AI For Enterprise – VIRTUAL

• For every success story, there are numerous examples of failure to implement and scale AI. Misalignment between leadership and technologists, an unclear understanding of what’s possible, or the lack of organizational support can drive organizations to a never-ending PoC cycle. It prevents them from harnessing AI’s actual potential value.
• In this session, we will explore the technological and use case landscape of AI projects. We will also explore a pilot-first approach to maximize the scalability of enterprise AI projects.

• How do you introduce a technology like AI into a working environment where one wrong decision could result in catastrophic disaster?!
• The session will explore the issues of bringing AI into safety a critical process like steel making but also explore the huge opportunities this technology delivers to improve production and aid with decarbonisation of the industry.

• Why is it important to consider sustainability when looking at digital services and infrastructure.
• What are the key considerations and opportunities
• How Bulk helped a UK based Fintech deliver a sustainable AI platform and significantly reduce cost at the same time.

• Artificial Intelligence is here to stay and is rapidly being adopted in virtually every domain – from healthcare to finance and public safety and entertainment, to name a few. However, most machine intelligence powered systems today are black-boxes that often function in oblique, invisible ways for developers, compliance staff, and regulators.
• This has led to increased business risks and ethical concerns about how AI may harm the most vulnerable.
• This session will discuss the importance of responsible AI through real-life Responsible AI use cases, with an overarching focus on accelerating adoption and impact of human-centric AI.

• The commoditisation of machine learning and AI is shifting the supply and demand of resources and insights for organisations. Where teams of data scientists were once required to solve challenges, solutions can now be purchased on-demand as a service 
• By abstracting away the complexity, Cognition as a Service (CaaS) disrupting the Data Science and AI market by making solutions accessible, on-demand, to all in a fast and cost-effective way. 
• Industrialised machine learning and robust high-end pre-packaged solutions are moving the needle for big and small players that want to access AI and ML solutions 
• The competitive advantage is gained through distributed learning frameworks to deliver improved performance and cost-efficiency, and address key privacy concerns 
• Machine Learning solutions are moving from a tailored approach, where frameworks are built around companies, towards a faster and more scalable one, where frameworks are built around challenges 

From fast food chains trying to avoid a chicken nugget shortage, to the NHS trying to stay one step ahead of a pandemic, good demand forecasting is what keeps the world running smoothly. AI can be a powerful tool for anticipating demand, but it needs to be deployed in such a way that it is actionable and impactful. It’s no mean feat to extract demand insights that are accurate, detailed enough to be actionable, and easy for users to understand. The session will explain how to build demand forecasting systems that organisations can actually use and trust to make better operational decisions covering:

• • Examples of where demand forecasting can be used in everyday business settings to drive value
  • The common challenges and pitfalls encountered when implementing demand forecasting
  • Practical guidance on how to deploy demand forecasting successfully with recent case study examples of this in action

• Current mainstream AI in industry is Narrow AI, that can only learn one specific task by memorising a tremendous amount of labelled data. Narrow AI faces great challenges like high cost of data labelling, lack of explainability, poor support for multiple tasks and vulnerability to outliers in data.
• Generative AI, like GAN, GPT-3 and Alpha Go, attracts great attention as it shows deep neural networks can learn itself to generate high quality data superior to Narrow AI.
• This talk will give insights into recent trend of applied Generative AI in computer vision, natural language processing and automated planning.

• Big Data projects and Data Pipelines have merged into the technology fabric of every organization. The tools for building and running application workflows must be not only easily versionable and testable but they must also provide access to business and non-technical users.  
• They must deliver the operational richness required to operate complex applications and deliver business services in accordance with service level commitments.  
• In this talk, Regardt Jacobs explains the need of production-ready application workflow orchestration for data-intensive applications and how automation, Big Data and IoT helped a leading vehicle manufacturer to cut fleet downtime. 

• How to move your project from “experimentation” to “live”  
• Turning AI into ROI – what does value mean to your business? 
• Top tips for moving your pilot to the next stage 
• R&D budget signoff – understanding why you need AI 

• Artificial Intelligence is becoming more mainstream and subsequently organisations are now increasingly investing and working with AI. However, most companies have trouble scaling their efforts and receiving satisfactory ROI. Peltarion offers a solution to this problem.
• With the Peltarion Platform, you can place the power of leveraging AI in the capable hands of employees throughout the organization. Thus, allowing you to scale your efforts and harvest the full potential of AI by allowing your domain experts to utilize state-of-the-art AI models to create value.

We will discuss some of the core principles of a sustainable data centric business strategy. We will also demonstrate how Dell Technologies can help the Enterprise broaden innovation opportunities and accelerate value realisation with data.