Powering Smarter AI And LLMs With The Right Data Strategy

I'll explore data-related challenges, the increasing importance of a robust data strategy and considerations for businesses ...
TechCrunch

The promise and perils of synthetic data

Is it possible for an AI to be trained just on data generated by another AI? It might sound like a harebrained idea. But it’s one that’s been around for quite some time — and as new, real data is ...
TechCrunch

Making AI models ‘forget’ undesirable data hurts their performance

So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material. But ...
Forbes

Data Quality For Business: Metrics To Measure And Understand

Zehra Cataltepe is the CEO of TAZI.AI, an adaptive, explainable AI and GenAI platform for business users. She has 100+ AI papers & patents. In many industries, including banking, insurance and ...
CoinTelegraph

Training vs. testing data in machine learning

Machine learning (ML) is a subset of artificial intelligence (AI) that involves using algorithms and statistical models to enable computer systems to learn from data and improve performance on a ...
Fast Company

The new AI paradox: smarter models, worse data

AI promises a smarter, faster, more efficient future, but beneath that optimism lies a quiet problem that’s getting worse: the data itself. We talk a lot about algorithms, but not enough about the ...
VentureBeat

Phi-4 proves that a 'data-first' SFT methodology is the new differentiator

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology ...
Microsoft

Detecting backdoored language models at scale

Learn how Microsoft research uncovers backdoor risks in language models and introduces a practical scanner to detect tampering and strengthen AI security.