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
Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese
The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process. Samsung, for instance, is adding AI to its memory chips to
Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality. IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible.
