Top 5 Deep Learning Trends That Will Dominate 2019

Deep Learning, under Machine Learning is a separate branch of IT technology, focusing on artificial intelligence. It collects data to help machines think and work like humans. The inception of neural networks has further enhanced the potentials of deep learning.

Below, we’ve top 5 Deep Learning trends you will find dominating 2019:

Biasness Influencing AI

Human biasness is itself challenging. It influences majority of decision-making models. Even, machine learning solutions have their own biases — sometimes they even compromise the integrity of data and outputs. This in return impact adversely real-world applications, including facial recognition algorithms, web-based marketing campaigns, etc.

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Secure Future of AI

Today, AI has become a reality. No more is it confined within the realms of sci-fi movies, instead it’s mainstream now tackling everyday challenges. Deep Learning has revolutionized the way we communicate with technology. Best examples of AI technology — Alexa, AmazonGo, etc.

AI Reality

Deep learning-powered robots are the future, but a distant one. Robots serving dinners, self-driven cars and drone-taxis are definitely fun, but they are not happening now, though an over-hype situation has been created, which may even urge venture capitalists to redirect their investment elsewhere, like quantum computing or 4D printing. To avoid that from happening, technology needs to understand that AI, ML and Deep Learning can be used mildly to make everyday living easier — thus, focusing on devising everyday solutions to tackle real-life challenges sounds a better option than waiting to explore the distant future of deep learning.

Importance of Audit Trails

Adaptability of AI in the strictly regulated industries is a major impediment — 2019 is marked as an year that would create AI audit trails, shedding light upon the fundamentals of how AI and Deep Learning works and reaches conclusion.

Cloud Adoption Capabilities

2019 is the year businesses will seek improvement in their infrastructural base and cloud hosting services to support machine learning and AI endeavors. As business starts innovating and working on their AI and machine learning infrastructure, more customized tools and techniques would be called for hosting cloud support for specific use cases, such as amalgamating satellite imagery with financial data to boost trading capabilities and so on.

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Source: Deep Learning on Medium