Generative Artificial Intelligence, in the evolving broader landscape of Artificial Intelligence (AI), has made a significant leap in recent years. Progress in this field has captured the attention of technologists, scientists, and researchers, further pushing the boundaries of AI and opening new avenues for businesses.
Understanding Generative AI
To understand Generative AI, one must ask, what makes it profound? It distinguishes itself from traditional AI systems that excel at tasks like classification, prediction, and automation. Its capability goes beyond mere machine understanding and ventures into the territory of creative expression. While "Generative AI" may not be a term familiar to everyone, its generated content, including text, images, and audio, has subtly been finding its way into various facets of our lives, sometimes without us even realizing it.
TCIN’s vision for Generative AI
At TCIN, we work on building connected applications that make lives easier for vehicle users. Going beyond user convenience, we aim to create meaningful touchpoints between the vehicle and the user, promoting safer and more focused driving experiences. We explore Generative AI’s vast potential to enhance our current vehicle support systems, providing real-time assistance in building a truly connected driving experience. Simultaneously, we dedicate our efforts to ensuring that our sensitive customer data remains securely within our premises. We also explore expanding its utility to non-English languages.
TCIN’s key contributions and innovation
Pursuit of innovation: TCIN works towards leveraging the transformative power of Generative AI in multiple ways, all geared towards enhancing user experiences and operational efficiency.
Synthetic Data Generation: Generative AI has opened a new world for data-driven innovation. Data lies at the heart of developing effective deep-learning models. Obtaining high-quality data has always been a persistent challenge. At TCIN, we have been exploring Generative AI to generate high-quality and diverse datasets. Augmenting existing datasets with synthetically generated data helps enhance the robustness of the existing AI models and helps with customer data privacy since these are synthetically created. Beyond data generation, we are also exploring the generation of test cases. These cases are designed to simulate a wide range of scenarios, including those that are hard to capture in a real-world setting and can be time-consuming to generate them manually.
Powering Data Analysis: Apart from data generation, this is also used by our team to analyze and detect anomalies in datasets with low-code or no-code written. This empowers our team to identify data irregularities efficiently, contributing to data quality assurance.
Call Dialogue Summarization: We have explored the power of call dialogue summarization in our call center setup. We have used Large Language Models (LLMs) to streamline conversation between a customer and an agent into concise and understandable content enabling faster feedback to agents, thereby improving the customer experience.
Enhancing Operational efficiency: While Generative AI has always been looked at and appreciated for its generative abilities, one of the undermined capabilities is it still can perform traditional AI systems tasks such as prediction and classification. In comparison, this achieves good accuracy. In our Japanese call center applications, we have seamlessly integrated LLMs to handle and categorize calls, and it demonstrates satisfactory performance. These applications are designed to cater to both English and Japanese languages, enabling our operations to enhance efficiency significantly.
Enterprise Search: While acquiring quality data has been critical, there is also the challenge of information abundance. Efficient access to relevant data is crucial for businesses to thrive. At TCIN, we have experimented with Retrieval Augmented Generation (RAG) using Large Language models (LLM) to answer relevant answers to questions in Question Answering (QA) style, that were not part of the model’s training phase. The knowledge base for these is from various documents, whether in text, tables, or PDF. We process this unstructured source, and retrieve relevant context which is then fed into LLMs to generate precise answers for the question. We have done this by leveraging the LangChain framework along with open-source LLMs.
While Generative AI demonstrates immense potential and promise, several challenges are associated with it. One of the primary challenges with Generative AI is to ensure the quality of generated content. These models can sometimes hallucinate and produce inaccurate or inappropriate outputs, which can be problematic in applications that are in production. Developing and training advanced Generative AI models requires significant computational resources that consume substantial energy. Another aspect to consider is whether Generative AI is truly creative, as it is claimed to be. These models are trained on large datasets, and the generated content we receive as output is a combination of these data created by humans, questioning the underlying creative component. At TCIN, we do not overlook these challenges, instead, we carefully examine them and diligently work to develop innovative solutions, instead.
The Future with Generative AI:
As we look ahead to the future of Generative AI, we are presented with limitless possibilities. The field continues to evolve rapidly and at TCIN, we stay committed to exploring this ever-evolving field, updating ourselves with the latest advancements, and solving real-world challenges. Our focus on data privacy and our strategic exploration of open-source Language Models (LLMs) ensure that we are at the forefront of tech innovation.
Our team constantly equips themselves with cutting-edge technology and actively looks to contribute to this space. To know more about the recent advancements in TCIN, and the Generative AI space, follow this space closely.
Our team also shares their expertise and knowledge through multiple channels. Techceleration is where we meet every quarter and discuss the evolving tech space. We talk on LLMs here