Post-event information




The video of this event will be posted (here) later this year.


Professor Lawrence's presentation may be found HERE.

Deeper dive

More information about Professor Lawrence's work may be found HERE.

More information about AI@Cam may be found HERE.

Pre-event information


  • Tickets are £17.80 + Eventbrite fees and may be purchased HERE

Directions and parking

Information re venue location, parking, and layout may be found HERE.


18:00 Pull up a groove and get fabulous! (a.k.a. registration – please arrive early)

18:30 Scheduled talk: "AI needs to serve people, science, and society"

19:30 Pizza pizza pizza pizza pizza! (Stimulating conversation encouraged…)

20:30 Event close – you don’t have to go home, but you can’t stay here!


Let's imagine that we're designing an AI, or even an AGI. Many approaches to this problem have been proposed (in the last 70 years or so), only some of which have been implemented. For our present purposes, we will consider two contrasting approaches. One might be called the high-level approach (often called the symbolic approach), the other the low-level approach (often called the connectionist approach).

Broadly speaking, with the high-level approach, we first try to visualise what's going on at the higher levels of cognition (at the "conscious" level in those humans, in particular, who seem to be especially good at critical thought and problem solving), and then try to implement that behaviour (explicitly) in software. With the low-level approach, we start by implementing (some approximation of) what we think is going on at the lower levels of cognition (at the "subconscious" level in humans), in the hope that cognitive behaviour broadly corresponding to the higher levels of cognition will ultimately emerge from this low level cognitive behaviour.

The high-level (symbolic) approach is particularly well-suited to applications requiring exact pattern matching, whereas the low-level (connectionist) approach is particularly well-suited to applications requiring approximate or probabilistic pattern matching.

Thus the high- and low-level approaches are (in many ways) complementary, and attempts to combine these two approaches are generally referred to as the neurosymbolic approach. Any AI/AGI constructed in this fashion would potentially bear a striking resemblance to the ideas propounded by Daniel Kahneman in his bestselling book Thinking, Fast and Slow — the low-level (connectionist) components would broadly correspond to Kahneman’s fast-thinking System 1, and the high-level (symbolic) components would broadly correspond to Kahneman’s slow-thinking System 2.

On 8 March, Professor Lawrence Paulson delivered a CAIS Lecture on automated theorem proving, which epitomises the high-level, symbolic camp.

This month's speaker, DeepMind Professor Neil Lawrence, is a world-renowned expert on neural networks and machine learning, i.e. the low-level, connectionist approach.

On 11 October, Professor Alessandra Russo is scheduled to deliver a CAIS Lecture on neurosymbolic AI, i.e. the hybrid approach.

Note that the low-level, connectionist approach is currently dominant in the AI field by at least an order of magnitude!

About the talk

Title: "AI needs to serve people, science, and society"

Abstract: As artificial intelligence becomes ubiquitous in our homes and workplaces, we need to develop a widespread understanding of what it is and how we use it in the interests of our societies. Professor Lawrence will discuss how the artificial systems we have developed operate in a fundamentally different way to our own intelligence and how this difference in operational capability leads us to misunderstand the influence that decisions made by machine intelligence are having on our lives. Without this understanding we cannot take back control of those decisions from the machine. This will set the scene for approaches we are taking in Cambridge to address these challenges such as AI@Cam, the University’s flagship mission on AI.

About the speaker

Lawrence graduated from the University of Southampton with a BS in Mechanical Engineering in 1994, and (after a brief period as a field engineer on North Sea oil rigs) obtained a PhD in neural networks from Cambridge University in 2000. After spending a year as a postdoctoral researcher at Microsoft Research in Cambridge, Lawrence spent six years at the University of Sheffield as a lecturer in machine learning and computational biology, before joining the Machine Learning and Optimisation research group at the University of Manchester School of Computer Science as a Senior Research Fellow. In 2010, Lawrence returned to Sheffield as collaborative Chair in Neuroscience and Computer Science, before becoming Director of Machine Learning at Amazon Cambridge in 2016. Since 2019, Lawrence has been DeepMind Professor of Machine Learning in the Department of Computer Science and Technology at the University of Cambridge, where he is currently academic lead for AI@Cam, which is Cambridge University's flagship mission on AI. Professor Lawrence has acted as editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence, the Journal of Machine Learning Research, and the Proceedings of Machine Learning Research, is a Senior AI Fellow at the Alan Turing Institute, and sits on the UK's AI Council (which advises the UK Government).

About CAIS

Cambridge AI Social (CAIS) seeks to deliver a series of in-person “AI + pizza” events (CAIS Lectures) in Cambridge (one of Europe’s hottest AI hubs!)

Fundamental to the CAIS vision are:

  • speakers must be a recognised world leader in their field
  • events are non-profit, with minimal cost to attendees
  • each event comprises a roughly one-hour talk followed by socialisation (with pizza!) 


CAIS would not be possible without the support of its sponsors:

Please note

  • This is an in-person event (audience and speaker), and will not be livestreamed.
  • A recording of the talk, and images of the event, will be captured as part of the event.
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  • In particular, CAIS may license the event video to e.g. commercial VoD channels.
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