The Meet & Greet is the perfect opportunity to reconnect with peers, expand your network, and discuss the state of ML across the cloud-edge continuum! Join attendees from both Edge AI Summit and AI Hardware Summit for this extended networking session.

We will soon announce a luminary guest speaker who will present in the middle of the function, followed by a drinks reception for both events.

Order of ceremony:
4:00 - 5:00 PM: Informal Networking
5:00 - 6:00 PM: Guest Keynote Speaker
6:00 - 7:00 PM: Drinks Reception

• Understanding the value of combing of real-world data and historic clinical trial data
• How can AI be used to analyse post approval data?

The true potential of AI rests on super-human learning capacity, and on the ability to selectively draw on that learning. Both of these properties – scale and selectivity – challenge the design of AI computers and the tools used to program them. A rich pool of new ideas is emerging, driven by a new breed of computing company, according to Graphcore co-founder Simon Knowles. At the AI Hardware Summit, Phil Brown, VP Scaled Systems Product discusses the creation of the Intelligence Processing Unit (IPU) – a new type of processor, specifically designed for AI computation. He looks ahead, towards the development of AIs with super-human cognition, and explores the nature of computation systems needed to make powerful AI an economic everyday reality.

• Highlighting the ethical and practical challenges of accessing patient data and how best to overcome shared challenges
• Developing trust across those involved in the formation of real-world data
• De-identify personal information using a risk-based approach and putting valuable data in the hands of data scientists

• Predictive approaches to treatment effect heterogeneity (PATH), and offer the potential to identify predictive biomarkers, and to understand which treatment a patient may be more likely to benefit from
• The two primary classes of PATH models include risk models and more flexible effect models, with a proliferation of newly published approaches in recent years
• Limitations of these approaches such as how to appropriately evaluate their accuracy are an area of active research

• Exploring the digital endpoint development process with a case-study
• Measuring what matters; identifying and addressing needs. Dealing with concerns around development, validation, regulatory concerns
• Putting endpoints to use via deployment in clinical trials

• Using AI to evaluate the demographic population and trial operational performance trade offs of exclusion of non-safety required chronic conditions in the eligibility criteria
• Benchmarking and differentiating between Schedule of Assessment(SoA) procedures relative to different time periods and peer groups to drive greater operational efficiency

• Assessing representativeness of randomized clinical trials using ML fairness metrics and surveillance data
• Using these metrics to assess and monitor representativeness of clinical trials
• New tools for designing representative and more efficient single and multi-site trials