Conversational AI Summit Conversational AI Summit schedule

Top 3 Most Complex Projects in the Current Environment

Even if AI provides “state of the art” solutions and research is gaining more ground, in real life Conversational AI doesn’t always have the impact expected by many. Florin and Zsolt Szekely will address the current challenges and will present a live demo of the latest and most complex developments. We at Bosch Service Solutions believe that the time for power point presentations, in which we create unrealistic expectations, need to be put behind us, and the focus should be on projects that can have immediate impact and the capacity to engage users, giving the same importance also to low-resourced languages.

This year, Florin together with his team started the implementation of complex Conversational AI frameworks (available for all languages, channels, and use-cases) designed to improve the AI based solutions within Bosch SO. The target is to continue the deployment of projects in each of the five Conversational AI pillars: Voicebots, Chatbots, Translation AI, Text/Email AI and Agent Assist AI. Prior to this role, Florin was involved in research, start-ups and other summits as keynote speaker - all these experiences giving him a 360 degree perspective on trends and challenges.

Superhuman Conversational AI

AI has reached superhuman levels in various areas such as playing complex strategic and video games, calculating protein folding, and visual recognition. Are we close to superhuman levels in conversational AI as well? In his talk, Yariv will address this question, sharing some of the recent developments from Google Cloud AI, Google Brain Research, Deepmind, and Duplex - across speech recognition and generation, and natural language understanding.

For the past 10 years, Yariv has been leading products and product teams at Google Zurich. In his current role, he is leading the Zurich product team working on the Google Assistant. Prior to this role, Yariv worked on a wide range of products, including proprietary Google infrastructure, privacy and security, products designed for the Emerging Markets, and even the notorious non skippable ads on YouTube. Before joining Google, Yariv was an engineering manager in various Israeli start-ups and companies.

Creating Conversational Assistants: Challenges and Solutions

Conversational assistants are all around us – as smart speakers in our homes, on our smartphones, in our cars. The Conversational AI market is expanding dramatically and major technology companies are producing ever larger language models and datasets to support the development of conversational assistants. However, the use of these assistants is currently mainly restricted to one shot conversations involving commands or questions, or in some cases to fulfilling well-defined tasks. More open conversations are still within the realm of research, yet these systems will be needed for new areas of application such as conversational commerce, healthcare and mental health support, and the promotion of active and healthy ageing in older people. Traditional approaches based on best-practice guidelines are compared with new neural dialogue based approaches. The presentation concludes with a brief look at some recent projects in which I have been involved and the different ways in which they are addressing the challenges faced by designers and developers of conversational assistants.

Michael McTear is an Emeritus Professor at Ulster University with a special interest in spoken language technologies. He has been researching in the field of spoken dialogue systems for more than 20 years and is the author of several books, including Spoken Dialogue Technology: Toward The Conversational User Interface (Springer, 2004), Spoken Dialogue Systems (Morgan and Claypool, 2010), with Kristiina Jokinen, The Conversational Interface: Talking to Smart Devices (Springer, 2016), with Zoraida Callejas and David Griol, and Conversational AI ( Morgan & Claypool 2020). Michael has delivered keynote addresses and tutorials at many academic conferences and workshops, including SpeechTEK, Conversational Interaction, ProjectVoice, REWORK AI Assistant Summit, and the European Chatbot Conferences. Currently Michael is involved in several research and development projects investigating the use of conversational agents in socially relevant projects such as mental health monitoring, and home monitoring of older persons.

Conversational AI: Mind The Knowledge Gap

Using NLP, NLG, Knowledge Graphs and Deep learning technologies makes it possible to build Conversational AI systems. Gaps in knowledge used by such a system are a critical challenge in achieving high accuracy and realistic Conversational AI. The knowledge gaps exist due to the lack of knowledge about the subject of conversation, or the knowledge the AI systems are built on becomes outdated. In this talk, Prof Thakker will present his research as part of an Innovate UK-funded project for building a Conversational AI system to support UK Immigration casework. Immigration law advice is a specialist domain, and underlying subject knowledge frequently changes due to changes in the immigration rules. Such knowledge dynamism is a common characteristic in other specialist domains such as other areas of law advice, financial advice, and medical advice. Prof Thakker will present work as part of this project in building NLP, NLG and Knowledge graph pipelines for conversational AI and explore techniques to overcome knowledge gap challenges. He will share lessons learnt with broader applicability in other domains.

Professor Dhaval Thakker is a Full Professor in Artificial Intelligence and the Internet of Things (IoT) at the University of Hull, UK. Dhaval has over fifteen years of working experience in the European Union(EU) and industrial projects researching and delivering innovative solutions. He has been a PI on projects with over €1m funding from Innovate UK, and European Commission. This includes the SCORE (Smart Cities and Open Data REuse) project with nine European cities and three other Universities and an Innovate UK-funded project on “Artificial Intelligence and UK Immigration law”, where his research focuses on Conversational AI.

AI for Spoken Communication

Do you want to know how chat bots like Amazon Alexa works? Why does Alexa understand what you say and how does she know what to answer? In this event we will discuss AI building blocks that Alexa has and what kind of improvement does AI provide in contrast to rule-based solutions

Elena is a manager in Applied Science in Amazon Alexa, working on developing new algorithms for natural language processing and speech. Before coming to Amazon, Elena made her PhD in Machine learning in the university of Nijmegen, the Netherlands. She also worked in Booking.com leading a team that was developing chat bot for Customer Service department.

Dr. Jeff Dalton is an Assistant Professor in the School of Computing Science at the University of Glasgow where he leads the Glasgow Representation and Information Learning Lab (GRILL) (https://grilllab.ai). His research focuses on text understanding and conversational information seeking. He completed his Ph.D. at the University of Massachusetts Amherst in the Center for Intelligent Information Retrieval. Later in Google Research, he worked on Information Extraction as part of the Knowledge Discovery Team (Knowledge Vault) and on language understanding in the Assistant Response Ranking team. He is the lead organizer for the TREC Conversational Assistance Track (CAsT) (http://treccast.ai) and previously helped organize the Complex Answer Retrieval track. He is the recipient of a prestigious UKRI Turing AI Acceleration Fellowship on Neural Conversational Assistants and received research awards from Google, Amazon, and Bloomberg. He is the faculty advisor for the 2021/2022 Alexa Prize Taskbot challenge team, GRILLBot. He holds multiple patents in retrieval, information extraction, and question answering.

Transfer Learning for Conversational AI: a Multidimensional Approach

The emergence of deep learning and recent advances in using large pre-trained language models have given conversational AI a big impulse. For a range of applications, models can be fine-tuned with only a limited amount of application specific data. However, for scalable development of spoken dialogue interfaces with true conversational intelligence, ultimately this approach may very well prove to be insufficient. In this presentation, I will argue for a multi-dimensional dialogue modelling approach, and discuss experiments, in which its transfer learning potential is demonstrated for the case of adapting reinforcement learning-based dialogue policies to a new application.

Simon Keizer is a research scientist specialized in conversational AI. He has a BSc/MSc in Applied Mathematics and a PhD in Computer Science, both from the University of Twente (The Netherlands). Simon has been active in spoken dialogue systems research for more than 18 years, focusing on topics such as statistical dialogue management, reinforcement learning, and user simulation. He has been involved in several national and international research projects at Tilburg University, University of Cambridge, Heriot-Watt University Edinburgh and the Vrije Universiteit Brussel, and is currently a research engineer in the Cambridge Research Lab, Toshiba Europe Limited.