AI Industry - Academia Summit, Where Research Meets Reality

Prof. Boaz Ganor is the president of Reichman University and the founder of the International Institute for Counter-Terrorism (ICT).

How to use AI effectively in education, research and medicine: Pittfalls and opportunities.

Deep thoughts on not-so-deep data: what between AI, archeology, and biblical studies

Abstract:
In the era of data proliferation, there are many domains, where collecting large datasets is simply not feasible. How, then, can we extract meaningful patterns and insights from limited data? This talk explores how machine learning offers powerful tools for analyzing "not-so-deep" datasets.
We will show how studying the geometry of data and comparing statistical distributions - whether derived from images or textual sources - can lead to compelling, statistically significant conclusions. Two case studies will illustrate this approach: the first analyzes ancient handwriting from around 600 BCE to investigate levels of literacy at the time of the Iron age; the second focuses on biblical text attribution, demonstrating how computational methods can uncover linguistic layers, infer authorship, and help disentangle the composite nature of sacred texts.
Join us as we explore how AI can illuminate the ancient world, even when working with sparse data - bridging the gap between cutting-edge technology and long-standing historical questions.

Efficient agents for AI PC

Building Voice Agents in the Real World: Architecture, Trade-offs, and the Future of Conversational AI

This talk is a deep dive into how we designed, built, and shipped at Lemonade a production-grade voice agent using OpenAI’s Realtime APIs. I’ll walk through the architecture and how we combined LLM-based reasoning with deterministic state machines — not just the “what,” but the “why” behind those choices.

More importantly, I’ll share the real-world challenges that don’t show up in diagrams: trade-offs between flexibility and control, managing latency at scale, designing for fast iteration while keeping quality, and deciding when to let the LLM lead vs. when to fall back on structure.

The talk blends technical insights with product and organizational lessons — useful for anyone building or planning to build with generative AI: engineers, product leads, researchers, and tech execs alike.

Despite dramatic gains in model capability, many GenAI initiatives fail as soon as they move beyond the prototype. It's not the model that breaks; it's the environment it’s deployed into. Fragile handoffs, missing feedback loops, and unclear team boundaries create the perfect storm for silent, cascading failures.

The rise of  where AI-generated code ships without proper review or integration has made these issues harder to detect and easier to scale. Public misfires from tools like Base44 illustrate how quickly AI can introduce instability when organizational readiness is lacking.

This talk draws on real-world AutonomyAI deployments to surface the most common failure modes in GenAI production. It introduces the , a practical framework for aligning product intent, architecture, communication, and task clarity. The goal: help teams build systems where GenAI can deliver lasting value, not just short-lived demos.

Title: From Guesswork to Greatness: Systematic AI Agent Optimization in Production

Talk Abstract:
Every engineer building AI agents has faced it: you tweak a prompt, swap a model, or change a RAG setting—only to realize it either made things worse or improved one thing while breaking another. Why does this happen? Because teams are testing just one version of the agent out of the million possible combinations—and hoping it works.

Today's evaluation tools are built for single-point assessment, not the kind of massive multi-dimensional comparison you actually need—sure, they might let you A/B test two prompts or pick between three models, but they can't help you explore hundreds of combinations across cost, latency, and accuracy all at once.

In this talk, we’ll show how a structured approach to testing alternatives can flip the script. Borrowing ideas from multi-objective optimization, we’ll show how our SDK and UI let engineers allocate their testing budget while Traigent intelligently explores high-potential configurations and surfaces the best tradeoffs. You’ll see how this approach leads to 4–7x quality gains and up to 90% cost savings—without blind guesses or manual trial-and-error.

Don’t Just Read It - Talk to It:
How I Transformed My Book into a GPT-Powered Learning Experience
What happens when a book stops sitting on the shelf – and starts talking to you?

In this talk, I’ll share the unconventional journey of how I took a professional book I wrote about personal branding and networking, and trained ChatGPT on it to turn it into an interactive experience.
Instead of buying and reading the book the “traditional” way, anyone can now ask it open-ended questions and receive personalized answers – including updated guides on branding in the age of AI. I’ll share what I learned from the process – from the technical aspects and copyright challenges to insights about the future of books and writing. This session is perfect for anyone who wants (and needs) to rethink what learning looks like in a world where books don’t have to stay silent.