THE EVOLUTION OF AI ASSISTANTS
AI has been around for a surprisingly long time. In 1308 'Catalan poet and theologian Ramon Lull published Ars Generalis Ultime (The Ultimate General Art)' which proposed a method of using paper-based mechanical means to create new knowledge from combinations of concepts. With thought on the matter having been around for 800 years, one would assume all advancements there are to be made in AI and teaching machines to learn independently would be long since achieved. As we all know however, this is not the case, as the potential for progress in AI is seemingly exponential. There have been progressions beyond those that Ramon could have possibly imagined and every week it would appear that more and more advancements are being made.
At the AI Assistant Summit in London this September we brought together leading researchers, data scientists, CEOs, CTOs, founders and industry professionals working with AI assistants to increase their efficiency. As these assistants become a more integral part of every day life, the research and progressions stretch from natural language processing to emotional understanding, deep text analysis, the challenges of designing virtual assistants for education, the behaviour of these assistants, how to build conversational AI at scale, and so many more things to consider.
Customer service is an area where AI assistants such as chatbots can provide really valuable customer service solutions. Adi Chhabra, Senior Product Manager of AI at Vodafone spoke about the evolution of machine learning and it's effect on customer satisfaction. 'With deep learning and neural network implementation, the traditional ML models are becoming dated. Often when a new technology has its breakthrough; it’s impact is only felt in hindsight. But it's different with AI and ML. Let’s talk about how ML powered Chatbots add value to the customer experience. From demand generation to fulfilment, all behind a seamless customer experience.'
Watch Adi's presentation:
In order to provide good customer service on an automated level, a competency in natural language processing is required from the model. It's important not only for the machine to not only understand the language it's being fed, but also the sentiment and context. Facebook have their own platform for text understanding which employs natural language understanding for relevance ranking, social recommendations, and marketplace suggestions. The platform, DeepText, is used not only to deliver the posts that a user is most likely to engage with but also to filter out any spam or undesirable content. David Testuggine, Applied Research Scientist at Facebook explained how they are building an understanding engine that can understand with near-human accuracy the textual content of several thousands posts per second in more than 20 languages.
Watch David's presentation:
Whilst AI is inherently rooted in technological progressions, many companies are working towards creating emotionally intelligent assistants. From mental health apps to weight loss assistants the empathetic potential of these models are potentially life changing. Andrew McStay, Professor of Digital Life at Bangor University explained how' artificial emotional intelligence (emotional AI) is under-represented, but is set to impact on society and diverse business sectors in important ways. That emotional life is becoming machine-readable brings benefits and concerns. This talk considers the following: what are the consequences of being able to see, read, listen, feel, classify, learn and interact with emotional life? What do citizens think?'
Watch Andrew's presentation:
Missed out on the AI Assistant Summit in London this year? We'll be back in London on March 15 & 16 with the AI Assistants Summit as well as the Deep Learning in Retail & Advertising Summit, and the Deep Learning in Finanace Summit. Super Early Bird discounted passes are end today (October 27), so register now to guarantee your place at the event.