AI Assistant Summit schedule

Helping Humans Understand Each Other - Emma, Your AI Psychologist

Ever wanted to scratch the surface and understand the inner motivations of the people you encounter in your professional life? Behind a CV or an online profile, there is a person with tons of drives that won't be possible to see for most humans. That is why we created Emma, a AI-powered psychologist that tells you what's behind the surface of your business partners, potential new hire or your boss. But the road to design a machine that can dig deeper than any human mind could go was definitely not an easy one: join this talk / session to discover how to make relationships more human thanks to AI.

Charles is co-founder at Bunch.ai and runs their engineering team. Bunch.ai is the team success platform for managing culture with data. Full stack software generalist with a knack for data, Charles is passionate about Software Craftsmanship, reactive architectures and Audio Synthesis. In addition to growing the engineers team at Bunch Charles mentors developers at askadev.org.

Intelligent Medical Decision Support in Conversations

At Corti, we imagine a future where every medical professional can perform at an elevated level thanks to a new kind of human-computer partnership. Corti provides this partnership by utilizing machine learning technology to enhance the decision-making abilities within real-time conversations. In this talk Lars will present Corti’s current machine learning stack and the research that is currently conducted to improve the system even further.

Lars is the Chief Technology Officer and co-founder of Corti and has previously worked with companies like Apple and Issuu. He holds a PhD from the Technical University of Denmark with an extensive research and publication history within semi-supervised and unsupervised machine learning.

Building Generative Models of Symptomatic Health Data for Autonomous Deep Space Missions

Krittika will speak about her work at NASA FDL in which she examined how AI can be used to support medical care in space. Future NASA deep space missions will require advanced medical capabilities, including continuous monitoring of astronaut vital signs to ensure optimal crew health. She will discuss how biosensor data collected from NASA analog missions can be used to train AI models to simulate various medical conditions that might affect astronauts. She will also discuss the future of AI and space medicine.

Krittika is a Gates scholar and Computer Science PhD student at Cambridge. Her work has had a far-reaching impact and been highlighted in National Geographic. Krittika has previously worked at Microsoft Research and Google. In 2018, she worked at the United Nations Pulse Lab in Indonesia, an innovation lab formed to harness data science insights for policy. She also works with NASA FDL to utilize AI for astronaut health monitoring in deep space missions.

Supervised Learning for Food Recognition

The colourful foods project at LiveSmart was built so that users could upload an image of their food plate to be analysed in order to obtain information about their diet. The analysis includes different steps, starting with the object detection using the YOLO model that will split the food plate into the different objects (food items) it contains. Then, we perform image classification on every detected object using the pretrained Inception model and assign a food category.

These two models need to be retrained using our 35K custom food images, which have been previously manually cropped and classified into 21 food categories. Finally, we perform a spatial analysis, a colour analysis and a nutritional content analysis in order to calculate the areas, colours and nutritional content of every food item in the plate. This will be used to understand how much variety the food plate has and how colourful the food plate is, which are both evidence of a healthy diet.

Laura Palacio García is a data scientist at LiveSmart, a health startup that provides an integrated solution to empower employees to optimise physical and mental wellbeing at work. She studied Biomedical Engineering in Barcelona, Spain and then did her MSc in Human and Biological Robotics at Imperial College London before starting to work for LiveSmart. She has previously worked as a data scientist in a biotechnology institute and a tech startup.

Reducing Customer Effort by Using AI Technology

The talk would present a use case of AI solution that helps Shell Energy to deliver great customer service 24/7 and make sure customers have a way to find information they need quickly and efficiently. It will touch on challenges of introducing such system and lessons learn over 5 years of its work.

Natalia Konstantinova is a great enthusiast with over 10 years' experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems. She got her PhD from the University of Wolverhampton and worked in various fields such as machine translation, ontologies, information extraction and currently dialogue systems and chat bots. She is currently a lead software engineer in the R&D department of First Utility, the UK's largest independent energy provider, supplying gas and electricity to around one million UK homes.

Natalia is a strong believer that modern technology can transform businesses and our everyday life.

Where is my parcel? Fast and Efficient Classifier to Detect User Intent in Natural Language.

We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS.com Customer Care Department, namely “Where is my order?”. These queries are characterised by the use of colloquialism, label noise and short message length. We conduct extensive experiments with two well established classification models: logistic regression via n-grams to account for sequences in the data and recurrent neural networks that perform the extraction of sequential patterns automatically. These models are calibrated to perform above Human performance (0.93 precision), revealing a small difference in Recall of 0.05 for the neural networks (training under 1hr), and of 0.07 for the linear n-grams (training under 10’). Overall our results indicate the latter as a judicious choice of model architecture in modern AI production systems.

