In the AI era, machines are now enabled to learn and perform tasks that previously required human intelligence. Machine learning technology is now making big waves by building and automating self-taught systems to make sense of analytics and data for making informed decisions – without explicit programming. If you think an autodidact machine is impressive (or alarmingly scary) enough, think twice since you are going to learn about a system that indeed, “thinks” like the way you do – deep learning.
What is deep learning?
Deep learning, a subset of machine learning, improves and enhances itself with algorithms inspired by and mimicked how human brains function. The algorithms are officially named artificial neural networks. Programmers can “train” these networks with an immense amount of unlabelled and complex data in the format of text, image or sound. The model would then “learn” from the “experiences” from time to time and eventually, accomplish futuristic accuracy or even surpass human capabilities.
Believe it or not, the concept of deep learning models can in fact be traced back to 1943 when 2 scientists built a many-layered neural network mirroring human brains.
Why is Deep Learning Called Deep?
Deep learning is described as “deep” due to the model’s learning procedures. Every passing minute the neutral networks rapidly dig and discover new levels of data. With the increasing number of levels, the model goes deeper and deeper to improve the learning process.
How does Deep Learning work?
Simply put, deep learning models learn as if they are human beings. Through repeated teaching and training, the AI builds a feature set by itself without supervision to expect and recognize outcomes.
Here is an example for better understanding: you can train a deep learning program to tell whether a creature is a dog by providing a massive set of dog and non-dog pictures. With the training data, the AI program will utilize the information it receives to create a feature set for dogs and build a predictive model accordingly. Starting from that moment, the deep learning model would begin looking for similar patterns of pixels to differentiate dogs and non-dogs.
Deep learning vs. Machine learning
They vary in the type of data that they work with and the methods with which they learn.
Note that this does not mean that machine learning programs cannot handle unstructured data. However, generally, they go through different pre-processing stages to organize data into columns and rows. At the same time, the deep learning algorithms can handle unstructured data like text and images along with the automation feature extraction, making it less dependent on human input.
When it comes to the learning part, machine learning requires more constant human intervention and supervision to bring results. And yet, deep learning is a more complex system to be set up but minimizes human intervention with unsupervised learning thereafter.
Example of Deep Learning in Real Life
The concept and technology of deep learning are widely used in everyday life and various fields, from art, science, and finance to healthcare and sci-fi blockbusters – you name it.
Check below for some most common usage of deep learning in our daily routines.
1. Virtual Assistants
The personalized response generated by our virtual assistants is powered by deep learning technology. Thanks to the combination of AI, deep learning, natural language processing and so much more, our best friends Cortana, Siri, Alexa, and Google Assistant can now find insights and provide smart recommendations by pulling from contexts such as our metadata, previous conversations, knowledge bases, geographical location, and other databases.
This is one of the most dominant examples of how powerful deep learning’s speech and language translation function is.
2. Translations
As aforementioned, one of the mightiest functions of deep learning is to translate human speech into different languages automatically, both spoken and written. I mean, see how often you use Google Translate! This software has been particularly helpful for language learners, the travel industry, and politicians – all those that may find multilingual communication challenging in the past.
4. Self-driven, Autonomous Cars
By allowing cars to collect data on their surroundings and understand road scenarios, from different signs to road cameras and other sensors, deep learning empowers the creation of self-driven, autonomous cars.
With the large data collected every time in training, deep learning algorithms will be improved and eventually speed up the decision-making flow. Forward-looking Tesla is a keen example of the leading edge of autonomous driving technology, making use of real-world data to advance in this field.
5. Service and Chat Bots
Deep learning makes the existence of AI chatbots possible to help facilitate better customer service in the commercial world. For brands, this saves a lot of time in originally labor-intensive work of responding to tricky questions and dealing with demanding customers.
6. Facial Recognition
Due to its extremely high accuracy in leveraging large sets of data, deep learning technology is heavily used to develop facial recognition features. Plenty of sectors, including security, fitness, tech, arts, entertainment, and more, rely on this function. For example, social media giants like Facebook and Instagram build tagging mechanisms/features based on facial recognition.
However, if deep learning models first approach facial recognition tasks, they would not be able to recognize the same person with weight gain, weight loss, beard or new hairstyles, etc.
7. Shopping and Entertainment
E-commerce stores and entertainment applications are other industries harnessing the power of deep learning. All the “For You” and “You may like to watch/buy” suggestions you see when browsing Amazon, Netflix, or Spotify results from deep learning.
Deep learning technology personalizes your feed to encourage future buying and watching by analyzing your data, browsing history, and online behaviors.
8. Pharmaceuticals and Healthcare
In the preliminary stage of drug discovery and design, deep learning plays a significant role by being involved in the initial screening of drug compounds and predicting success rates based on biological data and factors.
Deep learning and machine learning are also the frontiers in making precision andcustomized medicinesbased on the particular genome and diseases. Hence, the technologies are currently the center of attention of the largest pharmaceutical companies.
Besides that, other deep learning applications includefraud detection in finance,law enforcement, and many more.
Latest update of Deep Learning
The deep learning market is expected to reach USD 179.96 billion by 2030. We can expect to see the impacts of deep learning, machine learning, and AI in companies across virtually all industries, paving the path to more exciting innovations like pizza-making robots!
Source: Grand View Research, Analytics Step, IBM
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