10 Applications of Deep Learning in Various Industries

You've probably heard about deep learning and how this disruptive technology is changing how computers understand and manipulate data, but do you know what it is?

Deep learning is a term for a type of machine learning based on the exhaustive processing of data. It is a form of advanced artificial intelligence in which neural network systems are used to find complex patterns in large amounts of data.

Deep learning is responsible for processing large data sets algorithmically so that the algorithm can "recognize" certain patterns in that data. It can be considered a way of automating predictive analysis through the use of a neural network made up of a large number of hierarchical levels.

Deep Learning is one of the technologies behind Artificial Intelligence and works like the human brain to create patterns and make decisions.

Deep Learning is a subset of Machine Learning behind another well-known one: Artificial Intelligence.

AI has several levels of maturity. When it works like a human brain to process data learning, it will use multiple processing layers based on data input.

As this publication from Data-Driven Science, an educational platform focused on AI, shows, "Learning" refers to the number of layers in which the data will be processed. This allows the technology to learn patterns from:

• Unstructured data: when it is impossible to identify how the data is organized. Example: conversations on social networks, SMS, and text documents.
• Unlabeled data: when the machine does not know the input of this data.

By discovering these patterns, the Deep Learning algorithm will perform tasks repeatedly, improving its results. 

This potential is already well known. One example is autonomous cars and even voice assistants. These are technologies already available on the market due to the advancement of Artificial Intelligence.

10 Deep Learning AI Applications Present in Everyday Life

If the history of Artificial Intelligence dates back to the 1950s, the same cannot be said of Deep Learning. Projects using this technology date back to 2010; since then, it has gained more space in various projects.

Therefore, according to data-driven science, we have summarized five artificial intelligence applications of Deep Learning in everyone's daily lives.

1. Speech Recognition

Siri, Cortana, Alexa, and even Q, the genderless voice assistant, all have one thing in common: They are all based on Deep Learning. This technology will allow these agents to perform tasks or services for a user based on verbal commands.

In other words, the assistants will interpret human speech. And the more people interact with these devices, the more training data they receive. This makes it possible to determine users' behavior and preferences, making it easier for them to interact with a machine.

This AI technology is improving customer service at banks, insurance companies, contact centers, fast food chains, and hotels.

2. Facial Recognition

In Deep Learning, facial recognition identifies or verifies a person from an image or video. This technology compares facial features with others in a database.

For example, many smartphones have a facial recognition unlocking feature. Based on an extensive database, Facebook can identify you in a photo on an unknown person's profile and recommends tagging you in the post.

Easy recognition is related to security actions—such as identifying criminals, validating entry to buildings and residences, or unlocking electronic devices. Still, it can also support actions in the health area (monitoring medication use or detecting genetic diseases) and marketing and retail (analyzing consumer behavior on a consumer journey).

3. Personalized Recommendations

You open Netflix and get recommendations for a new series or movie. Deep Learning algorithms will show you what content might interest you next. This recommendation feature is based on two types of filtering:

• Collaborative filtering: the algorithm recommends content based on ratings that users with the same profile as you have watched and liked.
• Content filtering: This is when the algorithm identifies films and series with the same characteristics as the content you consume on the platform.

Of course, the example here is Netflix. However, the same goes for Spotify, other streaming services, and even e-commerce stores, which can cross-reference this data to spark your interest in buying a product purchased by another consumer.

4. Diagnostics in the Health Sector

The technology is widely used in pharmaceutical and medical companies for applications ranging from diagnostics to image segmentation.

Deep Learning solutions will help doctors diagnose more accurately, predict health, and determine the best patient treatment. In addition, machines will help analyze images, such as MRI or X-ray results.

5. Identifying Fake News

Fake news is manipulated news that spreads through social media to harm people and organizations. With Deep Learning, it is possible to create classifiers that will help detect fake news, remove it, and even notify the user about it.

On the other hand, on a more positive note, the technology can also recommend news to users based on the definition of personas. This is convenient for those who need to be informed and only sometimes have time to curate what will be necessary for decision-making.

6. Failure Prediction

Deep learning can help factories accurately predict whether equipment is about to fail and plan preventative maintenance to avoid unexpected production downtime.

7. Anomaly Detection

Deep learning also makes it possible to detect any anomaly in industrial processes accurately to avoid failures and minimize risks.

8. Process Automation

This technology can help companies automate complex processes like quality control, material inspection, or pattern recognition. It is also key in the identification and classification of products that are not identical due to their morphology (for example, broccoli ).

9. Resource Optimization

Any business can benefit from the help it offers in optimizing resources and manufacturing processes, thereby reducing production costs.

10. Supply Chain Control

Deep learning allows us to automate supply chain monitoring and control, ensuring products arrive on time and in good condition.

As you can see, deep learning can generate or improve various tasks within the industry, especially process automation and product quality improvement, which exponentially increases companies' productivity.

Key Takeaways

• Deep Learning is a branch of Artificial Intelligence that can simulate a human brain.
• Technology is still new; it has been studied for 10 years.
• Deep learning can be used in many industries.

Conclusion

Deep Learning, a powerful subset of Artificial Intelligence, is revolutionizing various industries with its ability to simulate human brain functions. Over the past decade, this technology has made significant strides, finding applications in everyday life such as speech and facial recognition, personalized recommendations, health diagnostics, and fake news detection. 

As Deep Learning continues to evolve, its potential to enhance personal and professional experiences becomes increasingly evident. Embracing these advancements can offer valuable opportunities for businesses and individuals alike.