Day 1 - 11 May 2022

09:45AM

(PDT)

Enterprise AI: Chairpersons Welcome

Chairpersons welcome and opening remarks. 

10:00AM

(PDT)

AI Enabled Healthcare – Aiding the Patient Experience

  • Human-centred AI for the optimum patient experience. 
  • AI enhancing the human expertise, not replacing it.  
  • Private and sensitive data – how it is handled and processed is more important than ever.  
  • Adopting AI quickly into existing processes, think automating routine tasks.  

10:30AM

(PDT)

Keynote: The Augmented Workforce

Fearing that disruptive technologies would replace workers; it is now clear that these innovations are there to aid employees’ skills and capabilities – realising them from automated tasks and giving them the time to work on new projects. Here we will look at the role of AI and ML in helping to build a digitally enabled and efficient workforce. 

11:00AM

(PDT)

Panel: Keeping it Ethical in AI

  • Human-in-the-loop review processes – how can we decide where the buck stops in decision making?  
  • Reconfiguring data input to tackle non-ethical AI bias. 
  • The potential of labour replacement and corporate practice that can be done to mitigate the risk of job-loss. 

11:40AM

Networking Break

12:00PM

(PDT)

Progressing Robotic Process Automation Capabilities

  • Driving AI and ML forward through RPA. 
  • Focusing on solving problems that already exist and maturing processes through RPA.  
  • Examples of RPA in action.

12:30PM

(PDT)

RPA led Transformation

  • Collaborating with the market and across landscapes as the enabler for optimised RPA.  
  • Focusing on the people as well as the technology. 
  • How can RPA be utilised across sectors as it becomes more widely embedded into enterprise systems? 

01:00PM

(PDT)

The Optimum Automation – Hyperautomation

  • Saving time and resources by automating repetitive, information heavy processes.  
  • Starting, stopping and altering automation in real-time – how AI automation keeps optimising throughout the chain.  
  • Automating with improved accuracy, speed and reliability. 

01:20PM

(PDT)

Industry Use-case

To follow soon …

01:50PM

Networking Break

02:30PM

(PDT)

Panel: Evolving the Enterprise with Language Modelling

  • NLP (Natural Language Processing) progressions like OpenAI for enhanced user experiences and automated responses.  
  • How these advancements are enhancing workforce and service capabilities, as well as increased data literacy.  
  • What are the challenges that need to be considered here?  
  • Accelerating processes with deep learning and neural networks – looking at the expansion of deep learning at scale across complex tasks.

03:10PM

(PDT)

Let’s have a Chat about Conversational AI

  • The present level of utility of conversation AI vs the future potentials. 
  • A case of Her – what are the ethical boundaries of creating human-like virtual company?  
  • A flow chart or something more? What are the alternative technical approaches to on-boarding conversational AI? 

03:30PM

(PDT)

Operational Al and ML Building Business Capabilities

  • Harnessing multi-sensor ML – where to start in building this data rich infrastructure.  
  • Solving big problems with operational AI. 
  • Working with unbiased ML and transparent AI for true insights.  

03:50PM

(PDT)

Chanchal Chatterjee

Artificial Intelligence Leader, Google Cloud

Google

Associated Talks:

03:50PM - Day 1

View From Concept to Production – MLOps for Your Entire ML Journey

01:50PM - Day 2

View Panel: Data led Intelligent Decision Making

View Full Info

From Concept to Production – MLOps for Your Entire ML Journey

  • End-to-end MLOps platform for 6 different ML frameworks and custom accelerators to transform your ML use cases from concept to production.
  • The platform offers 3 steps of MLOps starting with the “Dev” stage where we unit test the ML modules required in your use case.
  • This is followed by the “Test” stage where we build ML pipelines with Kubeflow and Airflow.
  • Finally the “Prod” stage deploys the ML components into production with model monitoring and CI/CD.
  • The platform is supported by code in GitHub with examples.
. Chanchal Chatterjee, Artificial Intelligence Leader, Google Cloud, Google

04:20PM

(PDT)

End of Day

View day 2 content here: Applied Data & Analytics

Data Optimisation | Big Data | Intelligent Decision Making | Data Storage | Industry Use-cases