AI Assistant Summary

AI ASSISTANT SUMMIT

LONDON SEPTEMBER 19-20, 2019

EVENT REPORT: Highlights, attendee & speaker feedback, agenda overview, photos, videos, interviews & more. Thank you to our sponsors:

B AI ASSISTANT SUMMIT

500 3 60 2 DAYS ATTENDEES TRACK ACCESS SPEAKERS

12+ HOURS OF 10 20 13 NETWORKING EXHIBITORS INTERVIEWS SPONSORS

JOB ROLES COMPANY SIZE LOCATION CTO and CEO Data Scientists Just me! 2 - 10 US Europe Academics Solutions Architect 11 - 50 51 - 100 UK Asia Software Engineer 101 - 1000 1001+ Other

A NOTE FROM THE FOUNDER Since the inaugural summit 3 years ago, Sessions included Interpretation of Natural we've seen rapid progress in the evolution Language Rules in Conversational Machine of AI Assistants, in both their capability and Reading; Scaling Custom Virtual Assistants; impact, increasing in efficiency, accuracy and and How to Build Trust with an AI Assistant. emotional intelligence. At the 2019 edition, we also held the annual At this year's event, we heard from a range of Deep Learning Summit as well as the AI in speakers, from leading industry players Retail and Advertising Summit in parallel, including Salesforce, Samsung and Mozilla, to increase collaboration and cross-industry to influential researchers from DeepMind, learning at the event. UCL and the University of Cambridge, as well as emerging startups such as PolyAI and We're now looking forward to the 2020 San LiveSmart. Francisco edition, to hear further developments from the likes of Google, Viv We also witnessed a development in the Labs and Alexa AI. We hope to see you topics explored at the event, including more there! emphasis on natural language processing and real-world applications. Nikita Johnson CEO & Founder RE•WORK ATTENDEE FEEDBACK

“I’ve been coming to your summits since they started and it’s amazing to see how they’ve grown. There are so many AI conferences out there it’s hard to know where to go. It’s busy here which is a telling sign you’ve got it right. They keep getting better!” Ali Shah, ICO

“As a Data Scientist it’s nice to have a mix of technical and overview presentations to gain insights into both sides. The organisation, the attendees and the food is excellent. “

Hugo Palmer, Blablacar

“It’s a great mixture of business and technical. It’s also excellent to find an AI Conference that is not solely for blokes, and is inclusive.” Andrew Smith, ICS.ai

“Meeting some awesome mothers in tech. Great to see so much diversity in the speakers and attendees at s tech conference.” Mahtab Mirmomeni, IBM

“I haven’t been to one of your events before and I like that it’s more technical than lots of others. I wish I could be in every session at once though!” Catherine Breslin, Cobalt.ai

“I think you have got the format perfect, it’s not all pitches to sell but actual research and application. Over the last 3 years I have attended and the content has gone from new exciting ideas to the actual application of them and it’s great to see.” Nevena Franetic, Shopify AI ASSISTANT SUMMIT

This summit focused on AI Assistants: case studies, business insights, research & results of implementation within large companies.

Speakers shared their insights & lessons learned to provide advice based on their own experiences, case studies & applications across industries such as retail, utilities and space exploration.

EXPERT SPEAKERS INCLUDED:

Verena Dieser Aditya Guglani Marzieh Saeidi Thomas Wolf Professor in Artificial Data Scientist Research Scientist Chief Science Officer Intelligence Uber Facebook Hugging Face Heriot-Watt University

Krittika D’Silva John Spindler Nikola Mrksic Laura Palacio Garcia Lead Software General Partner Co-founder & CEO Senior Vice President Engineer AI Seed PolyAI LiveSmart Shell Energy Retail

Francesca Warner Martin Goodson Fabon Dzogang Jonathon Wright Co-Founder and CEO Chief Scientist and Lead Scientist on European AI Alliance Diversity VC CEO Conversation AI European Evolution AIA ASOS.com Commission PRESENTATION HIGHLIGHTS

“What can we expect from virtual assistants in the future? We will see systems become much more customisable, they will have much more information from context and finally-the holy grail- they need to become much more conversational.” Catherine Breslin, Cobalt AI

“We’re using end-trend response generation which is really exciting- with dialogue for the first time we have really large data sets from the likes of Reddit, Facebook and Twitter.”

