Discover AI Best Practices, How to Harness Potential, and Deploy AI Responsibly from Leading Experts

What are the best practices to align technical considerations with business opportunities? What are the main advantages of using GANs over other AI models from a technical & business perspective? What kind of internal structure is needed in business to ensure ethical AI?

What better way to demystify these questions than to hear the answers from the technical experts themselves?

RE•WORK are hosting a series of panel discussions at the Deep Learning 2.0 Virtual Summit, inviting experts to uncover the latest research advancements in generative models and cross-industry applications, the potential for AI to transform enterprise & responsible approaches to developing AI for the common good.

The 2-day Virtual Summit will incorporate the following three panel discussions. Here’s what you can expect from each:

Panel Topic 1: “Best Practices for Realising ROI on AI Projects”

The first panel of the 2-day Summit will take place on the Enterprise AI Virtual Stage (Jan 28), exploring the “Best Practices for Realising ROI on AI Projects” with Evgeny Blaichman, Machine Learning Group Manager at Samsung, Claire Lebarz, Head of Data Science at AirBnb, Mario Lois, Global Head & Senior Director of AI at GE Healthcare, and Adri Purkayastha, Global Head of AI and Digital Risk Analytics at BNP Paribas Group.

This panel will cover the requirements for an AI project from a technical business perspective to best drive digital transformation, including considerations such as:

• What is your end goal?
• How can an AI strategy be developed to optimally reach it?
• How can you develop an ML lifecycle? (end-to-end or otherwise).
• What are the MLOps considerations when building an AI project/product?
• How do you build AI teams?
• What are the best practices to align technical considerations with business opportunities?
• How can we avoid common pitfalls in AI adoption in the Enterprise?
• What is the best and most effective way to build and deploy solutions in X industry/ in X type of company?
• What are the data & infrastructure limitations/requirements?

Panel Topic 2: “How Can We Best Harness The Potential of GANs & Overcome Challenges for Innovative Applications?”

The panel discussion on the Generative Models Virtual Stage (29 Jan) will cover how to tackle challenges in GANs (e.g. mode collapse, non-convergence and instability, high sensibility to hyper-parameters and evaluation markers, data efficiency) with Nikolay Jetchev, Senior Research Scientist at Zalando, Krishna Kumar Singh, Research Scientist at Adobe, Alexia Jolicoeur-Martineau, Research Scientist at MILA, and Jiangbo Yuan, Applied Researcher III at eBay.

Some considerations that are likely to be touched upon are:

• Why are these drawbacks contributing to the limitation of applications?
• What is the most appropriate network architecture & optimisation algorithm?
• What are the latest advancements in the design of GANs and optimisation solutions that can lead to innovative applications within industry & society?
• What are the main advantages/disadvantages of using GANs over other AI models from a technical & business perspective?

Panel Topic 3: “Ensuring Responsible AI”

During this panel, Myrna MacGregor, BBC Lead, Responsible AI & ML at BBC, Daniel Gifford, Senior Data Scientist at Getty Images, Engin Bozdag, Senior Privacy Architect II at Uber, and Maria Luciana Axente, Responsible AI Lead at PwC, will discuss how to ensure that businesses are deploying AI that is ethical. Some considerations include:

• How do you create a responsible AI Strategy?
• What are the current guidelines across countries & how can we progress these further?
• What are some of the main problems we still face in promoting the use of responsible AI?
• What kind of internal structure is needed in business to ensure ethical AI?

Join us at the Deep Learning 2.0 Virtual Summit to hear the discussions that take place across all panels, as well as for the opportunity to listen in on technical expert presentations, contribute to live Q&A sessions, partake in roundtable discussions & collaborate with others through 1:1 networking opportunities. It’s not too late to register for the RE•WORK Deep Learning 2.0 Virtual Summit. You can purchase your last chance tickets here.