Space is getting incredibly crowded. With over 40,000 tracked objects zipping around at ten times the speed of a bullet, managing our orbits has shifted from a traffic problem to a critical national security mission.

In this episode, we discover Project Aemelia, a pioneering initiative funded by the UK Space Agency. We see how a collaboration between the Alan Turing Institute and the University of Birmingham is rewriting the rules of Space Domain Awareness (SDA).

Join host Amelia Jabry, Dr Victoria Nockles, Principal Investigator - Defence AI Research (DARe), and Professor Marina Gashinova, University of Birmingham, to explore:

• The Space-to-Space Challenge: Why observing fast-moving satellites from other satellites is an engineering hurdle of cosmic proportions.
• SAR vs. ISAR: How Inverse Synthetic Aperture Radar (ISAR) is being used to map the 3D structures of uncooperative satellites, rockets, and orbital debris in real time.
• Autonomous Missions: How researchers on project Aemelia are going one step further and not just looking at new imaging systems, but designing fully-autonomous space missions.

Whether it's defending vital communications satellites or dodging space debris, find out how autonomous digital twins are safeguarding our future above the clouds.

Read more about Project Aemelia here: https://www.turing.ac.uk/research/research-projects/aemelia

https://www.birmingham.ac.uk/staff/profiles/eese/gashinova-marina

Chapter markers

0:00 Intro – the tricky problem of tracking satellites in space

1:44 What is space domain awareness?

3:20 How much space is there in space, or, where are the satellites?

4:30 How do you traditionally monitor satellites in space?

5:50 Introducing Synthetic Aperture Radar imaging (SAR) and how it works

7:20 The ‘space-to-space’ challenge

8:00 Now invert it: Inverse Synthetic Aperture Radar imaging (I-SAR)

9:00 The challenge of using I-SAR in a space-to-space setting

10:40 dealing with extreme speeds in real-time

13:00 Introducing Project Aemelia

14:18 The multidimensional aspect of I-SAR: multistatic setups, varied polarisation, and high frequencies

17:40 Setting up an autonomous space mission as we move further and further from Earth

19:45 Applications of this new technology

22:30 The case of the inspector satellite: how to deal with threats in space

24:00 The second space race – an explosion of capability (cubesats and beyond)

26:20 Stop interfering! The electromagnetic interference problem

28:00 The journey behind AEMELIA – a successful marriage

31:00 What is next on the horizon for I-SAR and SDA?

32:30 A sci-fi anecdote

34:20 Where to find out more

In an era of increasingly unpredictable extreme weather, getting the forecast right is no longer just about what you need to wear during your morning commute. It’s about national resilience, food security, and technological sovereignty.

In this episode, host Amelia Jabry is joined by Dr Scott Hosking (Mission Director for Environmental Forecasting at the Alan Turing Institute) and Professor Kirstine Dale (Chief AI Officer at the Met Office) to introduce FastNet: a groundbreaking, home-grown AI weather model developed right here in the UK.

Together, they demystify how FastNet uses 3D geometry (specifically, an icosahedral mesh) to map our round planet, how it spots patterns across 40 years of climate data. They also explore why we still desperately need physics to prepare for unprecedented ‘Grey Swan’ climate events like a looming Super El Niño. They tackle the vital questions of making our AI sustainable, the critical importance of a diverse AI workforce, and why the UK must maintain its own independent weather modelling capabilities rather than relying on global tech giants.

Read more about FastNet here: https://www.turing.ac.uk/research/research-projects/fastnet

Chapter markers

0:00 Intro

1:04 Introducing Scott Hosking and Kirsten Dale

1:50 The Raucous Revolution in weather forecasting of 2022

04:17 how AI weather prediction models work and how they differ from traditional weather prediction models

05:15 Why traditional physics based models still remain important in forecasting

07:00 The training data AI models rely on

7:50 The uses of AI weather models and the advantages of being computationally light

09:16 Turing’s partnership with the MET Office – why is the weather important?

11:50 Introducing Fastnet: How cutting-edge weather models can tackle climate insecurities

12:30 How AI weather models differ from LLMs

13:15 How Fastnet uses an  icosahedral mesh to map the Earth.

16:05 Grey Swan events and Super El Niño

19:40Comparators to Fastnet and introducing a regional model for the uk

20:30 Improving the accuracy of fastnet

24:55 Green AI

26:30 The importance of diversity in AI modelling

28:30 Why it is important for the UK to have its own weather model

30:30 The future of Fastnet

32:13 Outro and find out more

This special episode of the Turing Podcast revisits Project Bluebird – the project that featured on our very first ever episode on the podcast in 2020! Host Amelia Jabry is joined by Ben Carvell, an aviation researcher leading AI agent design on Project Bluebird, and Andrew “Andy” Pace, a London terminal air traffic control officer (ATCO), who describes ATCO work, sectorisation, radar-based operations, and the still-analogue reliance on voice. They examine the growing complexity of air traffic control as traffic returns to, and surpasses, pre-COVID levels and the added challenges from drones, new aircraft types, and emissions constraints, stressing human cognitive limits in a safety-critical environment. Bluebird is a multi-year partnership between the Alan Turing Institute, NATS, and the University of Exeter with three themes: building a high-fidelity digital twin, developing virtual controller agents using methods from rules to optimisation and reinforcement learning, and addressing trust/explainability. Join Amelia, Andy and Ben as they discuss the challenges and opportunities of building an AI system to assist ATCOs in one of the most safety-critical industries in the world.

00:00 The Hidden Chessboard at the heart of Air Traffic Control

00:37 Meet Ben Carvell and Andy Pace

03:20 What Air Traffic Control Officers (ATCOs) Do

06:20 Safety Rules for aviation and the moral responsibility of ATCOs

08:00 Introducing Project Bluebird

08:52 3 Main themes of Bluebird’s research: Digital Twins, AI Agents for Air Traffic Control, and Trust

10:30 The legal and moral responsibility of Air Traffic Control

12:20 The Science: The Physics Informed ML (PML) behind Bluebird. Including dealing with variability and the unknown.

14:55 Rules Optimisation versus Reinforcement Learning (RL)

15:50 A blast from the past

17:40 A collaboration with Johns Hopkins University tackling the issue of explainability in RL introducing ‘Relevant traffic’

20:36 TRUST: Safety Minima vs Collisions in Airspace – what is ‘non-negotiable’?

23:40 Interrogating the AI’s logic and maintaining trust

24:00 A curriculum for machines? The AI’s and ATCOs final exam

26:00 Bringing an ATCO onto project Bluebird – tool support for ATCOs

30:00 Intuition vs learning – the ‘mystical ATCO scan’

32:30 The ever-crowding airspace and increasing challenges to ATCOs

33:30 They history of Bluebird: 2020 versus now

35:40 How ATCOs really feel about Bluebird

36:02 Introducing Falcon - Are humans the ultimate guarantor of safety, or is our cognitive limit the ceiling for system efficiency?

40:15 Move 37 and the potential for Discovery

43:20 Collaboration between institutions and the real outputs Bluebird has made (the game!)

44:22 Wrap Up and Find Out More