Verena Dieser, Heriott Watt

“Natural language generation is turning concepts into writing or speech while delivering information. It also conveys the personality in the assistant.”

Dan Borufka, Samsung

“The change in performance shows the value of AI Assistants and Deep Learning. An Additional 5 million revenue from rolling out deep learning models.” Aditya, Uber AI

“Our most popular query? Where is my order! We have to develop capabilities to deal with enquiries like this. These include document classification, sentiment analysis, topic modelling, language generation and named early recognition.”

Fabon Dzogang, ASOS

“For machines to assist in information gathering it is essential to enable them to answer conversational questions.”

Marzieh Saeidi, Facebook AGENDA OVERVIEW DAY 1

CURRENT LANDSCAPE Usually when you present a data driven approach, the role based systems aren’t that strong, but in our case the rules were What can we expect from virtual assistants in really solid. the future? We will see systems become Verena Rieser, Heriot Watt much more customisable, they will have much more information from context and We are doing a lot more with less: less and finally-the holy grail- they need to people and fewer labelled data. This is why become much more conversational. we’re using transfer learning and multitask learning. Catherine Breslin, Cobalt AI John Glover, Aylien Biggest problem in conversational machine reading, as in other areas, is having a large AI Assistants behave poorly when asked data set which is difficult to acquire. some questions. On one hand they want to Marzieh Saeidi, Facebook give the best possible answer while they also want to protect their companies. There are two approaches to developing Dan Borufka, Samsung conversational interfaces. Traditional design and conversational AI. Conversational ai can Transfer learning is a way to try and get include symbolic approaches and data machines to learn from experience. When driven approaches. we have a new task we start with the Michael McTear, Ulster University model we’ve trained rather than starting Sensed information about human behaviour again. can be used to build models and make Thomas Wolf, Hugging Face predictions. But it can also be used to influence the behaviour of the individuals DESIGNING AI ASSISTANTS themselves. Mirco Musolesi, UCL

NATURAL LANGUAGE PROCESSING

Chatbots give you a hit of dopamine, they make you want to engage and keep engaging. Tim Gane, Lloyds Banking Group

Natural language generation is turning concepts into writing or speech while DEPLOYING AI ASSISTANTS delivering information. It also conveys the personality in the assistant. Dan Borufka, Samsung

When we applied machine learning, we Empowering customers to speak naturally. found that our models can predict stance We take our encoder and fine tune it. We with an 88% accuracy. embed sentences. Walid Madgy, Alan Turing Institute and Pawel Budzianowski, PolyAI University of Edinburgh DAY 2

START UP SESSION APPLICATIONS OF AI ASSISTANTS IN INDUSTRY & SOCIETY

One thing we are keen on is being able to work with a limited amount of data, huge data sets are a waste of time. We are constantly trying new methods, including active learning - through this method we are able to get the We’re working to augment healthcare same accuracy with three times less the solutions for patients like GPS does for training data. navigations. This is really broad so we need Martin Goodson, Evolution AI to start somewhere, so we’ve started with emergency services. They receive around 1 We are aggregating over 450k articles a week billion phone calls a year, so we have so from 15,000 sources to filter out fake news. much data we can use to help citizens. This can be very slow, so we started to look at Lars Maaløe, Corti how we could use tech to speed this up - full automation is not quite there. We have to be We’ll be using two supervised learning 100% accurate as it would be poor if we models. We will be retraining existing can’t. We fact check images too, photoshop models. The reason for that is that they have is a big part of this. We generate images so been retrained on large image data sets. that people can see the claims which have They also have optimal network architecture. been input and what our judgement is it. We We will be taking advantage of these models vectorise claims, capturing the meaning of using transfer learning. This will reduce our the claim which is used to pair the vector to training time which will improve our model similar claims and fact checks in our database performance. - if we find something similar then we can Laura Palocia Garca, Livesmart mark it as true or false. Ben Alter, Logically There is an expectation of conversation to be available 24/7. This is where AI Assistants We use slack data to help build an idea of come in. We have been developing the company culture through archetypes. We something over 5 years- we were the first can provide information on strengths and utility company to have a chatbot. This was a weaknesses as well as recruiting, you can run leap of faith from our management team. and EMMA on candidates to see how they Natalia Konstantinova, Shell Energy Retail would be best fitting in your team. Sam Edds, Bunch AI It’s an exciting time for space exploration, as we send people to the moon, Mars and beyond its important to look after our astronauts health. The target is to have an AI doctor on board which would help to combat the health issues which arise from further away missions. Krittika D’Silva, Nasa Frontier Development Lab DEEP DIVE TRACK: HIGHLIGHTS

