The Future of Artificial Intelligence and Quantum Computing
Published in Military & Aerospace Electronics
Written by JR Wilson
Until the 21st Century, artificial intelligence (AI) and quantum computers were largely the stuff of science fiction, although quantum theory and quantum mechanics had been around for about a century. A century of great controversy, largely because Albert Einstein rejected the quantum theory as originally formulated, leading to his famous statement, “God does not play dice with the universe”.
Today, however, the debate over quantum computing is largely about when — not if — these kinds of devices will come into full operation. Meanwhile, other forms of quantum technology, such as sensors, already are finding their way into military and civilian applications.
Military Quantum Computing
AI-HPEC would give UAVs, next-generation cruise missiles, and even maneuverable ballistic missiles the ability to alter course to new targets at any point after launch, recognize countermeasures, avoid, and misdirect or even destroy them.
Quantum computing, on the other hand, is seen by some as providing little, if any, an advantage over traditional computer technologies, by many as requiring cooling and size, weight, and power (SWaP) improvements not possible with current technologies to make it applicable to mobile platforms and by most as being little more than a research tool for perhaps decades to come.
Perhaps the biggest stumbling block to a mobile platform-based quantum computing is cooling — it currently requires a cooling unit, at near absolute zero, the size of a refrigerator to handle a fractional piece of quantum computing.
“A lot of work has been done and things are being touted as operational, but the most important thing to understand is this isn’t some simple physical thing you throw in suddenly and it works. That makes it harder to call it deployable — you’re not going to strap quantum computing to a handheld device. A lot of solutions are still trying to deal with cryogenics and how do you deal with deployment of cryo,” says Tammy Carter, senior product manager for GPGPUs and software products at Curtiss-Wright Defense Solutions in Ashburn, Va.
Tackling the AI Paradox at the Tactical Edge
In the defense industry, AI is on the path to becoming a loyal companion to the warfighter and manufacturers alike. As technology progresses, so does the range of defense capabilities that AI could not only supplement but eventually manage.
Artificial Intelligence and Embedded Computing for Unmanned Vehicles
The latest generation of unmanned vehicles operating on land, in the air, and at sea no longer simply are remotely operated. These advanced systems have built-in intelligence to learn from their experiences and make their own decisions.
How Can I Teach My Machine to Learn?
Like humans, machines learn from experience. They make observations from inputs of images, text, or other data, and then look for patterns. After the machine runs through the mathematical layers, it learns to make better decisions based on the examples it was given.
A GPU complements a processor to offer display graphics or can serve as a math/vector acceleration engine. As a display processor, a GPU supports real-time graphics using OpenGL and media codecs such as MPEG and H.254, and can drive multiple displays simultaneously. When used as a math/vector accelerator, a GPU can perform floating-point calculations using hundreds or thousands of parallel floating-point units via the use of OpenCL and CUDA software frameworks. In data-intensive applications, such as imaging enhancement and mosaicking, GPUs can stitch together input from multiple sensors or process radar data faster than general-purpose CPUs.