Artificial intelligence (AI) stands at the forefront of enterprise innovation, offering unparalleled opportunities for growth, efficiency, and competitive advantage. However, the journey toward AI optimization is fraught with challenges, from legacy infrastructures to strategic alignment and workforce readiness. This keynote speech will navigate the complex landscape of AI integration, presenting a comprehensive roadmap tailored for Fortune 500 companies ready to harness the power of generative AI.

In an era where artificial intelligence is not just an asset but a necessity, understanding the intricacies of Large Language Models (LLMs) has become paramount for enterprises. This session, 'Understanding and Mitigating Hallucinations in Large Language Models', offers a deep dive into the phenomenon of LLM hallucinations – a critical challenge in the deployment of AI technologies in business environments.

We will explore the mechanics behind LLM hallucinations, shedding light on how these AI models, despite their sophistication, can generate inaccurate or misleading information. From the subtlety of input-conflicting hallucinations to the complexity of context and fact-conflicting errors, we will dissect various types of hallucinations with real-world examples, including notable instances from prominent LLMs.

This talk will not only focus on the identification and detection of such hallucinations but will also present effective strategies for mitigation. We will discuss the role of data quality, model fine-tuning, and advanced techniques like Reinforcement Learning with Human Feedback (RLHF) in reducing the risks of inaccuracies. Furthermore, the session will highlight the importance of balancing the creative potential of LLM hallucinations with the need for factual accuracy, especially in high-stakes business decisions.

Attendees will leave with a comprehensive understanding of the challenges and opportunities presented by LLM hallucinations. This knowledge is crucial for enterprises looking to leverage AI responsibly and effectively, ensuring that their use of these powerful tools aligns with the highest standards of accuracy and reliability in the business world.

This presentation explores the integration of generative AI in healthcare and pharmacology, highlighting advancements in prompt engineering and its impact on decision-making. The session will examine the complexities and variability of AI responses and the difficulties in establishing a reliable ground truth, emphasizing the need for structured and reproducible outputs to support clinical and business processes efficiently.

This engaging panel discussion delves into the critical differences between proprietary and public data, emphasising the distinct advantages and disadvantages associated with each. Explore how the accessibility and vast quantities of public data facilitate robust generalisation within AI models, contrasting with the nuanced strengths of proprietary data.

Public data's accessibility and abundance offer significant advantages, enabling broad generalisation within AI models. Conversely, proprietary data boasts higher quality, enhanced control, and minimal risk of contamination, catering specifically to niche topics with detailed coverage.

Delve into the advantages of public data, its scalability, and the challenges it poses, juxtaposed against the precise and controlled nature of proprietary data. Gain valuable insights into navigating the trade-offs between the two, understanding their impacts on model performance, ethical and regulatory considerations, and innovation within the realm of AI.

This talk delves into the forefront of AI reliability, presenting sophisticated strategies that address core challenges in the field. Our focus encompasses the intricacies of hallucination prevention, the refinement of data batching processes, and the criticality of compliance in AI development. Leveraging deep insights from cutting-edge research and practice, we offer a comprehensive perspective on enhancing AI systems' accuracy and ethical integrity. This discourse is designed to equip practitioners and researchers with advanced methodologies, fostering the next wave of AI innovations grounded in robustness and responsibility.

Transformers are in high demand, particularly in industries like BFSI and healthcare, for language processing, understanding, classification, generation and translation. The parameter counts for models like GPT, that are fast becoming the norm in the world of NLP, are mind-boggling, and the cost involved in training and deploying even more so. If the vast potential for LLMs is to extend beyond the wealthiest companies and research institutions on the planet, then there is a need to evaluate how to lower the barriers of entry for experimentation and research on models like GPT. There's also a need to discuss the extent to which bigger is better, in the field of practical and commercial NLP.

This panel will look at the state of play of how enterprises are using large language models today, what their plans are for future research in NLP, and how hardware & systems builders and organizations like HuggingFace can help bring state-of-the-art performance into production in smaller, more resource-constrained enterprises and labs.