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Exploring Generative AI with EdgeRed

EdgeRed x Gen AI

Today, we’re diving into the captivating world of Generative AI and its implications for data analysts like us at EdgeRed. Let’s unravel the intricacies of this technology and explore its potential and limitations.


What is Generative AI?


Generative AI refers to a subset of artificial intelligence technologies that can generate new content. It leverages machine learning models, specifically generative models, to produce data that is similar to, but not exactly the same as, the data it was trained on. This can include text, images, music, and more. The AI learns the underlying patterns and distributions in the training data to create something new and original. It’s like teaching a computer to be an artist or a composer, only that the canvas or symphony is data.


What are some examples of Gen AI in real life?


Now, let's take a moment to appreciate the practical magic of Generative AI through real-life examples that are reshaping industries and even our daily lives.

  • Art and Design: Generative AI has revolutionised the world of art and design, giving rise to AI artists that can create stunning visual art pieces. Design tools such as Canva (AI Image Generator) and Envato (AI ImageGen) have embraced GenAI to empower artists and content creators.

  • Music Composition: AI is now composing music, whether in the style of Bach or the Beatles, that resonates with human emotions. These AI-generated compositions are not just novel experiments but are being used in films, games, and even as album tracks.

  • Content Creation: In the world of marketing and social media, Generative AI (such as OpenAI's GPT-3) is being used to generate creative content. From drafting promotional text to creating images for social media posts, AI is helping content creators to be more productive and innovative.

  • Personalised Experiences: E-commerce platforms including The Iconic are using Generative AI to create personalised shopping experiences. From virtual try-ons with AI-generated clothing to personalised product recommendations, AI is making shopping more interactive and tailored.


What is the difference between Generative AI and Machine Learning?


While Generative AI is rooted in machine learning, it differs from traditional machine learning, which usually focuses on predictive or classification tasks. Machine learning models take input data and give you an output, like a forecast or a category, based on that data. Generative AI, on the other hand, is all about creation. It’s like the difference between predicting the weather and actually creating a simulation of a weather pattern.


What is the difference between Natural Language Processing (NLP) and Gen AI and Large language models (LLM)?


Natural Language Processing (NLP) is a branch of artificial intelligence that deals with enabling computers to understand, interpret, and respond to human language in a meaningful way. It involves tasks like language translation, sentiment analysis, and content categorisation. At EdgeRed, we use NLP techniques to draw insights from textual data and automate processes, as seen in our survey analysis and transaction categorisation projects.


Generative AI and Large Language Models (LLMs) are subsets of AI that focus on content creation. Generative AI is known for creating new data similar to its training set, useful for tasks like data augmentation and simulations. LLMs, such as GPT and BERT, are advanced versions of Generative AI that process and generate human-like text at scale. EdgeRed applies these technologies to enhance data analysis, generate reports, and create meaningful visual content for our clients.


How Can Generative AI be Used by Data Analysts?

Data analysts can harness Generative AI in numerous ways. Some examples may include,

  • Data Augmentation: One potential application is data augmentation, where analysts can create additional data for training models, especially when the original dataset is too small or imbalanced.

  • Conduct Scenario Analysis: Another exciting use is in the realm of simulation and scenario analysis, which is invaluable for risk assessment and strategic planning.

  • Automated Anomaly Detection: Generative AI can automatically identify anomalies in datasets by learning normal data patterns, aiding analysts in detecting outliers efficiently.

  • Reporting & Presentations: Generative AI can assist in generating realistic and coherent reports or presentations, a task that traditionally takes up considerable time for analysts.


Limitations of Using Generative AI in Companies

Like any technology, Generative AI comes with its set of limitations, including:

  • Output Quality: The output quality from Generative AI is highly dependent on the quality and volume of input data.

  • Bias and Realism: There's a risk of producing biased or unrealistic data if the AI isn't well-trained or monitored.

  • Computational Resources: Significant computational resources are required to train complex generative models, posing challenges for smaller companies.

  • Copyright Concerns: Navigating copyright and intellectual property laws is crucial since generated content may infringe on existing copyrights.

  • Legal Compliance: Enterprises must ensure training data is free of copyright violations and that generated outputs are carefully checked for potential issues.

  • Expertise Requirement: Implementing Generative AI requires technical expertise as well as a comprehensive understanding of intellectual property rights to prevent legal problems.


Some examples of Gen AI at EdgeRed

Here are some pertinent examples of how we've implemented Generative AI at EdgeRed:

  • Text Summarisation: Developing a Generative AI-powered tool that automatically summarises lengthy documents or reports, saving time for analysts and decision-makers. This is added to our EdgePreso product, generating executive summaries and insights.

  • Data Augmentation: Implementing a Generative AI to augment datasets by generating synthetic data points, enhancing the robustness and diversity of training datasets for machine learning models without requiring the use of actual client data.

  • ERICA: Utilising Generative AI to develop ERICA (EdgeRed Internal Chatbot Assistant), a dynamic platform facilitating company operations and providing real-time assistance to EdgeRed employees. ERICA leverages natural language understanding and generation to support tasks such as onboarding new starters, offering support with company tools and platforms, answering FAQs, aiding in seamless team integration, generating blogs, and creating project summaries for proposals.


Generative AI is an exciting frontier for data analysts, offering immense potential when navigated with curiosity and caution. Stay tuned as we, at EdgeRed, continue to push the boundaries of what’s possible with data, proudly as part of the CSIRO AI Discovery portal.


Additionally, don't miss our moderated panel discussion on Generative AI in partnership with CBA and Konnecta this Thursday.


EdgeRed x Konnecta x CBA

Frequently Asked Questions


What is generative AI and what are some of its uses?

Generative AI refers to artificial intelligence that can generate new content or data that is similar but not identical to the training data. It includes applications such as image and voice generation, data augmentation, and content creation.

How is EdgeRed integrating generative AI into their services?

Can generative AI improve the accuracy of predictive models?

What are the potential benefits for EdgeRed's clients from using generative AI?


 

This blog is written by our team members, Erica & Monica.


About EdgeRed

​EdgeRed is an Australian boutique consultancy specialising in data and analytics. We draw value and insights through data science and artificial intelligence to help companies make faster and smarter decisions.


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