Day 1

In recent months, the landscape of Generative AI has undergone a transformative shift, challenging the long-held dominance of Big Tech giants. This talk delves into the rapidly innovating world of open-source LLMs, highlighting how they are reshaping the field. The latest advancements in both model development and infrastructure are not only democratizing access to generative AI but also presenting cost-effective alternatives to proprietary systems.

Join us in this session, tailored for businesses looking to leverage the power of GenAI, as we unravel the intricacies of building and scaling an in-house Generative AI platform. We will compare the financial implications, performance metrics, and long-term viability of proprietary versus open-source solutions.

This session would be a journey exploring how machine learning, a powerful tool, is enhancing the field of neurotech and contributing to advancements in healthcare. It will delve into real-world applications where machine learning assists in analyzing brain scans, potentially leading to earlier diagnoses and improved treatment and surgery options. It will cover recent research and experiments utilizing machine learning models, offering us a glimpse into the potential of this technology. Additionally, it will compare and contrast how machine learning’s impact on healthcare differs from its contributions in other areas, highlighting its unique potential to improve human lives. This session promises to be informative and thought-provoking, offering valuable insights into the exciting intersection of AI and neurotechnology.

The session emphasizes the strategic alignment of competency skill sets with the ever-changing technology landscape. The synopsis underscores the importance of staying updated by learning the latest advancements, including LLM (Large Language Models) and VLM (Vision Language Models) with Generative AI Evolution. This approach aims to cultivate competencies that effectively tackle contemporary challenges, ensuring professionals stay pertinent in the swiftly evolving digital era. As individuals, the key focus is on intelligently navigating dynamic career paths, remaining at the forefront of innovation, and adeptly contributing solutions to the intricate challenges present in today’s professional landscape.

In the dynamic world of investment firms, the investment committee’s role in evaluating potential investments is pivotal. The foundation lies in meticulously prepared investment memos by advisors. Traditionally, this demanding process involves extensive due diligence, analyzing company documents, extracting insights, and market research. This research explores Large Language Models (LLMs) as tools to enhance efficiency and accuracy. LLMs streamline data synthesis from varied sources, aiding in generating investment hypotheses, memo drafting, and precise Q&A support. Preliminary results show a twofold efficiency boost in tasks from due diligence to committee reviews and an 85% accuracy rate in responses. This signifies LLMs’ potential to transform manual investment decision-making in firms.

This research highlights the integration of Deep Reinforcement Learning (DRL) with Deep Deterministic Policy Gradient (DDPG) in Adaptive Cruise Control (ACC), a key part of Advanced Driver Assistance Systems (ADAS). Using a simulation environment featuring a lead car and a Battery Electric Vehicle (BEV), the DRL controller manages the BEV’s velocity, addressing multi-objective ACC goals. Driving objectives like tracking accuracy, ride comfort, and safety are translated into reward functions for the DRL. Comparing it with Model Predictive Control (MPC) in highway scenarios, the DRL excels in ride comfort while matching MPC in accuracy and safety. Moreover, the DRL exhibits robustness and efficiency across various driving scenarios, showcasing superior performance and strong adaptability.

In the year 2023, we saw the rise of a new industrial revolution – Large Language Models (LLMs). Bringing in a step function growth in how we process information, provide support to customers, and even build applications. We saw a glimpse of what Artificial General Intelligence (AGI) could be with autonomous agents working together. Following the hype of LLMs was not AGI but RAG, Retrieval Augmented Generation.

So, what is RAG?
If LLM is the most powerful car engine, RAG is the wheels.
You can’t reach your destination with the most powerful engine without converting the mechanical energy of the engine to the kinetic energy of the wheels as motion.

With the current LLM architecture, the knowledge of the world and the understanding to process the information are embedded in the network in an intertwined manner, which means the model’s view of the world is static and not easy to refresh without training. This is a big limiting factor.

Even if we choose to refresh LLM’s knowledge of the world, LLMs are not good at information recall (or retrieval) because they learn a compressed probabilistic view of the world. The fragmented information storage can’t provide retrieval. LLMs, being generative models, will always generate based on existing understanding, which leads rise to the problem of hallucination.

Retrieval Augmented Generation (RAG) is a technique of providing LLMs with context relevant to user queries to help LLM ground answers and provide citations.

But, building a RAG application hasn’t been easy, it is a continuous cycle of build -> learn -> improve.
A lot of this is because of the paradigm shift in how we build applications. Our programming language has changed from structured programming languages to unstructured language of English. This directly impacts how we process data and build execution flows, requiring us to rethink and build new mental models.

In this talk, I will take the first principles approach to break down the shift we are seeing, how we are building RAG applications, and leave the audience with thoughts to think about while building RAG application themself.

This research delves into lookalike modeling’s methodology, seeking congruent traits among novel customers mirroring high-value ones. Targeting expanded market reach, it pinpoints potential customers keen on products or services. Employing a designated subset (seed-set) of high-value customers, machine learning uncovers commonalities. Leveraging job-related attributes, the model identifies similar users from vast datasets via a fast multi-graph approach, boasting efficiency. Employing a distributed architecture ensures scalability, managing 500 recommendations/second with 80 million interactions. Evaluation shows up to 90% precision enhancement for 200 Lookalike candidates in digital marketing. Further, preprocessing optimization mitigates noise issues, enabling broader model applicability across diverse datasets. This study champions collaboration and innovation, promoting an accessible open-source framework.

This research paper innovates traditional market survey methods by integrating advanced Generative Pre-trained Transformers (GPTs) and ensemble Language Models (LLMs). Emphasizing efficient synthetic data generation while upholding stringent privacy and ethical standards, our methodology utilizes these AI techniques for transparent dataset synthesis. Through real-world scenarios and detailed case studies, it demonstrates substantial efficiency gains, acknowledging limitations transparently. Prioritizing user privacy and data security, our approach complies with rigorous regulations, accelerating survey processes and fostering personalized engagement. This pioneering paradigm shift reshapes market survey practices, establishing an industry benchmark for ethical and efficient data synthesis.

This paper navigates challenges faced by foundational language models, particularly combating Hallucinations in models like GPT. Detailing a methodical strategy, it regulates GPT’s responses by tapping into knowledge solely from PDF, HTML, and Word Doc sources. The approach, integral to a regulatory Query Bot application, prioritizes hallucination-free responses. Leveraging Retriever Augmented Generation, unique prompt engineering, and a scoring mechanism for context passages, it ensures accurate responses despite GPT’s innate limitations. This solution significantly reduces turnaround time by 150%, introduces Query Status Tracking, and enhances User Experience, marking a pivotal advancement in controlling misinformation while harnessing the potential of large language models.