MIT researchers have developed a computer interface that can transcribe words that the user concentrates on verbalizing but does not actually speak aloud. The system consists of a wearable device and an associated computing system. Electrodes in the device pick up neuromuscular signals in the jaw and face that are triggered by internal verbalizations -- saying words "in your head" -- but are undetectable to the human eye. The signals are fed to a machine-learning system that has been trained to correlate particular signals with particular words. The device also includes a pair of bone-conduction headphones, which transmit vibrations through the bones of the face to the inner ear.
One of the things that's so fundamental in software development that it's easy to overlook is the idea of a repository of shared code. As programmers, libraries immediately make us more effective. In a sense, they change the problem solving process of programming. When using a library, we often think of programming in terms of building blocks -- or modules -- that can be tied together. How might a library look for a machine learning developer?
This is an article I had originally written as part of a stream of work that has now been put on hold indefinitely. I thought it a shame for it to languish in OneNote. Well that is a very good question. To be perfectly frank, not that much has changed of late in the world of Artificial Intelligence (AI) as a whole that should justify all the current excitement. That's not to say that there isn't cool stuff going on; there really is great progress being made… in the world of Machine Learning.
If you'd go by the marketing newsletters of leading IT solutions vendors of the world, it would appear that artificial intelligence and machine learning are ideas that have come into being, almost magically, in the past two to three years. Artificial intelligence, in fact, is a term that was coined way back in the 1950s by computer programmers and researchers to describe machines that could respond with appropriate behaviors to abstract problems without human input. Machine learning is one of the more prominent approaches to making artificial intelligence a reality. It is centered on the idea of creating algorithms that are inherently capable of identifying patterns in data and improving their outcomes based on the large datasets. This guide is dedicated to helping you understand and identify the fundamental skills you need to master machine learning technologies and find fulfilling employment in this hot and growing field.
Machine learning is very much the'topic of the moment'. It is being discussed everywhere, whether as machine learning, or as artificial intelligence. Machine learning techniques and tools have been around for a while, however, so why now? Is it just an idea whose time has come, or have there genuinely been new developments that have made a difference?
Andrej Karpathy has an article "Software 2.0" that makes the argument that Neural Networks (or Deep Learning) is a new kind of software. I do agree that there indeed a trend towards "teachable machines" as opposed to the more conventional programmable machines, however I do have an issue with some of the benefits that Karpathy mentions to back-up his thesis. Certainly Deep Learning is already eating the Machine Learning world with advances across the board. Karpathy mentions several well known ones: visual recognition, speech recognition, speech synthesis, machine translation, robotics and games. This frames his argument about the sea change in computing and perhaps its time to think about a new kind of software (I guess the kind that you teach like a dog instead of programming).
As another academic year got under way at Imperial College London, a senior professor was bemused at the absence of one of her students. He had worked in her lab for three years and had one more left to complete his studies. But he had stopped coming in. Eventually, the professor called him. He had left for a six-figure salary at Apple.
HPE has just announced a bunch of new things to help organisations everywhere tap into AI. The new offerings include an integrated hardware-software solution, a set of guiding tools, a research collaboration platform, and a place to get access to the latest expertise. Deep Learning, as a subset of AI, is key for things like facial or voice recognition, image classification or other challenging tasks. It requires a high performance compute infrastructure to build and train learning models that can handle vasts amount of data, and that is something many organisations lack. This is also the core problem HPE is trying to solve with its new solution.