As AI becomes more integrated into our lives, building it with privacy at its core is a critical frontier for the field. Differential privacy (DP) offers a mathematically robust solution by adding calibrated noise to prevent memorization. However, applying DP to LLMs introduces trade-offs. Understanding these trade-offs is crucial. Applying DP noise alters traditional scaling laws — rules describing performance dynamics — by reducing training stability (the model’s ability to learn consistently without experiencing catastrophic events like loss spikes or divergence) and significantly increasing batch size (a collection of input prompts sent to the model simultaneously for processing) and computation costs.
Our new research, “Scaling Laws for Differentially Private Language Models”, conducted in partnership with Google DeepMind, establishes laws that accurately model these intricacies, providing a complete picture of the compute-privacy-utility trade-offs. Guided by this research, we’re excited to introduce VaultGemma, the largest (1B-parameters), open model trained from scratch with differential privacy. We are releasing the weights on Hugging Face and Kaggle, alongside a technical report, to advance the development of the next generation of private AI. — Read More
Daily Archives: September 15, 2025
AI Will Not Make You Rich
Fortunes are made by entrepreneurs and investors when revolutionary technologies enable waves of innovative, investable companies. Think of the railroad, the Bessemer process, electric power, the internal combustion engine, or the microprocessor—each of which, like a stray spark in a fireworks factory, set off decades of follow-on innovations, permeated every part of society, and catapulted a new set of inventors and investors into power, influence, and wealth.
Yet some technological innovations, though societally transformative, generate little in the way of new wealth; instead, they reinforce the status quo. Fifteen years before the microprocessor, another revolutionary idea, shipping containerization, arrived at a less propitious time, when technological advancement was a Red Queen’s race, and inventors and investors were left no better off for non-stop running.
Anyone who invests in the new new thing must answer two questions: First, how much value will this innovation create? And second, who will capture it? Information and communication technology (ICT) was a revolution whose value was captured by startups and led to thousands of newly rich founders, employees, and investors. In contrast, shipping containerization was a revolution whose value was spread so thin that in the end, it made only a single founder temporarily rich and only a single investor a little bit richer.
Is generative AI more like the former or the latter? Will it be the basis of many future industrial fortunes, or a net loser for the investment community as a whole, with a few zero-sum winners here and there? — Read More
The Data Backbone of LLM Systems
QCON London: Drawing from his 8 years of experience in AI, Paul Iusztin breaks down the core components of a scalable architecture, emphasizing the importance of RAG. He shares practical patterns, including the Feature Training Inference architecture, and provides a detailed use case for creating a “Second Brain” AI assistant, covering everything from data pipelines to observability and agentic layers. — Read More
AI-Ready Data: A Technical Assessment. The Fuel and the Friction.
Most organizations operate data ecosystems built over decades of system acquisitions, custom development, and integration projects. These systems were designed for transactional processing and business reporting, not for the real-time, high-quality, semantically rich data requirements of modern AI applications.
Research shows that 50% of organizations are classified as “Beginners” in data maturity, 18% are “Dauntless” with high AI aspirations but poor data foundations, 18% are “Conservatives” with strong foundations but limited AI adoption, and only 14% are “Front Runners” achieving both data maturity and AI scale. — Read More
When the government can see everything: How one company – Palantir – is mapping the nation’s data
When the U.S. government signs contracts with private technology companies, the fine print rarely reaches the public. Palantir Technologies, however, has attracted more and more attention over the past decade because of the size and scope of its contracts with the government.
Palantir’s two main platforms are Foundry and Gotham. Each does different things. Foundry is used by corporations in the private sector to help with global operations. Gotham is marketed as an “operating system for global decision making” and is primarily used by governments.
I am a researcher who studies the intersection of data governance, digital technologies and the U.S. federal government. I’m observing how the government is increasingly pulling together data from various sources, and the political and social consequences of combining those data sources. Palantir’s work with the federal government using the Gotham platform is amplifying this process. — Read More
Rick's Cafe AI 