Extracting maximum value from data while protecting consumer privacy. Jason McFall, Privitar
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Big organisations have a wealth of rich customer data which opens up huge new opportunities. However, they have the challenge of how to extract value from this data while protecting the privacy of their individual customers. He will talk about the risks organisations face, and what they should do about it. He will survey the techniques which can be used to make data safe for analysis, and talk briefly about how they are solving this problem at Privitar.
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At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space models (SSMs) with transformers for use in this constrained regime. While transformers rely on a read-write key-value cache, SSMs can be constructed as read-only architectures, enabling the use of novel memory types and reducing power consumption. Furthermore, SSMs require significantly fewer multiply-accumulate units—drastically reducing compute energy and chip area.
New techniques enable distillation-based migration from transformer models such as Llama to SSMs without major performance loss. In latency-sensitive applications, techniques such as precomputing input sequences allow SSMs to achieve sub-100 ms time-to-first-token, enabling real-time interactivity. Lewis presents a detailed side-by-side comparison of these architectures, outlining their trade-offs and opportunities at the extreme edge.
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We’ll examine the shift from routine coding to creative leadership, highlighting the new developer superpowers of vision, integration, and innovation.
We'll touch on security, legacy code, and the future of democratized development.
Whether you're AI-curious or already a prompt engineering, this session will help you find your rhythm in the new dance of modern development.
Oracle Cloud Infrastructure Generative AI Professional
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Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...
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LinkedIn: https://www.linkedin.com/in/anishkumar/
This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks).
This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights.
Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.
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LSNIF: Locally-Subdivided Neural Intersection Function
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https://arxiv.org/abs/2504.21627
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online businesses that required endless hours of setup and maintenance. But now,
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automation in the background; we’re entering a world where AI creatives actively
design, produce, and monetize content and products, opening the floodgates for
passive income like never before.
Imagine AI tools writing books, designing logos, building apps, editing videos, creating
music, and even selling your digital products 24/7—without you lifting a finger after
setup. This isn't the future. It’s happening right now. And if you act fast, you can ride
the wave before it becomes saturated.
In this in-depth guide, we’ll show you how to tap into AI creativity for real, sustainable,
passive income streams—no fluff, no generic tips—just actionable, traffic-driving
insights.
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Foundation Models show impressive results in a wide-range of tasks on scientific and legal content from information extraction to question answering and even literature synthesis. However, standard evaluation approaches (e.g. comparing to ground truth) often don't seem to work. Qualitatively the results look great but quantitive scores do not align with these observations. In this talk, I discuss the challenges we've face in our lab in evaluation. I then outline potential routes forward.
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Domino IQ – What to Expect, First Steps and Use Cases
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HCL Domino iQ Server – From Ideas Portal to implemented Feature. Discover what it is, what it isn’t, and explore the opportunities and challenges it presents.
Key Takeaways
- What are Large Language Models (LLMs) and how do they relate to Domino iQ
- Essential prerequisites for deploying Domino iQ Server
- Step-by-step instructions on setting up your Domino iQ Server
- Share and discuss thoughts and ideas to maximize the potential of Domino iQ
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance
Extracting maximum value from data while protecting consumer privacy. Jason McFall, Privitar
- 1. Extracting maximum value from data while protecting consumer privacy Jason McFall [email protected]
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- 11. 12 https://tech.vijayp.ca/of-taxis-and-rainbows-f6bc289679a1#.jbjimbh1f
- 14. What are we doing at Privitar? • Building privacy protection into Hadoop – we’re a spark application that runs on customers’ Hadoop clusters • Building a product around the latest state of the art techniques, and driving collaborative industry/academic research • Developing statistical approaches to protect privacy while preserving analytic power • We’re a software product company, working mostly with banks and telcos initially, but with far wider applications
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- 19. Interested in working in this important area? [email protected] 20