Artificial intelligence is an innovative concept that has changed the world of computing. Just like electricity brought light to the world, AI has already changed the way we work and is expected to change everything in the future. Deep learning plays an important role in technological progress. In the digital age, deep learning trends will surface, leading to the development of artificial intelligence.
Deep learning is a revolutionary technology that benefits artificial intelligence. It is an innovative technology that can perform complex tasks such as speech recognition, subtitle generation and text translation between languages. Although companies are trying to make the most of deep learning technology, much of its potential is yet to be discovered. Researchers are using paradoxes to test the effectiveness of deep learning networks. Mathematical theories are also shedding light on how the technology works, allowing for different architectures and leading to important breakthroughs. While the evolution of deep learning is unconditional, deep learning trends leading to the development of artificial intelligence are becoming a new topic of discussion. The evolution of deep learning began in 2012 and has contributed significantly to the development of artificial intelligence.
Top deep learning trends complementing AI
Moving away from convolutional neural networks (CNNs)
If it wasn’t for Geoffrey Hinton, we don’t know when, if ever, we would have realized the importance of deep learning. As mentioned above, it all started in 2012 when Geoffrey Hinton, the “godfather of artificial intelligence”, and his team won the ImageNet Challenge competition with a model based on Convolutional Neural Networks (CNNs). However, there are some issues with CNNs that need to be addressed as the technology evolves. CNN can identify objects, but compared to the human visual system, it cannot identify things seen from different angles, against different backgrounds, or in different lighting conditions. Instead of getting stuck with CNNs and limiting deep learning, we should spread this technology to different systems so that we can try new experiences.
Neuromorphic computing to patch the Artificial Neural Gap
In the early days, before Alan Turning created the world of “artificial intelligence“, scientists were working on anonymous papers to find innovative solutions. The result was an artificial intelligence designed to behave like humans. The main reason humans created machines was to find a mechanism to mimic their function. But that’s not how deep learning works. As mentioned, a deep learning CNN is not as accurate as a human visual system. So the technology has found a replacement, called “neuromorphic computing”. Neuromorphic computing refers to hardware that simulates the structure of the brain. They bridge the gap between human expectations and the shortcomings of artificial neurons.
Addressing ethical issues in Deep Learning
As artificial intelligence, deep learning and many other technologies proliferate, it is important to talk about ethics. While it is easy to create smart technology capable of making autonomous decisions, the implications of its development are often overlooked. Therefore, people need to address the ethical issues before they start tipping the scales.