Yael Daihes
Founder
Tel Aviv, Israel
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Yael Daihes is an AI leader and ML scientist with a foundation in cybersecurity, built over eight years in the Israeli defense forces. She went on to lead AI R&D initiatives in the private sector — including at companies that were later acquired — and advises companies across industries on transforming their core logic and algorithms with AI. That path led her to found Lumina: a new kind of social platform for women's health, where community, search, and real insights actually work. Live in production, Lumina answers women's health questions from the collective lived experience of thousands of women — and when an answer doesn't exist yet, it connects women on the same path so they can discover it together.
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AI Wasn't the First to Hallucinate
The most dangerous answer I ever got didn't come from an LLM — it came from a doctor. Confident, fluent, completely wrong. I lived the cost of that answer for ten years. Then I watched LLMs make the same mistake at scale, and realized: we didn't invent hallucination, we just named it.
Every mature cybersecurity product has an "unknown" verdict, but LLMs has exactly one verdict: answer. This talk is the story of building the alternative — a live production AI for women's health, in Hebrew, that refuses to answer without evidence; a feature every dashboard says is failing, and every user says is why they trust it. My doctor's certainty was meant as a kindness. It cost me ten years. So I built a machine whose kindness is honesty.
Hit the (ML) road(map) Jack
With the advancement of AI, it seems there isn’t a task unsolvable by this amazing technology, and no wonder every type of product wants to leverage it. You hire the most brilliant researchers and wait for the magic to happen, but apparently, not every scientist can translate a business problem into a research task, and not every business leader can vividly define achievable goals for the ML organization.
Imagine a digital health company that holds electronic medical records of thousands of patients. I believe we would all immediately assume their ML organization is creating some form of a diagnostics model, to ultimately improve the patients’ health. But, is that necessarily the best way to improve someone's health given this type of data? Maybe it would be more helpful to predict the severity of each patient's case and offer prioritization? And who said that improving patients’ health was even the company's objective? Maybe their objective is to optimize the doctor’s clinic operation.
Therefore, it is key to master the art of understanding the business needs and translating them to an AI research project. In this talk I will share my technique for learning a new business from a data science point of view and provide the pillars for building a realistic roadmap for an ML organization in a way that optimizes the business’ needs.
AI as My Co-Founder: Bootstrapping a Tech Startup Solo
In this talk, I'll walk you through my journey building a consumer app from scratch using AI as my virtual team. I'll share how I leveraged various AI tools to overcome traditional startup barriers—coding the application without development experience, gathering and analyzing market data, creating compelling marketing materials, and preparing presentations, all while maintaining a lean operation.
Whether you're an entrepreneur looking to bootstrap your own idea or a product builder interested in maximizing AI capabilities, this case study will provide practical insights into building a technology business in today's AI-powered landscape.
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