Hot Takes | Cold Proofs

About Hot Takes | Cold Proofs

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.

Publisher

University of West London — School of Computing and Engineering

Category

technology

Language

en

Explicit

No

First episode

30 Jul 2026

Latest episode

14 Oct 2026

Latest episodes

13 episodes in the feed.

1. 

   14 Oct 2026

   Season 2 Episode 1: Paper and People (Part 2)

   Professor Phil Cox, Dean of the School of Computing and Engineering at the University of West London, was our first guest in Episode 1. He has been through three REFs and helped write two of them, so we asked him to explain it.The Research Excellence Framework comes round roughly every seven years. Universities put forward their best papers, their impact case studies and an account of how they support research, a panel of assessors reads all of it, and a league table comes out at the end. UWL made the top 100 last time. The target this time is 70th.Phil walks through the mechanics: the three units of assessment the school is aiming for (computing, engineering and the built environment), who gets submitted with which case study, and what assessors who read a hundred papers a day are looking for. He is direct about the limits. It is a snapshot. It favours universities with deep resources, and the funding that follows the ranking makes that self-fulfilling. A post-92 has to go the extra mile because the main business is teaching.So where can a small university win? By doing a few things well and being noticed for it. Reflectors at Heathrow to measure how the ground moves, which he says no other hub airport in Europe has allowed. AI for acoustic imaging of the heart. And archaeoacoustics: rebuilding ancient monuments in a computer to hear how they sounded, including an unfinished Roman amphitheatre, with a grant in preparation alongside Sapienza University of Rome.His own view of impact is whether the work is on a global stage. His view of the REF's real value is staff: a good result brings research-active people in, their research reaches the teaching, and the people already here have a reason to stay.The second half is about the school itself. Around 40 per cent of colleagues are women, close to a hundred PhD students share a workspace, and Phil explains what a mixed staff room does for how people behave. He closes on the sector: too many degree providers, too many metrics, and a case for welcoming international students back.His last line gave us the title. Universities make two things, paper and people.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.

2. 

   7 Oct 2026

   Season 2 Episode 1: Paper and People (Part 1)

   Season 2 opens with a returning guest. Professor Phil Cox, Dean of the School of Computing and Engineering at the University of West London, was our first guest in Episode 1. He has been through three REFs and helped write two of them, so we asked him to explain it. The Research Excellence Framework comes round roughly every seven years. Universities put forward their best papers, their impact case studies and an account of how they support research, a panel of assessors reads all of it, and a league table comes out at the end. UWL made the top 100 last time. The target this time is 70th. Phil walks through the mechanics: the three units of assessment the school is aiming for (computing, engineering and the built environment), who gets submitted with which case study, and what assessors who read a hundred papers a day are looking for. He is direct about the limits. It is a snapshot. It favours universities with deep resources, and the funding that follows the ranking makes that self-fulfilling. A post-92 has to go the extra mile because the main business is teaching. So where can a small university win? By doing a few things well and being noticed for it. Reflectors at Heathrow to measure how the ground moves, which he says no other hub airport in Europe has allowed. AI for acoustic imaging of the heart. And archaeoacoustics: rebuilding ancient monuments in a computer to hear how they sounded, including an unfinished Roman amphitheatre, with a grant in preparation alongside Sapienza University of Rome. His own view of impact is whether the work is on a global stage. His view of the REF's real value is staff: a good result brings research-active people in, their research reaches the teaching, and the people already here have a reason to stay. The second half is about the school itself. Around 40 per cent of colleagues are women, close to a hundred PhD students share a workspace, and Phil explains what a mixed staff room does for how people behave. He closes on the sector: too many degree providers, too many metrics, and a case for welcoming international students back. His last line gave us the title. Universities make two things, paper and people. 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.

3. 

