Deep learning trends driving AI development is contributing to the growth of technology

Humans have entered a phase of life where technology is considered a need. Moreover, artificial intelligence is an innovative concept that changed the world of computing. Similar to how electricity brought light to the world, artificial intelligence has already evolved the way we work and is anticipated to change the future of everything. Deep learning plays a big role in contributing to the growth of technology. In the digital era, deep learning trends driving AI development are taking centre stage.

Deep learning is a disruptive technology that is powering artificial intelligence. Are you wondering how machines are able to learn the complicated representation that is required for difficult tasks? Then the answer is deep learning. Deep learning is the trailblazing technology that is capable of carrying out complex tasks like speech recognition, giving captions to photographs, and translating text between languages. Amazing! Isn't it? Although companies are trying to reap the maximum out of deep learning technology, much of its possibilities are yet to be explored. Scientists are investigating the effectiveness of deep learning networks with the help of paradoxes. Mathematical theories also illuminate how the technology functions, allows access to various architectures and leads to major improvement. While the development in deep learning is unconditional, deep learning trends driving AI development is emerging as a new topic of discussion. Ever since the deep learning evolution began in 2012, it has contributed greatly to artificial intelligence's improvement. In this article, Analytics Insight lists some deep learning trends that are driving AI growth.

Top Deep Learning Trends Complementing Artificial Intelligence

Moving Away from Convolutional Neural Networks (CNNs)

If it wasn't for Geoffrey Hinton, we don't know when or if we might've realized the importance of deep learning. As mentioned earlier, it all started in 2012 when Geoffrey Hinton, the 'Godfather of AI,' and his team won the ImageNet Challenge with a model based on convolutional neural networks (CNNs). But there are a few issues in CNN that need to be addressed as the technology's adoption intensifies. CNN can recognize objects clearly, but comparing to the human visual system, it can't identify things when shown from different angles, backgrounds, or lighting conditions. Therefore, instead of confining into the CNN and limiting deep learning's possibilities, the technology should spread across various systems to try out new ventures.

Neuromorphic Computing to Patch the Artificial Neural Gap

In the initial days when Alan Turning was yet to frame the world 'artificial intelligence,' researchers were engaged in anonymous works to come up with a disruptive solution. As a result, they gave birth to AI. The concept of artificial intelligence is to function like humans. The major reason why humans created machines was to find a mechanism that could imitate his works. But deep learning doesn't function in that way. As mentioned earlier, deep learning's CNN is not as accurate as the human visual system. Therefore, the technology has found a substitute called 'neuromorphic computing.' Neuromorphic computing refers to hardware that simulates brain structure. They patch the gap between human expectations and the shortcomings of artificial neurons.

Addressing Deep Learning Ethical Issues

As artificial intelligence, deep learning, and many other technologies are also on the rise, it is essential to talk about ethics. Although it is easy to create a technology that is intelligent and has independent decision-making capabilities, the aftermath of its development is often ignored. What if an autonomous car fed infused with deep learning technology fails to identify a human who crosses the street unannounced? Can deep learning take accountability for that? No, they can't. Therefore, humans should address the ethical issues before they could turn things upside down.