Applications of dynamic neural networks
So the architecture of dynamic neural networks is complicated – but what are its applications in the real world?
Currently, one of the top applications of the dynamic neural network is to understand social media users, already a powerful marketing tool for businesses across a range of retail sectors.
Artificial ‘neurons’ used dynamically, can not only collate huge amounts of data from the avid social media users’ devices, but can also study their behaviour patterns. This data can then be shared virtually in order to analyse the competition in a particular marketplace. Future-proofing of businesses can then be achieved by being able to predict not only social media trends but also the users’ specific habits and desires. Any edge, or advantage, in the field of social media marketing will be jumped on by business in any landscape, and dynamic neural network technology is a means to this end.
Aerospace engineering is one sector that has jumped on the new technology to improve business models. Improved fault diagnostics, which improve passenger safety, are key to any aerospace company’s success. The non-linear and pattern-recognition capabilities typical of the new functionality of these advanced computer systems can future-proof the safety and subsequently the commercial viability of these industries.
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Deep Learning
Global defence operations are another sector that are also now applying neural networks to their planning. Deep learning, as it is also known in technological sectors, is currently shaping the defences of nearly every advanced economy in the world. Logistics, object recognition, object location, automated drones, attack analysis and a multitude of other scenarios can all be enhanced with the new data.
Another crucial are where artificial neurons are enhancing the business landscape is telecommunications, an area where individuals and businesses are able to interact through technology. While recent decades have seen a massive transformation in this regard, dynamic neural networks promise even more. Again, pattern-recognition and fault management are set to transform customer and business owner experience alike.
Finally, one of the most exciting applications of deep learning is in the world of medicine. While humans can offer empathy, understanding and a certain level of expert knowledge in a healthcare situation, the new dynamic networks can not only assist in terms of diagnosis, and avert potential crises, but can also predict potential healthcare issues and solve them. Multiple hospital disciplines will benefit, not least the world of the Intensive Care Unit.
Data is practically ‘the new oxygen’. From the moment we open our eyes, we consume it, distribute it and collate it. Our smart technology has become our life support system and not simply a distraction, infiltrating our personal and professional environments in every corner.
Individuals who are unaware of the potency of their algorithms will be ill-equipped to navigate the future landscape, and businesses that do not expand their training to incorporate the deep-learning potential of dynamic neural technology will be sidelined.
Governments are defending their countries with these networks, patients are relying on them for their health and conservationists eliciting change as a result of their predictive patterning to save the planet. Dynamic Neural Network technology continues to push boundaries, and will continue to pervade every aspect of our lives, from medical diagnosis to banking, entertainment and navigation. Dynamic neural networks could make life easier for all of us, and put to work on problems such as climate change, could even save the planet – in theory.