MIT to Create Plant Cyborg

Elowan the robot with the plant drives to the light source necessary for photosynthesis
06 December 2018   128

Specialists at the Massachusetts InstituteĀ 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.

Microsoft to Open ONNX Runtime Source Code

It's a high-performance engine for machine learning models in the ONNX (Open Neural Network Exchange) format
07 December 2018   97

Microsoft announced the deployment of ONNX Runtime source code on GitHub. The project is a high-performance engine for machine learning models in the ONNX (Open Neural Network Exchange) format, ensuring compatibility of ML models with free AI frameworks (TensorFlow, Cognitive Toolkit, Caffe2, MXNet). Therefore, ONNX Runtime is used to optimize computations in models of deep learning of neural networks.

With the translation of the project into open source, the company hopes to attract more people to the development of machine learning. Moreover, Microsoft promised to respond quickly to commits.

To use ONNX Runtime, it is necessary to determine the ONNX model and select a tool for it. Their list and instructions are available on the GitHub page. Microsoft offers several options for those who do not know where to start:

  • Download ready-made ResNet or TinyYOLO models from ONNX Model Zoo;
  • Create your own computer vision models using Azure Custom Vision Service
  • convert models created in TensorFlow, Keras, Scikit-Learn or CoreML using ONNXMLTools and TF2ONNX;
  • train new models using Azure machine learning and save the result in ONNX format.

According to Microsoft spokesman Eric Boyd, the Bing Search, Bing Ads and Office services teams were able to achieve twice the performance of ML models using ONNX Runtime compared to standard solutions. Therefore, it is important to support the project by both users and large companies. As for the latter, while they embody the following projects:

  • Microsoft and Intel are implementing the nGraph compiler;
  • NVIDIA is working on TensorRT integration;
  • Qualcomm is looking forward to developing the Snapdragon mobile platform.

In early December 2017, ONNX was transferred from the stage of early access to a project corresponding to the conditions of industrial operation. Companies urged the community to join the project and help create a unified platform for engaging with in-depth training tools.