Deep Art

------ http://www.deepart.io/ ------ 

Abstract: In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.

Original article

Training eagles to intercept drones?

A "catchy" idea for the moment, but unrealistic in terms of induced cost. Makes me wonder of the reaction of the eagle in the scenario where the drone is equipped with a very high-frequency sound emitter to deafen the eagle. 

Object recognition App for the visually Impaired - Aipoly

http://aipoly.com/

Help EMILY robot help drowning refugees

Friends of CRASAR (Center for Robotic-Assisted Search & Rescue)

We can't wait any longer- people are drowning and the technology exists to help. We have permission from the Hellenic Coast Guard and partial funding but are short $5,000. We will send 2 small robot marine vehicles (EMILY, which are used by the Los Angeles County Fire Department to rescue drowning victims) and a team of 4 to rescue drowning refugees fleeing from Turkey to Greece. Check fundraiser site below

https://www.gofundme.com/Friends-of-CRASAR

Ancient humanoids: the automatic maid of Philon of Byzantium

Translated from Ancient Hellenic Technology:

The automatic maid of Philon:
(the first operational robot in history)

   This is about a humanoid robot in the form of a maid (in natural size), holding on the right hand a wine jar. When a visitor placed a cup on the palm of her left hand, she would initially pour wine and subsequently mix it with water following the visitor' s desire.

Description of operation: There are two containers in the interior of the maid, filled with water and wine, respectively. Two tubes that start at the bottom of the containers go through the right hand and conclude at the tip of jar. Two air tubes are exposed to the top of the two containers and go through their interior, passing through the stomach. Her left hand is connected through a joint to her shoulder while a spring placed at its center lifts the arm upwards. Another two tubes commence from the same point and go down (going through and blocking the perforated corners of the air tubes). The tubes of the joint dispose two holes/openings at their endings, the hole that connects with the wine container preceding that which connects with the water container. When the cup is placed at the palm of the maid, her left hand goes down and the tubes of the joint go upwards. The opening of one of the tubes is aligned with the air tube of the wine container, air enters the container and wine flows from the tube of the wine jar to the cup. When the cup becomes half full with wine, the hand (due to its weight) goes further down, the opening of the air tube for the wine is blocked and the flow is interrupted. At the same time, the opening of the second tube is aligned with the air tube for the water container and water starts to flow in the cup for diluting the wine. When the cup is entirely full, the hand continues to go further down (due to its weight), the opening of the air tube to the water container is blocked and water flow is interrupted. Moreover, if the cup is removed from the hand at any time, the left hand goes upwards, the tubes of the joint go down, thus blocking the air tubes and creating a void in the containers and the flow of liquids is halted. Thus, the maid fills our cup with pure wine or wine diluted with water at the desired ratio, depending on the time when we remove the cup from her hand.

Next INNOROBO in Paris!

From 24 until 26 of May, INNOROBO exhibition will take place in Paris, France. I will try to keep this post updated with new information related to the event.

Full information below:

http://innorobo.com/en/home/

Robots that can adapt like animals

Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret, Robots that can adapt like animals, Nature, 2015.

Abstract: Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets to deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, 11, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

Article reference:

http://dx.doi.org/10.1038/nature14422