Collaborating Authors

AI as the new UI – Accenture Tech Vision


Moving beyond a back-end tool for the enterprise, artificial intelligence (AI) is taking on more sophisticated roles within technology interfaces. From autonomous driving vehicles that use computer vision, to live translations made possible by machine learning, AI is making every interface both simple and smart–and setting a high bar for how future experiences will work. AI is poised to act as the face of a company's digital brand and a key differentiator – and become a core competency demanding of C-level investment and strategy.

TUM launches Munich Center for Machine Learning (MCML)


The Technical University of Munich (Technische Universität München TUM) has launched the Munich Center for Machine Learning (MCML). MCML is funded by Germany's Federal Ministry of Research. Artificial intelligence and machine learning are crucial technologies for today's and tomorrow's digital economy. TUM says that MCML will be connecting key areas of expertise from computer science, data science, and statistics. AI or artificial intelligence includes software technologies that make machines and other devices think like humans.

Conventional computer vision coupled with deep learning makes AI better


Computer vision is fundamental for a broad set of Internet of Things (IoT) applications. Household monitoring systems use cameras to provide family members with a view of what's going on at home. Robots and drones use vision processing to map their environment and avoid obstacles in flight. Augmented reality glasses use computer vision to overlay important information on the user's view, and cars stitch images from multiple cameras mounted in the vehicle to provide drivers with a surround or "bird's eye" view which helps prevent collisions.

AI Researchers Propose a Machine Vision Turing Test

AITopics Original Links

Computers are getting better each year at AI-style tasks, especially those involving vision--identifying a face, say, or telling if a picture contains a certain object. In fact, their progress has been so significant that some researchers now believe the standardized tests used to evaluate these programs have become too easy to pass, and therefore need to be made more demanding. At issue are the "public data sets" commonly used by vision researchers to benchmark their progress, such as LabelMe at MIT or Labeled Faces in the Wild at the University of Massachusetts, Amherst. The former, for example, contains photographs that have been labeled via crowdsourcing, so that a photo of street scene might have a "car" and a "tree" and a "pedestrian" highlighted and tagged. Success rates have been climbing for computer vision programs that can find these objects, with most of the credit for that improvement going to machine learning techniques such as convolutional networks, often called Deep Learning.

Computer Vision Meetup


Please don't hesitate to get in touch if you have a topic you'd like to talk about or a project you want to present! - [masked]:) Anyline is going to sponsor free drinks at the beginning of the evening. Agenda: 7pm: Grab a welcome drink 7.30pm: Is the Singularity near? Where technology and AI could lead us: Facts, forecasts and disruptive projections. A talk by Michael Sprinzl Abstract: Baseline detection is still a challenging task for heterogeneous collections of historical documents. We present a novel approach to baseline extraction in such settings, turning out the winning entry to the ICDAR 2017 Competition on Baseline detection (cBAD).