Hot Takes | Cold Proofs

University of West London — School of Computing and Engineering

Every field has its hot takes — the things people in the room believe, argue about and repeat, but rarely have to defend in public. Hot Takes | Cold Proofs is the podcast from the School of Computing and Engineering at the University of West London. Each episode puts two academics in a room to say what they actually think about AI and technology, then asks the harder question: what's the cold proof? Hosted by Atiyeh Ardakanian and Nino Auricchio. No press-release positivity, no doom. Just people who know their subject, disagreeing productively.

Episodes

  1. 2d ago

    Episode 7: You Always Take Yourself With You

    He opens by taking away the word the whole subject is built on. There is no such thing as a gamer, he says, because on personality, values and how they handle the hassles of ordinary life, gamers are indistinguishable from everybody else. They are just people who do this thing. We don't talk about badminton players or cyclists so much. And the industry is bigger than film and music combined. So the question is not who plays. It is what happens to you while you do. Professor Mark Coulson studies the traffic between a person and a virtual world. He borrows a line from a French travel writer: the problem with travel is that you always have to take yourself along with you. His better metaphor is a semi-permeable membrane. He can't carry an object into a game or take gold out of one, but he can take his experiences out. Some participants told him the question was beside the point — I'm only driving a character through a world, none of it touches me. That, he says, is simply denial. The surprise is about love. His interest started with a game he played himself and finished thinking he had genuinely cared about those characters. Other players report attachments that measure comparably to enduring love. Asked whether the appeal is a companion who never rejects you, he says the opposite. Game characters are far from sycophantic; some take an instant dislike to you. He thinks the interactions people have with them are less sycophantic than the ones they report having with AI chatbots, where the model shapes itself around your beliefs until you are both inside a bubble. He doesn't think that is psychologically healthy. Is any of it rehearsal for real life? Absolutely, he says — a safe space to experiment with behaviour, choices and your own sexuality. The strongest evidence is not from his lab. The None in Three group builds simple games co-designed with the children who play them; the UK one is about coercive control, and it measurably increases what a player understands as domestic abuse. The counterweight is the last honest thing he says. Two stories about acting out darker impulses somewhere consequence-free: you let it out and you're fine, or you desensitise yourself and grow more confident you could do it for real. The same behaviour, opposite consequences. He doesn't know which is right, and doesn't pretend to. Also in this episode: why gaming disorder is in the WHO's ICD-11 but only a condition of interest in the DSM, and what games won't let you do. Hosted by Atiyeh Ardakanian and Nino Auricchio, from the School of Computing and Engineering at the University of West London. Produced and edited by Jack Hoggard. Recorded at LSFMD, UWL, with Tim Bowerman and Arthur Page. #Psychology #VideoGames #Gaming #OnlineBehaviour #GamingDisorder #AIChatbots #CoerciveControl #Research #Podcast #UWL #UniversityOfWestLondon

