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GenAI is here, and it’s making huge waves in the digital world. But how much of the hype is applicable to the tangible, physical challenges industrials face? Dr. Christopher Nguyen, CEO and Co-Founder of Aitomatic, cuts through the noise, drawing on his vast experience leading global industrial AI at Panasonic to demystify how industrials can practically deploy GenAI in their AI ecosystems. With a focus on concrete applications, Dr. Nguyen will shift your perspective from AI as a mere Q&A tool to a powerful problem solver. He will share insights on how industry leaders avoid pitfalls like vendor lock-in, and crafting independent AI strategies that are fast, small, and industry-specific with domain knowledge and Small Specialist Agents (SSAs). Come to learn about GenAI beyond the digital hype and leave with a clear, actionable path to making GenAI your most valuable operative ally in the industry.
Watch nowGenerative AI’s untapped potential thrives in its synergy with knowledge graphs – dynamic structures of interconnected facts and concepts that enable insights and predictions from generative AI systems to be more accurate, transparent, and explainable. Neo4j, the graph database and analytics leader, has pioneered this work to enhance AI capabilities. Join Sudhir Hasbe as he unravels the essential relationship between knowledge graphs and generative AI, including its: 1. Contextual precision in moving beyond pattern recognition to achieve contextual understanding; 2. Semantic depth when knowledge graphs infuse domain-specific knowledge into AI outputs;3. Real-world applications and deployments, and the role of technologies like vector search; and 4. Challenges and prospects as we navigate the landscape over the next 3-5 years.This talk will immerse you in the profound impact of knowledge graphs on generative AI. You will gain insights on how they are shaping AI’s future, and transforming the way we interact with artificial intelligence to drive better and more informed decisions and predictions.
Watch nowThrough Dr. Kadous' work at Anyscale, they have seen many applications built using LLMs. In this presentation, Dr. Kadous will share 6 key real use case patterns we are seeing today: summarizing, talking to your data, question answering, diagnostics and advanced autocomplete. He will give concrete examples of each of these and share some of our experiences. At the end of this presentation, you should have an improved intuition for being able to look at a problem and identify if and how LLMs could help with.
Watch nowIn this talk, Daniel Lenton will explore how the AI deployment stack has become increasingly fragmented in recent years, with new serving technology, compression techniques, compiler workflows and hardware all entering the scene at unprecedented speed. All of this results in tools that are not compatible, excessive friction, and lost performance. Daniel explores a way forward, through unification at the level of the machine learning frameworks, and he will explain significant runtime improvements which can be achieved by simply converting code between different ML frameworks, each of which have different runtime characteristics on different hardware. We will then discuss a more holistic approach to unification, looking at tools such as MLIR, and the role this plays in combining different low-level compiler toolchains such as TensorRT, OpenAI Triton, OpenXLA, OpenVINO etc., before finally exploring how the AI stack might continue to evolve in the years to come.
Watch nowThe evolution to 6G promises communication-and-compute networks that are larger in scale and with significantly heterogeneous edge devices. These modern networks challenge our ability to design and optimize efficiently. While cognitive networks promise to introduce agility and intelligence, there is a need to significantly scale up such approaches in an internet-of-everything world. Dr. Mitra and her team present modeling strategies that effectively capture the dynamics and heterogeneity of these modern networks – however, the models come at the price of complexity. To this end, they propose a multi-pronged approach to network design and optimization via reinforcement learning. They review strategies exploiting graph signal processing for network optimization including new representations for network behavior. The new representations allow for efficient graph reduction and enable low complexity optimization of network control policies. An exciting consequence is that the graph representations allow for the efficient creation of related synthetic networks, or digital cousins, that accurately capture network behavior without the need for excessive trajectory sampling of the actual network. A novel on-line/off-line Q-learning methodology is proposed enabling ensemble learning across the digital cousins. The proposed strategy offers significantly improved convergence rates and performance versus current state-of-the-art learning methods including those based on neural networks. Theoretical guarantees can be provided, and the proposed methods offer strong performance gains across a variety of networks. The ensemble learning can be adapted to general graphs described by Markov chains.
Watch nowWhile general-purpose large language models (LLMs) and other language models are producing promising performances, its application in enterprises remain a challenge due to multiple mathematical and practical reasons. In this talk, we will cover three main topics: why a specific small model (SSM) strategy is better for building domain expert models, why AI warrants its own infrastructure strategy, and how Lepton AI's solution helps enterprises to better build one's own AI strategy.
Watch nowJoin us on a journey into the world of transferring know-how of global manufacturing companies. In this talk, we’ll explore how Fenrir with aiVA helps manufacturing companies improve service quality and productivity efficiency. For manufacturers with global service and factory operations, it is difficult to keep the same level of service or productivity as HQ provides because of many differences including languages, hiring circumstances, and regional characteristics. The combination of aiVA and Fenrir’s application which is optimized for users' business workflow provides a way to overcome those difficulties.
Watch nowJoin Dr. Hagiwara and Mr. Yu in exploring the world of cold-chain installation and troubleshooting in convenience stores. In this technical talk, they will dive into how aiVA is revolutionizing EMS systems, equipment management, and supplier components. Learn how aiVA's Chinese UI interface empowers field engineers with rapid access to precise information, streamlining troubleshooting and reducing post-installation issues. Discover significant improvements in lookup time, installation success rates, and efficient troubleshooting, all thanks to aiVA's capabilities.
