Day 1 - 30th SeptemberWednesday, 30 September 2020

Organizations in virtually every industry worldwide are exploring the benefits of Artificial Intelligence and Machine learning. But with this increased use comes some danger. Cybersecurity expert and McAfee CTO, Steve Grobman will explore the dark side of AI. We tend to think that tech is only used for good or by AI experts, but that’s not always true. The low barrier to entry and technologies focused on making AI fail, “adversarial ML”, create intended and unintended negative consequences in the world of AI that we now live in.

Takeaways:

1. How AI and ML can be used to inflict damage and why organizations and consumers need to be aware of it.

2. How does AI’s low barrier to entry present unintended negative consequences for organizations?

3. How will AI become the snake oil of the 21st century?

• What does AI Governance practice look for?
• Who are the leaders in the space and what are they doing that others aren’t?
• Are we actually seeing a techlash from exceptional early-career talent? Or is it overblown?
• How can you create a culture of cross-functional an integrated ethics and compliance?
• Who is accountable for AI good governance in your organisation? And why?
• Should you start incentivising ethical behaviour?
• Creating AI with the human – and the non-tech functions – within the loop. What are the early successes here?

Seemingly overnight, the world we knew has been up ended by the Covid-19 pandemic. Providing virtual self-service is becoming the primary means for customers to obtain the help and support they need from public and private organizations. With inquiry volumes rocketing, artificially intelligent, conversational chatbots are fast becoming the first-line solution.

But scaling a chatbot up to production readiness can often be challenging. It’s therefore critical to consider any conversational AI implementation as part of a wider digital transformation strategy that delivers the necessary resilience and continuity to drive the business forward - instead of treating it as a point solution that fixes an individual pain point.

Based on first-hand experience and real-life examples, Andy will examine conversational AI in action, discuss how it is revolutionizing business processes, and provide the five key steps that are essential for successful implementation.

To deliver the business value promised by AI and ML technologies IT needs to support the move to an industrialized stance.

But as an IT executive this change can be daunting. How can we test, secure and integrate all the new AI/ML tools our Data Scientists are demanding?

How can we leverage our existing platform investments? What skills do the IT teams need? What part will Kubernetes play with these complex stateful deployments? How can we deliver an omni-channel experience when deployments are spread across data centres, clouds and edge?

These questions cross every industry and organizations large and small. During this session, we will review the gaps that often exist between typical business requirements and the common analytical platforms in use today. We will then take a look at how current trends such as cloud operating models, containerization and Kubernetes could play a role in filling capability gaps when adopted successfully and how best to do that in these challenging times. We’ll end by discussing how to best approach the Industrialization process overall and just what the end-state might look like.

Companies and organizations of all sizes and across all industries are turning to AI to solve real-world problems, deliver innovative products and services, and to gain an advantage in an increasingly competitive marketplace. As organizations increase their use of AI, they face two major challenges – data availability and workload scalability. In this session, we will describe how AI workloads and petabytes worth of training data can be seamlessly scaled together across GPU nodes and regions through the use of NVIDIA DeepOps, Kubeflow, Apache Airflow and the NetApp AI Control Plane. These tools facilitate the rapid provisioning of GPU-accelerated data science workspaces and jobs, the efficient maintenance of dataset and model versions at the data science experiment level, and the automation of data ingestion from a variety of sources across disparate environments. The end result is less idle time for GPUs and data scientists, and an increase in data availability leading to more accurate models.

• How can we build processes whereby our data science and machine learning teams don’t overlook domain expertise when leveraging AutoML to automate different stages of the pipeline?
• How to interweave domain expertise and deep understanding of data context to ensure AutoML delivers for your teams
• Leveraging representation engineering to map structured and unstructured data into a custom data architecture
• Avoiding overreliance on single data type when using AutoML.

• Where are the safe bets? Which vendors in the market are leading the way in terms of successful case studies?
• Where is the next big OPEX reducing use case in your business? What are you doing to identify it?
• First things first: does your data science and engineering team have the necessary tools to optimise any current investments?
• Is operational intelligence about your business driven by AI? And if it isn’t, how can you justify the investment to senior management?