managed to get quadrotors flying through windows using only onboard sensing and computing, meaning that no window is safe from a quadrotor incursion. When you watch quadrotors flying indoors, if you look closely, you'll almost always see a motion-capture system in the background: Arrays of external cameras mounted on the walls that work together to collect very precise positional information hundreds of times every second. With the data that a system like this provides, a computer has no problem issuing very precise commands to a quadrotor flying under remote control to get it to do just about whatever you want.

I don't think K-means clustering in R has any special meaning, whatever package you use the basic K-means algorithm remains the same.

The theory of six degrees of separation, first proposed in 1929, suggested that every individual in the world was connected to anyone else in no more than five links. Today, social networking tools and graph technology can accurately map and extract valuable insights from the relationships between various entities in a network. Networks can also be analysed by machine learning, a technique in which a computer can adapt its own algorithms. Modern manufacturing equipment has been advancing rapidly; plants are filled with sensors to monitor equipment performance. The number of sensors that allow devices to connect to the internet is growing and so too is the volume and complexity of data available to plant managers.

Deep Learning: Keras Short Video Tutorial (youtu.be)

Why we urgently need to measure AI's societal impacts How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see.

Cops are already using computers to stop crimes before they happen, academics have warned. In a major piece of research called "Artificial Intelligence and life in 2030", researchers from Stanford University said "predictive policing" techniques would become commonplace in the next 15 years. The academics discussed the crime fighting implications of "machine learning," which allows computers to learn for themselves and then solve problems just like a human. This technique will have a major effect on transport, healthcare and education, potentially bringing massive benefits as well as putting millions of jobs at risk. But in the hands of cops, AI has the potential to have a massive impact on society by allowing law enforcement to have an "overbearing or pervasive" presence.

"Artificial intelligence" is a term often reserved for the likes of Skynet in the "Terminator" films or ENCOM's mainframe in the "Tron" movie, but the reality is that AI has been around for decades-- and experts say the rumors of a forceful robotic takeover are exaggerated. Instead, they see AI as an indispensable tool for supporting humans in virtually every aspect of life, especially in commercial applications. To find out more about how AI is rolling out in business and how it might develop in the future, Business News Daily spoke to industry insiders about the evolution of artificial intelligence. Rather than serving as a replacement for human knowledge and ingenuity, AI is generally seen as a support tool for the humans using the technology. Although AI currently has a difficult time completing common-sense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could.

Grand Theft Auto may not be the first place you'd go to learn better driving skills, but researchers are now using this fictional world to train self-driving cars. A team has discovered that machine learning can extract maneuver and landscape data much faster in these virtual settings than with traditional methods. Using computer vision algorithms, the team labeled thousands of images in just a few hours, which will be used to teach autonomous cars to recognize different objects. Cars use object identification data to'learn' how to recognize objects, such as pedestrians or cyclists. Gathering this information is usually conducted by researchers, who comb through scenes from real life footage and draw borders around objects by hand.

I recently participated in a weekend-long data science hackathon, titled'The Smart Recruits'. Organized by the amazing folks at Analytics Vidhya, it saw some serious competition. Although my performance can be classified as decent at best (47 out of 379 participants), it was among the more satisfying ones I have participated in on both AV (profile) and Kaggle (profile) over the last few months. Thus, I decided it might be worthwhile to try and share some insights as a data science autodidact. The competition required us to use historical data to create a model to help an organization pick out better recruits. The evaluation metric to be used for judging the predictions was AUC (area under the ROC curve).