   30 Sept 2026

   Episode 10: Jam Session

   Five days, one brief, and a rule that nothing new goes into the game in the final 24 hours. This episode is about the UWL Game Jam, from two of the people who run it: Ryan Callard, Course Leader for BA (Hons) Esports at the London School of Film, Media and Design, and Dr Malte Ressin, Associate Professor in Computer Science in the School of Computing and Engineering. Neither arrived by the direct route. In 2009 Malte was leading a software localisation team that had been hitting the same problem for years, until an email about Vice-Chancellor's scholarships at UWL turned it into his PhD. Ryan came at around the same time as a music technology undergraduate at the London College of Music (Nino was one of his lecturers), and found his way to games through voiceover work. The jam started five or six years ago as a small event. The latest one, which finished three days before this recording, brought together students from around ten courses across four schools, with Sega Europe back as partner sponsor and a brief built around reefs. The longest they ever ran lasted ten days. The student feedback: please never do that again. Malte's entry requirement is short. No fear of operating a computer, a willingness to put the work in, and the drive to create; if you know what a pixel is, you can contribute. Ryan points to two law students who joined a team this year. The payoff is practical. You can't walk into a games studio and say you studied X, Y and Z, give me a job, and for a computer science student the first side project that actually gets finished and shipped could just as easily be a game. Then the harder parts. Five days drops a team straight into crunch, which is why Ryan compares a jam to making an album in five days: scope is everything. They also get into safeguarding in multiplayer games, why ethics boards get nervous about horror games, the years Doom spent restricted in Germany, and a Sega brief that let teams destroy the reef as long as the game showed the consequences. It closes on what comes next: the first UWL Game Awards, and rumours that competitive game jams are being optioned for TV. A few moments to jump to 00:00 Welcome, meet Ryan Callard and Dr Malte Ressin00:41 Malte: a localisation problem and a VC scholarship02:27 Ryan: music technology, voiceover, then games04:40 What a game jam is, and the ten-day one08:17 What you need to take part15:41 You need a portfolio25:50 Ethics, safeguarding and games made for cats35:02 Scope is everything 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. #GameJam #GameDev #GameDesign #Esports #ComputerScience #HigherEducation #Podcast #UWL #fyp

4. 

   28 Sept 2026

   Episode 9: Who Watches The Watchers (Part 2)

   Before she was training the next generation of journalists at LSFMD, Alison Hawkings was choosing a call sign for her dog tags inside an American flight simulator at a 1990s defence-tech showcase. She called herself Lash. This episode traces thirty-plus years in journalism, from the dot-com boom's flown-in junkets and 1991 email addresses to a present she describes, bluntly, as a "techno-feudalistic period." The two eras rhyme more than you'd expect. Alison remembers a Silicon Valley hedonistic enough that she once set it as a student essay topic; she sees the same money-first energy now in AI and the billionaires steering it. Where the 90s produced Steve Jobs and free, GNU-and-Linux-style software, she argues, today's version has the propensity to completely undermine democratic fundamentals in countries. She's just as candid about the industry she came up in. Breaking into journalism in the early 90s meant a hard, male-dominated newsroom before the Equality Act or sexual harassment law existed - first in, last out, expected in the pub because that's where the stories were. That history sits behind her three-word test for the job, and for citizenship generally: be curious, investigate, serve the public. Those values are her answer to a post-truth world. Her line for it: the pen is still mightier than the sword. Misinformation, algorithmic bias and a "push culture" that delivers content rather than letting people choose it all make media literacy - asking who wants me to know this, and why - more urgent, not less. She's blunt about the mechanics behind it too: say yes to any free site or app, she tells her media law students, and you are the product. AI gets the same unsentimental treatment. Trust but verify, she says, applies to a chatbot exactly as it applied to a source - and she pushes back on the word we've settled on for when it gets things wrong: "I think hallucinate is the wrong word. I think it just lies," because it's built to give you an answer it thinks you want, consciousness or not. The conversation closes on hope rather than alarm: a proposed university "switch off all technology" day, and her advice to any student weighing a career in journalism - stay curious, because every person, even the humble apple, is a story. A few moments to jump to 00:00 Welcome, meet Alison Hawkings02:26 Silicon Valley in the 90s, and choosing the call sign "Lash"15:14 The pen is mightier than the sword17:00 We're in this kind of techno-feudalistic period27:20 We didn't have the Equality Act56:38 You are the product1:02:07 I think hallucinate is the wrong word, I think it just lies1:06:47 Advice for aspiring journalists: the humble apple 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.

5. 

