Project OpenDR which PAL Robotics is a project partner in has released a toolkit for deep learning in robotics – one of the first of its kind, that provides over 20 methods for human pose estimation, face detection, face recognition, facial expression recognition, object detection and more. The aim is to enable a wide range of capabilities in robotics more easily using the toolkit. Advances in deep learning have brought about significant developments in technology such as self-driving cars and algorithms that are able to understand and answer questions. However, the application of deep learning in robotics creates challenges in learning, reasoning, and embodiment problems and research questions that are often not addressed by the computer vision and machine learning communities. The team at PAL Robotics will use multiple capabilities developed in the OpenDR toolkit on the TIAGo robot.

All employees are expected to contribute to excellence through high quality research and teaching. The working environment for this position will be at CBU, in the Systems Biology & Machine Learning group headed by Prof. Tom Michoel. The aim of the INTRePID project is to create intelligent systems for personalized and precise risk prediction and diagnosis of non-communicable diseases using multi-omics data, by developing, implementing and validating novel algorithms for structure learning and inference in large-scale, multi-organ causal Bayesian gene networks. The project will have privileged access to a unique resource of multi-omics data from four Nordic countries generated by the project partners for a proof-of-concept application in cardiovascular medicine. The person appointed on this position will develop and apply models and algorithms for causal inference, graph representation learning, and inference in large-scale Bayesian networks.

Over 30 percent of that is already destroyed in the production process. In the Resource-efficient Intelligent Foodchain ("REIF") projects working to combat this food waste. In this undertaking, artificial intelligence (AI) can be a valuable asset. Cheese, bread, meat, and other food products can be efficiently produced using data-based algorithms. Germany has committed to the United Nations goal to reduce food waste by half by the year 2030.

RO-MAN 2021 (IEEE International Conference on Robot and Human Interactive Communication) is coming soon, from 8 to 12 August 2021, and we are taking part in the conference once again, this year as silver sponsors. RO-MAN 2021 (this year taking place virtually) is a key event in the community of Robot & Human technologies, which we at PAL Robotics are committed to contributing to. RO-MAN this year celebrates its 30th anniversary. This annual academic conference was launched in 1992 in Tokyo, Japan and is sponsored by the IEEE Robotics and Automation Society. RO-MAN 2021 is being organised by the University of British Columbia and the University of Waterloo, Canada.

Over 30 percent of that is already destroyed in the production process. In the Resource-efficient Intelligent Foodchain ("REIF") project, the Fraunhofer Institute for Casting, Composite and Processing Technology IGCV is working with partners to combat this food waste. In this undertaking, artificial intelligence can be a valuable asset. Cheese, bread, meat, and other food products can be efficiently produced using data-based algorithms. Germany has committed to the United Nations goal to reduce food waste by half by the year 2030.

According to experts, the mobility of the future will be characterized by artificial intelligence (AI) and digitization. Autonomous driving will make traffic more efficient, safer and environmentally friendly, as well as more cost-effective. But for people to accept this new mobility, it must be extensively tested and demonstrated in real test environments. As part of the new "BeIntelli" research project, an interdisciplinary team of scientists from the Technical University of Berlin (TU Berlin) and partners in the field want to develop and field-test the possibilities of AI for the mobility of the future based on platform economics. They also want to create a "showcase" that allows AI applications in mobility to be experienced.

Autonomous vehicles and devices for intelligent homes are becoming increasingly complex. A new system based on machine learning is being designed to make the soft- and hardware used for these applications more robust, powerful, and energy-efficient. The new project VEDLIoT is being funded by the European Commission, with approximately eight million Euro over the course of three years. The project is coordinated by Bielefeld University's CoR-Lab. In an intelligent home - a "Smarthome" - residents have devices at their fingertips that are designed to make lives easier: imagine a refrigerator that re-orders food when it is running low, and can, at the same time, communicate with the oven.

THuMP is a multi-disciplinary project, with the ambitious goal of advancing the state-of-the-art in trustworthy human-AI decision-support systems. ThUMP will address the technical challenges involved in creating explainable AI (XAI) systems, with a focus on Visualization for Explainable Planning and Argumentation, so that people using the system can better understand the rationale behind and trust suggestions made by an AI system. This project is conducted in collaboration with three project partners: Schlumberger and Save the Children, which provide use cases for the project, and a law firm whowill cooperate in considering legal implications of enhancing machines with transparency and the ability to explain. The candidate will be responsible for conducting research around the interfaces required to support explainability in the context of decision making in human-machine partnerships. Tasks will involve designing new visual layouts, building the interaction infrastructure for the project, developing a prototype interface for communicating with users, designing and conducting experiments with human subjects based on the use cases that will be co-created with the project partners.

The government has awarded grants totalling over £6.4m to 18 legal artificial intelligence (AI) and data analytics projects. The projects span the whole range of legal services, from City law firm DLA Piper and private client specialists Withers to consumer forum Legal Beagles and Islington Citizens Advice Bureau. The biggest grant of £1.53m from the Next Generation Services Industrial Strategy Challenge Fund went to a project focusing on the acquisition of confidential data. The project partners include Withers, Imperial College in London, Oxford University and Genie AI. The second biggest, £1.36m, went to help develop AI software that "detects and interprets emotion and linguistics from voice" to combat insurance fraud through "credibility/vulnerability assessment".

The University of Sydney's Brain and Mind Centre will partner with the Sydney Neuroimaging Analysis Centre to improve diagnostic neuroimaging of brain ailments such as multiple sclerosis and dementia. The Brain and Mind Centre is an institute within the University researching and developing treatments for conditions of the brain and mind. According to the report released by the University, funding amounting to A$ 2.36 million will be awarded to the project through the government's Cooperative Research Centre-Project (CRC-P) Program as announced by Assistant Minister for Science, Jobs and Innovation the Hon Zed Seselja. The CRC-P Program is a competitive merit-based program supporting industry-led, outcomes-focused partnerships between industry, researchers and the community. The investment made by the government is matched by nearly A$ 2.8 million of cash and in-kind contributions by the project partners of both the University and the Brain and Mind Centre, including Sydney Neuroimaging Analysis Centre (SNAC) and the I-MED Radiology Network.