The Rise of Deep Learning
2018 saw the rise of deep learning as it evolved into one of the most discussed topics of the year. This was down to the amazing advances it facilitated in a variety of applications, ranging from traditional computer science fields such as computer vision to the unlikeliest of contributions in digital marketing. Continuing forward, as we step into 2019 with an increasing awareness of big data, deep learning will continue to play an increasingly tangible role in our lives.
Deep learning is a type of machine learning that mimics the way the human brain learns through algorithms called neural networks. These neural networks can contain thousands of neurons packaged in multiple layers. Despite their relatively recent rise to popularity, neural networks are an old concept dating back to 60s. Back then, they were merely an academic concept applied to sample problems and unable to solve anything meaningful due to the vast computational resources. Their recent rise to fame has been mainly motivated by the massive rise in high power computing clusters, especially GPU clusters, that have finally made training deep neural networks feasible.
In 2019, experts predict that we will continue to see deep learning and machine learning continue to play an important role in a variety of fields. Additionally, we will continue to see businesses and organizations making use of deep learning to gain an advantage over their competitors. As a result, HPC hardware consisting of CPU and GPU clusters will play a bigger role for companies to retain this advantage and leverage deep learning to its maximum potential. Here are some of the important applications we will see deep learning continue to play a major part in.
Digital Assistants and Smart Devices
Digital assistants like Google Assistant, Alexa and Siri are heavily reliant on deep learning to understand a user as well as to provide a meaningful response in a natural manner. Furthermore, we are seeing an increasing trend of these assistants being heavily integrated into a wide range of devices ranging from cars to microwaves. Especially with the advent of smart devices and the internet, these digital assistants will continue to get smarter and more useful in 2019.
Translation services such as Google Translate have improved tremendously over the last few years, mainly thanks to new innovations in deep learning. These improvements can be traced back to the start of the use of deep recurrent neural networks that showed remarkable efficacy in being able to translate languages. Recent improvements in deep learning algorithms coupled with the availability of more data will see machine translation continue to improve.
Images make up a huge chunk of data on the internet, and thanks to deep learning, it is easier than ever to recognize and classify them. Deep learning introduced a major innovation in computer vision through the use of convolutional neural networks, a particular neural network architecture that specializes in dealing with image data. However, images also tend to be quite large and processing them is computationally expensive, which makes it important to utilize GPUs to speed up the training process and keep training times feasible.
Advents in deep learning also played a major role in bringing us closer finally realizing the dream of autonomous cars. Deep learning algorithms thrive in data-rich environments and the large number of sensors and cameras on autonomous cars makes them ideal for this application. From recognizing objects in a car’s path to making safety critical decision, deep learning will continue to play an important role as we move towards completely autonomous vehicles.
From helping marketing professionals gauge the effectiveness of their campaigns to generating songs and images for marketing through Generative Adversarial Networks, deep learning is playing a role in revolutionizing the unlikeliest of professions. The accurate predictions offered by deep learning models makes them great at predicting customer demand, customer satisfaction and the possibility of churn. In 2019, machine learning and deep learning will be an invaluable asset for the modern marketing professional to keep their services competitive.
Deep learning is playing a major role in helping businesses improve their customer services. Chatbots are probably the biggest example of this. Trained on large volumes of conversational data, chatbots can not only understand requests but also guide customers and resolve their problems in a remarkably human-like manner. Not only does this save valuable customer time but also brings down costs for the business. In 2019, we will see more businesses take this more efficient avenue towards better customer service.
If you felt your Spotify and Netflix recommendations have been getting uncannily good, you can thank machine learning. Deep learning has been playing a major role in understanding consumer behavior and making apt recommendations to help them make choices for products and services. Not only does this apply to media consumption, but also internet commerce, with giants like Amazon and AliBaba investing heavily in deep learning to provide meaningful recommendations for their users.
Deep learning has finally allowed robots to step away from their conventional procedural programming and closer towards true artificial intelligence. Deep learning not only allows robots to perform tasks, whether it is in car plant or in military applications, but also improve and learn over time to do them better.
Colorizing Videos and Images
Machines can finally show off their creative flair thanks to deep learning. In 2017, we saw the first use of deep generative adversarial networks being used to color footage from World War 1 in a remarkably realistic manner. These services are becoming increasingly common and a favorite amongst the older generation who can finally see their old black and white photos in color.
Lastly, deep learning has been playing an important role in advancing medical diagnosis and research. Deep learning models have shown remarkable efficacy in diagnosing diseases from medical image data, even surpassing medical experts in some cases. Deep learning models are also contributing to improving the time-consuming process of synthesizing new drugs, not only producing results faster but also opening up new paradigms for drug researchers.