   16 Sept 2026

   Episode 9: Who Watches The Watchers (Part 1)

   Before she was training the next generation of journalists at LSFMD, Alison Hawkings was choosing a call sign for her dog tags inside an American flight simulator at a 1990s defence-tech showcase. She called herself Lash. This episode traces thirty-plus years in journalism, from the dot-com boom's flown-in junkets and 1991 email addresses to a present she describes, bluntly, as a "techno-feudalistic period." The two eras rhyme more than you'd expect. Alison remembers a Silicon Valley hedonistic enough that she once set it as a student essay topic; she sees the same money-first energy now in AI and the billionaires steering it. Where the 90s produced Steve Jobs and free, GNU-and-Linux-style software, she argues, today's version has the propensity to completely undermine democratic fundamentals in countries. She's just as candid about the industry she came up in. Breaking into journalism in the early 90s meant a hard, male-dominated newsroom before the Equality Act or sexual harassment law existed - first in, last out, expected in the pub because that's where the stories were. That history sits behind her three-word test for the job, and for citizenship generally: be curious, investigate, serve the public. Those values are her answer to a post-truth world. Her line for it: the pen is still mightier than the sword. Misinformation, algorithmic bias and a "push culture" that delivers content rather than letting people choose it all make media literacy - asking who wants me to know this, and why - more urgent, not less. She's blunt about the mechanics behind it too: say yes to any free site or app, she tells her media law students, and you are the product. AI gets the same unsentimental treatment. Trust but verify, she says, applies to a chatbot exactly as it applied to a source - and she pushes back on the word we've settled on for when it gets things wrong: "I think hallucinate is the wrong word. I think it just lies," because it's built to give you an answer it thinks you want, consciousness or not. The conversation closes on hope rather than alarm: a proposed university "switch off all technology" day, and her advice to any student weighing a career in journalism - stay curious, because every person, even the humble apple, is a story. A few moments to jump to 00:00 Welcome, meet Alison Hawkings02:26 Silicon Valley in the 90s, and choosing the call sign "Lash"15:14 The pen is mightier than the sword17:00 We're in this kind of techno-feudalistic period27:20 We didn't have the Equality Act56:38 You are the product1:02:07 I think hallucinate is the wrong word, I think it just lies1:06:47 Advice for aspiring journalists: the humble apple 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.

6. 

   9 Sept 2026

   Episode 8: It's Not Something You Can Manufacture

   What's the thing that actually holds a university together — the part nobody can point to on an org chart? Professor Anthony Hilton, UWL's Deputy Vice-Chancellor (Academic), and Paula Bruton, Director of Human Resources & Organisational Development, spend an episode chasing exactly that. Before either guest is properly introduced by title, Anthony explains how he ended up running a microbiology lab out of the back of an articulated lorry, parking it outside people's homes to show them the microbes living in, on and around them — a science-communication project born from a tension he still wrestles with: staying credible to fellow academics while staying accessible to everyone else. Then the conversation turns to what actually shapes a university day to day. Asked what's invisible but foundational, Paula reaches for a comparison rather than a definition: a sense of community that feels, she says, "like the pre-COVID feeling" — not something any policy can manufacture. Anthony agrees, but frames it institutionally: a can-do culture willing to rally around people outside your own school or department, in a way he says he hasn't seen at larger, more siloed universities. The sharpest stretch is about parenthood. Paula describes her own return to work after having her son — a senior HR role, six months off because six months was all they could afford, then a hunt for part-time senior work she calls "virtually impossible." Anthony connects it to the wider data: a "leaky pipeline" where the share of women thins at every career stage after the PhD, even though half of academia's workforce is female. Both are candid that legislation can make a pay gap measurable without making it close. The back half turns to leadership. Anthony defines authentic leadership as knowing your own values first, then creating psychological safety so people can be themselves rather than copies of you — including, he argues, in the hardest conversations, when redundancy or closure is genuinely the only option on the table. Paula adds the HR half of that: a leader's job in those moments is to explain the why, not just deliver the decision. They close on mythbusting — Anthony, ever the microbiologist, on why "a bit of dirt is good for you" doesn't mean poor hygiene builds character — and on what would help working parents more than any policy on paper: a manager who actually asks how you're doing, not just what you've delivered.

7. 

   2 Sept 2026

   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

8. 

   26 Aug 2026

   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.

9. 

