Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around
Published By: Polycom
Published Date: Jul 24, 2014
Build or buy? When manufacturers have sought new leaders, they have typically done both: grooming talent in-house and paying market rates for the best thinkers and managers.
With the growing STEM (science, technology, engineering, and mathematics) shortage, companies will be harder-pressed to buy – and keep – top technical talent. Emerging and established leaders will command premium prices and field continual offers, increasing the likelihood they’ll make frequent job changes, taking their industry and institutional knowledge with them. As a consequence, manufacturers will need to build their talent pipeline, empower knowledge workers with the tools and intelligence they need to succeed, and provide professional development opportunities that exceed what competitors offer.
This will be a marathon, not a sprint. What’s key to making the race for talent successful: virtual training environments that accelerate learning and innovation.