High profile Australian business and technology leaders Genevieve Bell and David Thodey are backing a push to create a new organisation to lead the development of an ethical framework for artificial intelligence. In an open letter to be released on Friday, Ms Bell and Mr Thodey say there are significant challenges that need to be addressed as AI becomes more commonplace, be it the further entrenchment of discrimination on the basis of gender or "minority status", creating "ethical algorithms for autonomous vehicles, bias in AI-powered hiring processes" or "the impact of fake news bots". They are joined by other notable industry figures such as H2 Ventures founding partner Toby Heap, Fujitsu Australia chief executive Mike Foster and KPMG Innovate national leader James Mabbott as being signatories to the open letter. Genevieve Bell is one of the leaders pushing for an AI body. Mr Mabbott said while the opportunities and benefits of AI were unprecedented, there were very real community concerns about how AI is adopted, relating to privacy, sustainability and quality of life.

This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First, we investigate semantic segmentation in the context of street scenes and train semantic segmentation networks on combinations of various datasets. In Chapter 2 we design a framework of hierarchical classifiers over a single convolutional backbone, and train it end-to-end on a combination of pixel-labeled datasets, improving generalizability and the number of recognizable semantic concepts. Chapter 3 focuses on enriching semantic segmentation with weak supervision and proposes a weakly-supervised algorithm for training with bounding box-level and image-level supervision instead of only with per-pixel supervision. The memory and computational load challenges that arise from simultaneous training on multiple datasets are addressed in Chapter 4. We propose two methodologies for selecting informative and diverse samples from datasets with weak supervision to reduce our networks' ecological footprint without sacrificing performance. Motivated by memory and computation efficiency requirements, in Chapter 5, we rethink simultaneous training on heterogeneous datasets and propose a universal semantic segmentation framework. This framework achieves consistent increases in performance metrics and semantic knowledgeability by exploiting various scene understanding datasets. Chapter 6 introduces the novel task of part-aware panoptic segmentation, which extends our reasoning towards holistic scene understanding. This task combines scene and parts-level semantics with instance-level object detection. In conclusion, our contributions span over convolutional network architectures, weakly-supervised learning, part and panoptic segmentation, paving the way towards a holistic, rich, and sustainable visual scene understanding.

An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain the generalization ability of deep nets even when the size of the dataset is smaller than that of the model. While the prior literature focuses on the classical supervised setting, this paper aims to demystify overparameterization for meta-learning. Here we have a sequence of linear-regression tasks and we ask: (1) Given earlier tasks, what is the optimal linear representation of features for a new downstream task? and (2) How many samples do we need to build this representation? This work shows that surprisingly, overparameterization arises as a natural answer to these fundamental meta-learning questions. Specifically, for (1), we first show that learning the optimal representation coincides with the problem of designing a task-aware regularization to promote inductive bias. We leverage this inductive bias to explain how the downstream task actually benefits from overparameterization, in contrast to prior works on few-shot learning. For (2), we develop a theory to explain how feature covariance can implicitly help reduce the sample complexity well below the degrees of freedom and lead to small estimation error. We then integrate these findings to obtain an overall performance guarantee for our meta-learning algorithm. Numerical experiments on real and synthetic data verify our insights on overparameterized meta-learning.

The next wave of AI will be powered by the democratization of data. Open-source frameworks such as TensorFlow and Pytorch have brought machine learning to a huge developer base, but most state-of-the-art models still rely on training datasets which are either wholly proprietary or prohibitively expensive to license [1]. As a result, the best automated speech recognition (ASR) models for converting speech audio into text are only available commercially, and are trained on data unavailable to the general public. Furthermore, only widely-spoken languages receive industry attention due to market incentives, limiting the availability of cutting-edge speech technology to English and a handful of other languages. The first is prohibitive licensing: Several free datasets do exist, but most of sufficient size and quality to make models truly shine are barred from commercial use. As a response, we created The People's Speech, a massive English-language dataset of audio transcriptions of full sentences (see Sample 1).

When minimising the empirical risk, the generalisation of the learnt function still depends on the performance on the training data, the Vapnik-Chervonenkis(VC)- dimension of the function and the number of training examples. Neural networks have a large number of parameters, which correlates with their VC-dimension that is typically large but not infinite, and typically a large number of training instances are needed to effectively train them. In this work, we explore how to optimize feature mappings using neural network with the intention to reduce the effective VC-dimension of the hyperplane found in the space generatedby the mapping. An interpretationofthe resultsofthis study isthat it ispossible to define a loss that controls the VC-dimension of the separating hyperplane. We evaluate this approach and observe that the performance when using this method improves when the size of the training set is small.

Patients are often encouraged to make use of wearable devices for remote collection and monitoring of health data. This adoption of wearables results in a significant increase in the volume of data collected and transmitted. The battery life of the devices is then quickly diminished due to the high processing requirements of the devices. Given the importance attached to medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data for network transmission may improve sensor battery life without compromising accuracy. There is a trade-off between efficiency and accuracy which can be controlled by adjusting the sampling and transmission rates. This paper demonstrates that machine learning can be used to analyse complex health data metrics such as the accuracy and efficiency of data transmission to overcome the trade-off problem. The study uses time series nonlinear autoregressive neural network algorithms to enhance both data metrics by taking fewer samples to transmit. The algorithms were tested with a standard heart rate dataset to compare their accuracy and efficiency. The result showed that the Levenbery-Marquardt algorithm was the best performer with an efficiency of 3.33 and accuracy of 79.17%, which is similar to other algorithms accuracy but demonstrates improved efficiency. This proves that machine learning can improve without sacrificing a metric over the other compared to the existing methods with high efficiency.

Florian Ellsaesser Frankfurt School of Economics and Finance Guido Fioretti University of Bologna Gail E. James Gail James contributed a unique series of cognitive maps with her PhD thesis at University of Colorado, Boulder, 1996. We would like to have her as a co-author. If anyone knows where she is, please contact us. Abstract Sometimes unexpected, novel, unconceivable events enter our lives. The cause-effect mappings that usually guide our behaviour are destroyed. Surprised and shocked by possibilities that we had never imagined, we are unable to make any decision beyond mere routine. Among them there are decisions, such as making investments, that are essential for the long-term survival of businesses as well as the economy at large. We submit that the standard machinery of utility maximization does not apply, but we propose measures inspired by scenario planning and graph analysis, pointing to solutions being explored in machine learning. We wish to thank Jochen Runde and Jean Czerlinki for helpful comments and remarks on previous versions of this manuscript. Introduction Sometimes, unexpected events destroy certain causal relations that used to provide a few firm signposts in spite of all uncertainty involved in managing a business.

Watson, a supercomputer by IBM, shot to fame in 2011 as the'brain' that beat two of the best contestants of Jeopardy! to win a million dollars. This system that combines artificial intelligence (AI) and sophisticated analytical software to answer questions was widely deployed in many industries. The supercomputer was developed in IBM's DeepQA project and was named after IBM's founder Thomas J. Watson. "You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that's where you'll find success – on the far side of failure."

What can AI do for you? A lot, as the recent Intel AI Summit 2021 has shown. Artificial intelligence opens up a broad range of possibilities, from tiny devices to the massive cloud. Suppose you don't know where to start or how to develop and scale up your ideas and innovation further. In that case, the two-day summit's contents are now available on-demand to inspire you with the latest from the Intel AI technology stack, as well as successful customer use cases from the Asia Pacific and Japan Territory (APJ-T).

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