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Journal Club 7:  Deep Reinforcement Learning and Curriculum Learning

23rd March 2021


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Don’t forget to join us on March 23rd at 6pm GMT for our last journal club of the year!

We have guest speaker, Minqi Jiang, who is a PhD student at UCL and a researcher at Facebook AI, presenting his exciting work on deep reinforcement learning and curriculum learning. To learn more about Minqi Jiang and his research, have a look at his website: https://minch.co

Introduction to AI in Robotics - Phase 4

15th March 2021



Come and join us on the 15th of March at 5PM GMT for the 4th session of our Introduction to AI in Robotics series!

During the penultimate session, we are going to focus on extending our knowledge in robotic vision as well as learning the basic principles of communication, and odometry. Finally, we are going to apply the things we acquired when building the double bot colour recognition system.

Risks from Learned Optimisation in Advanced ML Systems

12th March 2021



Evan Hubinger will discuss the paper of the same name (https://arxiv.org/abs/1906.01820), which tries to answer the question of how we might train models that end up pursuing objectives that are different from the loss functions they were trained under. Rather than the “outer alignment problem” of defining safe objectives, this talk will instead focus on the “inner alignment problem” of training models to robustly pursue such objectives—a solution to both problems being necessary for eventually producing safe AI systems.

More about Evan Hubinger:
Evan Hubinger is an AI safety research fellow at the Machine Intelligence Research Institute. Before joining MIRI, Evan was an AI safety research intern at OpenAI. His current work is aimed at solving inner alignment for iterated amplification. Evan was an author on “Risks from Learned Optimization in Advanced Machine Learning Systems,” was previously a MIRI intern, designed the functional programming language Coconut, and has done software engineering work at Google, Yelp, and Ripple. Evan has a B.S. in math and computer science from Harvey Mudd College.

Introduction to AI in Robotics - Phase 3

8th March 2021



Come and join us for Phase 3 of our Introduction to AI in Robotics series on the 8th of March at 5 PM!

During Phase 3, we are going to start working with “Webots” simulation software that will allow us to explore more complex concepts in the following weeks. In this session, we are going to apply the basic principles of robotic vision to build a bot that is able to differentiate the different colour objects.

Tutorial: Neural ordinary differential equations

4th March 2021



Describing complex phenomena directly is difficult to do. In those situations, an alternative, implicit definition can often help us model the phenomenon and perform simulations of it.

Ordinary Differential Equations (ODEs) do this by describing how a system changes, that is, they present equations that capture the "dynamics" of the system.

In this tutorial, we will begin to explore what learning these dynamics through neural network training allows us to do. Examples include making density estimation through normalizing flows continuous and allowing time-series models to take in data sampled at irregular intervals.

Introduction to AI in Robotics (Live Session 2)

1st March 2021


Robot Hand

Join us on Monday the 1st of March for our second live session of our Introduction to AI in Robotics series!

During the Phase 2 live session, students will learn how to construct and program a simple maze-solving robot. In addition to that, we are going to overview the solutions for the asynchronous material tasks.

Journal Club 6:  AI for Sustainability with Lily Xu

23rd February 2021


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On Tuesday 23rd of February at 6pm GMT we have Harvard PhD student Lily Xu talking about her research in AI applications in sustainability and social good at the AI Society Journal Club. Join us for a very exciting presentation and discussion!

Find out more about Lily Xu and her work at her website: https://lily-x.github.io

Women in Health Series: AI Solutions in Community Settings

19th February 2021


This will be our final event related to our Women In Health series, so come and join us for this talk on the Opportunities and Pitfalls in Developing AI Solutions for Community Settings, delivered by Dr. Rafah El-Khatib, Machine Learning Engineer at Feebris.

This is a great opportunity to learn more about AI and especially AI in healthcare!

Hand Gesture Recognition: Tutorial Project 1

18th February 2021


The majority of deaf-and-mute people use sign language produced by body actions such as hand gestures, body motion, eyes and facial expressions to communicate amongst each other and with non-impaired people in their daily life. However, it has become a barrier for mute and deaf communities which intend to integrate into society. To bridge the communication gap, a hand gesture recognition system for Sign Language Recognition (SLR) is required.

This project aims to design a real-time vision-based hand gesture recognition system with machine learning techniques, which potentially makes deaf-and-mute people life easier. In practice, signs are always continuously spelt words mixing both dynamic and static gestures, so the wanted recognition system should be able to recognize both dynamic and static gestures in ASL with promising accuracy.

