Construction 2.0 Embraces Technology Advances

Our industry is ripe for major disruptions, if not from within then from outsiders who see the opportunity. CII is leading a multi-million-dollar research effort supported by private firms and like-minded associations to determine how we create a new operating system for our industry. The old operating system of a plan the work, work the plan is not meeting the agile business needs of today’s world in a rapidly changing business environment.
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OTHER ON-DEMAND WEBINARS

AI and Robotic Process Automation: From Hype to Reality

Gartner

Intelligent robots have captured the attention of CIOs and business leaders by promising the ability to accelerate your digital transformation journey without the cost and complexity of traditional application development approaches. But do intelligent robots really deliver on this promise? Just how smart are intelligent robots?
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From Idea to Robotics Product in 3 weeks

The Construct

We will guide you step by step from idea to a real robotics product that you can start selling in three weeks from now. We will show you which materials to use, which parts to buy, how to program the whole robot to perform its task, and how to launch it to the world. We will use the example of Barista robot, a robot that serves coffee to the tables, made in 3 months by a couple of students with no prior knowledge of robotics.
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Improve Accuracy, Reduce Risk, & Automate Submittal Logs with Pype AutoSpecs

Contractors rely on submittals to maintain contract compliance and ensure that the project delivered is what the client intended to build. However, manually-created submittal logs often miss requirements and changes between spec versions, impacting both the quality and financial health of your project.
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Data Engineering Best Practices

Databricks

Making quality data available in a reliable manner is a major determinant of success for data analytics initiatives be they regular dashboards or reports, or advanced analytics projects drawing on state of the art machine learning techniques. Data engineers tasked with this responsibility need to take account of a broad set of dependencies and requirements as they design and build their data pipelines.
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