Deep Reinforcement Learning- DeepMind

“ We want to build on the intelligence of the general level. What would AGI look like? The agent is small and the world is large, there is only so much you can learn. First we automated physical solutions. The next step is to learn solutions from experience and then learn to Hado Van Hasselt find relevant experience. “ Senior Staff Research Scientist

Machine Yearning: A Detailed Study of taking ML to Production - Sainsbury’s Argos

“Be agile, have the minimum mpp that you can get away with and get to production ASAP. Have a cross functional team. We rejected linear, soiled working.”

“Not only were we trying to match a third party legacy Dave Romano- Critchley system, we were trying to match what is actually Principal Engineer happening in the business.”

“We replaced legacy third party service with in-house calculation. We followed standard software engineering practices. We kept stakeholders included throughout the process. This became a pipeline that we can use for other algorithms, not just price elasticity.” Sumanas Sarma Machine Learning Engineer

Rising Stars: The Next Generation of AI Pioneers “We are sleep walking into a global food crisis. Our idea is to use AI to monitor the conditions during hydroponic plant growth.” Hydroponics AI, team of teens from the Teens in AI initiative

“Green Feast is an automated web extension which reduces your carbon footprint and helps to make more sustainable purchases by telling you its carbon footprint and possible alternative purchases." Green Feast, team of teens from the Teens in AI initiative

“Early Catch is aiming to use AI to catch breast cancer earlier and prevent invasive diagnostic measures by using an AI model which tests biopsy and works out if they are cancerous more accurately. “ Early Catch, team of teens from the Teens in AI initiative INTERVIEWS & PODCASTS

As well has hosting events, RE•WORK has a We’re always open to new collaborations and catalogue of online resources with White Papers, love hearing from attendees and speakers alike. video interviews, our blog, and the Women in AI If you are working on something you think would podcast. At each event, we chat with speakers be valuable to our content hub, don’t hesitate to and industry experts who share their expertise. get in touch with Luke: [email protected]

Laura Palacio Garcia, Live Smart Aditya Guglani, Uber

Laura Palacio Garcia, data scientist at health We interviewed Aditya Guglani from Uber who startup that provides integrated solution to delved into further detail on Uber’s experience of empower employees to optimise physical and using deep learning to improve the customer mental well-being at work called LiveSmart, support experience. joined us for an episode of our Women in AI Podcast.

Catherine Breslin, Cobalt AI Shreyansh Daftry, NASA JPL

Catherine Breslin from Cobalt.AI joined us for an On day 2 we interviewed Shreyansh Daftry, from episode of our Women in AI Podcast. During this NASA JPL. He discussed how deep learning is episode she discussed scaling custom virtual being used in space exploration and how the assistants and the main challenges associated conditions in space can be emulated. with this.

Neil Lawrence, Shefﬁeld University Krittika D’Silva, Cambridge University

We spoke to Neil Lawrence, currently Professor We interviewed Krittika D’Silva, PhD student at of Machine learning at The University of Sheffield Cambridge University and discussed the work about what a typical day might look like for him. that she did as an AI Researcher at NASA Frontier Development Labs. WHAT’S NEXT?

Video presentations will soon be available online and you can access them here. On our video hub we also have extensive playlists from our past events.

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