Cohort Size
Mar 28 - Apr 7, 2024
10 days
January 21, 2024

Course Description

ML4Good is a bootcamp that aims to provide advanced training in deep learning to those who want to work towards making AI safe and beneficial to humanity.

This camp will fast-track your deep learning skills, inform you about AI safety research, allow you to explore conceptual challenges, and connect you with like-minded individuals for potential friendship and collaboration.


How will the days be spent? 

  • Peer-coding sessions following a technical curriculum with mentors.
  • Presentations by experts in the field.
  • Review and discussion of AI Safety literature.
  • Personal career advice and mentorship.
  • Evening activities - and time to rest!
  • The bootcamp is free. There is no fee for room, board, or tuition.
  • This bootcamp is aimed at people currently based in the UK and nearby countries. There will be more camps running in 2024 - please sign up on our website to be notified when these are confirmed and when applications open.
  • We ask participants to pay for their own travel costs - however, if this is preventing you from attending we will have the option to apply for travel support.

First part of the camp (7 days)

  • Implement ResNet from scratch in PyTorch, implementing all the layers from scratch and loading weights from a trained model.
  • Implement interpretability techniques on the ResNet.
  • Implement SGD and other local optimization algorithms, run remote hyper-parameter searches on a simple architecture.
  • Implement a simple clone of some of PyTorch, with particular focus on the implementation of back-propagation.
  • (Optional) CUDA programming day–write various CUDA kernels, see how close to the performance of PyTorch’s kernels you can get.
  • Implement GPT-2 from scratch, implement beam search.
  • Fine-tune BERT on classification, fine-tune GPT-2 on some specific corpus.
  • Look at various interpretability techniques on GPT-2.
  • Data-parallel training.
  • Conceptual lectures and discussions.

Second part of the camp (3 days)

AI Safety literature review and projects on topics such as:

  • Interpretability of language models.
  • Adversarial robustness of neural networks.
  • Mathematical frameworks for artificial agents’ behaviours.
  • Conceptual research on AI Alignment.
  • AI Governance: the semiconductors supply chain.

These are simply guidelines - anyone is welcome to apply.

This program is aimed at people in the UK and nearby countries who are comfortable with programming and at least one year’s worth of university level applied mathematics.

Despite our focus on machine learning and technical alignment in this course we think that ML4Good is a good use of time for some people who do not plan to do this kind of research long term, but who intend to work on other things where being knowledgeable about ML techniques is useful. If you are someone working in AI Governance and could do with a more technical foundation on which to act for example, this bootcamp may prove useful.

We welcome applications from those who fit a majority of the following criteria:

You are motivated to work on addressing the societal risks posed by advanced AI systems - ideally, motivated enough to consider making significant career decisions such as transitioning to technical alignment work, setting up a university AI safety group, or founding a project

You have a programming background and want to learn how to contribute your skills to the field of AI Safety

You have relatively strong maths skills (e.g. Mathematics level equivalent to at least one year of university education). Including:

  • Linear algebra (matrix operations, eigenvalues and eigenvectors, linear subspaces)
  • Analysis (multivariable calculus)
  • Probability (random variables, expected values, conditional distributions, Bayes theorem)

You have a high level of proficiency in English

You can commit to completing our prerequisite material before the bootcamp (we will send this to you upon acceptance)

  • We expect this material to take 10-20 hours
  • This will include AI Safety conceptual readings and may include programming or mathematics preparation depending on your strengths

How many people will attend the camp? There will be 20 participants and 6 staff members.

Will there be any spare time? There will be periods of leisure and rest during the camp. However, the course is intensive and full-time - don’t plan to do anything else during the camp.

What language will the camp be in? All courses, instruction, and resources will be in English.

What do you mean by AI Safety? By “AI Safety” we mean ensuring that AI doesn’t lead to negative outcomes for sentient beings or the premature disempowerment of humanity. In a recent open letter signed by many deep learning pioneers, it is stated that “mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” Avoiding these bad outcomes is a challenge that has to be tackled on a societal level. In this camp, we will focus on technical approaches to building safer AI systems, for example by making their internal processes more interpretable.

I am not sure my level of technical knowledge is sufficient. Please see the prerequisite section above to see what level of technical knowledge we are looking for. If you have particularly strong mathematics skills then we would accept less programming experience and vice-versa. If you are still unsure, feel free to contact us. Additionally, before the camp begins we will provide some preparation work.

How much do I need to know about AI Safety to apply? We expect participants to have a solid grasp of why and how an AI could become existentially unsafe for sentient beings and a general overview of proposed solutions. When it comes to theoretical AI Safety topics, we don’t expect an advanced level of knowledge. You will get more value out of the event if you have more familiarity with AI Safety beforehand. We will provide some reading before the camp for those less familiar.

What might an ideal candidate look like? We have particular interest in those who we can support in planning concrete actions towards working on the reduction of AI risks. Examples of promising candidates include:

  • You are an undergraduate in a technical subject with an active github account and you would consider setting up an AI Safety Reading Group at your university.
  • You are early in your career or are a masters student in a technical field and you are interested in exploring a future career in alignment to reduce risk from advanced AI.
  • You are a professional in the field of software engineering or data science and are looking for a way to alter the trajectory of your career towards work on AI Safety. You would be happy contributing engineering talent to open source tooling or helping found a new project.
  • You already have prior machine learning experience and are keen to apply your skills to reduce risk from AI and plan to act on this by e.g. changing jobs, or planning your career, or would be willing to join early stage projects.


Charbel-Raphael Segerie
Teacher, Curriculum Designer
Charbel is the co-head of the AI Safety Unit at EffiSciences. He will be the primary instructor for the coding parts of the event and has been the lead curriculum developer for all past iterations of ML4Good. He is teaching technical AI Safety in ENS Paris-Saclay in the Mathematics, Vision and Learning master.
Diego Dorn
Teaching Assistant
Diego is a Master student from EPFL in Switzerland, and currently working for the summer at David Kruger’s lab on detecting goal misgeneralisation in RL agents. He participated in the first ML4G, organised in France last summer and has now joined the other side of the organisation.
Nia Gardner
Nia studied Computer Science and Economics at university and has spent the past few years working as a software engineer.