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It's a unique opportunity to watch the most sought-after AI content – ordinarily reserved for AIAI Pro members. Each stream delves deep into a key AI topic, industry trend, or case study. Simply sign up to watch any of our upcoming live sessions.
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🗣 Hear from experienced AI leaders
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Take a look at what we've got coming up 👇
- Exploring a fully monetized generative AI ecosystem
- Generative AI from enterprise architecture strategy perspective
- Early target (drug) discovery with AI: challenges, progress, and future directions
Exploring a fully monetized generative AI ecosystem
April 29, 2024
11:00 pm EDT | 8:00 am PDT | 4:00 pm GMT
Join us in exploring a fully monetized generative AI ecosystem with Ruchika Sachdeva, the Head of Data Analytics at Coca-Cola HBC. Discover the challenges businesses face in monetizing the performance of generative AI applications due to the need for high amounts of data. Ruchika will share insights from the consulting and industry side, highlighting the potential of generative AI and its impact on various industries.
Don't miss this opportunity to learn about the latest advancements in AI and how it can transform your business. Register now and be part of the AI community!
Senior Data & Analytics Executive, specializing in data enabled business transformation. With 18+ years of cross industry expertise and a decade of consulting experience, I specialize in delivering business value from Data, Analytics and AI investments.
Generative AI from enterprise architecture strategy perspective
March 25, 2024
11:00 pm EDT | 8:00 am PDT | 4:00 pm GMT
In this session, the speaker discusses the evolution of machine learning (ML) and deep learning, highlighting the emergence of foundation models and their implications for enterprise use. She outlines the basic process of ML, transitioning to the complexities introduced by deep learning models, which can handle more complex inputs. Foundation models, with their large datasets and multimodal capabilities, offer new opportunities for reuse and fine-tuning.
The speaker emphasizes the shift towards treating models as software-as-a-service (SaaS) components, necessitating new governance and oversight processes within enterprises. She also addresses the challenges of multi-cloud environments and content moderation, advocating for a security-in-depth approach.
Furthermore, the speaker delves into the architectural design principles for deploying and managing AI models. She discusses considerations such as data compliance, orchestration, endpoint security, and threat mitigation. Finally, she outlines the development process for building and integrating AI models within a secure cloud environment.
Eyal Lantzman, GT Head of architecture AI/ML JPMorgan Chase & Co.
Expertise in designing, developing, and delivering intelligent systems (knowledge graph and machine learning) that scale and are operationally excellent.
Early target (drug) discovery with AI: challenges, progress, and future directions
February 27, 2024
11:00 pm EDT | 8:00 am PDT | 4:00 pm GMT
In this presentation, we'll delve into the fascinating world of using machine learning and AI for drug discovery. The potential of these technologies to uncover drugs that traditional methods might overlook and accelerate the typically slow drug discovery process is emphasized.
The speech highlights the importance of understanding cell biology and the role of DNA in disease development. Through machine learning, the goal is to identify differences between healthy and diseased cells, providing a blueprint for potential therapies.
The challenges in AI drug discovery are discussed, particularly the need for comprehensive and searchable data assets. Despite the high costs and complexities involved, the value of sharing data assets while recognizing the nuanced considerations involved in open-sourcing such resources is acknowledged.
This presentation offers a glimpse into the promising intersection of AI and pharmaceuticals, fueled by ongoing innovation and collaboration within the industry.
Shane Lewin, Vice President, AI/ML, GSK
Shane builds teams that leverage machine learning to solve impossible problems, now as VP in AI/ML at GSK working on early target discovery.
He has been building data-driven products for a little under 15 years, spanning early-stage start-ups, large organizations, and companies making the transition between the two.
He holds an MS in Computational and Mathematical Engineering from Stanford, and degrees in Mathematics and Molecular Biology from the University of Colorado at Boulder.
