Artificial intelligence is fast becoming an essential ingredient for an array of solutions across all industries – which promises to transform every aspect of our lives. While intelligence similar to (or perhaps even surpassing) that of humans may emerge in the not-too-distant future, it's clear from our collective everyday experience we are not there yet. We are, however, firmly on the path. And there are steps we can take to create the brighter future we desire. This is especially important when it comes to the media and publishing industry.
The 21st century has opened up a boundless mass of headlines, articles, and stories. This information influx, however, is partially contaminated: Alongside factual, truthful content is fallacious, deliberately manipulated material from dubious sources. According to research by the European Research Council, one in four Americans visited at least one fake news article during the 2016 presidential campaign. This problem has recently been exacerbated by something called "automatic text generators." Advanced artificial intelligence software, like OpenAI's GPT-2 language model, is now being used for things like auto-completion, writing assistance, summarization, and more, and it can also be used to produce large amounts of false information -- fast.
HELIXRE announces the launch of Enhanced Cloud Technology, an application of geometric deep learning (GDL) to greatly increase the benefits of digital building plans. HELIXRE now applies GDL to distinguish contents of multi-billion point clouds to allow semantic understanding of building spaces, identifying specific elements like walls, floors, ceilings, furniture, and typical office clutter. Defining and isolating these elements means no one creates more accurate building clouds faster and more cost efficiently than HELIXRE. GDL is a relatively new and cutting-edge form of machine learning, overcoming limitations of convolutional neural networks that solve 2D problems like image recognition, but are not well suited to the complexity of 3D spaces. By creating superpoints, HELIXRE reduces the problem domain to a smaller set of points to which graph convolutions are applied.