In my main position, as a data scientist at SIL International, I work on expanding language possibilities with AI. Practically this includes applying recent advances in Natural Language Processing (NLP) to low resource and multilingual contexts. We work on things like spoken language identification, multilingual dialogue systems, machine translation, and translation quality estimation.
For the newest instalment in our series of interviews asking leading technology specialists about their achievements in their field, we’ve welcomed Matthew Renze. Matthew is a specialist in artificial intelligence who has given over 100 talks on this very subject and is the author of numerous AI courses on Pluralsight.
The word “data” is ubiquitous in narratives of the modern world. And data, the thing itself, is vital to the functioning of that world. This blog discusses quantifications, types, and implications of data. If you’ve ever wondered how much data there is in the world, what types there are and what that means for AI and businesses, then keep reading!
Much of my focus over the last couple of decades has been in analytics, big data, and AI, and Joe DosSantos and I discussed the progression of these fields over time in a recent Data Brilliant podcast episode. My subtitle for that episode might be, “The Promise and Perils of a Hot New Field,” as we addressed several aspects of how these popular concepts have evolved in the first fifth of the 21st century.
At AgroScout, we’re taking on a massive challenge with some correspondingly exciting upside, both for us and for our customers: We’re creating an automated, AI-driven scouting platform for early detection of pests and disease in vast agricultural areas.
In our previous article, The Future of the Modern Data Stack, we examined the motivations of the modern data stack, its current state, and looked optimistically into the future to see where it is headed. If you’re new to the modern data stack, we highly recommend giving the aforementioned article a read. A question we often get from new adopters of the modern data stack is “What tech should we be looking into?”.
Congratulations on creating a clean(ish) dataset to use for training! Now while the dataset is stored where it’s accessible to everyone, the distribution itself is a hassle! Local workstations, local GPU machines, and cloud machines (that may be spun up and down without disk persistence) are getting data everywhere. …and to say it is annoying is an understatement!
A casual stroll through recent tech headlines in the past few years makes two things abundantly clear: investment in AI is at an all-time high, and companies really struggle to get value out of AI technology. At first glance, these ideas seem to be at odds with each other: why consider investing in a field that hasn’t lived up to the hype? If you dig into the details, you’ll notice that a gap exists between the development and production use of AI in many companies.