Fabon obtained his PhD in Computer Science in 2013 “on Learning and Representation from Texts for both Emotional and dynamical Information” at the University of Pierre et Marie Curie, DAPA department (LIP6). After graduating he held a post-doctoral position at LIP6, researching interpretable models for the classification of multivariate time series’ data. At this time he grew an interest in the analysis of time series’ data, and in the Fourier transform as a mean to extract meaningful features from data. He later joined the University of Bristol as a Research Associate in 2014 investigating efficient machine learning algorithms for data streams, and researching models to study our human behaviours at a collective level via the detection of periodic patterns in the social media or in large samples of press archives. Since 2018 Fabon is Lead Scientist ConversationalAI at ASOS.com working on the development of new NLP technology for Chatbot applications.

Deep Reinforcement Learning

Reinforcement learning is the science of how to learn to make decisions through interaction. Deep learning is the science of learning representations, mappings, and functions from data. The intersection of these two fields has great potential, because deep reinforcement learning algorithms give us a means to learn behaviour through interaction with complex problems, by using general learning algorithms rather than having to rely on extensive domain knowledge. In my talk, I will discuss why I'm excited about this research field, I will talk about open challenges, and discuss some recent successes in applying these algorithms to interesting problems.

Hado van Hasselt is a Senior Staff Research Scientist at DeepMind. He joined DeepMind in 2014, after doing a postdoc at the University of Alberta with Rich Sutton. He obtained his Ph.D. in artificial intelligence with a dissertation on reinforcement learning at Utrecht University in the Netherlands. His research focuses mostly on core learning algorithms that have the potential to scale to rich and interesting domains, and on the intersection of reinforcement learning and deep learning.

In early 2019, the High-Level Expert Group on AI presented a set of ethics guideliens for trustworthy AI. What do these mean and how can you apply best practice in your organisation? Join us for a 'town-hall' style session where attendees will have the opportunity to ask experts their questions and practical concerns, and to offer some key takeaways and best practice for attendees to apply in their own work.

Join industry leaders to discuss and share your challenges in integrating AI, ML & DL into your business.

Session takeaways: 1) What is emerging in the field? 2) What industry do the next generation believe will be most impacted by AI over the coming years? 3) What inspired these rising stars to explore STEM and what is the role of all stakeholders in encouraging young people to do the same?

As Machine Learning becomes a core component of any forward-looking company, how can we engage the entire workforce to help with ML and automation initiatives? This talk will cover how to adopt a "machine learning mindset" by 1. providing training to all employees (both technical and non-technical folks) on what ML is and how it works, including current applications and ethical considerations, 2. conducting structured brainstorming sessions to elicit automation opportunities, where everyone can contribute what ML could mean for their team or their customers, and 3. implementing a subset of those ideas by partnering with infrastructure, operations, and product teams, resulting in improved risk management, more efficient internal operations, and novel customer-facing product features.

BehindTheScenes - Salesforce Voice Assistant

Using Deep Learning technology, we intend to transform unstructured data like voice and text to more structured data which empowers users to execute actions. This is a huge problem statement in itself even if one company was to build an assistant to interact with their organization, but it is even more challenging to build this as a platform while being agnostic of underlying details.

Level of expertise to benefit from the session: It is appreciated for the audience to have some amount of technical knowledge as it would entail different parts required to build such an assistant.

Vishal is a Senior Member of Technical Staff at Salesforce, where he works on Einstein Voice Assistant, bringing the best in class NLP capabilities from Salesforce AI Research to production. Prior to Salesforce, he has contributed to research evaluating impact of lawyers voice in US Supreme Court case outcomes. He has also worked on predicting optimal distribution of bikes in New York City. He holds a Masters in Computer Science from New York University's Courant Institute of Mathematical Sciences where he spent his time studying Natural Language Processing, Computer Vision and more.

Session takeaways: 1) Why is data preparation important? 2) Where should you gather the data; 3) How do you handle missing data

This session is for everyone.

Session takeaways: 1) What are the short, medium and long-term challenges in investing in AI to solve challenges in business & society? 2) What are the main success factors for AI startups? 3) What are the challenges from a VC perspective?

Session takeaways: 1) Is AI the answer to solving this challenge, and if so, why? 2) What partnerships need to be formed to move this problem forwards? 3) What is the role of industry and academia to solve this challenge in question?