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FOUR DECADES IN CONVERSATIONAL AI Let’s set a line in the sand as we start this piece. This discussion is 100% about Conversational AI, not AI in other applications such as facial recognition, or recruitment triaging, or image recognition, or fraud detection, or autonomous cars, or the singularity where GAI (general AI) becomes as, indeed more, intelligent than humans leading to a world where Sundar Pichai’s assertion that Artificial Intelligence will have a more profound impact on humanity than fire will come to pass.
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ll of those topics are entirely relevant and have their pros and cons, controversies and successes but we’ll not concern ourselves with them here as this is not an area where 20 years of professional practice and 40 years of broader interest in how humans communicate with machinery will have any validity. This is the frame of reference, research and reality that the author brings to this piece.
The Author is a veteran player with experience building Conversational A.I. since 1982 when he built his first ChatBot on a ZX Spectrum computer. He and his Associates have created production conversational systems for; customer service, learning & development, technical support, classroom support for learners & teachers and playful systems aimed to generate discussion amongst many others. Since 2002 Elzware has been designing and making Conversational AI systems
using, evolving and training clients, on appropriate technologies and methods using engineering, social and computer sciences.
There have been some prototype systems for healthcare, virtual humans and other outliers, more than 80 systems in nearly 20 years. They are voice/text input and output,
mixed UI environments, multi modal, tied into back office systems for email and SMS, wrapped with code for access to web services, driving social media automatically. Creating nuance to lip-sync and Avatar expressions, we tune Avatars for attitude and see how people react to better understand. That is the position.
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SYNAPSE | 3RD QUARTER 2021
We are old skool, seen it, done it, watched fashions and hype cycles come and go. Now let’s get to the point. There’s been a lot of conversation about Chatbots and Conversational AI over the last few years, lots of talk of revolutions and scary tales of computers taking over the world to make us subservient, not just from the loony out-there correspondents but from respected traditional media outlets around the world both in print, online and on television. This is a shame as the market for Chatbots is far older than the current hype cycle and in many ways should be, the author believes, reflecting back on its roots to ensure that lessons previously learnt are blended into the best of the methods that are currently being trialled around the world particularly in simple transaction based interactions fit for customer service and before less adventurous human interaction sectors get drawn in to further trials. We are on the threshold of an amazing
phase in human to machine conversation but there are some significant roadblocks along the way. Purely data driven Conversational AI is blowing in like fog to cover everything in it’s path and while there is much talk about levels of Conversational AI being more or less autonomous, even sentient or conscious the reality is that fundamental problems with conversational data and large language models are presenting problems for those vendors that are not working with a hybrid architecture.
By hybrid is meant a blending of clear, concise, auditable and governance driven business and process rules and structures in a method that is transparent and explainable,
/ By Phil D Hall, Conversational AI Architect, Elzware Ltd / indeed interpretable to the common businessman and not just the esoteric and slippery methodologies of data science. I’m ignoring the costs of computing these methods and their impact on our planet for this piece. Since Elzware was set up in 2002 it has seen some peaks and troughs in the market for digital conversational. Work before Elzware was with a global systems integration company working with systems that were called ERMS (email response management systems ). These were built according to various NLP methods and worked side by side with call centre operatives to deliver transaction support and handover to humans. 20 years ago and the functionality is essentially no different to that attempted by the recent blizzard of companies that it seems, thankfully, to be thinning slowly out.
Why didn’t these systems continue to evolve? Social media is the short answer, marketeers and information specialists imagined a delivery mechanism where information was delivered once and people would find this through search engines and all would be good in the world, but let’s not get side-tracked into this grimy don’t-call-me-apublisher so I, the Big Tech Companies, can ignore the vitriol, anger and offensive content that is an unacceptable percentage of total social media output. Let’s not open the box on the Tay debacle and start a discussion about feedback mechanisms of autonomous generative and/ or adversarial AI system, let’s keep focussed on Hybrid AI and let’s talk about some of the systems over the last few years that Elzware has delivered that set a target for the heavy lifting that needs to be achieved if the crucial sectors of; healthcare, education and to a lesser extent entertainment are going to be presented with Conversational AI which is fit for purpose.
Echoborg – 2016 and still evolving Actions speak louder than words, so goto www.echoborg.com to check out some of our trailers. As words go though, The AI, built by Elzware, gives the impression of being on the brink of sentience. It speaks through a human or “Echoborg”. It is programmed to
