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Deep Learning and Knowledge Engineering

Deep Learning facilitates image classification, language translation, speech recognition, and solving pattern recognition problem without human intervention.
  • འཕྲུལ་རིག་གསར་བཏོད།
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  • སྤྱི་ཟླ་དགུ་པ། 20, 2021

Deep Learning is a subset of machine learning that is based on learning and improving on its own by examining computer algorithms. Deep learning works through artificial neural networks, which are inspired by the way humans think and learn. Advances made in Big Data Analytics have enabled larger, more sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans through knowledge engineering. 

Knowledge Engineering is a subset of AI that creates rules that can be applied to data in order to enable human-like decision making. The goal of Knowledge Engineering is to enable software to make decisions that human experts (such as financial advisors) would make. 

Considered the fastest-growing field in machine learning, deep learning represents a truly disruptive digital technology. Deep Learning facilitates image classification, language translation, speech recognition, and solving pattern recognition problem without human intervention. When you provide a neural network with many labeled examples of a specific kind of data, it can detect common patterns between these examples and transform it into a mathematical equation that will help classify future pieces of information. As a result, it is increasingly being used by more companies to create new business models.

Applications

Digital Assistants Natural Language Processing is used to understand and perform requested tasks. Alexa by Amazon, for example, uses deep learning to play music. Apple’s Siri takes the complexity a step further by adapting to user patterns and preferences. For instance, consistently setting an alarm may prompt Siri to suggest the same to its user when he/she forgets to do so. 
EnergyDeep learning applications like predictive maintenance and infrared technology make it possible for industry workers to adjust their production standards based on the data they receive. 
ManufacturingPredictive maintenance can help understand when to fix equipment and machines before they break, saving time, energy, and money. 
HealthcareComputer-aided disease detection and computer-aided diagnosis. Used extensively for medical research, drug discovery, and diagnosis of life-threatening diseases such as cancer and diabetic retinopathy through medical imaging. 
RoboticsBuilding robots to perform human-like tasks. It can be used to carry goods in hospitals, factories, warehouses, inventory management and manufacturing products, amongst other uses. 

Challenges

  • Bhutan needs to develop human capital to meet the appropriate of skills. Implementation will require an understanding of Deep Learning and education about potential uses of the technology.
  • Deep Learning requires adequate quality data. 
  • Pinpointing truly relevant areas and instances of application.
  • Establishing the underlying or foundational technologies required for Deep Learning.
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