Deep Learning Is Overtaking Classic Machine Learning Methods, Study Finds

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Deep Learning Is Overtaking Classic Machine Learning Methods, Study Finds

Tue, 10/29/2019 - 05:37
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AI experts at CognitionX and Peltarion release new report on where the value in Deep Learning (DL) lies, and where the trend for Deep Learning is moving, based on insight across industries from AI thought-leaders in Europe.

Peltarion, leading AI innovator and creator of an operational deep learning platform, and AI Knowledge Network CognitionX, organizer of the CogX festival of AI and emerging technology, today released a new study based on research and interviews with other AI industry leaders across Europe such as Amazon, Google, DeepMind, and JP Morgan. The report, “Deep Learning: Opportunities and Best Practice”, provides insight that deep learning is overtaking more classic Machine learning methods. However, several challenges remain; cost, complexity, and skills are yet to be solved to enable market growth.

The study is intended to serve as a primer for those with shallow knowledge about deep learning, and a guide for those with more experience. The report gives an understanding about deep learning, where its moving, and best practices from the field. Tabitha Goldstaub, Co-Founder of CognitionX comments "At a time when so many organisations are debating the risks and rewards of using AI, I'm thrilled to see this report give some practical guidance to businesses on where deep learning can be applied, along with options on how to deliver this technology and some pointers on where the technique will go in the future. This is a good starting point for business leaders who are thinking about adopting AI."

The report shares a brief history of AI in general and deep learning more specifically. It includes case stories of real-world applications from different verticals. Among them, you will find stories about:

  • Pattern-recognition in healthcare diagnostics
  • Real-time prediction of fraudulent transactions
  • Automation of complex tasks in manufacturing workflows

Still, a few challenges need to be solved for deep learning to meet its full potential. The report states “As with any large-scale IT project, deep learning projects often fail due to factors such as complexity, failure to clearly define requirements and lack of proper communication between business and technical teams.” And these challenges get even worse when tools are expensive and complex to use, if your data is not in order, and if you lack talent in your team. According to report contributor, Scott Penberthy, Director of Applied AI at Google: “We believe there are about 10,000 people in the world who really understand deep learning. There are about 100,000 deep learning practitioners and a million data scientists.”

Deep learning expert Luka Crnkovic-Friis, Co-Founder and CEO of Peltarion, argues that for deep learning to reach its full potential, it needs to be operationalized: “AI and deep learning will save millions of lives and improve the lives of billions. The technology will fundamentally impact health, food production, energy, business and creativity. But if the true potential of AI and deep learning is going to be reached, it needs to be practically accessible by innovators across the world – the many, not just the few. One of the suggested routes to making deep learning more accessible is via a platform model, which simplifies and automates many tasks and provides the capability for managing the end-to-end DL workflow in one place, with an easier transition to running these models in live production environments.”

The full report can be accessed here.