In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observe that the attention based knowledge retrieval mechanisms used in current NMNs restricts them from achieving their full potential as the attention process retrieves information based on a set of static connection weights. This is suboptimal in a setting where there are vast differences among samples in the data domain; such as anomaly detection where there is no consistent criteria for what constitutes an anomaly. In this paper, we propose a plastic neural memory access mechanism which exploits both static and dynamic connection weights in the memory read, write and output generation procedures. We demonstrate the effectiveness and flexibility of the proposed memory model in three challenging anomaly detection tasks in the medical domain: abnormal EEG identification, MRI tumour type classification and schizophrenia risk detection in children. In all settings, the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in-depth analysis demonstrating the utility of neural plasticity for the knowledge retrieval process and provide evidence on how the proposed memory model generates sparse yet informative memory outputs.

ABSTRACT There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time that, on English, hybrid ASR systems can in fact model graphemes effectively by leveraging tied context-dependent graphemes, i.e., chenones. Our chenone-based systems significantly outperform equivalent senone baselines by 4.5% to 11.1% relative on three different English datasets. Our results on Librispeech are state-of- the-art compared to other hybrid approaches and competitive with previously published end-to-end numbers. Further analysis shows that chenones can better utilize powerful acoustic models and large training data, and require context-and position-dependent modeling to work well. Chenone-based systems also outperform senone baselines on proper noun and rare word recognition, an area where the latter is traditionally thought to have an advantage. Our work provides an alternative for end-to-end ASR and establishes that hybrid systems can be improved by dropping the reliance on phonetic knowledge. Index T erms-- graphemic lexicon, hybrid speech recognition, chenones, acoustic modeling, librispeech 1. INTRODUCTION In the past decade, neural network acoustic models have become a staple in automatic speech recognition (ASR).

We explore a novel setting of the Multi-Armed Bandit (MAB) problem inspired from real world applications which we call bandits with "stochastic delayed composite anonymous feedback (SDCAF)". In SDCAF, the rewards on pulling arms are stochastic with respect to time but spread over a fixed number of time steps in the future after pulling the arm. The complexity of this problem stems from the anonymous feedback to the player and the stochastic generation of the reward. Due to the aggregated nature of the rewards, the player is unable to associate the reward to a particular time step from the past. We present two algorithms for this more complicated setting of SDCAF using phase based extensions of the UCB algorithm. We perform regret analysis to show sub-linear theoretical guarantees on both the algorithms.

Assessing regulatory compliance of personal financial advice is currently a complex manual process. In Australia, only 5%- 15% of advice documents are audited annually and 75% of these are found to be non-compliant(ASI 2018b). This paper describes a pilot with an Australian government regulation agency where Artificial Intelligence (AI) models based on techniques such natural language processing (NLP), machine learning and deep learning were developed to methodically characterise the regulatory risk status of personal financial advice documents. The solution provides traffic light rating of advice documents for various risk factors enabling comprehensive coverage of documents in the review and allowing rapid identification of documents that are at high risk of non-compliance with government regulations. This pilot serves as a case study of public-private partnership in developing AI systems for government and public sector.

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

The research also found U.S. employees are split on their perception of their readiness to work with AI. In fact, just over half (52%) of the U.S. employees surveyed believe they have the necessary skills to be successful in an AI-enabled workplace. However, almost as many (48%) doubt they have what it takes, with 20% saying they do not possess the right skills and 28% reporting they simply aren't sure. But confident Millennial employees are the most likely age group to feel their current skillset will meet the challenge of AI. "The most successful AI deployments take more than good data and the best technology – people are an equally important part of the equation. We believe that's why employers should be investing in their people to prepare them for a future workplace that will change as a result of this intelligent technology," said Merijn te Booij, chief marketing officer, Genesys.

In China, platforms and services like Alibaba's Alipay and Tencent's WeChat Pay have brought facial recognition payments to online and brick-and-mortar retail stores. But as biometrics and facial recognition technologies become mainstream, experts and regulators are concerned about the privacy and cybersecurity risks associated with these, according to a report by Abacus. Li Wei, director of the technology department of the People's Bank of China, said consumers should realize that when they are using these features, they are giving up privacy for convenience. Faces are very sensitive personal information, and it could have a critical impact on someone if it were leaked or stolen. While people can put their bank cards in their pockets, faces are out in the open all the time, Li said, adding that some companies have not considered these issues.

About 3.85 million people studied Japanese at a record 18,604 institutions overseas in fiscal 2018, with the number of institutions soaring in Asia, according to a survey released this week. The number of Japanese language institutions jumped nearly fourfold to 818 in Vietnam from the previous survey in fiscal 2015 and nearly tripled to 400 in Myanmar, said the survey by the Japan Foundation, a government-backed organization conducting international cultural exchange programs. The number of Japanese learners overseas rose 5.2 percent to 3,846,773, led by a 169.0 percent surge to 174,461 in Vietnam, it said. The survey found a record high 142 countries and territories offering Japanese language education, five more than the fiscal 2015 level. The five include East Timor, Zimbabwe and Montenegro.

It's 2019 and I have been alone for most of my adult life. As I get older, and because I am male, my loneliness is generally going to increase. If I lose my job chances are that I will become even more socially isolated. Compared to women, I'm three times more likely to take my own life because of loneliness and less likely to talk about it with anyone. But writing is my companion, it's my "talk-to".

Courses The major educational initiative of the JHUDSL is to create open-source online courses delivered through a range of platforms including Youtube, Github, Leanpub, and Coursera. Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Please note that this is an advanced course and we assume basic knowledge of machine learning. I am currently working as a data science researcher and trainee at Jheronimus Academy of Data Science.