AI

Simplifying and Humanising LegalTech

A Norwegian legal-tech start-up developed a niche product with proprietary and a unique AI algorithm capable of identifying patterns and relationships from a sea of data. While the product’s core capability worked well, its adoption was limited to a few users. In early 2024, the company collaborated with Sahaj Software to integrate AI algorithms into its case-management process for better adoption in areas such as disputes, litigations, investigations, and transactions. Typically, the legal space in Norway did not face any...

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Leveraging Large Language Models in Real-time Ad Bidding and Placement: Possible Perspectives for Enhanced User Targeting

Written by Oshin Anand and Kulbhushan Singhal In the ever-evolving digital advertising landscape, Real-Time Bidding (RTB) is a cornerstone of modern ad placement strategies. The integration of Large Language Models (LLMs) such as GPT-4, Bing Chat, and Gemini promises to revolutionize this process. This blog post explores the potential of LLMs to enhance the RTB value chain and both the opportunities and challenges that come with this technological advancement. The integration of Large Language Models (LLMs) into the RTB process...

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Exploring LoRA - Part 2: Analyzing LoRA through its Implementation on an MLP

Source: ChatGPT+ Part 1 delves into the concept and necessity of fine-tuning pre-trained large models for specialized tasks. It introduces the conventional method of fine-tuning, where only the top layers of the model are adjusted, and highlights its limitations, particularly in terms of computational and storage demands. To address these challenges, the article shifts focus to Parameter-Efficient Fine-Tuning (PEFT) methods, specifically the use of adapter modules, as proposed by Houlsby and colleagues. These adapters are small, inserted layers that allow for...

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Exploring LoRA — Part 1: The Idea Behind Parameter Efficient Fine-Tuning and LoRA

Source: ChatGPT+ Pre-trained large language models undergo extensive training on vast data from the internet, resulting in exceptional performance across a broad spectrum of tasks. Nonetheless, in most real-world scenarios, there arises a necessity for the model to possess expertise in a particular, specialized domain. Numerous applications in the fields of natural language processing and computer vision rely on the adaptation of a single large-scale, pre-trained language model for multiple downstream applications. This adaptation process is typically achieved through a...

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Soaring to New Heights: AI, Machine Learning, and Data Strategy in the Airline Industry

Written by Ravindrababu T and mckimmer In the fiercely competitive airline industry, the race to deliver a frictionless passenger experience is on. Air travel, once merely about getting from A to B, is now a journey laden with potential touch-points where airlines can either win loyalty or lose business. The key to success lies in leveraging machine learning, artificial intelligence (AI), and a robust data strategy to not only streamline this journey but also to unlock avenues for increased revenue. The application of...

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Data-Driven Customer Insights: A New Altitude for Airline Revenue Management and Retail Pricing

The aviation industry, with its dynamic pricing and complex service delivery, presents a fertile ground for AI driven data insights to revolutionize customer experience and revenue management. Understanding customer behavior through meticulous data mining is not just a competitive advantage; it’s a strategic imperative that is increasingly separating the successful airlines from those struggling to achieve profitability. The crux of the matter lies in mining and interpreting the vast array of customer data at each touchpoint — from booking a...

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That state-of-the-art LLM embedding may not work for your use case — here is why

With LLM embedding-based applications, it’s quite easy to understand and apply semantic meaning in machine learning algorithms. For example, you can use text embedding to feed to a classifier for rating user sentiment on user feedback or use it for RAG-based semantic search, etc. We understand words by mapping them in different dimensions. For example, relative terms such as good, better and best or low carb vs high carb food. If we represent the word by rating these values in...

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Enhancing Domain-Driven Design with Generative AI

Creating robust and maintainable applications that align closely with business requirements is a constant challenge in software development. Domain-Driven Design (DDD) has been quite successful in tackling this challenge. By placing the domain at the heart of the development process, DDD empowers developers to create highly scalable, modular, and business-oriented solutions. In this article, we will examine the potential of using Generative AI in Domain Driven Design. In our experiment, we developed a time-tracking application using Generative AI tools to...

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An Agile Kickstart with Generative AI for Business Analysis

Image courtesy of Adobe Stock Leveraging artificial intelligence (AI) tools can significantly expedite the business analysis process. By utilising advanced AI models like GPT, analysts can quickly analyse problem statements, define high-level features and objectives, and generate epics and user stories. AI tools can streamline requirements gathering and save time and effort while enabling structured and organised analysis. As part of an exploratory exercise, we tried to find out how OpenAI's GPT can be leveraged to kickstart the business analysis...

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Optimising Software Engineering Practices with AI

In the world of software development, Generative AI has been a topic of both excitement and concern. Its potential to automate and enhance various aspects of the development process raises questions about its impact on the developer community. As developers, it is crucial to understand the reality behind the hype and explore how Generative AI can be effectively utilised in real-world project scenarios.In this blog series, we capture our process and learnings from an experiment to develop a time-tracking application...

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