Engineering Firm to Invest $6m, Hire About 240 in Tennessee

U.S. News

The company plans to initially occupy a 60,000-square-foot (5,574-sq. It plans to have the new facility operational in January. The positions will be for engineering, management and technicians.


Penn Engineering - ENIAC: Celebrating Penn Engineering History

AITopics Original Links

Originally announced on February 14, 1946, the Electronic Numerical Integrator and Computer (ENIAC), was the first general-purpose electronic computer. Hailed by The New York Times as "an amazing machine which applies electronic speeds for the first time to mathematical tasks hitherto too difficult and cumbersome for solution," the ENIAC was a revolutionary piece of machinery in its day. It was constructed and operated here at The Moore School of Electrical Engineering, now part of the School of Engineering and Applied Science. Today, it is difficult to imagine how we could manage without the myriad electronic devices that we utilize each day. From our "smart" phones, touch screens, and tiny cameras to our automobiles, airplanes and medical equipment and devices, electronics is the engine driving us forward.


NX 12 Is Dedicated to Digitalization ENGINEERING.com

@machinelearnbot

The movement toward digital part manufacturing is part of a trend toward connecting all steps of the manufacturing process--from planning to production--with a single source of information, a so-called digital thread. By taking advantage of digitalization, manufacturers can embrace automation to achieve greater efficiency, reduced time to delivery and better production results.


r/MachineLearning - [D] Design methodology for ML engineering.

#artificialintelligence

I think you are putting your cart in front of the horse a little bit... That is before stating the problem mathematically you need to have an idea of what data is there. This means both talking to the people who already use it on a day-to-day basis in order to understand their process, and seeing where and how it is stored. Then figuring out if it is suitable for the task at hand and what the business wants. Only then can you bother with stating it as a math problem, feature engineering, etc. IMO most of your time will go into figuring out the problem, understanding the data and cleaning the data, so you should put some more attention to that.