Super-Recognizers

In 2009, psychological scientists first used the term super-recognizer to describe four participants in a study they conducted. According to the conclusions of the study, their findings demonstrate the existence of people with exceptionally good face recognition ability and show that the range of face recognition and face perception ability is wider than has been previously acknowledged. More recently, researchers at the University of Greenwich in London, is advancing this work. I talked to researchers to find out more about this fascinating field of research. By chance, they also one of the most recognizable people I have ever met.

According to them, approximately 1% of us may be super-recognizers. They might have this memory superpower. They have already identified well over 1000 super-recognizers from around the world. According them one of the problems is diagnosing super-recognition. Currently researchers have tended to use two main criteria. First, excellent performance on short term face memory tests using photos. Second, extraordinary subjective experiences of real person recognition. However, it can be difficult to know from lab-based tests how good a particular person is at real-life face remembering, he concedes.

More information:

https://blogs.scientificamerican.com/mind-guest-blog/super-recognizers-lurk-among-us/

Mapping Disaster Areas Using Drones & Insect Biobots

Researchers at North Carolina State University have developed a combination of software and hardware that will allow them to use unmanned aerial vehicles (UAVs) and insect cyborgs, or biobots, to map large, unfamiliar areas, such as collapsed buildings after a disaster. The biobots would be allowed to move freely within a defined area and would signal researchers via radio waves whenever they got close to each other. Custom software would then use an algorithm to translate the biobot sensor data into a rough map of the unknown environment.

Once the program receives enough data to map the defined area, the UAV moves forward to hover over an adjacent, unexplored section. The biobots move with it, and the mapping process is repeated. The software program then stitches the new map to the previous one. This can be repeated until the entire region or structure has been mapped; that map could then be used by first responders or other authorities. This has utility for areas, like collapsed buildings, where GPS can't be used.

More information:

https://www.sciencedaily.com/releases/2016/11/161117104402.htm

Can Pokémon Go Teach the World of Conservation?

The augmented reality game, designed for mobile devices, allows users to capture, battle and train virtual creatures called Pokémon that appear on screen as if part of the real-world environment. But can the game's enormous success deliver any lessons to the fields of natural history and conservation? A new paper by a group of researchers from the universities of Oxford and Cambridge, UNEP World Conservation Monitoring Centre, and University College London (UCL) explores whether Pokémon Go's success in getting people out of their homes and interacting with virtual 'animals' could be replicated to redress what is often perceived as a decline in interest in the natural world among the general public. Or, could the game's popularity pose more problems than opportunities for conservation?

In the paper, the researchers explain that Pokémon Go has been shown to inspire high levels of behavioural change among its users, with people making significant adjustments to their daily routines and to the amount of time spent outside in order to increase their chances of encountering target 'species'. There is also evidence that users are discovering non-virtual wildlife while playing Pokémon Go, leading to the Twitter hashtag #Pokeblitz that helps people identify 'real' species found and photographed during play. Pokémon Go, exposes users first hand to basic natural history concepts such as species' habitat preferences and variations in abundance. 'Grass Pokémon', for example, tend to appear in parks, while water-related types are more likely to be found close to bodies of water.

More information:

https://www.sciencedaily.com/releases/2016/11/161116103115.htm

AI Algorithm Keeps Learning

Researchers have designed an algorithm that learns directly from human instructions, rather than an existing set of examples, and outperformed conventional methods of training neural networks by 160 per cent. But more surprisingly, their algorithm also outperformed its own training by nine per cent -- it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap forward for artificial intelligence. This algorithm learns directly from human trainers.

With this model, called heuristic training, humans provide direct instructions that are used to pre-classify training samples rather than a set of fixed examples. The heuristic training approach holds considerable promise for addressing one of the biggest challenges for neural networks: making correct classifications of previously unknown or unlabeled data. This is crucial for applying machine learning to new situations, such as correctly identifying cancerous tissues for medical diagnostics, or classifying all the objects surrounding and approaching a self-driving car.

More information:

https://www.sciencedaily.com/releases/2016/11/161116102203.htm

Wireless VR Headsets

Dangling cables are one of the major problems of today’s virtual reality headsets. Wearing an HDMI cable reduces mobility and lead to users tripping over cords. A new solution developed by a group of researchers at Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to solve this problem. Named as “MoVR” the new system allows any VR headset to communicate without a cord.

The system uses millimeter wave, the technology that can help deliver 5G connectivity to smartphones. MIT says MoVR acts as a programmable mirror that detects the direction of the incoming mmWave signal and reconfigures itself to reflect it toward the receiver on the headset. MoVR can learn the correct signal direction to within two degrees, allowing it to correctly configure its angles. Researchers tested the system on an HTC Vive but say that it can work with any headset.

More information:

https://haptic.al/mit-wireless-virtual-reality-4e277bc37fd4#.bzmnklsgc