Here’s an interesting documentary (“Canada – The Rise of AI”, Ep. 11) on the “Canadian Silicon Valley.”

Silicon Valley may be home to some of the biggest tech giants in the world but it’s being challenged like never before. Crazy tech geniuses have popped up all over the planet making things that will blow your mind. Author and journalist Ashlee Vance is on a quest to find the most innovative tech creations and meet the beautiful freaks behind them. Bloomberg Businessweek presents an exclusive premiere of the latest episode of Hello World, the tech-travel show hosted by journalist and best-selling author Ashlee Vance and watched by millions of people around the globe.

SparkFun Electronics highlights the RP2040 for use in machine learning projects.

The RP2040 is supported with both C/C++ and MicroPython cross-platform development environments, including easy access to runtime debugging. It has UF2 boot and floating-point routines baked into the chip. The built-in USB can act as both device and host. It has two symmetric cores and high internal bandwidth, making it useful for signal processing and video. While the chip has a large amount of internal RAM, the board includes an additional 16MB external QSPI flash chip to store program code.

In 2020 the UK confirmed details of the country’s first commercial quantum computer. This is being build in Abingdon, near Oxford, and once complete will be made available to companies who want to explore the potential of quantum computing on their businesses.

In this video, Alexei Kondratyev talked through some of the aspirations in the Financial Services space, and in particular the potential applications of Quantum Machine Learning.

Yannic Kilcher covers a paper where Geoffrey Hinton describes GLOM, a Computer Vision model that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders and RNNs.

GLOM decomposes an image into a parse tree of objects and their parts. However, unlike previous systems, the parse tree is constructed dynamically and differently for each input, without changing the underlying neural network. This is done by a multi-step consensus algorithm that runs over different levels of abstraction at each location of an image simultaneously. GLOM is just an idea for now but suggests a radically new approach to AI visual scene understanding.