Peter leads us in Project Management with over 30 years experience in industry. He is critical of QA procedures and adherence to industry standards with regards to documentations, drawings and calculations. He is also skilled in SWOT analysis for future business planning. Peter has managed a wide range of European Union funded projects under grants by innovateUK. His exposure cuts across nuclear, marine and civil engineering related projects.

We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning. PQR does not assume that the reward solely depends on the state, instead it allows for a dependency on the choice of action. Moreover, PQR allows for stochastic state transitions. To accomplish this, we assume the existence of one anchor action whose reward is known, typically the action of doing nothing, yielding no reward. We present both estimators and algorithms for the PQR method. When the environment transition is known, we prove that the PQR reward estimator uniquely recovers the true reward. With unknown transitions, we bound the estimation error of PQR. Finally, the performance of PQR is demonstrated by synthetic and real-world datasets.

It has been theorized that positive intergroup relations can reduce prejudice and facilitate peace. However, supporting empirical evidence is weak, particularly in the context of real-world conflict. Mousa randomized Christian Iraqi refugees to soccer teams that were composed of either all Christian players or a mixture of Christian and Muslim players (see the Perspective by Paluck and Clark). Playing on the same team as Muslims had positive effects on Christian players' attitudes and behaviors toward Muslims within the context of soccer, but these effects did not generalize to non-soccer contexts. These findings have implications for the potential benefits and limits of positive intergroup contact for achieving peace between groups. Science , this issue p. [866][1]; see also p. [769][2] Can intergroup contact build social cohesion after war? I randomly assigned Iraqi Christians displaced by the Islamic State of Iraq and Syria (ISIS) to an all-Christian soccer team or to a team mixed with Muslims. The intervention improved behaviors toward Muslim peers: Christians with Muslim teammates were more likely to vote for a Muslim (not on their team) to receive a sportsmanship award, register for a mixed team next season, and train with Muslims 6 months after the intervention. The intervention did not substantially affect behaviors in other social contexts, such as patronizing a restaurant in Muslim-dominated Mosul or attending a mixed social event, nor did it yield consistent effects on intergroup attitudes. Although contact can build tolerant behaviors toward peers within an intervention, building broader social cohesion outside of it is more challenging. [1]: /lookup/doi/10.1126/science.abb3153 [2]: /lookup/doi/10.1126/science.abb9990

Artificial intelligence has been vital in controlling the spread of the coronavirus in the Arabian Gulf, a health conference has been told. Technology has forecast the pandemic's development and informed residents when they have been in contact with infected individuals, the Riyadh Global Digital Health Summit heard. The summit was also told that the rapid growth in telemedicine – such as video or telephone consultations – is not likely to be reversed when the pandemic is over. However, experts cautioned that organisations were not doing enough to share vital data that could save lives and certain ethical concerns about the use of data had not been resolved. Dr Esam Al Wagait, director of Saudi Arabia's National Information Centre, said the Kingdom's artificial intelligence (AI) based Covid-19 Index had been crucial in forecasting the virus's spread locally, including which areas would be most heavily affected and how many people would fall ill.

As roles and tasks shift in tandem with the expansion of new technologies, and the division of work between human and machine is redrawn, it is of critical importance to monitor how those changes will impact the evolution of economic gender gaps. Artificial Intelligence (AI) is a prominent driver of change within the transformations brought about by the Fourth Industrial Revolution (4IR), and can serve as key marker of the trajectory of innovation across industries.19 In partnership with the LinkedIn Economic Graph Team, the World Economic Forum aims to provide fresh evidence of the emerging contours of gender parity in the new world of work through near-term labour market information. The increasing expansion of AI is creating the demand for a range of new skills, among them neural networks, deep learning, machine learning, and "tools" such as Weka and Scikit-Learn. AI skills are among the fastest-growing specializations among professionals represented on the LinkedIn platform.

The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data. They leverage the Canonical and Tensor-Train decompositions to yield expressive context aggregation while limiting the number of model parameters. Finally, we define two novel neural recursive models for trees leveraging such aggregation functions, and we test them on two tree classification tasks, showing the advantage of proposed models when tree outdegree increases.

The 70-foot-tall colossus, called a "Field Scanalyzer," is the world's biggest agricultural robot, the project's researchers say. Resembling an oversize scaffold with a box perched in its middle, it lumbers daily over 2 acres of crops including sorghum, lettuce and wheat, its cluster of electronic eyes assessing their temperature, shape and hue, the angle of each leaf. The Scanalyzer beams this data--up to 10 terabytes a day, roughly equivalent to about 2.6 million copies of Tolstoy's "War and Peace"--to computers in Illinois and Missouri. Analyzing the range and depth of data generated is possible only with machine-learning algorithms, according to data scientists at George Washington University and St. Louis University, where researchers are teaching the computers to identify connections between specific genes and plant traits the Scanalyzer observes. Deep learning, a form of AI that uses conclusions from data to further refine a system, can also help pinpoint how some varieties of a plant may subtly differ from one another in ways that plant scientists may not anticipate, researchers say.

Varjo, the Finnish startup that has developed a virtual and mixed reality headset capable of "human-eye resolution" for use in various enterprise applications, has closed a $51.7 million in Series C funding. Existing investors including Lifeline Ventures, Atomico, EQT Ventures and Volvo Cars Tech Fund have also followed on. It brings total raised by Varjo to around $100 million to date. The company is also announcing the appointment of Timo Toikkanen, who was previously president and COO of Varjo, as its new CEO. Co-founder and previous CEO, Niko Eiden, becomes CXO where he'll be tasked with continuing to drive the company's technology innovations and, notably, remains a board member.

We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to evaluate language models trained on formal mathematical statements, such as type inference, suggesting missing assumptions and completing equalities. To train language models for formal mathematics, we propose a novel skip-tree task. We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs.

Tensor decomposition techniques have shown great successes in machine learning and data science by extending classical algorithms based on matrix factorization to multi-modal and multi-way data. However, there exist many tensor decomposition models (CP, Tucker, Tensor Train, etc.) and the rank of such a decomposition is typically a collection of integers rather than a unique number, making model and hyper-parameter selection a tedious and costly task. At the same time, tensor network methods are powerful tools developed in the physics community which have recently shown their potential for machine learning applications and offer a unifying view of the various tensor decomposition models. In this paper, we leverage the tensor network formalism to develop a generic and efficient adaptive algorithm for tensor learning. Our method is based on a simple greedy approach optimizing a differentiable loss function starting from a rank one tensor and successively identifying the most promising tensor network edges for small rank increments. Our algorithm can adaptively identify tensor network structures with small number of parameters that effectively optimize the objective function from data. The framework we introduce is very broad and encompasses many common tensor optimization problems. Experiments on tensor decomposition and tensor completion tasks with both synthetic and real world data demonstrate the effectiveness of the proposed algorithm.