This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications; and at researchers in ME, for whom we clarify: i) what the possibilities of the current AI technologies are, ii) what is still out of reach and iii) what is to be expected in the near future. We start our analysis by establishing a high-level taxonomy of AI tools that are found as components in digital ME applications. Then, we describe in detail how these AI tools, and in particular ML, are being used in two key applications, specifically AI-based calculators and intelligent tutoring systems. We finish the chapter with a discussion about student modeling systems and their relationship to artificial general intelligence.

A new recycling app will use artificial intelligence to help Australians work out if their rubbish can be recycled. Recycle Mate is designed to end widespread confusion about what goes into which coloured bin. "A lot that you think is recyclable is not and a lot that you think can't be recycled can be," new Australian Council of Recycling CEO Suzanne Toumbourou told AAP. "The app will help every Australian answer the question, 'What bin do I put this in?'" Keen recyclers can use their phones to take a photo of a waste item, such as a plastic container, and Recycle Mate will use artificial intelligence to identify it and determine whether the local council can recycle it. "The technological sophistication that underpins this is probably unprecedented," Ms Toumbourou said.

Continuous advances in artificial intelligence promise to shake up medical care in all kinds of exciting ways, with the ability to rapidly scan medical images and spot signs of disease far more efficiently than humans can. Scientists in Australia have now adapted this technology for the early detection of prostate cancer, with their software outperforming trained radiologists to detect cancerous growths in seconds. For many medical ailments, an early diagnosis can greatly improve the treatments available and therefore the chances of overcoming them. Improvements in machine learning and computing power have led to highly capable forms of artificial intelligence that could be invaluable in this regard. We've seen AI tools that can improve an ECG's ability to reveal heart dysfunction, more accurately predict survival rates of ovarian cancer and just this week, calculate diabetes risk by measuring fat around the heart. The latest example of this comes from researchers at Melbourne's RMIT University and St Vincent's Hospital, who started with CT scans of asymptomatic patients both with and without prostate cancer.

Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view. The advantages and shortcomings of these techniques have been discussed, and pointers to some future directions have also been provided.

Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical information. Chinese characters have evolved from pictographs since ancient times, and their structure often reflects more information about the characters. This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed MECT method.\footnote{The source code of the proposed method is publicly available at https://github.com/CoderMusou/MECT4CNER.

In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that can ensure monotonic policy improvement during learning. Based on the nature of entropy-regularized RL, we derive a new entropy regularization-aware lower bound of policy improvement that only requires estimating the expected policy advantage function. CPP leverages this lower bound as a criterion for adjusting the degree of a policy update for alleviating policy oscillation. Different from similar algorithms that are mostly theory-oriented, we also propose a novel interpolation scheme that makes CPP better scale in high dimensional control problems. We demonstrate that the proposed algorithm can trade o? performance and stability in both didactic classic control problems and challenging high-dimensional Atari games.

Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results. In the context of computational creativity, however, a major shortcoming is that they are unable to explicitly diverge from the training data in creative ways and are limited to fitting the target data distribution. To address these limitations, there have been a growing number of approaches for optimising, hacking and rewriting these models in order to actively diverge from the training data. We present a taxonomy and comprehensive survey of the state of the art of active divergence techniques, highlighting the potential for computational creativity researchers to advance these methods and use deep generative models in truly creative systems.

The oscillating performance of off-policy learning and persisting errors in the actor-critic (AC) setting call for algorithms that can conservatively learn to suit the stability-critical applications better. In this paper, we propose a novel off-policy AC algorithm cautious actor-critic (CAC). The name cautious comes from the doubly conservative nature that we exploit the classic policy interpolation from conservative policy iteration for the actor and the entropy-regularization of conservative value iteration for the critic. Our key observation is the entropy-regularized critic facilitates and simplifies the unwieldy interpolated actor update while still ensuring robust policy improvement. We compare CAC to state-of-the-art AC methods on a set of challenging continuous control problems and demonstrate that CAC achieves comparable performance while significantly stabilizes learning.

A new recycling app will use artificial intelligence to help Australians work out if their rubbish can be recycled.

Latest published market study on Global Artificial Intelligence (AI) in Construction Market provides an overview of the current market dynamics in the Artificial Intelligence (AI) in Construction space, as well as what our survey respondents--all outsourcing decision-makers--predict the market will look like in 2027. The study breaks market by revenue and volume (wherever applicable) and price history to estimates size and trend analysis and identifying gaps and opportunities. Some of the players that are in coverage of the study are Renoworks Software, SmarTVid.Io, Jaroop, Smartvid.io, Get ready to identify the pros and cons of regulatory framework, local reforms and its impact on the Industry. Market Factor Analysis: In this economic slowdown, impact on various industries is huge.