A roadmap for integrating deep learning in an enterprise

Deep learning will soon be an invisible part of every organization, says the 11th annual report on emerging technologies from The Future Today Institute. The report highlights massive increases in computational power and the availability of enormous amounts of data as two key drivers for wider adoption of deep learning within enterprises.

Photo by Franck Veschi on Unsplash

All of this seems to be in-sync with one broad consensus: this is the year AI will breakthrough into the mainstream. That said, every organization still requires a strategic road map to get to an intelligently-automated future — a future where complex decisions are made in real-time and at scale, leveraging self-developing algorithms and systems. And a foundation technology like Robotic Process Automation (RPA) could play a strategic role in enabling businesses to streamline and accelerate the journey to deep learning and real AI.

As much as RPA has proven to be a transformative technology, it is still seen as a tool to automate simple, rules-based, low-complexity processes within an enterprise. There’s no denying the cost, quality and productivity implications of RPA, but it still generally lacks the disruptive reputation afforded to deep learning and AI.

But there is a “particular opportunity with regard to RPA,” says Chris Mazzei, Global Chief Data & Analytics Officer, EY, in the race to AI adoption and intelligent automation. As Mazzei explains, combining AI’s intelligence with RPA’s operational efficiencies could help accelerate the pace at which emerging intelligent technologies are leveraged within the enterprise.

Most progressive RPA solutions already incorporate machine learning-powered cognitive components that deliver complex decision-making capabilities above and beyond simple rules-based automation. For instance, the automation toolkit from intelligent automation software provider WorkFusion includes a cognitive automation system that works with RPA to automate, and continuously learn from, more complex judgment activities.

Next-generation RPA solutions like this are designed to provide the foundation upon which businesses can integrate emergent iterations of intelligent automation technologies. These solutions are able to leverage the power of deep learning concepts such as Deep Neural Networks and NLP to augment and advance their self-learning capabilities.

Deep learning is also becoming more accessible as it slowly makes its way from research establishments into the mainstream. In fact, easy-to-use deep learning tools are predicted to become available as packaged and SaaS applications as well as function-specific libraries over the next three to five years. The open sourcing of tools, frameworks and libraries has also helped in mainstreaming the concept and accelerating research into the enterprise potential of deep learning. Today, deep learning pioneers like Google, AWS and Microsoft have all open sourced deep learning libraries, training algorithms and neural network models that make it easier for businesses to experiment with deep learning using their own data.

Even as deep learning goes mainstream — Gartner predicts that 80 percent of data scientists will have deep learning in their toolkit by 2018 — there are still challenges related to specialized requirements, in terms of technology and data expertise, that need to be addressed. But in spite of all that, it will become increasingly difficult for businesses to ignore the disruptive potential of deep learning to address complex business needs currently beyond the scope of conventional automation solutions.

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Posted from my blog with SteemPress : https://selfscroll.com/a-roadmap-for-integrating-deep-learning-in-an-enterprise/

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