The term “LLM” has been generating a lot of buzz on social media, with mentions often carrying an air of mystery or awe. What are LLMs exactly, and in which areas are they making a significant impact? This blog post is an attempt to shed light on these questions.

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In this blog post, we will explore the growing significance of chatbots for e-commerce, their limitations, and how GPT-3 can enhance their capabilities. We’ll discuss how GPT-3 can improve intent detection, entity extraction, and conversational flow building, and thus make chatbots more robust and efficient.

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In Part 1, we discussed how we extract text from invoices using PaddleOCR. Here we will outline the steps for retrieving relevant information from the extracted text using deep learning.

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Manual data entry presents a serious bottleneck for organizations that have a vast number of documents to process. With the coming of age of intelligent data extraction and document processing technologies (computer vision, natural language processing, machine learning, robotic process automation, etc.), they can now avail solutions that halve the labor and double the speed.

There are two major steps involved in intelligent invoice processing: text extraction from invoices and information extraction from the extracted text. We will cover these steps in a two-part series based on the intelligent invoice processing solution we built for a client.

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Graph Neural Networks (GNN) have proven their capability in traffic forecasting, recommendation systems, drug discovery, etc., with their ability to learn from graph representations. What I’m going to do here is take you through the working of a simple Graph Neural Network and show you how we can build a GNN in PyTorch to solve the famous Zachary Karate Club node classification problem.

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Scaling machine learning pipelines using PyTorch can be a pain. 

You typically start a PyTorch-based machine learning project by defining the model architecture. Then you run it on a CPU machine and progressively create a training pipeline. Once the pipeline is done, you run the same code on a GPU or TPU machine for faster gradient computations. You update the PyTorch code to load all the tensors to the GPU/TPU memory with a ‘.to(device)’ function call. Now comes the difficult part: what if you want to use distributed training for the same pipeline? You have to overhaul the code and test it to make sure nothing is broken.

Why sweat the small stuff? Let’s use PyTorch Lightning instead.

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Modality refers to how a particular subject is experienced or represented. Our experience of the world is multimodal—we see, feel, hear, smell and taste things. Multimodal deep learning tries to link and extract information from data of different modalities. Just as the human brain processes signals from all senses at once, a multimodal deep learning model extracts relevant information from different types of data in one go.

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Video tracking applications are becoming commonplace with a growing number of use cases such as perimeter surveillance, asset tracking, medical imaging, and traffic violation monitoring. One of the problems that a developer must tackle while building a video tracking application is server overload.

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Regardless of the many technological leaps made over the past decade, firms in the healthcare, insurance, and finance sectors still deal with a staggering amount of paperwork. Because of the lack of unified data platforms, it is still a common practice to use paper documents when there is a need for ad hoc data transfer between organizations. This is especially true in the case of insurance companies in India, which require you to attach medical records from hospitals with the claim request forms.

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Claim adjudication, the process of determining the financial liability of a claim by the insurance company, is quite complex and time-consuming. Adjudication can be quick if the received claim is clear to the dot, in the sense that all the information is accurate and the claim is within the limits of the policy. But, as with all things in life, this is never the case. 

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