Applied Machine Learning & AI

23 September 2020

Victoria Park Plaza, London




Session ONE – building your strategy, identifying your problems and sourcing your solutions

  • People, processes and technologies
  • AI risk perception and mitigation
  • Re-engineering your operations
  • Surviving disruption
  • Actionable AI
  • Establishing a successful data science function
  • Aligning the objectives of IT and lines of business
  • ROI at speed and supporting scalability
Conference Chair’s Opening Address
People, processes and technologies: what do you need to unpack and reconfigure?

What do you need to learn to become AI ready?

What do you need to do to become AI strong?

How can technology automate your basic processes before advancing towards more complex, cross-business, machine-led production?

In our opening address, we explore the key questions you need to ask in order to collate your desired outcomes and check against them at critical points in your AI journey.

  • Aligned Value
  • Problem – look for chaos
  • Purpose – tied to values
  • People – valuable to spend time with
  • Process – engineer flow
  • Platform – required resources
  • Profits – enough to be happy
  • Timing?
AI risk perception and mitigation: understanding your existing infrastructure

The journey towards becoming an AI powered company does not mean identifying and eliminating risks associated with adoption. Instead, the focus should be on understanding how to perceive and mitigate risks in a way which complements business and provides confidence that risks can be identified and managed within a space which is dictated by organisational culture and capacity for innovation.

We address:

  • The challenge of governing AI
  • Managing risks in an increasingly complex world
  • Enhancing existing processes to deal with AI manifestation
  • Identifying and managing AI-related risks and controls
  • Involving your three lines of defence in the framing process
Re-engineering your operations: abandoning traditional business models

Traditional models simply cannot be sustained in an environment of frequent disruption. In order to embrace AI, those with power and influence need to lead on the re-engineering of operations.

As a starting point, we explore the value of automating basic processes within a particular function rather than scaling at speed across the company. We then address how this approach allows you to focus on providing bespoke, relevant, customer-centric products and services.

Surviving disruption: how to respond

Putting the need to re-engineer your operations into a wider context, we explore how you can best engage with disruption brought about by AI technologies. Rather than fear disruption, it’s important to adopt the emerging tools and models it produces.

We address:

  • Developing AI products and services
  • Working in a timely and competitive way
  • Recognising that technology adds value
  • The expansion of your product offerings
  • How AI complements your workforce, cuts costs and increases productivity
Moving from concept to reality: actionable AI

One of the key stumbling blocks to translating data into actionable intelligence is the lack of company-wide access to, and interaction with, datasets. This in turn leads to the failure of AI-led initiatives as data usage isn’t utilised to its maximum extent, leaving many to feel that reality has not met hype.

Whether you’re a data mechanic, driver or passenger, this talk will help you better understand how best to approach data integration and relation in order to produce actionable intelligence.

Questions to the Panel of Speakers
Refreshment Break Served in the Exhibition Area

Session TWO – Implementation

Establishing a successful data science function

Many enterprises lack the vision required to successfully establish a data science function capable of accommodating current business demands, evolving in accordance with shifting global contexts and progressing towards the creation of new products and services.

We address:

  • What do we mean by data science as a function?
  • How do you determine the right organizational design?
  • What are the resistance points and barriers to setting up the ‘best’ design?
  • What are the measures of success – how do you know the design works?
Aligning your IT function with the wider business: successful AI deployment

Unless IT and lines of business have a shared objective, new enterprise technology initiatives are doomed to fail.

By understanding the challenges faced by lines of business within an organisation, businesses will be able to progress AI from a niche technology to a critical analytics function. This will then lead to a cross-section of departments deriving value from the potential of AI to analyse massive data sets and allow business unit leaders to evidence AI’s ability to deliver meaningful change.

AI to ROI: achieving ROI at speed and supporting scalability

One of the biggest barriers to implementation is proving ROI early on in the deployment process in order to support the extension of AI into other business functions.

We address how you can predict ROI, measure against investment, share the value gained with other business units and support the identification of new business cases

  • Reduce time to market pressures
  • Identify new business cases
  • Link business cases and corresponding applications
  • Develop information applications
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
Seminar Sessions
Networking Lunch Served in the Exhibition Area

Session THREE – Advancing from implementation to delivery: platforms, applications and tools

  • Applying deep learning to design systems
  • Using AI to create original product offerings
  • Going beyond chat bots
  • Tackling AI bias
  • Audit the algorithm
  • Deepening your AI security posture
Conference Chair’s Afternoon Address
Applying deep learning to design systems

Enterprises are increasingly turning to the design, implementation and use of complex neural networks in order to experiment at scale to deploy optimised learning models.

By deploying an experiment-centric deep learning service you will enable data scientists to visually design neural networks and scale out training runs, optimise in accordance with production environments, scale up training with a preferred deep learning framework and then deploy to the cloud or the edge.

Using AI to create original product offerings

The objective of marketing and AI is to use it to improve the response you get from marketing, to grow the business and develop your relationship with consumers, both engaged and prospective.

Ultimately, the goal of AI-led marketing is to better market the right product to the right person at the right time in the right place for the right price.

We address:

  • Products and services-why do your customers buy?
  • Person-the right target market
  • Place-knowing how customers wish to be reached
  • Price-acquisition cost and price to customer
  • Promotion-creating awareness
  • Process-moving the customer through the journey
Going beyond chat bots: leveraging the customer experience

This presentation explores practical applications of Marketing Intelligence Modelling and applying them in the cloud to better retain and understand your customer base, as well as your growth potential.

  • Using Predictive Modelling for churn events
  • Classification vs Survival Analysis
  • Setting up Machine Learning process in the cloud
  • Do’s and don’ts for optimal ROI
Questions to The Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
Tackling AI bias: for business and societal good

Unconscious bias is difficult to measure let alone prevent from becoming cooked in and escalated by AI systems. Human decisions can be flawed, shaped by individual and societal biases. The question is, will AI’s decisions be less biased than human ones?

We explore:

  • Where algorithms can help reduce disparities
  • Where more human vigilance is needed to critically analyse
  • Use AI to identify and reduce the effect of human biases
  • Prevent AI bias from scaling
AI ethics: Audit the algorithm

We look at examples of algorithmic biases within a variety of industry sectors, the automation of decision making, how to add transparency to the process and successfully audit your algorithms in order to ensure you are operating in an ethical manner.

  • Determining the importance of the algorithm
  • The process by which you audit
  • Communicating the outcome
Deepening your AI security posture: Text Analytics and NLP

By deploying NLP as a key part of your AI security infrastructure, you will be able to support text analytics to go beyond mere perception and advance towards a truly granular understanding of sentence structure and meaning, sentiment and intent through statistical and machine learning methods.

In our closing address we tie together the stream of security conscious, ethically minded, presentations to highlight the value of advancing the means by which organisations can make NLP a key part of their security infrastructure.

Questions to the Panel of Speakers
Closing Remarks from the Conference Chair
Conference Closes

Please note:
Whitehall Media reserve the right to change the programme without prior notice.