AI IN ROBOTICS: An introduction

15th February 2021


Join us for this new series of tutorials to discover basic robotic concepts!

Facebook event: https://fb.me/e/5STWoA3Qb

Journal Club 5: Applications of AI in Healthcare

9th February 2021


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Change of plan! At next week's journal club,  we have Chongyang Wang, PhD student at UCL presenting his work on Affective Computing for Chronic Pain!


More about Chongyang Wang here: https://uclic.ucl.ac.uk/people/chongyang-wang

Women in Health Series: AI in Drug Discovery

8th February 2021



Join us for our next event this Monday for a talk on AI in drug discovery, hosted by Aylin Cakiroglu, senior scientist at Benevolent AI.

A great opportunity: there will be information about internships at Benevolent AI (deadline on the 12th of February).

Here’s a link to learn more about their internship program:

This is a great way to learn more about AI and you even get to ask your own questions in the last 15 minutes of the talk!

Women in Health Series: AI in Biomedical Informatics

5th February 2021


In this first lecture and Q&A of our Women in Health series, Dr Maha Farhat, an Assistant Professor of Biomedical Informatics, Harvard Medical School, will discuss data and statistical considerations that have helped improve resistance accuracy and discuss remaining gaps in the accurate prediction of antibiotic resistance and treatment response. She will also touch upon the need for translational research including clinical trials to help improve the adoption of ML as a resistance diagnostic.

Note that the session will not be recorded as Dr Farhat is presenting unpublished work, so make sure to join us live!

Facebook event: https://fb.me/e/8ndXqXMzh 

Soft Robotics & AI: Prof. Helge Wurdemann

28th January 2021


We are delighted to have Prof. Helge Wurdemann with us to talk about his work in the field of soft robotics and AI. Prof. Helge will be sharing with us his research interests in the field of minimally invasive interventions, robots for industry and autonomous systems. This will be the first event of an exciting project, so please join us for this session to learn more!

Journal Club 4: Deep Multi-Agent Reinforcement Learning

15th December 2020


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For our last Journal Club of 2020, we have a very exciting guest speaker.
Mikayel Samvelyan is a PhD student at UCL and a research assistant at Facebook AI Research working in Deep Multi-Agent Reinforcement Learning. He will be giving an introduction to the topic and going into detail on one of his most recent papers: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Hope to see you all there!

RSVP via the Facebook event.

Journal Club 3: Fairness in Machine Learning

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1st December 2020


The papers we will be discussing:

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Racial Categories in Machine Learning
And a third paper if time permits:

Mathematical Notions vs. Human Perceptions of Fairness: A Descriptive Approach to Fairness for Machine Learning

Hope to see you all there!

RSVP via the Facebook event.

Journal Club 2: Neural Network Compression

17th November 2020


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The following paper will be presented:

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Guest speaker: Kamen Brestnichki

Please have a read of the leaflet for summaries of the papers we will be discussing.

Journal Club 1: Natural Language Processing

3rd November 2020


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• Unsupervised Question Decomposition for Question Answering

• Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic

• A Survey of Multilingual Neural Machine Translation

Please have a read of the leaflet for summaries of the papers we will be discussing.

AISoc Introduction 2020/21

6th October 2020



Meet the UCL AISoc committee for the first event following our freshers fair. Gain insights into the application process, get advance knowledge about upcoming research and dev projects, and learn to navigate our site.

Artificial vs Natural Intelligence: Connectomics

13th February 2020


Prof. Moritz Helmst​ädter from the Max Planck Institute for Brain Research gave a talk about his work in the field of connectomics. His lab uses cutting edge techniques such as Electron Microscopy to decipher the complex structure and wiring of neurons. His team has reconstructed of a piece of mouse retina (2013) and recently layer 4 of the mouse somatosensory cortex (2019) which constitute the largest connectomes to date!

Deep Learning 2: Neural Networks

30th January 2020


Learn about PyTorch in this event hosted by the AI Core, the UCL Artificial Intelligence Society, and the UCL Technology Society. 

Deep Learning 1: Intro to Machine Learning

23rd January 2020


What is ML?
- Linear regression
- gradient based optimisation
- multivariate regression and feature normalisation
- intro to pytorch
- hyperparameters and validation sets
- intro to regularisation