Deep and Machine Learning

In the face of growing digital-native competition for audiences and ad dollars, media and entertainment companies must invest in advanced technologies to remain relevant—and profitable. Machine learning (ML) and deep learning (DL) provide consumer insights, empower personalization, and optimize the customer experience. Media and entertainment brands that embrace and integrate ML/DL will achieve greater agility, efficiency, and sustainability.

Amazon and Netflix have not only served as disruptors to the status quo but also as enablers of subsequent digital-native media startups that are unburdened by the need to transform legacy operations and processes. As technology progresses, ML- and DL-empowered brands will continue to widen the gap over those that fail to adopt these innovations.

EXPLORING AI WITH UKRAINIAN MOTRIIKA DOLLS

While the emphasis of this article is primarily on differentiating ML and DL, it’s important to understand that both are subsets of artificial intelligence (AI). Without AI, there can be no machine learning or deep learning. To simplify the relationship between artificial intelligence, machine learning, and deep learning, one can think of them visually as technological nesting dolls, similar to traditional Ukrainian Motriika dolls.

We’ll start with the largest doll (AI), work our way inward to the mid-sized doll (ML), and finally arrive at the smallest doll (DL).

Artificial intelligence

The pursuit of defining machine-driven intelligence began more than half a century ago. In 1956, John McCarthy—a founding pioneer of AI — established the Dartmouth Conference to accelerate the serious development of artificial intelligence, stating:

Artificial intelligence drives the technologies that empower transformation: automation (of repetitive and error-prone processes), containerization and rendering (for scalable, on-demand content creation), and more. Artificial intelligence requires more human interaction than ML and DL due to AI’s foundational data dependency and its parental role to these subsets.

In contrast, ML and DL are learning technologies designed to replace repetitive and error-prone processes. Humans develop AI to be intelligent enough to drive these automation technologies, freeing people to focus on more productive areas such as sales, creative work, or advanced development. For insights into how AI and cloud are transforming media, explore our whitepaper, Three Technology Musts for M&E.

Continuing with our analogy, let’s move inward to the next two dolls: machine learning and deep learning. These two AI-driven technologies can be differentiated by their nature and objectives. Machine learning can be categorized as, “The objective is known,” while deep learning applies to scenarios where the objective is unknown.

ML: “Understood, I’ll take it from here.”

Three years after McCarthy’s proclamation of his vision for AI, Arthur Samuel — a pioneer in machine learning — defined ML as “a field of study that gives computers the ability to learn without being explicitly programmed.”

When exposed to data, ML can progressively adapt, much like how a child learns to crawl and eventually walk from birth. Machine learning algorithms optimize for a known objective — either to minimize error or to continually improve predictive accuracy.

DL: “Let’s find a more accurate answer — and faster.”

This subset is most closely associated with the functions of the human brain and processes vast data sets in a hierarchical manner.

Driven by graphic processing units (GPUs), DL solves more complex problems with record accuracy and is responsible for technological advances, including image and sound recognition, recommendation systems, natural language processing (NLP), and more.

Machine learning eliminates error-prone human processes, which not only improves efficiency and reduces loss but also frees talent to focus on more productive and innovative efforts. Pre-population of data in platforms, trafficking rules, and VAST tag management are areas where ML transforms efficiency.

Deep learning drives the most relevant advances in personalized user experiences through recommendation engines, chatbots, and even neuromarketing research. Read about optimizing repetitive M&E operations in our whitepaper, “Stop Watching Screens.”

In closing

Enterprise companies that are optimizing AI, ML, and DL today will gain a significant competitive advantage as these technologies evolve into industry standards within M&E infrastructure.

At SoftServe, our global team of experts is backed by decades of experience and thousands of enterprise success stories. Your journey to fully harness the potential of AI, ML, and DL is a process, but it starts with thinking big and taking small, impactful steps — today.