Enterprise AI

Chairpersons welcome and opening remarks. 

• 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.  

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. 

• 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. 

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

• 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? 

• 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. 

To follow soon …

• 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.

• 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? 

• 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.  

• 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.

View day 2 content here: Applied Data & Analytics

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