Artificial Intelligence technology can today automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired. Now nearing the end of January, the respiratory virus that's been linked to the city of Wuhan in China has already claimed the lives of more than 100 people.

In a recent work, we presented a discriminative backend for speaker verification that achieved good out-of-the-box calibration performance on most tested conditions containing varying levels of mismatch to the training conditions. This backend mimics the standard PLDA-based backend process used in most current speaker verification systems, including the calibration stage. All parameters of the backend are jointly trained to optimize the binary cross-entropy for the speaker verification task. Calibration robustness is achieved by making the parameters of the calibration stage a function of vectors representing the conditions of the signal, which are extracted using a model trained to predict condition labels. In this work, we propose a simplified version of this backend where the vectors used to compute the calibration parameters are estimated within the backend, without the need for a condition prediction model. We show that this simplified method provides similar performance to the previously proposed method while being simpler to implement, and having less requirements on the training data. Further, we provide an analysis of different aspects of the method including the effect of initialization, the nature of the vectors used to compute the calibration parameters, and the effect that the random seed and the number of training epochs has on performance. We also compare the proposed method with the trial-based calibration (TBC) method that, to our knowledge, was the state-of-the-art for achieving good calibration across varying conditions. We show that the proposed method outperforms TBC while also being several orders of magnitude faster to run, comparable to the standard PLDA baseline.

In machine learning, boosting is one of the most popular methods that designed to combine multiple base learners to a superior one. The well-known Boosted Decision Tree classifier, has been widely adopted in many areas. In the big data era, the data held by individual and entities, like personal images, browsing history and census information, are more likely to contain sensitive information. The privacy concern raises when such data leaves the hand of the owners and be further explored or mined. Such privacy issue demands that the machine learning algorithm should be privacy aware. Recently, Local Differential Privacy is proposed as an effective privacy protection approach, which offers a strong guarantee to the data owners, as the data is perturbed before any further usage, and the true values never leave the hands of the owners. Thus the machine learning algorithm with the private data instances is of great value and importance. In this paper, we are interested in developing the privacy-preserving boosting algorithm that a data user is allowed to build a classifier without knowing or deriving the exact value of each data samples. Our experiments demonstrate the effectiveness of the proposed boosting algorithm and the high utility of the learned classifiers.

In this paper, we analyze maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. One obstacle in this large dimensional setup is the singularity of the sample covariance matrix of the excess asset returns. To solve this issue, we benefit from a technique called nodewise regression, which was developed by Meinshausen and Buhlmann (2006). It provides a sparse/weakly sparse and consistent estimate of the precision matrix, using the Lasso method. We analyze three issues. One of the key results in our paper is that mean-variance efficiency for the portfolios in large dimensions is established. Then tied to that result, we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets. Furthermore, we provide convergence rates and see that the number of assets slow down the convergence up to a logarithmic factor. Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio. Finally, we provide consistent estimates of the Sharpe ratios of global minimum variance portfolio and Markowitz's (1952) mean variance portfolio. In terms of assumptions, we allow for time series data. Simulation and out-of-sample forecasting exercise shows that our new method performs well compared to factor and shrinkage based techniques.

Automatic music generation is an interdisciplinary research topic that combines computational creativity and semantic analysis of music to create automatic machine improvisations. An important property of such a system is allowing the user to specify conditions and desired properties of the generated music. In this paper we designed a model for composing melodies given a user specified symbolic scenario combined with a previous music context. We add manual labeled vectors denoting external music quality in terms of chord function that provides a low dimensional representation of the harmonic tension and resolution. Our model is capable of generating long melodies by regarding 8-beat note sequences as basic units, and shares consistent rhythm pattern structure with another specific song. The model contains two stages and requires separate training where the first stage adopts a Conditional Variational Autoencoder (C-VAE) to build a bijection between note sequences and their latent representations, and the second stage adopts long short-term memory networks (LSTM) with structural conditions to continue writing future melodies. We further exploit the disentanglement technique via C-VAE to allow melody generation based on pitch contour information separately from conditioning on rhythm patterns. Finally, we evaluate the proposed model using quantitative analysis of rhythm and the subjective listening study. Results show that the music generated by our model tends to have salient repetition structures, rich motives, and stable rhythm patterns. The ability to generate longer and more structural phrases from disentangled representations combined with semantic scenario specification conditions shows a broad application of our model.

