MIT to Create Plant Cyborg

Elowan the robot with the plant drives to the light source necessary for photosynthesis
06 December 2018   747

Specialists at the Massachusetts Institute of Technology (MIT) have developed the Elowan robot, which is in a kind of symbiosis with the plant. A living organism uses electrical signals to control the movement of the robot.

So far, the system only responds to some changes, such as lighting a plant or a lack of water. Silver electrods are used to transmit signals, which are implanted in the leaves, roots and stem of the plant. The direction of movement of the robot depends on the strength of the impulse. For example, in one of the experiments, the robot on which the pot with the plant stood, itself drove up to the light source necessary for photosynthesis.

The algorithm looks like this: the plant needs light, this causes certain biochemical changes that generate an electrical impulse. The robot captures it, decrypts and executes the desired command.

In the future, such systems may be useful for automatic farms, where robots themselves will take care of plants, responding to their needs for light, water or nutrients. Such technologies will be used in agriculture on Earth, as well as in distant space expeditions, where it will be necessary to grow plants on board a ship.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   864

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.