DeepMind is developing one algorithm to rule them all
DeepMind wants to enable neural networks to emulate algorithms to get the best of both worlds, and it’s using Google Maps as a testbed.
Read More →Machine learning at the edge: A hardware and software ecosystem
Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market. For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.
Read More →DeepMind aims to marry deep learning and classic algorithms
Will deep learning really live up to its promise? We don’t actually know. But if it’s going to, it will have to assimilate how classical computer science algorithms work. This is what DeepMind is working on, and its success is important to the eventual uptake of neural networks in wider commercial applications.
Read More →The state of MLOps in 2021
MLOps is the art and science of bringing machine learning to production, and it means many things to many people. The State of MLOps is an effort to define and monitor this market.
Read More →AI ethics in the real world: FTC commissioner shows a path toward economic justice
FTC Commissioner Rebecca Kelly Slaughter lays out machine learning and AI's potential for harm and outlines some ways for the FTC to counter it.
Read More →Building MLGUI, user interfaces for machine learning applications
Machine learning is eating the world, and spilling over to established disciplines in software, too. After MLOps, is the world ready to welcome MLGUI (Machine Learning Graphical User Interface)?
Read More →Open-source growth and venture capital investment: Data, databases, challenges, and opportunities
Open-source software used to be poorly understood by commercial forces, and it's often approached in a biased way. A new generation of investment funds goes to show that things are changing.
Read More →More than words: Shedding light on the data terminology mess
Data management, data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps. It's a data terminology mess out there. Let's try and untangle it, because there's more to words than lingo.
Read More →Machine learning at the edge: TinyML is getting big
Being able to deploy machine learning applications at the edge is the key to unlocking a multi-billion dollar market. TinyML is the art and science of producing machine learning models frugal enough to work at the edge, and it's seeing rapid growth.
Read More →Graphs as a foundational technology stack: Analytics, AI, and hardware
How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in trying to keep up with demand, probably.
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