Watch nowThis talk addresses how to leverage both natural language and graph technologies together for AI applications in industry. We’ll look at how LLMs get used to build and augment graphs, and conversely how graph data gets used to ground LLMs for generative AI use cases in industry – where a kind of “virtuous cycle” is emerging for feedback loops based on graph data. Our team has been engaged, on the one hand, with enterprise use cases in manufacturing. On the other hand we’ve worked as intermediaries between research teams funded by enterprise and open-source projects needed by enterprise – particularly in the open-source ecosystem for AI models. Also, there are caveats; this work is not simple. Translating from the latest research into production-ready code is especially complex and expensive. Let’s examine caveats that other teams should understand, and look toward practical examples.
Watch nowGenerative AI is the talk of the town and in this talk, we discuss topics at the intersection of Generative AI and Industrial AI, where we define Industrial AI as the application of Artificial Intelligence (AI), Machine Learning (ML) and related technologies towards addressing real-world use cases in industrial and societal domains. We will look beyond the hype to identify real use cases and applications, challenges ahead and promising research directions.
Watch nowGenerative AI models and applications are being rapidly deployed across several industries, but there are several ethical and social considerations that need to be addressed. These concerns include lack of interpretability, bias, and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this talk, Dr. Kenthapadi and his team first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models and provide a roadmap for thinking about responsible AI for generative AI in practice. Focusing on a real-world LLM use case, namely, Fiddler Auditor (https://github.com/fiddler-labs/fiddler-auditor), an open-source toolkit for evaluating robustness of LLMs, we present practical solution approaches/guidelines for applying responsible AI techniques effectively and discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice. By providing real-world generative AI use cases, lessons learned, and best practices, this talk will enable ML practitioners to build more reliable and trustworthy generative AI applications.
Watch nowWhat is generative AI? How does machine creativity relate to human creativity? What will become of us in the age of creative machines? We will delve into the essence of generative AI, drawing on comparisons to our own brains. A vision of the future of creativity will be presented, along with a discussion of potential risks brought on by these powerful creative machines. Ethical dimensions of generative AI applied to creative domains will also be addressed.
Watch nowIn this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
Watch nowIndustry 4.0, where maintenance undergoes a transformative revolution with Generative AI. Join Mr. Shashi Dande as he introduces the CMMS Marvel, a project reshaping maintenance and repair practices. Explore how Generative AI streamlines operations, redefining workflows for greater efficiency. Discover how Generative AI ignites data, providing actionable insights.
Watch nowIn the fishing field, the undersea is invisible, has disadvantages in network communications, and is rich in regional variations. Fishermen spend more than a decade honing their own experience and intuition to catch fish. For their problems that traditional machine learning has not been able to solve, we are trying approaches such as Al with built-in knowledge and LLM to collect their knowledge. It is expected to revolutionize the fishing industry, which has remained unchanged for 50 years.
Watch nowJapan faces a dual challenge of a shrinking, aging workforce and conservative IT management. This presentation explores how Generative AI can be the solution. Discover how Generative AI optimizes workforce efficiency amid a declining labor force. We’ll also discuss how it aligns with Japan’s traditional IT conservatism, offering innovation while respecting cultural values. Join us to explore ethical AI deployment in Japan’s unique business context.
Watch nowGenerative AI models and applications are being rapidly deployed across several industries, but there are several ethical and social considerations that need to be addressed. These concerns include lack of interpretability, bias, and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this talk, Dr. Kenthapadi and his team first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models and provide a roadmap for thinking about responsible AI for generative AI in practice. Focusing on a real-world LLM use case, namely, Fiddler Auditor (https://github.com/fiddler-labs/fiddler-auditor), an open-source toolkit for evaluating robustness of LLMs, we present practical solution approaches/guidelines for applying responsible AI techniques effectively and discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice. By providing real-world generative AI use cases, lessons learned, and best practices, this talk will enable ML practitioners to build more reliable and trustworthy generative AI applications.
Watch nowWhat is generative AI? How does machine creativity relate to human creativity? What will become of us in the age of creative machines? We will delve into the essence of generative AI, drawing on comparisons to our own brains. A vision of the future of creativity will be presented, along with a discussion of potential risks brought on by these powerful creative machines. Ethical dimensions of generative AI applied to creative domains will also be addressed.
Watch nowIn this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
Watch nowIndustry 4.0, where maintenance undergoes a transformative revolution with Generative AI. Join Mr. Shashi Dande as he introduces the CMMS Marvel, a project reshaping maintenance and repair practices. Explore how Generative AI streamlines operations, redefining workflows for greater efficiency. Discover how Generative AI ignites data, providing actionable insights.
Watch nowIn the fishing field, the undersea is invisible, has disadvantages in network communications, and is rich in regional variations. Fishermen spend more than a decade honing their own experience and intuition to catch fish. For their problems that traditional machine learning has not been able to solve, we are trying approaches such as Al with built-in knowledge and LLM to collect their knowledge. It is expected to revolutionize the fishing industry, which has remained unchanged for 50 years.
Watch nowJapan faces a dual challenge of a shrinking, aging workforce and conservative IT management. This presentation explores how Generative AI can be the solution. Discover how Generative AI optimizes workforce efficiency amid a declining labor force. We’ll also discuss how it aligns with Japan’s traditional IT conservatism, offering innovation while respecting cultural values. Join us to explore ethical AI deployment in Japan’s unique business context.
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