The discussion took place today at the Conference of Major Superannuation Funds put on by the Australian Institute of Superannuation Trustees. Walsh covers everything from myths about AI to Ethics and Artificial Intelligence.

When mapping the universe, it pays to have some smart programming. Experts share how machine learning is changing the future of astronomy. Astronomy is one of the oldest sciences and the first science to incorporate maths and geometry. It sits at the centre of humankind's search for its place in the universe. As we delve deeper into the space surrounding our planet, the tools we use become more complex.

The Mitsubishi Corporation has set up a joint venture company that will use artificial intelligence (AI) to control on-demand bus services in Japan. The new company, called Next Mobility, has been established by Mitsubishi and the Nishi-Nippon Railroad Company, a major Japanese bus operator. The joint venture will start a one-year trial in April at Island City, in the Higashi-ward of Fukuoka City. In a statement Wednesday, Mitsubishi said that the AI would be used to automatically generate routes, in real time, based on passenger requests that are made through a smartphone app. Deep learning will be used to collate "operational data" on both traffic conditions and passenger destinations.

Like many problems in AI in their general form, supervised learning is computationally intractable. We hypothesize that an important reason humans can learn highly complex and varied concepts, in spite of the computational difficulty, is that they benefit tremendously from experienced and insightful teachers. This paper proposes a new learning framework that provides a role for a knowledgeable, benevolent teacher to guide the process of learning a target concept in a series of "curricular" phases or rounds. In each round, the teacher's role is to act as a moderator, exposing the learner to a subset of the available training data to move it closer to mastering the target concept. Via both theoretical and empirical evidence, we argue that this framework enables simple, efficient learners to acquire very complex concepts from examples. In particular, we provide multiple examples of concept classes that are known to be unlearnable in the standard PAC setting along with provably efficient algorithms for learning them in our extended setting. A key focus of our work is the ability to learn complex concepts on top of simpler, previously learned, concepts---a direction with the potential of creating more competent artificial agents.

We propose a framework for learning discrete deterministic planning domains. In this framework, an agent learns the domain by observing the action effects through continuous features that describe the state of the environment after the execution of each action. Besides, the agent learns its perception function, i.e., a probabilistic mapping between state variables and sensor data represented as a vector of continuous random variables called perception variables. We define an algorithm that updates the planning domain and the perception function by (i) introducing new states, either by extending the possible values of state variables, or by weakening their constraints; (ii) adapts the perception function to fit the observed data (iii) adapts the transition function on the basis of the executed actions and the effects observed via the perception function. The framework is able to deal with exogenous events that happen in the environment.

He has demanded "goddamned steam" to power the Navy's aircraft carriers and prefers a wall to drones and other technology to secure the country's southern border. He has rejected the scientific consensus on climate change and repeatedly, wrongly, pointed to occasional wintry weather as proof that he's right. And this week, amid a safety scare involving Boeing's 737 MAX 8 and MAX 9 airplanes, President Trump complained that modern jets are "too complex to fly." He added: "I see it all the time in many products. Always seeking to go one unnecessary step further, when often old and simpler is far better."

Recent advances in Artificial Intelligence are creating huge opportunities for process improvement in the Financial Sector but do have some challenges. The financial services industry in Australia has not been without it's challenges in the last few years. Downward moving real-estate and investment markets in Australia, increased competition from global players and the recent high degree of inspection and intervention from Australian regulators has forced Financial Services companies to on the one hand focus more on customer service and doing the "right thing' for their customers. Whilst on the other hand this same environment is pushing these same companies to deploy new technologies to improve costs and to increase efficiencies to offset the additional burdens that new regulation is bringing. In an era of big data with large volumes of transactions the deployment of AI represents an opportunity for Financial Services organisations to improve their operations through increased cost efficiencies whilst at the same time providing better services to their customers.

BUSINESSES in Australia such as law and accounting firms, technology companies, and medical facilities are staffed with professionals certified by government bodies. In order to ensure these professionals stay up-to-date and relevant, the governing bodies often require that they receive training on an ongoing basis. CPA Australia and the Lawyers Society of South Australia, for example, require members undergo 20 and 10 hours of CPD training per year and offer seminars and sessions to help meet that requirement. However, practically speaking, the training on offer might not be directly relevant to the businesses or jobs that these professionals are performing on a daily basis. For example, CPA Australia might offer a seminar on understanding wealth management in the accounting context. Although that knowledge is relevant to a CPA in general, it might not be suited to someone handling internal audit for a manufacturing entity.

Generative Adversarial Networks (GANs) have become exceedingly popular in a wide range of data-driven research fields, due in part to their success in image generation. Their ability to generate new samples, often from only a small amount of input data, makes them an exciting research tool in areas with limited data resources. One less-explored application of GANs is the synthesis of speech and audio samples. Herein, we propose a set of extensions to the WaveGAN paradigm, a recently proposed approach for sound generation using GANs. The aim of these extensions - preprocessing, Audio-to-Audio generation, skip connections and progressive structures - is to improve the human likeness of synthetic speech samples. Scores from listening tests with 30 volunteers demonstrated a moderate improvement (Cohen's d coefficient of 0.65) in human likeness using the proposed extensions compared to the original WaveGAN approach.

This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components - namely, the prediction And-Or graph (AOG) model for recognizing and localizing concepts of interest in input data, and the XAI model for providing explanations to the user about the AOG's predictions. In this work, we Figure 1: Two frames (scenes) of a video: (a) focus on the XAI model specified to interact top-left image (scene1) shows two persons sitting with the user in natural language, at the reception and others entering the auditorium whereas the AOG's predictions are considered and (b) top-right (scene2) image people running given and represented by the corresponding out of an auditorium. Bottom-left shows the parse graphs (pg's) of the AOG. AOG parse graph (pg) for the top-left image and Our XAI model takes pg's as input and Bottom-right shows the pg for the top-right image provides answers to the user's questions using the following types of reasoning: direct evidence (e.g., detection scores), Consider for example, two frames (scenes) of part-based inference (e.g., detected parts a video shown in Figure 1. An action detection provide evidence for the concept asked), model might predict that two people in the scene1 and other evidences from spatiotemporal are in sitting posture. User might be interested context (e.g., constraints from the spatiotemporal to know more details about the prediction such surround). We identify several as: Why do the model think the people are in sitting correlations between user's questions posture? Why not standing instead of sitting? and the XAI answers using Youtube Action Why two persons are sitting instead of one?