  2. Aug 26

    Epidode 6: The Person Who Can Help You Is in the Next Room

    Every research culture has a version of the same problem. The people with the least power are the ones who most need to be in the room, and they are the first thing cut when the diary is full. This episode is about what a university does about that. Dr Livia Lantini coordinates UWL's Early Career Researcher Network, which began as a conversation with the head of school: Professor Phil Cox asked why there wasn't something for people at the start of their careers, and she went and built it. It started in the School of Computing and Engineering, then spread. Six schools and colleges are in it now. Professor Fabio Tosti directs the Faringdon Research Centre, and his answer to how it came to lean on early career researchers is that it never didn't. He was one himself when the centre was founded; Livia joined it as a PhD student. His statistic, offered reluctantly — he says he doesn't much like going by statistics — is that more than seventy per cent of the people contributing to Faringdon's research are formally early career researchers. Not assisting on it. Contributing to it, in leading positions, across the centre's disciplines. The most useful advice in the episode is also the least glamorous. Livia's recommendation to any institution is to put people in a room and let them talk, because the person who could help you most — as a collaborator, as a mentor — is often sitting next to you or one door along, and neither of you knows what the other does. They have watched projects start exactly that way, between two people who met at an ECR meeting. "Sometimes being an academic is a solitary journey," Nino says, and nobody rushes to disagree. Then there is the sharper version of the same argument. Representation is the thing people forget, Livia says: if you are not there and you hear it secondhand, you are not learning, you are not growing, and you are not representing your view. Fabio makes it structural. He has just been inaugurated as president of one of the EGU's divisions — a union whose annual assembly in Vienna draws more than twenty thousand scientists across five days — and the first item on his agenda there is the same as the one at UWL. If there is no representation of the young generation, there is no continuation. When a mandate ends, somebody has to already be ready to take it. There is a definition worth stealing, too. The UK convention for "early career" is seven or eight years past the PhD. UWL made it ten, because people arrive from industry, or take time out for parental responsibilities, and a clock that starts at the viva punishes both. The other rule is a ceiling rather than a floor: senior lecturer at most. Not because associate professors stop needing support, but because the ECR groups are led by ECRs, and learning how to lead is the whole point. One group leader in another school, on being promoted, handed the role to someone more junior on purpose — and then taught her how to do it. The ending is the reason to listen. Livia describes being a postdoc with no permanent job, a first teaching load, no idea how to manage her time or how to apply for a grant. She was lucky enough to be able to walk up to Fabio and ask. Not everybody has this luck. The network exists so that the answer to "I don't know how to do this" can be a group of people who say: we got you. Also in this episode: the Faringdon Journal Club as collective supervision, where PhD students argue with senior academics at the same level; why getting a PhD student into a meeting with a council or an international partner is worth more than a report about it afterwards; diversity as a team-building problem rather than a compliance one; and whether a sector being told to do more with less can afford to treat early career researchers as the overhead rather than the answer. Hosted by Atiyeh Ardakanian and Nino Auricchio, from the School of Computing and Engineering at the University of West London.

  3. Aug 19

    Episode 5: Can You Build an AI That Isn't Biased?

    Professor Julie Wall and Dr Nasim Dadashi Serej on where bias actually comes from, and what you can do about it Nobody in this episode claims you can build an AI with no bias in it. The interesting question is what you do once you accept that. Dr Nasim Dadashi Serej's answer runs through the whole episode as one long metaphor: you train a model the way you raise a child. You feed it your own opinions and your own insights, you reward what you like and discourage what you don't, and then you are surprised when it comes back sounding like you. Reinforcement learning, she points out, is that analogy made literal — the agent explores, and you hand out rewards or punishments. Which prompts the obvious question from the hosts: so how do we punish ChatGPT? Professor Julie Wall approaches it from the other end. A speech recognition system trained mostly on one accent will be better at that accent — that is not a scandal, it is what training data does. The work is in knowing the bias is there and deciding what you are willing to trade for it: a model tuned for equal error rates across categories will usually be less accurate overall than one tuned for accuracy alone, and that is a choice somebody has to make on purpose. She also corrects a premise mid-episode, politely and completely: these models were not born in Silicon Valley. AI goes back over eighty years, to the Second World War. The healthcare section is the one that stays with you. In supervised learning somebody has to label the data, and in medical imaging that somebody is a clinician — so the model inherits not just their expertise but their off days. Nasim's term for it is intra-observer error: the same doctor, same case, different answer, because they are tired or in a low mood and want to get to the end of the list. Between two clinicians it becomes inter-observer error. Her argument for AI is not that it is smarter than a doctor; it is that it gives the same answer every time. And a distinction worth stealing: the industry says "responsible AI" rather than "ethical AI" because ethics are not the same in every country a company operates in. GDPR works here. It does not travel. Also in this episode: whether language models have been trained into agreeing with us, what happens to guardrails when people treat them as a puzzle to solve, the uncomfortable question of who does the content-moderation work that makes a model safe, and why explainable AI is the thing that gives both of them hope — asking a model, as Nasim puts it, the same thing you would ask a child: how did you get to that answer? Chapters 00:00 Welcome, and why bias this time00:27 What is bias?01:39 Can you get rid of it?02:30 Models trained around the world, with different ideologies03:16 Accents, dialects and low-resource languages04:38 The useful kind of bias05:38 How do you detect it?07:42 Reinforcement learning, and raising a child09:40 Have these models been trained to agree with us?11:41 Healthcare: whose labels are you learning from?15:18 Accuracy or representativeness — the trade-off16:26 Pain, gender and ethnicity in the clinic17:58 Intra-observer and inter-observer error20:13 "I don't think the models started in Silicon Valley"22:41 Guardrails, and people who treat them as a game25:11 Who does the moderation work26:24 Responsible AI, not ethical AI27:06 The same prompt in a different country29:31 What gives them hope: explainable AI31:28 Inside CAINT33:46 CloseHosted by Atiyeh Ardakanian and Nino Auricchio, from the School of Computing and Engineering at the University of West London. Produced and edited by Jack Hoggard. Recorded at LSFMD, University of West London, with Tim Bowerman and Arthur Page. #ArtificialIntelligence #AIBias #ResponsibleAI #ExplainableAI #MachineLearning #AIinHealthcare #AIEthics #NLP #Research #HigherEducation #Podcast #UWL #UniversityOfWestLondon

  4. Aug 12

    Episode 4: How Do You See What's Under the Ground?