The world's tech powers are sending giant sums of money spinning into Canada, but while many see this as a sign of success, others are worried about researchers and intellectual property being swallowed wholesale. The country is in the midst of an artificial intelligence (AI) boom, with Google, Microsoft, Facebook, Huawei and other global heavyweights spending millions or even hundreds of millions of dollars on research hubs in Quebec, Ontario and Alberta. Canadian doors are open – some fear too open. Jim Hinton, an IP lawyer and founder of the Own Innovation consultancy, reckons that more than half of all AI patents in Canada end up being owned by foreign companies. What we need to be doing is getting money out of our ideas ourselves, instead of seeing foreign talent scoop it all up," said Hinton. "Otherwise we'll never have a Canadian champion." The country is home to hundreds of fledgling AI companies, including much-talked-about start-ups like Element AI and Deep Genomics, but they remain relatively small. "They don't have a strong market position yet," Hinton says. Deep learning pioneers such as Yoshua Bengio and Geoffrey Hinton (no relation to Jim) have nurtured top-notch talent in AI in Canada for years, back when AI was an emerging field. But despite Canadian inheriting this brilliant AI lead from the country's AI "godfathers", big foreign players have an unassailable advantage over homegrown efforts, Hinton said. "It's not an easy go for the average company to make a business out of AI.

When a fifth of the people living in the city of Wakayama faced a three-day water stoppage last month to fix a 60-year-old pipe network, they rushed to get ready, only to learn that the repairs could be made without a shutdown. Some 3,000 complaints were filed with city officials, who said they had no way of knowing until they dug up the pipes. Cities across the world are facing similar challenges in dealing with deteriorating infrastructure because of a lack of precision in where and when to fix aging water pipelines. Now, some cash-strapped cities are embracing new technology to make water repairs more efficient, with the goal of cutting construction costs and lowering utility bills. The need is pressing, as global climate change, with an increasing frequency of floods, droughts and warmer weather, is overloading water systems.

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm. Unfortunately, such queries can be expensive or time-consuming, mandating the careful consideration of which node pairs to query. In this paper we estimate the improvement in link prediction accuracy after querying any particular node pair, to use in an active learning setup. Specifically, we propose ALPINE (Active Link Prediction usIng Network Embedding), the first method to achieve this for link prediction based on network embedding. To this end, we generalized the notion of V -optimality from experimental design to this setting, as well as more basic active learning heuristics originally developed in standard classification settings. Empirical results on real data show that ALPINE is scalable, and boosts link prediction accuracy with far fewer queries.

SINGAPORE--(BUSINESS WIRE)--Jumio, the leading provider of AI-powered end-to-end identity verification and authentication solutions, has partnered with CIMB Bank Philippines' all-digital bank to provide a simple, hassle-free and convenient digital onboarding solution to Filipinos. In its first full year of formal operations, CIMB Bank Philippines signed in almost 2 million Filipinos via the CIMB Bank PH digital platform, 30% of which are first-time bankers, making CIMB Bank PH the fastest-growing all-digital bank in the Philippines and ASEAN, and a winner of eight banking awards in 2019. Driving financial inclusion, the all-digital, mobile-first bank offers the best-in-market savings interest rates of 4% with zero transaction fees and minimum balance, and seamless account opening and personal loan applications. CIMB Bank PH's mobile app integrates Jumio's AI-driven identity verification technology to provide a safe, secure and fast digital onboarding experience -- what used to take 15 minutes with a video KYC process now takes less than five minutes, resulting in an increase in conversions and happier customers. Jumio's identity verification solution uses machine learning, AI, certified liveness detection and face-based biometrics to ensure the person behind a digital transaction is who they say they are by matching a user's live selfie with the photo shown on their government-issued ID. "Our partnership with Jumio has been integral in achieving our milestones so far as an all-digital bank in the Philippines to deliver a safe and secure banking experience," said Vijay Manoharan, CIMB Bank PH CEO.

One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear (partial and ordinary) differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations dense in the solution set of such systems, in particular any solution (and only such solutions) can be represented to arbitrary precision by Gaussian process regression. The construction is fully algorithmic via Gr\"obner bases and it does not employ any approximation. It builds these priors combining two parametrizations via a pullback: the first parametrizes the solutions for the system of differential equations and the second parametrizes all functions adhering to the boundary conditions.