   19 Aug 2026

   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 time 00:27 What is bias? 01:39 Can you get rid of it? 02:30 Models trained around the world, with different ideologies 03:16 Accents, dialects and low-resource languages 04:38 The useful kind of bias 05:38 How do you detect it? 07:42 Reinforcement learning, and raising a child 09:40 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-off 16:26 Pain, gender and ethnicity in the clinic 17:58 Intra-observer and inter-observer error 20:13 "I don't think the models started in Silicon Valley" 22:41 Guardrails, and people who treat them as a game 25:11 Who does the moderation work 26:24 Responsible AI, not ethical AI 27:06 The same prompt in a different country 29:31 What gives them hope: explainable AI 31:28 Inside CAINT 33:46 Close 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, University of West London, with Tim Bowerman and Arthur Page. #ArtificialIntelligence #AIBias #ResponsibleAI #ExplainableAI #MachineLearning #AIinHealthcare #AIEthics #NLP #Research #HigherEducation #Podcast #UWL #UniversityOfWestLondon

10. 

    12 Aug 2026

    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.

11. 

    5 Aug 2026

    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 Welcome 00:24 How Eugenio and Daniel got into sound 04:39 Favourite mispronunciation stories 06:36 What mispronunciation detection and diagnosis is 07:11 Britain has no speech data for this 09:20 Building the datasets from scratch 11:42 The word nobody could say 12:33 Regional accents and dialects 15:02 Pronunciation training for a dialect you choose 17:46 The lift that can't understand your accent 19:15 Detection vs diagnosis 21:20 Where the research goes next 23:05 Annotating vowels by hand 26:04 Why you sound like you 28:35 Accent, identity, and who decides 31:17 Deepfakes and voice as a health marker 33:22 Supervisor and PhD student 35:17 Life outside the research 38:02 Doing a PhD through lockdown Produced 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

12. 

    30 Jul 2026

    "It Should Be Called Ethical AI"

    The industry says "responsible AI". Prof. Julie Wall thinks that word was chosen carefully — ethics differ by market, and the rebrand quietly drops the part that binds you. Professor Julie Wall and Dr Nasim Dadashiserej get into what that looks like in practice: deepfakes that have stopped being a celebrity problem and become something ordinary people use on each other, AI agents whose upside is real and whose blast radius is enormous, and regulation that is always, structurally, written for the technology we have already left behind. Nasim's route into healthcare AI runs through six hours in A&E with her son, one nurse, and a single judgement call — an argument for decision support that no whitepaper makes as well. (00:00) Introduction (02:21) Six hours in A&E: why an engineer moved into healthcare AI (04:38) "It should be called ethical AI" (09:44) The more advanced the technology, the more vital the hand of the musician (13:30) Regulation is always written for the last technology (16:04) A teacher deepfaked his own head teacher (20:41) Hundreds of agents, one blast radius Hosted by Atiyeh Ardakanian and Nino Auricchio. Hot Takes | Cold Proofs is produced by the School of Computing and Engineering at the University of West London.

13. 

    30 Jul 2026

    "You Don't Understand Anything About Humans"

    A computing dean and a psychologist on why technology keeps getting built for a "typical user" who doesn't exist — and what happens when AI averages out the very people it was meant to serve. Prof. Phil Cox (Dean of the School of Computing and Engineering) reads AI research proposals, and he has a blunt verdict on a lot of them: the coding is excellent, the understanding of people is absent. Prof. Jen Mayer (Head of the School of Human and Social Sciences), a psychologist, has the mirror-image problem — the outliers her field exists to study are exactly the ones a model is trained to discard. Between them they get at something that goes well past AI: what we lose when we optimise for the average, and why the interesting cases are always the ones that get smoothed away. Also in this episode: what happened when a dean built an AI avatar of himself from a one-minute recording and quietly ran it past his students, and an extraordinary food-engineering case study about what happens when brilliant technology meets actual human behaviour. (00:00) Introduction (04:24) "You don't understand anything about humans" (11:59) The fat-free crisp that human behaviour defeated (16:22) AI gives you the average — the outliers disappear (23:45) A dean clones himself with AI Hosted by Atiyeh Ardakanian and Nino Auricchio. Hot Takes | Cold Proofs is produced by the School of Computing and Engineering at the University of West London.

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