business

In “What ‘Pondering’ and ‘Reasoning’ Really Mean in AI and LLMs,” you address the semantic gap between human and machine reasoning. How does understanding this distinction impact the way in which you approach model...

And do we wish to? The post Can We Save the AI Economy? appeared first on Towards Data Science.

Much of the momentum is being driven by two related forces: the rise of AI agents and the rapid democratization of AI tools. AI agents, whether designed for automation or assistance, are proving especially...

As an information scientist your job is to leverage data to unravel business problems and convey value, normally by constructing models. This typically involves running a series of experiments where numerous ideas are iterated...

In Belgium's Flanders region, 600,000 smart meters watch every drop of water flowing through homes and businesses. When a meter spots trouble—say, three liters trickling away every hour for 3 straight days—it immediately triggers...

As data scientists, we’ve turn out to be extremely focused on constructing algorithms, causal/predictive models, and advice systems (and now genAI). We optimize for accuracy, fine-tune hyperparameters, and search for the subsequent big fancy...

attract all of the hype today inside data science, but I’d argue they’re each secondary to a more vital—and often-ignored—section of the sector. When coping with data, there are two essential steps: Processing and analyzing...

Parts 1 and a couple of of this series focussed on the technical aspect of improving the experimentation process. This began with rethinking how code is created, stored and used, and ended with utilising...