In 98 percent of large building projects, logistical challenges result in cost overruns of more than 30 percent. Moreover, 77 percent of new construction projects are completed at least 40 percent late. Construction errors and risk mitigation costs contribute 10 percent to 30 percent to this figure. What's an earnest contractor to do? Tel Aviv-based startup SiteAware, which just raised a $15 million Series B financing round, offers an AI-powered Digital Construction Verification (DCV) platform that creates a "digital twin" of a building under construction. Every stage of new construction -- core, shell or interior -- is documented using drones, on-site cameras or people on the ground.

Let's Make the Learning Data Science Efficient and Impactful! Every year the number of Students, Professionals stepping into Data Science is increasing Exponentially. Even though there are plenty of resources and structured curriculums in place, Many of the beginners are struggling to learn them properly and build their skillsets demonstratable. The main reason could be random and distracted learning. In this article, we will be discussing the most effective ways to learn Data Science and its related fields.

Not a week goes by without hearing about new applications of computer vision. If you take a look at the job market for machine learning, you'll notice that there are so many companies using computer vision to do all sorts of cool things. This is thanks to deep learning! I've seen mobile apps that use computer vision to tell you how many calories you have in your food from a picture of your plate. I've seen products that use computer vision to detect ships docked in the port.

Data science is one of the most buzzed about fields right now, and data scientists are in extreme demand. And with good reason -- data scientists are doing everything from creating self-driving cars to automatically captioning images. Given all the interesting applications, it makes sense that data science is a very sought-after career. Data science is applied in many field, including in developing self-driving cars. If you're reading this post, I'm assuming that you'd like to learn how to become a data scientist.

To most of us, a 3-D-printed turtle just looks like a turtle; four legs, patterned skin, and a shell. But if you show it to a particular computer in a certain way, that object's not a turtle -- it's a gun. Objects or images that can fool artificial intelligence like this are called adversarial examples. Jessy Lin, a senior double-majoring in computer science and electrical engineering and in philosophy, believes that they're a serious problem, with the potential to trip up AI systems involved in driverless cars, facial recognition, or other applications. She and several other MIT students have formed a research group called LabSix, which creates examples of these AI adversaries in real-world settings -- such as the turtle identified as a rifle -- to show that they are legitimate concerns.

Using lidar and a healthy dose of AI, a new robot can check that building projects are going to plan. How it works: Once a construction site shuts down for the night, a small robot deployed by startup Doxel can get to work. Using lidar, it scans the site and uploads data to the cloud. There, deep-learning algorithms flag anything that deviates from building plans so that a manager can fix it the day after. Why it matters: If errors aren't noticed immediately on a work site, they can create compounding issues that take time and money to put right down the line.

As a teenager working for his dad's construction business, Noah Ready-Campbell dreamed that robots could take over the dirty, tedious parts of his job, such as digging and leveling soil for building projects. Now the former Google engineer is turning that dream into a reality with Built Robotics, a startup that's developing technology to allow bulldozers, excavators and other construction vehicles to operate themselves. "The idea behind Built Robotics is to use automation technology make construction safer, faster and cheaper," said Ready-Campbell, standing in a dirt lot where a small bulldozer moved mounds of earth without a human operator. The San Francisco startup is part of a wave of automation that's transforming the construction industry, which has lagged behind other sectors in technological innovation. Backed by venture capital, tech startups are developing robots, drones, software and other technologies to help the construction industry to boost speed, safety and productivity.

For the non-earlybird, you say that R Implementation of all projects WILL BE included if 20K is crossed. I just want to know if it will be included for the earlybird project as well. Since you are including it in the non-earlybird, you will have already generated the necessary code so it should not be difficult to include the code in the earlybird level as well. I do find that I learn better when I have an example to work from.