    Professor Fabio Tosti, the director of UWL's Faringdon Research Centre on how you survey a bridge, a road or a ruin without laying a finger on it, why Heathrow behaves like a city in miniature, and the question his team is still trying to answer — whether the communities living with that infrastructure can actually use any of the data.You can learn a great deal about a bridge without drilling into it. Ground-penetrating radar sends electromagnetic waves into the subsurface and reads what comes back — moisture, cracks, cavities, decay — in much the same spirit as an X-ray, and without touching the structure at all.Professor Fabio Tosti directs The Faringdon Research Centre at the University of West London. It started as a ground-penetrating radar centre; it now runs interferometric radar, satellite remote sensing, embedded sensors, robotics and immersive technology, and Fabio would rather call it a centre for smart technology and society. His teams work with Ealing Council and the London boroughs, with Historic England on how ruined structures are damaged by environmental stressors, and with the Royal College of Art on where science, technology and art meet.In this episode: what non-destructive testing actually means and where it stops being remote sensing, why Heathrow is best understood as a city in miniature, how you explain a radar output to someone who has never seen one, and the research question with no precedent behind it — can high-spec data be turned into something a community can genuinely use?Hosted by Atiyeh Ardakanian and Nino Auricchio, from the School of Computing and Engineering at the University of West London.

  5. Aug 5

    Episode 3: Who Decides the Right Way to Speak?

    Daniel Tweddle and Dr Eugenio Donati on accents, AI and who gets understood. Speech technology has a data problem. Almost every benchmark dataset used to train systems that assess pronunciation is American English — so if you speak with a Geordie, Glaswegian or Brummie accent, the technology was never built with you in mind. Daniel Tweddle is building the British English speech datasets that don't exist yet, annotating every mispronunciation by hand. His supervisor, Dr Eugenio Donati, has been through the same thing — he collected his PhD data during lockdown by recording himself. In this episode: what mispronunciation detection and diagnosis actually means, why a voice-activated lift can't understand a Scottish accent, whether pronunciation training can target a dialect you choose rather than a "correct" one, and the question Daniel gets asked more than any other — who gets to decide what the right way to speak is? Hosted by Atiyeh Ardakanian and Nino Auricchio, from the School of Computing and Engineering at the University of West London. Chapters 00:00 Welcome00:24 How Eugenio and Daniel got into sound04:39 Favourite mispronunciation stories06:36 What mispronunciation detection and diagnosis is07:11 Britain has no speech data for this09:20 Building the datasets from scratch11:42 The word nobody could say12:33 Regional accents and dialects15:02 Pronunciation training for a dialect you choose17:46 The lift that can't understand your accent19:15 Detection vs diagnosis21:20 Where the research goes next23:05 Annotating vowels by hand26:04 Why you sound like you28:35 Accent, identity, and who decides31:17 Deepfakes and voice as a health marker33:22 Supervisor and PhD student35:17 Life outside the research38:02 Doing a PhD through lockdownProduced and edited by Jack Hoggard. Recorded at LSFMD, University of West London, with Tim Bowerman and Arthur Page. Hashtags: #SpeechTechnology #AI #Accents #Linguistics #Phonetics #MachineLearning #SpeechRecognition #PhDLife #HigherEducation #Podcast #UWL #UniversityOfWestLondon

About

Every field has its hot takes — the things people in the room believe, argue about and repeat, but rarely have to defend in public. Hot Takes | Cold Proofs is the podcast from the School of Computing and Engineering at the University of West London. Each episode puts two academics in a room to say what they actually think about AI and technology, then asks the harder question: what's the cold proof? Hosted by Atiyeh Ardakanian and Nino Auricchio. No press-release positivity, no doom. Just people who know their subject, disagreeing productively.