Day 2

28 June 2018

AI Technologies

This track covers the full spectrum of AI technologies, how they are being developed and the real life scenarios where they are being utilised.  Expect to hear about chatbots, visual recognition technologies, robotics and machine learning.


AI Technologies: Chair’s welcome and opening comments


Keynote: Realities of Bot & VA Development and Implementation

  • Deep Learning, natural language processing and voice recognition implementation
  • The key elements to building a multi platform bot
  • The challenge of discovery and on-boarding
  • Implementing successful engagement strategies


Developing a conversational chatbot / VA

  • The pros and cons of conversation for UX
  • Conversational skills & character creation to educate your bot
  • Understanding natural language processing
  • How do we inject personality into your bot / VA
  • Can machines become creative


Networking Break


AI for coding

A case study presented by a leading AI company about how AI can aid with traditional coding tasks: bug fixing, test writing, finding and fixing exploits, refactoring code, translating from one language to another, and creating original code to fit specifications.


Designing for AI

Steve Brendish

Founder & Creative Director

2 Dam Creative

Associated Talks:

11:45AM - Day 2

View Designing for AI

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  • What is AI Product Design?
  • What are the challenges?
  • Good and bad AI Design
  • Designing the right Product/Designing the Product right
  • What Does the future hold for AI Design
. Steve Brendish, Founder & Creative Director, 2 Dam Creative


Deep Learning on Mobile Devices

  • Building convolutional neural networks (CNN) architectures to memory- and power-constrained devices like smartphones, wearables, and drones.
  • How can deep learning principles and algorithms be applied to sensor inference problems
  • Practical tips from real-world scenarios


Networking Break


AI and computer vision: Cameras that see like humans


Chihuahua or Muffin? Object recognition

  • Challenges of developing object recognition systems
  • Training models using large image data sets
  • Training deep neural networks
  • Testing with real-life images


The rise of smarter robotics

  • Already being used in cognitive factories – what industries could benefit next from hyper-intelligent machines?
  • Creating a strategic vision and roadmap for RPA and AI
  • How will jobs be affected?


Session Close