A Crash Course in Machine Learning and Analytics

June 8, 2017 David Vuong

No matter what industry you work in, you’ve likely been hearing about the importance, and prevalence, of machine learning and analytics. But what do they mean, and what impact will they have on the EHSQ industry? We’ve put together a list of the most insightful articles out there to help you get a better grasp on machine learning and analytics and where it’s headed in the next few years.

 

Target – How Target Figured Out a Teen Girl Was Pregnant Before Her Father Did

Introduced by Target’s statistician Andrew Poll, the retail giant can now predict when their customers are expecting a child. By assigning each shopper with a personal ID number, Target can track and analyze customers purchases to identify if, and when, they’ll be adding a new member to the family. Target is taking data mining to the next level, seemingly predicting the future and boosting revenues as a result.

Read the full article here.

 

Data Scientist – The Sexiest Job of the 21st Century

To gain a competitive edge in the tech industry, large organizations have turned to data scientists to help improve business functions. This new type of scientists can create structure in large amounts of data and make analysis to promote growth. They are a crossbreed of computer hackers, business analysts, communicators and trusted advisor. With their ability to create value and innovate business processes, data scientists are in high demand, yet short supply, and thus one of the most highly sought after position by tech companies today.

Read the full article here.

 

Beyond Automation

As technology advances, so does automation in the workplace. Machines are replacing people at an alarming rate, and we’re letting it happen. Instead, automation should present an opportunity for augmentation, where machines are used alongside people to expand, rather than diminish, their job roles and achieve more than ever before.

Read the full article here.

 

Deep Learning Will Radically Change the Ways We Interact with Technology

Deep learning, a branch of artificial intelligence, has greatly evolved in the last few years. In the past, algorithms needed to be put in place for a computer to perform a task – a labor intensive method. The emergence of deep learning has eliminated the need for individual algorithms, as these systems can now make sense of data on their own almost instantaneously, and similarly to humans, learn from experiences. Deep learning systems can identify images, recognize speech and innovate the way we conduct business.

Read the full article here.

 

What Artificial Intelligence Can and Can’t Do Right Now

Artificial Intelligence (AI) can process an input to provide an output, also known as supervised learning. While supervised learning is effective and useful, it is very time consuming as it requires huge amounts of data. AI automates tasks that humans can do within a second of thought, but struggles with the understanding of some higher levels of intelligence. While AI is useful, it can inflict harm on, and negatively impact, people.

Read the full article here.

 

The Future of Wearable Technology in the Workplace

Wearable technology can be a valuable addition to the workplace and become more than just a product for personal enjoyment. Google made headlines by creating the wearable computer “Google Glass”, with Microsoft following suit shortly after, announcing the “HoloLens”. Wearables can increase productivity and accuracy in the workplace, though must be carefully introduced to avoid negative implications.

Read the full article here.

 

Artificial Intelligence: The Impact on Jobs – Automation and Anxiety

Artificial intelligence is disrupting the labor market as computers gain the ability to complete jobs faster and more accurately than trained workers. In the past, automation was generally only used for manual work, but now these machines have the ability to perform more intricate tasks done by knowledge workers as well. Workers must now focus on acquiring new skills in order to “survive” automation.

Read the full article here.

 

To hear more about machine learning and analytics as they relate to EHSQ, be sure to come back to our blog or reach out to a Medgate representative today.

About the Author

David  Vuong

David Vuong is the Product Manager of Analytics at Medgate, where he oversees the product development roadmap for Medgate’s Analytics solution. He joined the Product Management team as the Product Manager of Business Intelligence in March 2015, where he developed a long-term plan to elevate Medgate’s Business Intelligence suite to world-class levels. Prior to Medgate, David was in the Business Intelligence industry for over five years where he led new and established best practices in data visualization and design. He has worked with clients in industries such as mining, telecommunications, and logistics. 

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