I am interested in discussing an intriguing concept in machine learning, which promises to revolutionize the way we approach learning in robotics: World Models.

At a high level, World Models aim to create a compact and controllable representation of the world. Think of it as a mental simulation or an internal mini-world where AI can experiment, explore, and ‘imagine’ different scenarios, all without the need for real-world interactions. It’s like creating a sandbox game for AI, where it can learn the ropes before stepping out into the real world. ??

Let’s contrast this with the conventional end-to-end learning methods. These traditional approaches typically require vast amounts of real-world data and intensive training, which can be time-consuming, computationally expensive, and let’s face it, data-inefficient.

That’s where the beauty of World Models shines. By allowing AI to ‘dream’ or simulate possible scenarios in their internal model of the world, they can learn faster and more efficiently. They can plan and strategize better by running various ‘what-if’ scenarios within their world model. Imagine playing chess and being able to simulate all possible moves in your mind before making your move – that’s the advantage of World Models in a nutshell! ??

The ‘DayDreamer’ paper is a fantastic resource if you’re keen to delve into the specifics of this innovative approach. It opens up new vistas in our quest for smarter and more data-efficient learning in robotics.

In a world where data is king but also a constraint, World Models are pioneering a path towards more strategic, efficient, and thoughtful AI. So, let’s continue learning, exploring, and innovating. After all, the future of AI is as exciting as we dare to imagine!

#WorldModels #MachineLearning #DataEfficiency

G Research, an industry partner and supporter of our CDT, recently ran a competition for our PhD students, summarising their PhD research field and sharing how their work is both novel and has an impact in its field.

At G-Research we value supporting talent and innovation at institutions world-wide. We were thrilled to sponsor the UCL CDT in Foundational AI PhD prize. The video submission format provided the opportunity for PhD students to present their research in a concise way. We are grateful of the chance we were given to join in on talks and the poster session at Cumberland Lodge. We look forward to repeating the experience next year and meet more of the staff and students. – Dr Charles Martinez, Academic Relations Manager

There were three prize winners overall

1^st Jakob Zeitler
2^nd Augustine Mavor-Parker
3^rd Jake Cunningham

The standard of submission was excellent. All videos were very high quality, interesting and informative and we’re very proud of our students.

The prize is important since it has encouraged students to focus on learning the skills to communicate scientific ideas well and to a broader audience. The entries were also quite creative and we hope that all entrants found this a useful and enjoyable process.

Below is the winning entry video from Jakob Zeitler

We are delighted to that our students are excelling in their field and providing new ideas for the future of AI research.

The CDT wishes to thank G Research for its support.