Council Post: How Advanced Databases Can Enable Deep Learning To Address Some Of The World's Great Problems

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One of the most critical components in machine learning projects is the quality of an organization’s database management system. And as artificial intelligence (AI) continues to grow more complex, access to adequate data is an increasingly important component of a company's success.

For deep learning, forward-thinking companies must choose to upgrade to more robust and efficient databases.

As reported by the World Economic Forum, the “deep” in deep learning refers to the depth of layers in a neural network. A neural network consisting of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm.

Deep learning has the potential to solve big real-world problems, from curing diseases (subscription required) to image analysis to delivering effective cyber defense to ending traffic deaths. But in order to reach this potential, databases must evolve to meet the needs of more advanced AI algorithms.

In general terms, deep learning is a type of machine learning designed to imitate the way humans gain certain types of knowledge. And while machines are capable of processing massive amounts of data at a rate far exceeding that of the human brain—which allows them to help improve productivity, increase retention and drive revenue—sound oversight structures are needed to ensure positive results.

The evolution of next-generation AI to deep learning will require optimized, powerful databases with unlimited throughput, scalable processing power and zero latency. By integrating AI with these more optimized databases, algorithms can be used to train machine learning models, which can run other algorithms.

In addition, increasingly powerful databases can help bridge the gap between current AI models and more advanced and evolving deep learning capacity.

Companies in almost every industry are discovering new opportunities through the connection between AI and machine learning, including retail, banking, healthcare and the hospitality industry. New possibilities are emerging constantly.

With augmented systems, businesses can quickly sort a large amount of data, and leaders can gain meaningful insights from it. Indeed, there’s no avoiding the necessity of upgrading to meet growing database demand and AI infrastructure—one recent study projects the global deep learning market will grow to $526 billion by 2030.

Deep learning has recently become much more popular because of its success in many complex data-driven applications. The database community has worked on data-driven applications for many years and should continue to play a lead role in supporting this new wave.

The most effective solution for modern enterprises seeking to build deep learning solutions is to ensure their strategies begin with addressing database performance and efficiency.

Fully optimized databases are the only way to enable the deep learning applications of tomorrow—applications capable of instantly accessing and understanding data to reach conclusions and make recommendations without human intervention.

Data is increasingly becoming an organization’s—and the world’s—most important asset. In order to unlock the promise of deep learning and solve some of the world’s biggest problems, from energy production to a cure for cancer, we need to start with stronger and more efficient databases.

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