Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.

In this work we discuss the incorporation of quadratic neurons into policy networks in the context of model-free actor-critic reinforcement learning. Quadratic neurons admit an explicit quadratic function approximation in contrast to conventional approaches where the the non-linearity is induced by the activation functions. We perform empiric experiments on several MuJoCo continuous control tasks and find that when quadratic neurons are added to MLP policy networks those outperform the baseline MLP whilst admitting a smaller number of parameters. The top returned reward is in average increased by $5.8\%$ while being about $21\%$ more sample efficient. Moreover, it can maintain its advantage against added action and observation noise.

Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-of-the-art global causal structure learning algorithms, a set of state-of-the-art local causal structure learning algorithms, a set of state-of-the-art MB learning algorithms, and functions for evaluating algorithms. The data generation part of Causal Learner is written in R, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to provide researchers and practitioners with an open-source platform for causal learning from data and for the development and evaluation of new causal learning algorithms. The Causal Learner project is available at http://bigdata.ahu.edu.cn/causal-learner.

Anyone still doubting whether fintech is disrupting Chicago's financial services industry only needs to look at a handful of recent partnerships to see that innovative technology is necessary for large enterprises to stay competitive in an ever-evolving market, lest they become obsolete. Take the following inked deals, for example. Amount -- a digital credit solution provider -- partnered with TD Bank and HSBC last year to help the two large institutions streamline their personal loan services, reflecting a marketplace that grew by $21 billion in 2018 to a record high of $138 billion, according to credit reporting agency TransUnion. Meanwhile, AI-powered financial compliance solution Ascent recently partnered with global information tech company IBM to help banks and other financial entities meet changing regulatory requirements. These types of partnerships help banks and financial institutions react to market changes and prepare for the future of finance; namely, by giving consumers more of the seamless user experiences they're used to and leveraging AI to streamline manual regulatory processes, saving valuable time and resources.

Machine learning technology is set to reduce herbicide use by up to 80 per cent, according to John Deere's Australia and New Zealand managing director Luke Chandler. Mr Chandler said this would be achieved by moving from a whole-of-field approach to a plant-by-plant management strategy. He said global agriculture was at an inflection point and technological developments would help drive productivity. Farmers across the world have been chasing economies of scale and the company's focus had been on building bigger, faster and stronger machinery to meet that growth, he said. "That's still important but as we shift towards this next frontier of agriculture, we really see machinery being driven by automation, easier to use, more precise types of technologies."

This post is an introduction to time-series forecasting with torch. Central topics are data input, and practical usage of RNNs (GRUs/LSTMs). Upcoming posts will build on this, and introduce increasingly involved architectures.

The course enables novice practitioners who are interested in entering the field of AI to become job-ready engineers in just six months. There will be a skill assessment to evaluate the student's current skills and experience, but the only prerequisites are a basic understanding of mathematics and statistics and, or alternatively, relevant experience in the industry, such as an internship. According to the 2020 IT Skills and Salary Report, 'AI, cognitive computing and machine learning' was reported as the second weakest IT skillset in organisations across the world, presenting serious hiring challenges for IT managers.[1] The lack of skilled AI professionals forms part of a broader technology skills shortage in Australia, with new research commissioned by AWS revealing that Australia will require an additional 6.5 million newly-skilled and reskilled digital workers by 2025 to meet future demand for technology skills (79 per cent more than Australia currently has). Jon Lang, CEO of AIICT said, "We're seeing a severe shortage of skilled AI professionals and in many instances, organisations are simply unable to fill these roles. With AI and ML becoming a critical part of the digital transformation process for many organisations, the demand is on the rise for engineers with these specific skills across a wide range of industries."

The America's Cup, the oldest trophy in international sport, is competitive sailing's most coveted prize. When the 36th edition begins in early March, the race's defending champion, Emirates Team New Zealand, will hit the water having utilized a new crewmember: an AI bot created by McKinsey. Winning the America's Cup has always been as much about technology and innovation as it is about sailing. Boat designs are governed by the competition's "Class Rule, " which leaves small opportunities for design tweaks that might give one team an advantage over another. Those opportunities, if brilliantly seized, can translate into a shining race-day performance.

SCA has become an early-stage investor in Melbourne-based Sonnant Pty Ltd, a transformational artificial intelligence (AI) and machine learning (ML) company that provides content discovery for the spoken word. According to the GfK Australian Share of Audio 2019, consumption of digital audio is growing in Australia and is expected to reach 80% of the population by 2024 and SCA's recent launch of LiSTNR is designed to be a best in class consumer digital audio experience. SCA's in-house technical capability will be enhanced by Sonnant's AI intelligence, providing an in-depth of understanding of SCA content that allows for rich meta data and exciting new user experiences. Sonnant allows SCA to enhance the key product pillars of LiSTNR delivering a personalised experience to each user based on the topics they most enjoy listening to. SCA Head of Digital and Innovation, Chris Johnson, says "SCA is a proud supporter of the Australian entrepreneurial ecosystem, so when we found a company of Sonnant's calibre in Melbourne, we knew that an investment partnership would provide significant long term value to both parties. "We were delighted when Sonnant's tech outperformed several'off the shelf' products we pitted it against in testing.

Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perceive and understand the physical world. In this paper, we observe that there are three "drivers" that impose requirements on spatially intelligent agents and thus VPR systems: 1) the particular agent including its sensors and computational resources, 2) the operating environment of this agent, and 3) the specific task that the artificial agent carries out. In this paper, we characterize and survey key works in the VPR area considering those drivers, including their place representation and place matching choices. We also provide a new definition of VPR based on the visual overlap -- akin to spatial view cells in the brain -- that enables us to find similarities and differences to other research areas in the robotics and computer vision fields. We identify numerous open challenges and suggest areas that require more in-depth attention in future works.