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Table of contents
Transfer learning
ONNX architecture
Machine comprehension
Edge Intelligence
Last Updated: Mar 27, 2024

5 On-Going Trends in the Field of Deep Learning

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Ashwin Goyal
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Deep learning has been popular for quite some time now. It has brought several benefits to many businesses since deep learning has boosted the effectiveness of pattern recognition, fraud detection, and many other fields. However, it has not been very well injected in many modern organizations. There are justifiable reasons for it. Deep learning is difficult to integrate and plus, it requires a complete makeover of the technology of the organization.

However deep learning trends continue to rise and trudge into the world of business. Some of the trends that might rule 2023 are:

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Transfer learning


Transfer learning is an immensely popular technique where a model which has been designed to do one task can be modified to perform another similar task. For example, imagine that you have an object classifier which can only detect objects like trucks. Now, you can modify it to detect cars. The method combines the quick learning approach with deep learning. And since you use pre-trained the models from different open source networks, it does not require you to start from scratch.

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VUI (Voice User Interface or Vocal User Interface) is the interface that is used to enable a conversation to be set up between machines and humans. Can you think of any such interface? Ask Alexa. Yep, it may have started with cell phones, but now it has moved into our home automation systems. Deep learning algorithms like language modelling, speech recognition and translation keeps running in the background. These voice assistants are always getting modified by adding something called ‘skills’. Everything is a skill, including changing the date each day. In recent times, with the inclusion of smart lights and speakers, the VUI seems to boom in 2019 and beyond.


ONNX architecture


A programmer’s dream — the ONNX or Open Neural Network Exchange is a kind of open format that could be used to represent several deep learning models. It would be possible for AI developers to move from one model to another using ONNX. Imagine you use TensorFlow library for developing a deep learning model and now, it can only run in TensorFlow library. With ONNX, you can now have interoperability between different models and use it in a different model base too. Now, most libraries use ONNX model — a true game changer for 2019 and beyond.

Also See, Agents in Artificial Intelligence

Machine comprehension


This is what you might have thought AI domination, as in the sci-fi movies, meant. In Machine comprehension, AI models are used which provide the computer with the ability to read documents and answer questions in it. Basic task for humans, monumental for AI. Stanford Question Answering Dataset is the dataset that is being used. This is a popular field as almost all major players, from Microsoft, Google and Facebook are working on it.


Edge Intelligence


We already know about cloud computing and IoT. It has revolutionised the world of technology but how storage and extraction work. But so many connections are just messing things up. With Edge, data is no longer stored in the cloud but close to the data source. The result: there will be much less delay in the communication process and so, you have improved results coming in real time. Since IoT is bound to increase in the future, Edge Intelligence doesn’t seem far away.


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