Once every 2 weeks, we collect the most interesting news from the AI world for our staff. We thought you may find them worth reading, too!
In this first issue of our #InfocusAI digest, you will discover how Microsoft’s neural network learns in polynomial time, why it takes so much effort to recognize speech from brain activity, and how a robot with computer vision patrols secured facilities in Australia. You will also find out how AI can control traffic safety and help architects with their creative projects.
AI focused digest – News from the AI world
Issue 1, 02-15 September 2022
Microsoft and Harvard present a neural network architecture that can learn well in polynomial time
A research team from Microsoft and Harvard University has come up with a neural network architecture that learns in polynomial time. The architecture is based on concurrent sharing of weights between both recurrent and convolutional layers, which permits reducing the number of parameters to a constant value, even in networks built from trillions of nodes. The research shows that simple network architecture is capable of learning just as well as any other limited sample size algorithm. The authors refer to this attribute as “Turing optimality.” Read more
AI learns to recognize speech from brain activity
A recently published
Australia will get a site patrol robot
Stealth Technologies and Honeywell struck hands to put on the market a patrol robot for secured facilities. This fully autonomous vehicle will be able to cruise the area and stream video to the base, meanwhile testing microwave, photoelectric and electromagnetic security sensors on the go. This robotic security guard can run for up to eight hours on lithium-ion batteries and uses computer vision technology to recognize faces and license plates. Up until now, the solution has only been tested at a correctional facility in Western Australia. Its developers now want to give the system a trial run and then start selling it to telecom and defense customers in Australia and New Zealand. Read more
Artificial intelligence for architects
We keep tracking the progress of Midjourney and Dalle-2 neural networks. This time our focus is on “text-to-image” – an AI-enabled technology that converts text into images and is being actively mastered by leading architecture firms. Neural networks that were trained on billions of images and their textual descriptions can generate breathtaking visuals and inspire creative professionals. Architects and designers use these tools early on in their projects to quickly develop novel visual concepts for buildings and test how they fit into the urban environment. For example, HDR, an international architecture firm, used AI to design a new building in Ontario: a neural network studied the city’s cultural heritage sites and generated sketches of the new building that matched the local landmark style. Read this
Canada runs AI pilot to detect distracted drivers
The University of Alberta in Edmonton is