Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a patient's outcome. Compared to classic machine learning methods, evaluation and validation of causal inference analysis is more challenging because ground truth data of counter-factual outcome can never be obtained in any real-world scenario. Here, we present a comprehensive framework for benchmarking algorithms that estimate causal effect. The framework includes unlabeled data for prediction, labeled data for validation, and code for automatic evaluation of algorithm predictions using both established and novel metrics. The data is based on real-world covariates, and the treatment assignments and outcomes are based on simulations, which provides the basis for validation. In this framework we address two questions: one of scaling, and the other of data-censoring. The framework is available as open source code at https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework

Deep learning defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. Deep learning (DL) has been widely adopted in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the robustness of a DL system against adversarial attacks is usually measured by the accuracy of test data. Considering the limitation of accessible test data, good performance on test data can hardly guarantee the robustness and generality of DL systems. Different from traditional software systems which have clear and controllable logic and functionality, a DL system is trained with data and lacks thorough understanding. This makes it difficult for system analysis and defect detection, which could potentially hinder its real-world deployment without safety guarantees. In this paper, we propose DeepGauge, a comprehensive and multi-granularity testing criteria for DL systems, which renders a complete and multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, with four state-of-the-art adversarial data generation techniques. The effectiveness of DeepGauge sheds light on the construction of robust DL systems.

In this paper, the estimation problem for sparse reduced rank regression (SRRR) model is considered. The SRRR model is widely used for dimension reduction and variable selection with applications in signal processing, econometrics, etc. The problem is formulated to minimize the least squares loss with a sparsity-inducing penalty considering an orthogonality constraint. Convex sparsity-inducing functions have been used for SRRR in literature. In this work, a nonconvex function is proposed for better sparsity inducing. An efficient algorithm is developed based on the alternating minimization (or projection) method to solve the nonconvex optimization problem. Numerical simulations show that the proposed algorithm is much more efficient compared to the benchmark methods and the nonconvex function can result in a better estimation accuracy.

Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition. Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech. Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.

In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause $P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as independent random variables, we propose to infer the causal direction via comparing the distance correlation between $P(X)$ and $P(Y|X)$ with the distance correlation between $P(Y)$ and $P(X|Y)$. We infer "$X$ causes $Y$" if the dependence coefficient between $P(X)$ and $P(Y|X)$ is smaller. Experiments are performed to show the performance of the proposed method.

Arabic comics have in recent years grown into a thriving creative movement. BEIRUT--A computer scientist at the American University of Beirut is using artificial intelligence to classify the content of Arabic comics, applying the computer-based science to this cutting-edge art form in the Arab world. Artificial-intelligence specialists are always trying to stretch the capabilities of computer brainpower. If artificial intelligence can be used to play the ancient Chinese board game Go, or the American TV quiz game Jeopardy, then Arabic comics are also fair game. "I try to look for unusual applications for artificial intelligence and machine learning," explained Mariette Awad, the associate professor in the department of electrical and computer engineering at the American University of Beirut who is leading the project.

This machine learning, which the developers call C-3PO (heh), worked by analyzing a person's browsing history, shopping history and financial details. The data was provided by Leopard Mobile, a Taiwan-based internet company. The neural network then analyzed the pop-up notifications people were getting and which ones they clicked on. As a result, the AI was able to make push notifications "smarter," reducing the number of overall notifications and increasing the click through rates on the ones that did appear, according to the article. The team still has work to do.

The US Federal Trade Commission (FTC), Washington's consumer watchdog, has filed a lawsuit against two businesses it accuses of operating cryptocurrency pyramid schemes. The FTC is taking action against Bitcoin Funding Team and My7Network over what it defines as "chain referral" scams, in which participants pay upfront entry fees in order to be able to recommend others to follow suit. The companies allegedly promised customers who made an initial investment of just $100 (£71) that they could earn an $80,000 (£56,938) monthly income from doing so - although payouts seldom amounted to anything like that. The two businesses defrauded an estimated 30,000 people worldwide between them, the lawsuit alleges. "Bitcoin Funding Team's structure, which created a continual chain of recruitment and recruitment-related payments, ensured that few participants would obtain the results depicted or projected by the defendants," the FTC's complaint reads.

These three companies--the so-called BATs--are plowing millions of dollars into electric-vehicle startups, car-sharing services and online retailers, as well as software platforms for autonomous driving and online car selling. U.S. tech companies, notably Alphabet Inc. and its self-driving car unit Waymo, also are pushing into the auto sector. But the BAT companies have a big advantage in China, where tight government internet controls make it difficult for foreign enterprises to compete. For example, non-Chinese companies aren't allowed to operate digital mapping systems needed for autonomous driving. That has prompted both foreign and domestic auto companies like Ford Motor Co., BMW AG, SAIC Motor Corp. and Zhejiang Geely Holding Group Co. to seal tech partnerships with the BAT firms.

You don't have to be a technology expert to know that the hottest topic in technology today is Artificial Intelligence. AI combined with human ingenuity is already helping people do amazing things. Thanks to AI advances, we are changing the transportation landscape, discovering new ways of protecting the planet, reinventing healthcare to connect with customers, and so much more. At Microsoft, we are infusing intelligence throughout our own products to enable millions of people to realize the benefits of AI today and advance our AI platform. Discuss the future of human interactions as we cover top AI topics with well-known speakers.