If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
One of the coolest devices for technically minded musicians is the MOD-Duo from MOD-Devices. Not only is it a standalone audio processor with 300 built in audio/MIDI effects, but it also acts as a server that you can interact with through a browser to build any kind of pedalboard imaginable. The sheer number of possibilities on these devices (now upgraded to the MOD-DuoX and most recently MOD-Dwarf), is daunting, but it has the power to revolutionize how people make music. What does this have to do with neural networks? If you are familiar with my work on GuitarML, you know that I develop guitar plugins that use neural networks to mimic analog amplifiers and pedals.
Allan: Machine learning is traditionally associated with heavy duty, power-hungry processes. It's something done on big servers. Even if the sensors, cameras, and microphones taking the data are themselves local, the compute that controls them is far away. The processes that tend to make the decisions are all in the cloud. This is now changing, and that change is happening remarkably quickly and for a whole bunch of different reasons. For anyone that's been around for a while, this isn't going to come as a surprise. Throughout the history of our industry, depending on the state of our technology, we seem to oscillate between thin and thick client architectures. Either the bulk of our compute power and storage is hidden far away in sometimes distant servers, or alternatively, it's in a mass of distributed systems much closer to home. We're now on the swing back, towards distributed systems once again, or at least a hybrid between the two. Machine learning has a rather nice split that can be made between development and deployment. Initially, an algorithm is trained on a large set of sample data, that's generally going to need a fast powerful computer or a cluster, but then the trained network is deployed into the wild and needs to interpret real data in real time, and that's a much easier fit for lower powered distributed systems. Sure enough this deployment or inference stage is where we're seeing the shift to local or edge computing right now, which is a good thing. Let's be entirely honest with ourselves, as an industry, we have failed to bring people along with us as we've moved to the cloud. We have increasingly engineered intricate data collection, storage, and correlation systems. We have lakes, we have siloes, we have piles of unrelated data that we'll never touch again.
This article looks at the unique challenges introduced by Edge computing for AI/ML workloads, which can have a negative impact on results. It applies available machine learning models to real-world Edge datasets, to show how these challenges can be overcome, while preserving accuracy in the dynamic nature of Edge environments. The field of machine learning has experienced an explosion of innovation over the past 10 years. Although its roots date back more than 70 years when Alan Turing devised the Turing Test, it has not matured significantly until recently. Two primary contributing factors are the exponential growth in both compute power and data that can be used for training. There is now enough data and compute power (some in specialized hardware like GPUs/FPGAs) that new, real-world problems are being solved every day with machine learning.
On November 13, 2015, Google had open-sourced TensorFlow, an end-to-end machine learning platform. Apart from marking five years of being one of the most popular machine learning frameworks, last week was even more significant as TensorFlow crossed the 160 million downloads. This article lists some interesting TensorFlow projects, in no particular order, which enthusiasts can try their hands on. This Handwritten Text Recognition can be implemented using TensorFlow. In this project, the system is trained on the IAM off-line dataset.
Welcome to a very fun hodgepodge of geek goodies on sale at Amazon for Prime Day. We cover a bunch of deals in this guide, including some good Raspberry Pi kit discounts and a really good price on a Creality 3D printer you can control with a Pi board. Deals, deals, deals: Business Bargain Hunter's top picks for Prime Day Look, we'll admit that the most desirable robot gadgets aren't on this list because they're not on sale for Prime Day. But if you want to do a little virtual window shopping and come home with something you can put together and tinker with, we have a great little selection. Oh, and if you're just getting started with Raspberry Pi making, here's a quick intro.
Welcome to a very fun hodgepodge of geek goodies on sale at Amazon for Prime Day. We cover a bunch of deals in this guide, including some good Raspberry Pi kit discounts and a really good price on a Creality 3D printer you can control with a Pi board. Deals, deals, deals: Business Bargain Hunter's top picks for Prime Day 2020. Look, we'll admit that the most desirable robot gadgets aren't on this list because they're not on sale for Prime Day. But if you want to do a little virtual window shopping and come home with something you can put together and tinker with, we have a great little selection.
Programming expert Rongzhong Li has designed a complex 3D printable robotic cat equipped with artificial intelligence and numerous cool features. The fake feline, which contains an Arduino Pro Mini and a Raspberry Pi 3 Model B, has a "Super Hard" classification on hackster.io. Robotic pets have long fascinated the consumer market, with toys like the Sony AIBO enjoying big success. In fact, the legendary AIBO is now back after an 11-year hiatus, to the delight of many. Unfortunately, it comes with a $1,800 price tag.
The Raspberry Pi 3 Model B is the latest version of the $35 Raspberry Pi computer. The Pi isn't like your typical machine, in its cheapest form it doesn't have a case, and is simply a credit-card sized electronic board -- of the type you might find inside a PC or laptop but much smaller. See also: Raspberry Pi: The smart person's guide As you can see below you can use the Pi 3 as a budget desktop, media center, retro games console, or router for starters. However that is just the tip of the iceberg. There are hundreds of projects out there, where people have used the Pi to build tablets, laptops, phones, robots, smart mirrors, to take pictures on the edge of space, to run experiments on the International Space Station -- and that's without mentioning the wackier creations -- self-driving goldfish anyone?
This RetroPie really happened: Watch (above) as our own Adam Patrick Murray and Alaina Yee build a RetroPie system after they weren't able to buy an SNES Classic. Go ahead, laugh at (and learn from) our mistakes. For the past 20 years, retrogaming enthusiasts have dreamed of building a "universal game console" capable of playing games from dozens of different systems. Their ideal was inexpensive, easy to control with a gamepad, and capable of hooking into a TV set. Thanks to the Raspberry Pi 3 hobbyist platform and the RetroPie software distribution, that dream is finally possible. For under $110, you can build a very nice emulation system that can play tens of thousands of retro games for systems such as the NES, Atari 2600, Sega Genesis, Super NES, Game Boy, and even the PlayStation. All you need to do is buy a handful of components, put them together, and configure some software. You'll also have to provide the games, but we'll talk about that later.