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Last week, we held our Bridging the AI Skills Gap webinar featuring Varun Ganapathi, head of our AI and Data Engineering and Mat Leonard, Product lead for our School of Artificial Intelligence.

The conversation centered on five key areas:

  1. AI vs Machine learning vs Deep learning
  2. How companies are using these technologies today?
  3. Skills gap and talent shortage
  4. Common use cases and outcomes
  5. How to overcome the skills gap

AI, machine learning, and deep learning are easily confused and overlap with each other. The panel did a good job of breaking down the definitions:

AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

“AI is any technology that enables a system to demonstrate human-like intelligence,” explained Varun Ganapathi. “Machine Learning (ML) is one type of AI that uses mathematical models trained on data to make decisions. As more data becomes available, ML models can make better decisions.”

Watch Webinar Recording

Today, different AI technologies are finding a place in various industries. For instance, Banking and Financial Services companies are using chatbots or virtual assistants to help customers with routine tasks like scheduling payments, automate most frequently asked questions. Predictive Analytics’ to reduce the risk of loan defaulters. Machine learning to identify patterns of transactions that might indicate fraudulent activity.

The expanding applications for AI continues to create a shortage of qualified workers in the field. AI is moving fast and enterprises need talent today. However, not just any talent. What once was a shortage of coding and software engineering expertise has now evolved into an overall shortage of skills in machine learning, robotics and algorithmic engineering.

Product Lead

“If you’re considering working in AI as a data scientist or machine learning engineer you need to find a good starting point, and it starts with knowing Python, C++, and learning mainstream deep learning libraries like TensorFlow or PyTorch,“ said Mat Leonard, Product Lead at Udacity’s School of Artificial Intelligence.

AI and machine learning are driving innovation and transformation. They are embedded in how we sift through large volumes of data and content and how we interact, connect, and buy today. They are the engines underlying many of our products and services.

Hear more from Varun and Mat about steps your organization can take to embrace AI and close the skills gap. Listen now.