Anyone who speaks to Siri or Google’s language assistant is already using deep learning as a matter of course. Without the technology, the voices from smartphones and speakers would not provide appropriate answers and understand our spoken language.
Deep learning is not new, but it has only been improved in the last few years to the extent that it delivers real added value and has made its way into our everyday lives for the first time. And the development in the field is proceeding at top speed. Megacorporation’s like Google are intensively researching deep learning – not only so that we can one day have human-like conversations with the Google Assistant. The areas of application are diverse.
We explain how the technology works, its strengths and weaknesses, and what areas of application are emerging today and in the future.
What Is Deep Learning?
For computers to be intelligent in the eyes of humans, they must do one thing above all: learn independently . Deep learning is thus an area of the scientific research field of artificial intelligence. More precisely: It is part of machine learning. There are many different methods to implement machine learning; one method is deep learning.
Artificial neural networks are used for information processing, consisting of an input layer, middle layers (layers) and an output layer. Information hits the input layer as an input vector, is weighted by artificial neurons in the intermediate layers, and finally, a specific pattern is an output on the output layer. The more layers an artificial neural network contains, the more complex the AI can handle.
Application Areas For Deep Learning
DL is already being used in various industries and will be found in many more areas of our everyday life in the future.
Some chatbots are already being optimized via deep learning to respond better and better to customer inquiries and relieve human customer support.
DL is used in various language assistants such as Alexa, Google Assistant or Siri. They independently expand their vocabulary and understanding of the language.
DL is already being used in some translation programs. The technology can also automatically translate dialects and images into other languages, which was impossible with previous machine learning applications that relied on structured data.
Computers can use deep learning to create text that is correct in grammar and spelling and mimics an author’s style – given enough training material. In the first attempts, AI systems made articles for Wikipedia and deceptively authentic Shakespeare texts thanks to deep learning.
Due to their independent and continuous learning, AI systems with deep knowledge are particularly suitable for detecting irregularities in system activities. In this way, you can draw attention to possible hacker attacks. By using video material, the system also makes it possible to secure particularly endangered locations such as airports better because the computer recognizes abnormalities in normal airport activity.
The ability to detect anomalies is particularly useful in the sensitive area of financial transactions. If the algorithm is trained accordingly, attacks on bank networks and credit card fraud can be warded off more effectively than before.
Marketing And Sales
Artificial Intelligence systems can use deep learning to perform sentiment analysis, they can filter customer messages (chat and email) for anger and then prioritize them to human agents.
The systems could also take independently defined measures to restore customer satisfaction and prevent termination. By evaluating the customer data collected in the CRM, systems with deep learning AI could also make predictions about how the customer will behave in the future so that targeted measures can be played out to customers who are ready to buy or to customers who are considering canceling.
Cars without human drivers being safe on the road are still a future vision. But the technology exists. It combines various deep learning algorithms: one algorithm recognizes traffic signs, for example, while another specializes in locating pedestrians.
Robots with deep learning AIs could be used in many industrial sectors. By observing a human, the systems could learn how to operate machines and then optimize themselves.
Especially in the field of industrial maintenance, there are important application possibilities. In complex systems, many parameters must be continuously monitored to ensure safety. Deep learning could monitor the dynamic systems for error-free functioning and make predictions as to which units of a system will soon need maintenance (predictive maintenance).
Deep learning AIs can scan images for anomalies far more accurately than a human eye, even a trained one. With the help of intelligent systems, diseases can be detected earlier than before on CT or X-ray images, which improves the patient’s chances of recovery.