How combining linguistics and mathematics turned into a successful company

In this episode, we sit down with Marco Antonio Stranisci, a postdoctoral researcher in Natural Language Processing at the University of Turin and founder of aequa-tech, an AI startup focused on social impact. Marco shares his journey from humanities to computer science, his activism against hate speech, and the creation of Debunker-Assistant, a tool designed to combat misinformation.We explore the ethical dimensions of AI, the challenges of launching a startup in a crowded tech landscape, and the importance of participatory design in building inclusive technologies. Marco also offers advice to young researchers navigating academia and industry, and invites listeners to contribute to his open-source initiative, the Citizen Dataset Lab.We express our gratitude to University of Turin and personally Lucia Salto for the guest of the podcast Marco Antonio Stranisci.🔑 Key Topics CoveredMarco’s academic journey: from humanities to computational linguisticsThe intersection of activism and AI: detecting hate speechFounding aequa-tech and building Debunker-AssistantChallenges of entrepreneurship in the AI spaceEthical concerns in AI development and data collectionParticipatory design and citizen involvement in techOpen-source vs. closed-source models in AIAdvice for PhD students and early-career researchersThe future of NLP and interdisciplinary AIThe Citizen Dataset Lab initiative⏱️ Question Timestamps01:33 – Marco’s academic journey: switching from humanities to IT02:57 – How activism led to a PhD in computer science03:53 – Translating research into startup innovation05:12 – Why Marco chose entrepreneurship06:00 – Emotional highs and lows of startup life07:26 – Benefits and drawbacks of leaving academia08:45 – The crowded AI landscape post-ChatGPT11:56 – Marco’s elevator pitch for aequa-tech13:45 – Debunker-Assistant and participatory design15:02 – Challenges of analyzing social media data16:38 – Open-source values and transparency17:58 – Future plans: interdisciplinary and efficient AI19:00 – Advice for PhD students using AI21:45 – Ethical concerns and the importance of learning23:01 – Humanities in STEM: is something missing?26:18 – How listeners can help: Citizen Dataset Lab27:36 – Multilingual participation and open collaboration Send us a message!