Rotem Pinchover
Head of AI at definity
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Rotem Pinchover is Head of AI at definity, where she develops AI solutions for data engineering and observability. Previously, she was a Senior AI Scientist at Intuit, building large-scale processes focused on customer needs and measurable business results. Her work spans agentic AI, generative AI, and classic machine learning. Her talks explore the practical challenges of building AI systems, from prototyping and architectural debt to bias measurement and GenAI security. Rotem holds an M.Sc. and a B.Sc. in Industrial Engineering from Tel Aviv University and serves as the Israeli Ambassador for Women in Data Science (WiDS).
Leading the Fair Fight: Strategies to Combat Bias in LLMs
Fasten your mental seatbelts—we're rocketing through a rollercoaster ride unraveling the hidden biases of Large Language Models (LLMs). These AI powerhouses, from which we demand superhero-like feats of intellect, often come with their own kryptonite - ingrained biases that could trip us up in the most unexpected ways. From advising women conservatively on financial matters to misdiagnosing medical conditions based on gender, LLMs can perpetuate stereotypes and deepen societal divides.
We’ll delve into heart-racing real-world consequences of these biases and the cutting-edge strategies we’re deploying to tackle them head-on. Explore the landscape of bias types that plague these systems, from stereotypical inferences to discriminatory compliance.
This session packs a punch, aimed at not only highlighting the issues but also spotlighting actionable insights and proven tools to enhance sensitivity and accuracy in our digital interactions. Leading an initiative for bias measurement and mitigation across all generative AI applications at Intuit, I bring firsthand experience and expertise to this critical discussion. Gear up for a session that promises not just to inform but also to ignite a spark for change, empowering you to refine the brains behind the bits.
Who Wants to Be an AI Practitioner? The Million-Token Gameshow
Welcome to the Reversim ultimate tech reality check game: "Production Drop or AI Flop?"
In this fast-paced interactive lightning session, a live gameshow is coming straight to the crowd. Get ready to answer the ultimate question: Are these genuine engineering wins, or just incredibly expensive architectural illusions?
The Generative AI gold rush has turned engineering ecosystems into a high-stakes guessing game. Driven by relentless industry FOMO, developers are treating basic API wrappers like architectural triumphs, PMs are shipping features based entirely on hype metrics, and Data Scientists have traded rigorous statistical validation for the chaotic dopamine rush of prompt engineering. The industry has become so blinded by the shiny brilliance of LLMs that deterministic bounds have been completely traded away for probabilistic, "vibes-based" outcomes.
No marketing fluff, no boring slides—just a brutal, hilarious, and deeply necessary intervention for a room full of builders.
How I Left the Corporate to Vibe-Code an AI Startup (And Built a Prompt-Engineered Nightmare)
Entering a startup as the first AI builder after years in the corporate world is a whirlwind. No data? No problem, Claude to the rescue.
It felt like a superpower, until I had to sit down with our lead developer and try to explain the sprawling, hyper-complex prompt-and-code hybrid I had just built.
In this talk, I’ll take you inside the chaos of the "vibe coding" loop and the culture shock of trading enterprise guardrails for pure startup speed. You'll walk away with a practical look at the prototyping traps that build instant architectural debt, and real-world strategies for keeping your AI code lean, explainable, and production-ready when building from zero.
GenAI in the Wild: Protecting Your Herd from Predators
The adoption of GenAI for various use cases is on the rise, with its power exponentially increasing with each new implementation. However, this power is also a magnet for attackers who look to exploit vulnerabilities. This makes the need for GenAI security more critical than ever before. Our talk will provide an overview of GenAI attacks and explain why GenAI needs to be secured.
We will delve into various use cases and discuss the complexity of fitting detectors to each unique case. The number of use cases is constantly growing, evolving and they are not well defined, posing a significant challenge in terms of detecting malicious activity. We will also present a scalability demonstration, showcasing the sheer numbers of use cases and threats that need to be addressed.
We will then introduce GenOS and GenSRF's general structure, which will highlight the challenges we've encountered, including managing use case-detector fit, balancing FP/specificity of detectors and contending with the large variation in types of data and formats. Furthermore, we'll discuss the lack of labeled data, leading to difficulties in evaluating detector real-world performance and monitoring as well as the ambiguity around trusted/untrusted inputs.
Lastly, we will explain why this is still an unexplored and misunderstood domain, requiring us to pave the way and invent the wheel to defend against new types of attacks that we must design ourselves. Join us as we unravel the complexities of securing GenAI and discuss our efforts to stay one step ahead of the hackers.
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