CAMBRIDGE AI SUMMIT

Connect with experts, expand your network and upskill in Artificial Intelligence

MAIN CONFERENCE

Join our community of hundreds of researchers, analysts and data scientists for an opportunity to network, develop new skills and gain insight into the evolving field of data science.

Hear from industry and academic speakers representing a range of sectors, from research and bioinformatics to business and finance
Learn about the practical application and implementation of the latest tools, techniques to industry case-studies.
Share knowledge, pick up new ideas and connect with developers, analysts, researchers and executives.
Cambridge AI Summit

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Cambridge AI Summit

The Data Science Summit’s are all about putting research into action. You can see how the latest techniques are implemented, network with other leaders and specialists in the field who make research actionable, and get insight on how you can help transform your company, teams and the way you work.

Sarah Curshen, Director of Executive Education Custom Programmes, Cambridge Judge Business School

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Featured Speakers

Cambridge AI Summit
Prof. Kenneth Benoit

Professor of Quantitative Social Research Methods, London School of Economics

Session: Quantitative Text Mining, the Social Scientific Way: Mining Social Media on Brexit

Abstract: Text mining and text analytics form increasingly important subsets of data science. This activity may be geared toward extracting value from commercial data, improving policy delivery, studying human speech, or analyzing literature or the arts. In this talk, I present a distinct perspective for analyzing text as data from a social science perspective, meaning that text is used as data for qualities that we cannot observe more directly. I will discuss the implications of this perspective, and provide examples through ongoing work on text analysis of tens of millions of Tweets about Brexit, including machine learning to predict a user’s preference for Leave v. Remain, sentiment analysis, and topic models.

Cambridge AI Summit

Dr. Sebastian Kaltwang and Brook Roberts
Machine Learning Engineer, FiveAI

Session: Overcoming the Data Bottleneck for Self-driving Cars

Abstract: How can we efficiently obtain millions of annotated images for model training? State-of-the-art deep learning models have been able to achieve superhuman performance on various object recognition challenges. This makes them a suitable candidate for the safety critical perception tasks required in self-driving cars. There is one caveat: these methods require large amounts of data, which is typically obtained via a costly and time consuming manual annotation process. We at FiveAI work on urban travel that’s safe for everyone — without costing the earth. Not willing to make any compromises on safety, we needed to find a way to efficiently label large amounts of images. We noticed that driving the car is itself a form of annotation. Using this as a starting point, we can estimate a road plane in 3D based on where the car has driven and are able to project manual labels from this plane into all images of a video sequence. Using this semi-automated labelling process, we have been able to reduce the labelling time from the order of minutes down to 5 seconds per image.

Cambridge AI Summit
Kevin Nelson

Cloud Developer Advocate, Google

Session: Google Cloud AutoML

Abstract: Thanks to machine learning and AI, applications are now being created that can see, hear, and understand the world around them. Learn how you can easily infuse AI into your business today. In addition to a guided walkthrough and some fun demos of Google Cloud’s easy-to-use machine learning APIs: Cloud Vision, Cloud Video Intelligence, Cloud Speech, Cloud Natural Language, and Cloud Translation, we’ll demonstrate how Google Cloud AutoML enables developers with limited machine learning expertise to train high quality models by leveraging Google’s state of the art transfer learning, and Neural Architecture Search technology.

Cambridge AI Summit
Alison Lowndes

Artificial Intelligence DevRel EMEA, Nvidia

Session: Artificial intelligence and the evolution of the computing platform

Abstract: Artificial Intelligence is impacting all areas of society, from healthcare and transportation to smart cities and energy. AI won’t be an industry, it will be part of every industry. NVIDIA invests both in internal research and platform development to enable its diverse customer base, across gaming, VR, AR, AI, robotics, graphics, rendering, visualisation, HPC, healthcare & more. Alison’s talk will introduce the hardware and software platform at the heart of this Intelligent Industrial Revolution: NVIDIA GPU Computing. She will provide insights into how the computational demands for AI have impacted hardware evolution & how academia, enterprise and startups are applying AI, offering a glimpse into state-of-the-art research.

Cambridge AI Summit
Dr Haitham Bou-Ammar

Head of Reinforcement Learning and Tuneable AI, Prowler

Session: Data-Efficient Reinforcement Learning

Abstract: Thanks to machine learning and AI, applications are now being created that can see, hear, and understand the world around them. Learn how you can easily infuse AI into your business today. In addition to a guided walkthrough and some fun demos of Google Cloud’s easy-to-use machine learning APIs: Cloud Vision, Cloud Video Intelligence, Cloud Speech, Cloud Natural Language, and Cloud Translation, we’ll demonstrate how Google Cloud AutoML enables developers with limited machine learning expertise to train high quality models by leveraging Google’s state of the art transfer learning, and Neural Architecture Search technology.

Cambridge AI Summit
Dr Maksim Sipos

CTO, causaLens

Session: Automated feature extraction and selection for challenging time-series prediction problems

Abstract: In the case of predictive modelling, feeding time-series data directly into a machine learning algorithm often leads to sub-optimal performance. Most modern algorithms tend to be slow at learning the embedded time dynamics. This is especially the case in challenging problems such as datasets of small sample size and datasets containing low signal to noise ratio. A common solution is to include a pre-processing step, namely feature extraction. Given that many features can be extracted from each time-series, this leads to an exponential increase in the dimensionality of the data. Optimal feature set selection can be a time-intensive process and the optimal solution is a function of the choice of algorithm and parameters. The talk will focus on how including automated feature extraction and selection as part of a full machine learning optimisation pipeline can lead to superior results, especially in the case of challenging time-series problems.

Cambridge AI Summit
Dr Jeremy Bradley

Lead Data Scientist, Royal Mail

Session: Data Science as a Transformative process

Abstract: Data Science is often misunderstood or misused in a commercial environment as a means of creating more detailed insights in an existing operation – whether that be in ops, finance or marketing. This is a waste of the science and the talent. The real power of using science in a commercial environment is to link its results through well engineered tools to decisions – maybe in an automated, semi-automated or curated fashion. I will talk about some of my experiences of doing this at Tesco and at Royal Mail in this talk. Far from leading to a business operation with less human understanding and characteristics, I will argue that a new data science approach can lead businesses to take greater care of both employees and customers and benefit both as a result.

Cambridge AI Summit
Dr Steven McDermott

Qualitative Analysis and Social Media Lead, HMRC

Session: AI as Moderator/Mediator in the Recognition of Citizen’s Voice with Social Media

Abstract: Government departments are now utilising customer feedback channels and social media in an attempt to respond to crowdsourced insights and eventually informing policy. They are also using social media listening platforms to listen in to conversations taking place regarding their departments. They are also taking tentative steps into machine learning and AI techniques. The debates surrounding these tools have tended to frame such activity as surveillance and opening up the possibility of Armageddon with the rise of the machines. However, how can the voice of the citizen be recognised and responded to if these departments are discouraged from listening and using the latest tools? Does the utilisation of social media, machine learning and AI offer the potential means of escaping from the stranglehold of top–down, stage–managed politics. If millions of people could be the producers as well as receivers of political messages, could that invigorate democracy? And what role will machine learning and AI play in this emerging new media ecology? I intend to present a peak behind the curtain regarding the level of listening that is taking place and how machine learning and AI are being applied. Asking can this be done ethically and to enhance democratic processes and improve evidence based policy decisions. In which ways will democratic institutions have to change in order to meet these challenges?