Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - wsj.com
Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models wsj.com
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AI is Driving Cognitive Surrender Whilst Influencing Confidence Levels
AI has rapidly transformed how people access information and make decisions. Tools like ChatGPT offer speed, convenience and support for everyday tasks, however growing evidence suggested overreliance on AI may influence how we think, reason and evaluate information. The research from the University of Pennsylvania’s Wharton School of Business has reviewed 1,300 subjects use of [ ] The post AI is Driving Cognitive Surrender Whilst Influencing Confidence Levels appeared first on DIGIT .
98% of Firms Struggling to Manage Wireless as AI Explodes
Wi-Fi has evolved into a strategic growth engine delivering exponential value for enterprises, according to new research from Cisco, to the extent that a single network investment drives returns across employee productivity, customer engagement, and revenue. Polling more than 6,000 global wireless professionals, Cisco’s latest State of Wireless report found that 80% of large businesses [ ] The post 98% of Firms Struggling to Manage Wireless as AI Explodes appeared first on DIGIT .
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
arXiv:2512.16284v2 Announce Type: replace Abstract: Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for practitioners to evaluate and benchmark the degree of privacy protection offered by synthetic data. In this paper, we propose a framework to empirically assess the efficacy of tabular synthetic data privacy quantification methods through controlled, deliberate risk insertion. To demonstrate this framework, we survey existing approaches to synthetic data privacy quantification and the relate
A technical, 100% local writeup on how I replicated and then surpassed the Secret Detection model from Wiz (and the challenges along the way) - including labeling an entire dataset with local AI
Hey everybody, I have a strong interest in offloading work to small, specialized models that I can parallelize - this lets me scale work significantly (plus, I am less dependent on proprietary APIs) Some time ago, I saw a blog post from Wiz about fine-tuning Llama 3.2-1B for secret detection in code. They got 86% Precision and 82% Recall. I wanted to see if I can replicate (or beat) those numbers using purely local AI and produce a local specialized model. After a couple of weekends of trying it out I managed to get a Llama 3.2-1B hitting 88% Precision and 84.4% Recall simultaneously! I also benchmarked Qwen 3.5-2B and 4B - expectedly, they outperformed Llama 1B at the cost of more VRAM and longer inference time. I’ve put together a full write-up with the training stats, examples, and a st
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