Generative AI Application Landscape: All That You Need to Know

As Sora disrupted the scene upon its release, anticipation brews for new generative AI applications poised to reshape markets. With the recent news of Adobe Premier Pro integrating Generative AI tools and forming partnerships with third-party integrators like Runway, Pika Labs, and OpenAI's Sora Models, people are eager to immerse themselves in this transformative generative AI application landscape and explore its potential and intricate challenges.

Llama2 Project for MetaData Generation using FAISS and RAGs

Downloadable solution code | Explanatory videos | Tech Support

Start Project

In ProjectPro's recent podcast episode, renowned industry expert Rajdeep Arora lends his insights, addressing pertinent queries concerning the Generative AI application landscape today and shedding light on the prevailing challenges data scientists encounter in this evolving domain.

Evolution of Data Science Roles with Changing Gen AI Landscape

Rajdeep Arora points out that while LLMs have been extensively utilized for tasks like content generation and searches, the fundamental responsibilities of BI engineers, data scientists, and data engineers have remained essentially unchanged.

He further explains that professionals in this field grapple with integrating this powerful technology into their current workflows. It is a complex task to identify suitable Generative AI use cases or applications to maximize their potential efficiency. The emergence of LLMs has shifted the paradigm from using technology to solve problems and identify significant issues/challenges they can address.

Code Generating LLMs in Machine Learning Project Lifecycle

Rajdeep points out significant enhancements and efficiencies that the industry is seeing from integrating LLMs and using generative AI applications. LLMs have notably accelerated the establishment of functional pipelines, showcasing their effectiveness in generative AI applications such as automating coding tasks. The key strength of LLMs in a machine learning project lies in their ability to help quickly create these pipelines. LLM fine-tuning is comparatively simpler than building pipelines from scratch, thus reducing the time required to initiate any machine learning project.

However, Rajdeep highlights that this relevancy problem, especially in light of hallucinations, is a significant hurdle for LLMs at present. Additionally, bias remains a crucial issue, and extensive research is underway to denoise bias in the LLM agents and make them neutral, which would definitely contribute to reducing the time for the deployment of a machine learning project.

Generative AI Applications and Use Cases Beyond Summarization

Further discussing the Generative AI application landscape, Rajdeep sheds light on the top generative AI use cases, with one notable area being Personalized Advertising, where targeted messaging in accordance with individual preferences has shown a substantial return on investment (ROI) for companies heavily reliant on ad revenue. You need to know what to target, whom to target, and where to target, like mobile notifications, home page, email channel, etc.

LLMs are also increasingly utilized in question-and-answer chatbots like this knowledge-grounded chatbot. These chatbots enable natural language interactions and simplify complex tasks like SQL queries. This advancement has expanded the user base by catering to non-technical audiences, enhancing business-specific solutions, and fostering greater audience engagement.

Emerging RAG-like technologies in Gen AI Application Landscape

Rajdeep highlights the approach of parameter-efficient fine-tuning (PEFT), where the model's structure is modified to tailor it to specific requirements. Techniques like LoRA and QLoRA exemplify this strategy, comprehensively adapting the model's parameters. Another technology is In-context learning, where the model parameters remain unaltered, and the focus shifts toward understanding contextual nuances through methods such as prompt engineering and RAG. 

However, amidst these advancements, challenges persist, prompting the need for robust evaluation mechanisms post-generation. This entails assessing factors like content quality, relevance, and coherence. By devising metrics to gauge these aspects, they can be applied across diverse contexts.

Challenges in Scaling LLMs: Limited Context Windows

Rajdeep points out that the challenge with many existing LLMs is that they are trained on uncurated datasets comprising vast amounts of internet text, leading to a potential disconnect between generated content and its relevance to specific contexts. Currently, these models exhibit the capacity for diverse reasoning, but their outputs often lack context-specific accuracy. 

Rajdeep explains this issue by examining generative AI business use cases, such as those encountered in retail giants like Walmart. In the context of Walmart's operations, the term "fuel" assumes a distinct meaning compared to its general usage, implying energy sources and fuel pump stations. This highlights the importance of contextual relevance in refining the outputs of generative AI models for specific business applications.

Rajdeep stresses the necessity of a curation window to refine models' understanding of the world, distinguishing between generic and organization-specific contexts. This process facilitates the creation of models tailored to specific domains, enhancing their practical applicability and minimizing irrelevant outputs.

Shift from Traditional to Generative AI?

While discussing this, Rajdeep emphasizes that skipping traditional regression and classification methods can seem tempting, but grasping the fundamental intuition behind building models is essential. Deep learning is crucial as it is the foundation for Natural Language Processing (NLP) and computer vision tasks. 

An aspiring data scientist can choose between Engineering and Linguistics. Regardless of the preferred path, one will still need a solid understanding of both aspects. Engineering skills are necessary for implementing models using standard APIs like TensorFlow or PyTorch, while linguistic knowledge helps grasp language intricacies and predictions. 

Rajdeep expresses his excitement and optimism, stating that the field offers ample opportunities for those willing to dive in. With many unsolved problems, there is ample room for innovation and relevance. These skills can be enhanced by implementing real-world LLM projects to understand the technology and its challenges better.

Innovations and Research in Gen AI Application Landscape

Rajdeep concludes the podcast by delving in-depth into the primary areas of research and innovation currently shaping the landscape of generative AI, which revolve around model efficiency, natural language understanding, task decomposition, and knowledge representation. These areas contribute to enhancing AI technologies and their real-world applications in the following ways:

1. Model Efficiency: Researchers are working on making generative AI models smaller and smarter by squeezing their size to fit into devices like smartphones and smart home gadgets. This is essential not only to make AI more accessible but also to ensure it works smoothly on devices with limited resources. Additionally, they're questioning whether massive models with billions of parameters are necessary for every task, pushing for efficiency without sacrificing performance.

2. Natural Language Understanding: One of the best generative AI use cases is the development of conversational AI assistants and chatbots that can understand and respond to human language more naturally. Researchers are teaching these models to comprehend better and generate human-like responses, making interactions with computers more intuitive.

3. Knowledge Representation: Besides improving natural language understanding, there's a significant focus on enhancing how AI represents and retrieves information. While traditional techniques have been helpful, researchers are exploring new methods, such as graph-based representations, to help AI better understand complex relationships and retrieve relevant information with more depth and relevance. Generative AI use case examples in this area include improving information retrieval, question-answering, and knowledge synthesis across various domains.

We hope this podcast provided you with current updates and insights into the generative AI application landscape and highlighted the most compelling generative AI use cases to demonstrate the efficiency and potential of LLMs. Explore other top-trending data science podcasts with industry experts on ProjectPro’s YouTube channel with industry experts. Check out the ProjectPro repository, which has over 250+ solved end-to-end hands-on enterprise-grade data science and big data projects.