The Rise of Machine Learning and Generative AI
- Ekta Verma
The Matrix and Machine Learning: A Glimpse into the Future
In the iconic movie The Matrix, Neo becomes a martial artist by plugging electrodes into his brain and training for hours. While humans haven’t reached this level of instantaneous learning, machines have made remarkable strides in a similar direction. Just over 20 years since the movie’s release, machines can now be trained in hours to perform tasks that once seemed unimaginable.
The Evolution of Machine Learning
The concept of training machines, though formulated in the 1950s, couldn’t gain traction initially due to two primary limitations: insufficient data and inadequate computing power. The digital revolution of the 1990s and advancements in computing power, particularly with cloud technologies, overcame these hurdles. This abundance of data and enhanced computing capabilities led to the true onset of the AI revolution, transforming machines into entities capable of more than just executing specific instructions.
Understanding Machine Learning
Machine learning involves training computers to predict outcomes based on data. Consider predicting if it will rain in the next five minutes. Humans consider factors like cloud formation, wind speed, temperature, and more. Similarly, machines use data points, known as variables or features, and relationships between these points, called models, to make predictions. The output of these predictions is referred to as labels.
So, you see, if we can create a simple table with all the features as columns and the values as rows, we can make a dataset that can be used to train a computer model to predict whether it is going to rain or not. This is Machine Learning in a nutshell. Machine Learning is a subset of Artificial Intelligence (AI) that encompasses various ways and forms machines can learn and make decisions and/or predictions.
Generative AI: The Next Frontier
Despite advancements, traditional AI struggled with content creation. This changed with the advent of Generative AI, spurred by the 2017 white paper “Attention is All You Need,” that introduced the concept of Transformers.
Researchers from all over started experimenting with transformers, coming out with something called Large Language Models (LLM) that could churn out text when some text is entered. In a few years, they would go on to train computers to create art, music, all from pure text entered by a human.
Generative AI marked a significant shift in how machines interact with data. Previously, machines could only analyse and predict based on existing data. With Generative AI, they can create new content, opening up possibilities for creativity and innovation in various fields.
Understanding Generative AI and Transformers
Transformers revolutionised the field of AI by enabling models to process and generate language with unprecedented accuracy. They work by focusing on the relationships between words in a sentence, rather than processing each word individually. This approach allows them to understand context better and generate more coherent text.
Large Language Models (LLMs), such as OpenAI’s GPT-3, are built on transformer architecture. They are trained on vast amounts of text data, allowing them to generate human-like responses to text inputs. These models have been used for various applications, from chatbots to content creation, demonstrating the power of Generative AI.
Retrieval-Augmented Generation (RAG): Pushing Boundaries
The desire for human-like chatbot interactions, particularly those accessing company-specific knowledge, fuelled the development of RAG technology. Here’s how it bridges the gap:
- Structured and Unstructured Data Repository: RAG establishes a comprehensive data repository encompassing both structured data (spreadsheets, CSV files) and unstructured data (documents like PDFs and Word files).
- Vector Database: Unlike traditional relational databases, RAG utilises a unique storage system called a vector database. This database efficiently stores and retrieves information based on its semantic meaning, allowing for faster and more relevant search results.
The Mechanics of RAG
- Vector Database: Stores data in a format that allows for efficient retrieval based on similarities and relevance.
- Chatbot Engine: Processes user queries and interacts with the LLM to generate responses.
- Large Language Model (LLM): Generates human-like text based on retrieved data.
- Chatbot User Interface: The front-end interface through that users interact with the system.
When a user inputs a query, the chatbot engine processes it and uses the LLM to understand the context. It then retrieves relevant information from the vector database, that is summerised and presented as a coherent response. This approach ensures that the responses are accurate and up-to-date, addressing the limitations of traditional LLMs.
Applications and Benefits of RAG
RAG technology is particularly beneficial for corporations. It streamlines data retrieval and analysis, providing timely and accurate information without extensive manual effort. For instance, an executive seeking a sales summary can get instant results from a chatbot instead of waiting for a subordinate to compile the data. This boosts productivity and efficiency across various business functions, from managerial queries to customer interactions.
Imagine if a busy executive needs the sales summary of a particular country. A typical path for him to get the answer would be to send an email to a subordinate. The subordinate would run a few reports in the SCM software, then possibly collate the information and report back to the executive. The time taken for this could be a few hours, depending on the systems used and how fragmented the data is.
With RAG, the most relevant data is fed into the vector database daily. The executive would simply have to ask the chatbot, that would summarise this data and reply with the answer in seconds. This productivity improvement eliminates the need for human intervention.
This technology is useful in all kinds of businesses, from manufacturing to aerospace. Managers can query any data stored in the vector database, saving hours of manual effort and allowing them to focus on more strategic tasks. Customers can interact with chatbots to find out the status of their orders, eliminating the need for large call centers.
Overcoming LLM Constraints with RAG
While LLMs have revolutionised AI, they come with limitations. They are trained on static datasets, meaning they lack knowledge of events or data beyond their training cutoff date. For example, an LLM trained until June 2022 would not have information on events occurring afterward. Additionally, LLMs typically do not have access to proprietary or internal corporate data.
RAG addresses these limitations by integrating real-time and proprietary data into the AI’s knowledge base. This ensures that responses are not only accurate but also relevant and up-to-date. By leveraging a vector database, RAG can provide specific, context-aware answers that are crucial for business operations.
InspireXT's Role in Leveraging RAG
InspireXT offers a RAG chatbot called Connected Intelligence, designed to integrate with diverse data sources and provide instant, tailored information. Our services encompass identifying business needs, managing data stores, customising bot engines, and maintaining LLMs. By deploying Connected Intelligence, we help businesses enhance process efficiency and improve their bottom line with cutting-edge AI technologies.
How InspireXT Can Help
There are various aspects to implementing a RAG chatbot:
- Identify Business Needs: Understanding what information is critical for the business.
- Identify Business Data: Recognising data sources that are necessary for the chatbot to access.
- Prepare and Maintain Vector Databases: Ensuring the data is stored in a format that the chatbot can efficiently retrieve and use.
- Customise the Bot Engine: Tailoring the chatbot’s functionality to meet specific business requirements.
- Identify and Maintain the LLM: Choosing and updating the appropriate language model.
- Build Custom IT Infrastructure: Develop the necessary backend to support the chatbot.
- Deploy the Bot: Setting up the chatbot on the cloud or customer premises.
- Maintain and Upgrade the Bot Engine: Regularly updating the chatbot to ensure it remains effective and relevant.
InspireXT’s Connected Intelligence can be integrated into all kinds of data sources, allowing users to converse with the chatbot to get the information they need almost instantly and in the way they want it. By bringing in the latest AI technologies, InspireXT accelerates customers’ businesses by improving process efficiency and enhancing their bottom lines.
Do you want to know more about Connected Intelligence and how it can help you? Contact us by filling out the form. We’d be delighted to get you started on your journey with Generative AI.
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