Australians needs to have a diplomatic discussion about the potential impact of advanced artificial intelligence (AI) and the boundaries that need to be established to ensure AI is developed and used for good, according to federal parliamentarians Bridget McKenzie and Ed Husic. Speaking at the Australian Computer Society's (ACS) Reimagination Thought Leaders Summit, Senator McKenzie, chair of Foreign Affairs, Defence, and Trade Legislation Committee, said if bright minds like Stephen Hawking and Elon Musk are warning of "evil AI" destroying humankind if not properly monitored and regulated, then this is something that as a nation needs to be publicly discussed. "I think'man against machine' has been a powerful narrative with our species for a very, very long time," McKenzie said during a panel discussion. "We always end up winning because somehow we always write the script so that we're smarter in the end than the machine." "But I think when the creators of this technology ... have concerns, I think we mere mortals really should pay attention because they're the guys that have actually developed this technology, they understand its potential. "Sometimes I think we can get very excited about the potential development of the next step in your scientific endeavour, and forget that it is part of a wider ... society and a civilisation." McKenzie referenced the view of Swedish philosopher Nick Bostrom, that we could end up creating and designing our own demise. "I think we do need to be very, very cognisant of that fact because there's not a lot of research.

So where did AI come from? The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning. Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

Renowned physicist Prof Stephen Hawking has said robots will eventually completely replace humanity. Prof Hawking said that he believes artificial intelligence (AI) will eventually reach a level where it will essentially be a "new form of life that will outperform humans" in an interview with WIRED magazine. He said: "I fear that AI may replace humans altogether. If people design computer viruses, someone will design AI that improves and replicates itself. This will be a new form of life that outperforms humans."

In 2016, restless tech-industry forecasters enjoyed a rare moment of consensus: Whatever else might be coming next, everyone seemed to agree that bots would be a big part of it. The analyst Benedict Evans, in a representative essay, located a promising future specifically in chat bots -- conversational interfaces for artificial intelligence, designed to assist with particular tasks. Facebook, the year before, created a personal-assistant chat bot, and the company would soon open its Messenger app up to outside developers, who it hoped would create more bots to help people shop, look things up or otherwise organize their lives. Amazon's Echo, by then already a surprise mainstream success, provided a tailwind: Here was a widely used artificial intelligence just sitting there on millions of countertops. These predictions were self-interested, of course.

Investigators from Brigham and Women's Hospital and Dana-Farber Cancer Institute are leveraging the power of artificial intelligence to develop a new technique to detect ovarian cancer early and accurately. The team has identified a network of circulating microRNAs - small, non-coding pieces of genetic material - that are associated with risk of ovarian cancer and can be detected from a blood sample. Their findings are published online in eLife. Most women are diagnosed with ovarian cancer when the disease is at an advanced stage, at which point only about a quarter of patients will survive for at least five years. But for women whose cancer is serendipitously picked up at an early stage, survival rates are much higher.

Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on unifying two of the most widely used link-formation models: the stochastic blockmodel (SBM) and the small world (or latent space) model (SWM). Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse graphs for both models. When the network comes from an SBM, the algorithm outputs a block structure. When it is from an SWM, the algorithm outputs estimates of each node's latent position.

Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics. A widely used method for hypergraph partitioning relies on minimizing a normalized sum of the costs of partitioning hyperedges across clusters. Algorithmic solutions based on this approach assume that different partitions of a hyperedge incur the same cost. However, this assumption fails to leverage the fact that different subsets of vertices within the same hyperedge may have different structural importance. We hence propose a new hypergraph clustering technique, termed inhomogeneous hypergraph partitioning, which assigns different costs to different hyperedge cuts. We prove that inhomogeneous partitioning produces a quadratic approximation to the optimal solution if the inhomogeneous costs satisfy submodularity constraints. Moreover, we demonstrate that inhomogenous partitioning offers significant performance improvements in applications such as structure learning of rankings, subspace segmentation and motif clustering.

Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random. We analyze a variant of the Dantzig selector [9] for estimating the regression model and we use a de-biasing argument to construct component-wise confidence intervals. Our first main result is to establish upper bounds on the estimation error as a function of the model parameters (the sparsity level s, the expected fraction of observed covariates $\rho_*$, and a measure of the signal strength $\|\beta^*\|_2$). We find that even in an idealized setting where the covariates are assumed to be missing completely at random, somewhat surprisingly and in contrast to the fully-observed setting, there is a dichotomy in the dependence on model parameters and much faster rates are obtained if the covariance matrix of the random design is known. To study this issue further, our second main contribution is to provide lower bounds on the estimation error showing that this discrepancy in rates is unavoidable in a minimax sense. We then consider the problem of high-dimensional inference in the presence of missing data. We construct and analyze confidence intervals using a de-biased estimator. In the presence of missing data, inference is complicated by the fact that the de-biasing matrix is correlated with the pilot estimator and this necessitates the design of a new estimator and a novel analysis. We also complement our mathematical study with extensive simulations on synthetic and semi-synthetic data that show the accuracy of our asymptotic predictions for finite sample sizes.

It's commonly billed as the single most influential position in the global economy – chairman of the US Federal Reserve. President Donald Trump has promised to announce who will be filling the role for the next four years by the time he sets off on a visit to Asia on Nov 3, or in other words any day now. Many column inches have already been expended speculating on who he might choose – a hawk or a dove – and I don't intend to add to them. But here's what is possibly the more interesting question; does it actually matter any more? I suppose it might, in the sense that any choice thought likely to be compliant with the president's political demands would be taken badly by markets, degrading a generally competent institution and haven of economic expertise.

Ceres may have once had a global ocean - and part of it could still remain, NASA has revealed. The dwarf planet, best known for its strange'alien spots', is seen as being a record of the early solar system. Now, the Dawn mission has found minerals containing water are widespread on its surface. Ceres is 590 miles (950 km) across and was discovered in 1801. It is the closest dwarf planet to the sun and is located in the asteroid belt between Mars and Jupiter, making it the only dwarf planet in the inner solar system.