Enterprise AI

Chairpersons welcome and opening remarks. 

• How to move your project from “experimentation” to “live”.
• Turning AI into ROI – understanding why you need AI.
• Top tips for moving your pilot to the next stage.

Business users are increasingly expected to apply machine learning to improve prediction quality and increase business outcomes. However, developing data science and data engineering skills can be time-consuming.

This session gives an overview of AWS machine learning capabilities for builders of all levels of expertise. You will be introduced to:

• AI Services, which allow developers to easily add intelligence to any application, helping organizations do things like personalize the customer experience, forecast business metrics, translate conversations, extract meaning from documents and more.
• Amazon SageMaker Canvas, which will enable you to easily build high-quality ML models using Amazon SageMaker and AutoML capabilities without having to write any code.

• You do not need to be a programming expert to develop and deploy AI enabled algorithms with MATLAB & Simulink. 
• Developing and deploying AI enabled algorithms with Model-Based Design facilitates regulatory compliance for Medical Devices and Digital Health. 
• MathWorks has helped to operationalize AI on Embedded, Edge, and Cloud, leading to new applications in Automotive, Aerospace, Industrial Machinery, and Healthcare. 

• 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 legal side of AI – delving into the new European AI Act.
• What needs to be considered next from a legal and regulatory perspective to help keep the growth of AI ethical?

• To reduce energy consumption manufacturing companies, must accurately know where they are consuming energy.
• With standard out-of-the-box reports, analytics, and dashboards it is possible to be operational within 5 days.

IT departments made sense back when technology was separate from the business. But today, having IT as a standalone department with its own management hierarchy and budget slows innovation and business modernization initiatives. Of course, the problem isn’t with the people or leaders. It’s the outdated assumptions from which IT departments were created.

Fortunately, there is a better way. Hear from Professor Joe Peppard, former Principal Research Scientist at MIT and Wall Street Journal contributor. He’ll share insights from pioneers who are ditching their IT departments and making IT part of every business unit.  This shift helps to eliminate the massive schism between the business and IT and offers a model for others to do the same. The way forward is clear: Organizations need IT. But they almost certainly don’t need an IT department.

To follow soon…

To follow soon …

• The new capabilities of RPA and AI combined for complex business tasks.  
• How do you ensure information exchange is consistently accurate?  
• What IA looks like across industries.  
• As many view IA as the future of work, how quickly will this come into play on a mass scale?  

Given the current changes in purchasing habits, it is important for individual stores and large chains to create new sales experiences across both online and in-store channels. Creating a 360° brand experience that goes beyond just the physical store and now is available to all customers, physical and online. These results can be achieved thanks to advancements such as: new ways of collecting and managing data; the move from micro to nano service paradigm and to deep changes in skills and organisations.

The ability of artificial intelligence to gather and analyse information from massive datasets and easily finds correlations that are invisible to the human eye places AI as one of the most disruptive technologies in healthcare in the 21st century. In this session, we will focus on harnessing the power of AI in the healthcare industry to gain a strong competitive advantage by using real-time intelligence, holistic technology and an ecosystem approach.

Graph AI can achieve the state of the art on many machine learning tasks regarding relational data. One of them is recommendation which can be found in a lot of services such as content streaming, shopping or social media. We developed, together with Fujitsu Research, a performance-based Graph AI solution that takes advantage from the graph to do better recommendations and we tested it against a real retail fashion dataset.

• How AI-generated postage stamps became a reality. 
• The neural networks that created unique stamps. 
• What will the future bring?  

View Day 2 Content Here:

Applied Data & Analytics – Data Optimisation | Big Data | Intelligent Decision Making | Data Storage | Industry Use Cases

Transformational AI – Personalization | Edge AI | Future Progressions | Innovations | Industry Use-Cases