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.

On the face of it, it seems like some kind of impossible oxymoron. But it makes perfect sense… (Or at least it does to me now that I've attended the NZ Tech Marketers September event, Unleash your creativity with the power of AI.) Auckland's Tech Marketer contingent had the benefit of insights from Amanda Johnston-Pell, IBM's Chief Marketing Officer for Australia and New Zealand, and our Wellington and Christchurch cohort were joined by the ever-impassioned Isuru (Issy) Fernando, IBM New Zealand's Chief Design and Technology Officer. Both shared findings from the recent IBM 2019 Marketing Trends report: Nine factors reshaping marketing and how you can stay ahead of them. Doing this makes it less scary, binary and wo/man v. machine-ish. Issy says: "There's a lot of hype and uncertainty – and a lot of fud – out there about what AI is. We need to think of AI as less artificial reality and more augmented reality. It really changes the conversation. "If you look at humans across civilisation we've been augmenting ourselves with machines all the time.

SEEK Limited (ASX: SEK) is one of Australia's most entrepreneurial digital businesses and also one of the heaviest investors in new tech, product development, and start up or early stage ventures (ESVs) for long-term growth. In fact it's ready to wear $25 million to $30 million in ESV losses over FY 2020 such is it's commitment to sacrificing the short term for long term success. It now has an aspirational revenue of $5 billion by FY 2025 versus the $1.54 billion delivered in FY 2019, which would be an impressive result if achieved. It recently reported how it's an Australian market leader in artificial intelligence (AI) investment with a team of more than 100 specialist data scientists and software engineers building AI that learns from how candidates search job ads to better target ads for advertisers. According to SEEK the new AI has resulted in an 11% increase in job ad click through rate, a 10% uplift in candidate applications per session and 600,000 more applications per month across its platforms.

We propose $\tt RandUCB$, a bandit strategy that uses theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), uses randomization to trade off exploration and exploitation. In the $K$-armed bandit setting, we show that there are infinitely many variants of $\tt RandUCB$, all of which achieve the minimax-optimal $\widetilde{O}(\sqrt{K T})$ regret after $T$ rounds. Moreover, in a specific multi-armed bandit setting, we show that both UCB and TS can be recovered as special cases of $\tt RandUCB.$ For structured bandits, where each arm is associated with a $d$-dimensional feature vector and rewards are distributed according to a linear or generalized linear model, we prove that $\tt RandUCB$ achieves the minimax-optimal $\widetilde{O}(d \sqrt{T})$ regret even in the case of infinite arms. We demonstrate the practical effectiveness of $\tt RandUCB$ with experiments in both the multi-armed and structured bandit settings. Our results illustrate that $\tt RandUCB$ matches the empirical performance of TS while obtaining the theoretically optimal regret bounds of UCB algorithms, thus achieving the best of both worlds.