In 1928, London was in the middle of a terrible health crisis, devastated by bacterial diseases like pneumonia, tuberculosis, and meningitis. Confined in sterile laboratories, scientists and doctors were stuck in a relentless cycle of trial and error, using traditional medical approaches to solve complex problems. This is when, in September 1928, an accidental event

The rise of the vector database As a result of the rapid advancement of generative AI in recent years, many companies are rushing to integrate AI into their businesses. One of the most common ways of doing this is to build AI systems that answer questions concerning information that can be found within a database

Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company. Machine learning processes are complex, and it’s very easy to do things that will cause overfitting without it being obvious. In the 20 years or so that I’ve

The history of each living being is written in its genome, which is stored as DNA and present in nearly every cell of the body. No two cells are the same, even if they share the same DNA and cell type, as they still differ in the regulators that control how DNA is expressed by

One of the most fascinating problems that a computer scientist may be lucky enough to encounter is a complex sociotechnical problem in a field going through the process of digital transformation. For me, that was fish counting. Recently, I worked as a consultant in a subdomain of environmental science focused on counting fish that pass

In this article, we will talk about classical computation: the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates. Of course, given The Gradient’s

This essay first appeared in Reboot. Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both evocative of and directly derived from children’s cartoons — might show up uncritically in the New Yorker? More than ever, the public discourse about AI

Once considered a forbidden topic in the AI community, discussions around the concept of AI consciousness are now taking center stage, marking a significant shift since the current AI resurgence began over a decade ago. For example, last year, Blake Lemoine, an engineer at Google, made headlines claiming the large language model he was developing