Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i.e., the gap between images' visual features (low-level) and labels' semantic features (high-level). This issue will be even more challenging if visual features cannot be retrieved from images, that is, when images are only denoted by numerical IDs as given in some real datasets. The typical way of existing VSE methods is to perform a uniform sampling method for negative examples that violate the ranking order against positive examples, which requires a time-consuming search in the whole label space. In this paper, we propose a fast adaptive negative sampler that can work well in the settings of no figure pixels available. Our sampling strategy is to choose the negative examples that are most likely to meet the requirements of violation according to the latent factors of images. In this way, our approach can linearly scale up to large datasets. The experiments demonstrate that our approach converges 5.02x faster than the state-of-the-art approaches on OpenImages, 2.5x on IAPR-TCI2 and 2.06x on NUS-WIDE datasets, as well as better ranking accuracy across datasets.

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations.

A version of this article appeared in the Spring 2018 issue of strategy business. If you haven't noticed, a high-stakes global game of digital disruption is currently under way. It is fueled by the latest wave of technology: advances in artificial intelligence, data analytics, robotics, the Interne...

The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for processed data in order to work -- they can only make predictions from numeric data. This data is composed of relevant variables, known as "features." If the calculated features don't clearly expose the predictive signals, no amount of tuning can take a model to the next level.

As we sweep into the 4th Industrial Revolution driven by artificial intelligence, organisations are scrambling to implement Chatbots to be the face of their new machine-driven operations. Chatbots are often just seen as an automated text chat channel, replacing a human to support customers on a website. But Chatbots are capable of much more. By adding a voice interface to a chatbot platform it can answer phone calls, replacing traditional Interactive Voice Response (IVR) and speech recognition technologies. Chatbots can also respond to emails making them truly multi-channel.

NEW DELHI: To counter China's commitment towards artificial intelligence, the government has formed a high-level committee headed by NITI Aayog vice chairman Rajiv Kumar to lay out a roadmap for India's research and development on AI and its applications.

Artificial intelligence will continue to be buzzing in wealth management in 2018. But there's a short list of professionals who actually understand AI and can clearly explain how advisors and wealth management firms will benefit from it now and in the future. To help break it down, WealthMangement.com We asked Fritz to unpack AI in a way anyone in the industry can understand and even act on it. Prior to founding F2 Strategy, Fritz was the CTO for First Republic Private Wealth Management.

This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics; 2) we develop upper and lower (minimax)bounds on the generalization error; 3) we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric;4) we also bound the accuracy of the learned metric relative to the underlying true generative metric. All the results involve novel mathematical approaches to the metric learning problem, and lso shed new light on the special case of ordinal embedding (aka non-metric multidimensional scaling).

Much has been said and written about disruptive technologies and how they are shaking things up across industries. But it is clear by now that businesses have to constantly track the course of technological innovation, which is coming through at a rapid pace making their strategies and planning almost obsolete, to understand what future has in store for them. The entire banking industry is now being disrupted by new technologies, and artificial intelligence (AI), above all else, has taken it precedence. One can clearly see an increased enthusiasm across the industry to introduce AI into business owing to the potential of this cutting-edge technology to transform the way banks do business. Drawing inspiration from the country, which is at the forefront of the global technological revolution, leading banks in the UAE have also joined their international counterparts in applying the intelligent technology in their day-to-day operations. Nonetheless, a sector-wide adoption is still far from reality.

As healthcare steps closer to each it's becoming increasingly clear that an LHS will be almost dependent on cutting-edge technologies. "Learning Health Systems continually improve by collecting data and processing it to inform better decision making. As the amount and complexity of big data continues to increase, organizations are challenged to fully take advantage of it," said Kenneth Kleinberg, Vice President at Chilmark Research. "AI systems are particularly suited to analyze huge data sets to discover meaningful and actionable insights, and even to carry out actions." A big reason that Kleinberg pointed to is the reality that the more good data people can feed AI systems, the better the insights they get back.