The Future of Deep Learning

Deep learning is a type of machine learning and artificial intelligence (AI). It has taken off in the last few years and has transformed how we can recognize and process images, speech and user behavior data. Many critical developments have used deep learning, including car automation and facial recognition. Many specialists are working in the field, furthering the advances of this technology. 

The human brain operates in a non-linear pattern. Deep learning works similarly. Like the human brain, it learns from examples and imitates how humans acquire certain kinds of knowledge. It processes information in a similar way, and this means that it can do things that people do, such as learning how to drive a car. 

It uses automated predictive analytics to identify trends and customer buying patterns. When someone buys an item on a retail website, they are often shown related items that are frequently bought together. These come from predictive deep learning algorithms that use past buying patterns and the current search to offer more items that the customer might want. 

Deep learning has significant capabilities mainly because of increased computing capability, more available data and improved modeling. It requires vast amounts of data and can analyze large datasets more quickly than a human. Pioneers of deep learning have claimed that enhanced neural network architectures will eventually encapsulate all aspects of human and animal intelligence. 

What is deep learning, and how are our attitudes to data and the world changing? For those who wish to learn more about this subject, it is possible to complete an online master’s in computer science, with courses including data visualization, cloud computing, applied data science, and data mining and analysis. 

Entrepreneur and scientist

Andrew Ng is a British-born American technology specialist focusing on machine learning and AI. He is internationally known as a leader in AI and co-founded Coursera, founded DeepLearning.AI, and is an adjunct professor at Stanford University. Ng grew up in Hong Kong and Singapore and was about five years old when he learned to use BASIC programming. 

Ng believes that researchers have only touched on the potential of machines. He thinks that at some point, people will speak to their smartphones when they want something rather than tap. Computer vision technology is doing new things, but he thinks that there is much more to come. 

With self-driving cars, machine learning technology has achieved 99% accuracy, but the challenge is to reach 99.99%. Another development using deep learning consists of computer vision algorithms and wearable devices to help blind people engage with their surroundings. 

Deep learning has taken off over the last few years and has transformed how we recognize and process images, speech and user behavior data. Much has been achieved, and Ng believes that there is more progress ahead over the next several years, and the best possible things will be created for humanity.

Facial recognition technology

Baidu is a Chinese search engine. It is the fifth-most-visited website in the world. Baidu launched a service that uses AI technology to find missing persons. Individuals have to upload a photo to the website, and it will be compared with those on the Ministry of Civil Affairs and missing people charities’ databases. The technology can recognize people from old photos to identify those who went missing years earlier, even if their appearance has changed significantly. This was demonstrated when a family uploaded a photo of their four-year-old son who went missing 20 years ago and was found and reunited with them.

Research shows that many businesses will use facial recognition technology in the future. With adherence to data protection laws and privacy codes in place, companies will use facial recognition for advertising and target marketing toward their possible user base. Faces can be scanned, and attributes such as emotions and age can be determined, giving businesses crucial consumer data to improve product offerings and direct product promotions. This could be the future of targeted advertising.

Looking to the future  

The business and academic worlds have shown much interest in deep learning. Research has shown that AI continues to add value in organizations, while the use of deep learning in business is still in the early stages. However, the innovations from this aspect of AI are believed to have great potential.  

Deep learning has provided efficient ways to decrease labor costs, improve business productivity and make image product searches more convenient for consumers. A lot of money is being invested into deep learning in financial services, where it can reduce risk, detect fraud, advise investors, and automate trading. 

Deep learning has already transformed some industries, such as finance, automotive and healthcare. Companies have enormous amounts of data and require it to be understood and analyzed, which can be done accurately with deep learning.    

Whilst deep learning systems are improving as data scientists use more data to train them, there are still issues to be faced. Despite its numerous applications in many industries, it is a little slow compared to other machine algorithms. However, it is much more beneficial because of its multiple real-world applications, such as in medicine, manufacturing, and supply chains.

Conclusion

Deep learning has made its mark, and numerous businesses have benefited from this innovative technology. Despite advances such as self-driving cars and facial recognition, there is enormous potential for more. Businesses have already seen how their processes can be fine-tuned and improved, and as this technology advances, deep learning should become even more prevalent. Specialists such as Andrew Ng are exploring the possibilities and finding new ways to utilize this technology. 

If you are interested in pursuing a career in deep learning, consider enrolling on a computer science master’s degree.