The market has twice the number of jobs as jobseekers, indicating an urgent need for professionals to upskill, said the study by online and hybrid education company Great Learning More than 50,000 jobs in data science and machine learning are lying vacant due to shortage of qualified talent, says an industry report. Such is the situation that the market has twice the number of jobs as jobseekers, indicating an urgent need for professionals to upskill, said the study by online and hybrid education company Great Learning, shared exclusively with ET. "India lacks massively when it comes to talent in data science, machine learning and artificial intelligence," Hari Krishnan Nair, co-founder of Great Learning, said. "With more and more jobs increasingly becoming data-driven, the need of the hour is for professionals to upskill themselves to stay relevant." The primary reason for the demand supply gap, according to Nair, is that the kind of projects companies are implementing require a higher order of data skills that do not exist. "What people learn in colleges is not in sync with what is being used in industry," he said.

What's going on: As researchers keep pushing AI forward, increasing types of businesses are experimenting with basic forms of the technology to cut costs, resolve problems efficiently -- and achieve a little wow factor. The big picture: To hear Friday's tell it, few aspects of its global enterprise haven't been injected with AI. The company is also experimenting with micro-targeting to lure customers more often, Sherif Mityas, chief experience officer at Friday's, tells Axios. The bottom line: This is a far cry from futuristic robot restaurants in Beijing and Tokyo, but the algorithms at Friday's reflect the mundane ways AI will soon pervade business. Go deeper: More on AI-powered micro-targeting at T.G.I. Friday's (WSJ)

Actually, most people have very little knowledge of how artificial intelligence works, or for that matter, how broadly it is used in everything from daily financial transactions to determining your credit score. Take the stock market, for example. Only a tiny amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It's preprogrammed so that if the price of soybeans or oil goes down, all kinds of additional steps will take place.

Let's face it, Boston Dynamics is the SpaceX of Robotics, and if you're a fan of robotics like I am, you can't wait to see what they come up with next. While we wait, let's check out some robots developed in Japan capable of doing amazing things. First is the HRP-5P Developed by Japan's National Institute of Advanced Industrial Science or AIST and Technology. There is legitimate concern over robots taking away jobs in the future, Japan, however, wants this to happen. This is because the country will have a workforce shortage in the future due to declining birth rates and strict immigration laws.

Kai-Fu Lee became a legend in artificial intelligence research and the tech world because of his groundbreaking work the past three decades with Apple, Microsoft, and Google. But Lee says cancer has radically changed the way he views technology, his life, and the world of medicine. In September 2013, the former head of Google China was given a diagnosis of stage IV follicular non-Hodgkin's lymphoma. The cancer diagnosis put his career and life on the line. Then, it put his career and life in a new light.

There are many examples of 'wisdom of the crowd' effects in which the large number of participants imparts confidence in the collective judgment of the crowd. But how do we form an aggregated judgment when the size of the crowd is limited? Whose judgments do we include, and whose do we accord the most weight? This paper considers this problem in the context of geopolitical event forecasting, where volunteer analysts are queried to give their expertise, confidence, and predictions about the outcome of an event. We develop a forecast aggregation model that integrates topical information about a question, meta-data about a pair of forecasters, and their predictions in a deep siamese neural network that decides which forecasters' predictions are more likely to be close to the correct response. A ranking of the forecasters is induced from a tournament of pair-wise forecaster comparisons, with the ranking used to create an aggregate forecast. Preliminary results find the aggregate prediction of the best forecasters ranked by our deep siamese network model consistently beats typical aggregation techniques by Brier score.

Tuning machine learning models, particularly deep learning architectures, is a complex process. Automated hyperparameter tuning algorithms often depend on specific optimization metrics. However, in many situations, a developer trades one metric against another: accuracy versus overfitting, precision versus recall, smaller models and accuracy, etc. With deep learning, not only are the model's representations opaque, the model's behavior when parameters "knobs" are changed may also be unpredictable. Thus, picking the "best" model often requires time-consuming model comparison. In this work, we introduce LAMVI-2, a visual analytics system to support a developer in comparing hyperparameter settings and outcomes. By focusing on word-embedding models ("deep learning for text") we integrate views to compare both high-level statistics as well as internal model behaviors (e.g., comparing word 'distances'). We demonstrate how developers can work with LAMVI-2 to more quickly and accurately narrow down an appropriate and effective application-specific model.

Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake. It always involves a defending side, usually a classifier, and an attacking side that aims to cause incorrect output. The earliest studies on the adversarial examples for machine learning algorithms start from the information security area, which considers a much wider varieties of attacking methods. But recent research focus that popularized by the deep learning community places strong emphasis on how the "imperceivable" perturbations on the normal inputs may cause dramatic mistakes by the deep learning with supposed super-human accuracy. This paper serves to give a comprehensive introduction to a range of aspects of the adversarial deep learning topic, including its foundations, typical attacking and defending strategies, and some extended studies.

We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been used as a mapping function to estimate time-frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error (MSE). Since OSQA scores have been used widely for sound-quality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create high-quality output signals. However, since most OSQA scores are not analytically tractable, \textit{i.e.}, they are black boxes, the gradient of the objective function cannot be calculated by simply applying back-propagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of \textit{black-box optimization}, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized.

This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art technique for unsupervised-ADS. To decrease the false positive rate (FPR), the AE is trained to minimize the reconstruction error of normal sounds and the anomaly score is calculated as the reconstruction error of the observed sound. Unfortunately, since this training procedure does not take into account the anomaly score for anomalous sounds, the true positive rate (TPR) does not necessarily increase. In this study, we define an objective function based on the Neyman-Pearson lemma by considering ADS as a statistical hypothesis test. The proposed objective function trains the AE to maximize the TPR under an arbitrary low FPR condition. To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm. Through experiments using synthetic data, we found that the proposed method improved the performance measures of ADS under low FPR conditions. In addition, we confirmed that the proposed method could detect anomalous sounds in real environments.