Last week, Kenneth Hayne QC handed down his royal commission final report that recommended banning banks from paying trail commissions to mortgage brokers from mid-next year. Instead, the borrower will likely be required to pay an upfront fee for the service. UBank, a subsidiary of NAB, doesn't pay mortgage brokers, but its new robot-like home loan aid gives a glimpse into how the service could be provided in the future. Many commentators are speculating only the wealthy will be able to afford a broker, while regular Aussies will have to rely on an automated service. The artificial loan aid, named Mia (My Interactive Assistant) and powered by AI start-up FaceMe, will speak directly to customers through a desktop or smartphone advising on questions such as what's a variable rate to what classifies as an expense, the bank says.

SABRA LANE: Much of the traditional car making industry around the world could go bust during the next decade, as the cost of electric cars plunge and become affordable for average motorists. That's the prediction of a top researcher and investor who thinks a world of self-driving electric cars, including autonomous taxis, is only a few years away. Brett Winton, the director of research at the US based Ark Invest, believes a disruptive day of reckoning is coming for complacent car making giants, who've failed to adapt their manufacturing models to confront new challengers like Tesla. Mr Winton is visiting Australia and he spoke about a not too distant "new world" of artificial intelligence with Senior Business Correspondent, Peter Ryan. You can look at some of the advances in artificial intelligence and see that computers are going to have the capability to solve the game of driving the car, and likely a lot safer than humans.

There's been some interest in the Explorer Challenge which is a 1 million dollar competition combining machine learning and geology to come up with the best prospect. A funny video and detailed slides have been released. I was also at the presentation in Perth, Australia so feel free to clarify things with me if the slides aren't clear. This is an interesting competition because geology seems like it could be disrupted by the application of ML, but it's also challenging because of the large amount of contextual and qualitative data that goes into making decisions.

In 1950, Norbert Wiener's The Human Use of Human Beings was at the cutting edge of vision and speculation in proclaiming: But this was his book's denouement, and it has left us hanging now for 68 years, lacking not only prescriptions and proscriptions but even a well-articulated "problem statement." We have since seen similar warnings about the threat of our machines, even in the form of outreach to the masses, via films like Colossus: The Forbin Project (1970), The Terminator (1984), The Matrix (1999), and Ex Machina (2015). But now the time is ripe for a major update with fresh, new perspectives -- notably focused on generalizations of our "human" rights and our existential needs. Concern has tended to focus on "us versus them" (robots) or "gray goo" (nanotech) or "monocultures of clones" (bio). To extrapolate current trends: What if we could make or grow almost anything and engineer any level of safety and efficacy desired?

The vast amount of information encoded in an individual's DNA tells great tales of one's health and disease conditions. When the first human genome was sequenced, the project that began in 1990 took over 10 years and cost around $2.7 billion. According to Andrew Underwood, CTO, HPC & Artificial Intelligence, Dell EMC, Australia and New Zealand, data intensive computing is fast becoming a dominant approach. Especially in R&D, it is a rapidly growing field of research built on data that is generated from scientific instruments, people, machines and IoT devices. Data comes in high velocities and in large volumes – requiring scientists to harness the power of high performance computing to analyze data faster for timely insights in their field of research.

Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams. We present an alternative dynamic paired comparison model which uses a Gaussian Process (GP) as a prior for the time dynamics rather than the Markovian dynamics usually assumed. In addition, we show that the GP model can easily incorporate covariates. We derive an efficient approximate Bayesian inference procedure based on the Laplace Approximation and sparse linear algebra. We select hyperparameters by maximising their marginal likelihood using Bayesian Optimisation, comparing the results against random search. Finally, we fit and evaluate the model on the 2018 season of ATP tennis matches, where it performs competitively, outperforming Elo and Glicko on log loss, particularly when surface covariates are included.

Summary: Artificial Intelligence in Healthcare Market Overview: Artificial intelligence (AI) can be defined as the science and engineering adopted to design intelligent machines, especially intelligent computer programs. AI is an intelligent system that applies various human intelligence based functions such as reasoning, learning, and problem-solving skills on different disciplines such as biology, computer science, mathematics, linguistics, psychology, and engineering. AI is widely applicable in medication management, treatment plans, and drug discovery. The global AI in healthcare market was valued at $1,441 million in 2016, and is estimated to reach at $22,790 million by 2023, registering a CAGR of 48.7% from 2017 to 2023. The growth of the global AI in healthcare market is driven by the ability of AI to improve patient outcomes, need to increase coordination between healthcare workforce & patients, increase in adoption of precision medicine, and a notable rise in venture capital investments.

It was one of the most infamous companies in Japan, rocking the nation with a corporate scandal that ousted a prime minister and then nearly collapsing under a mountain of debt. Now, Recruit Holdings Co. is back, reinvented by a group of employees who quietly turned the magazine publisher and job placement firm into an internet giant that touches the lives of almost every consumer in the world's third-biggest economy. If Recruit were a U.S. company, it would be like having LinkedIn, Zillow, Yelp, eHarmony, Booking.com, "We are there, every time people choose to do things," said Masumi Minegishi, Recruit's 55-year-old chief executive officer. As the biggest internet companies compete for world domination with apps that track consumers and use artificial intelligence to crunch data and provide tailored services, Recruit is Japan's leading contender.

Alita: Battle Angel is an interesting and wild ride, jam-packed full of concepts around cybernetics, dystopian futures and cyberpunk themes. The film – in cinemas now – revolves around Alita (Rosa Salazar), a female cyborg (with original human brain) that is recovered by cybernetic doctor Dyson Ido (Christoph Waltz) and brought into the world of the future (the film is set in 2563). Hundreds of years after a catastrophic war, called "The Fall", the population of Earth now resides in a wealthy sky city called Zalem and a sprawling junkyard called Iron City where the detritus from Zalem is dumped. We follow Alita's story as she makes friends and enemies, and discovers more about her past. Her character is great – she has many of the mannerisms of a teenage girl combined with a determination and overarching sense of what is right – "I do not stand by in the presence of evil."

Nowadays the increasing and rapid progress of technology and the availability of electronic documents from a variety of sources have made a huge amount of textual data available. Hence, one of the prominent research topics of statistical andmachine learning communities is to provide suitable and feasible methods to extract high-quality information from unstructured textual data (Lata and Loar, 2018) for the different purposes of clustering, classification and document retrieval (Khan et al., 2010). This work originates from an empirical problem of classification of the content ofcalls made to the customer service of an important mobile phone company inItaly. The received calls are written down by an operator and classified into relevant classes (e.g.