The chatbot, based on Kasisto technology, can answer customers' questions via the bank's mobile app or its website. Standard Chartered Hong Kong has become the latest bank to turn to artificial intelligence (AI) for customer-facing tasks. According to a report in the South China Morning Post, an AI chatbot is about to be rolled out by the bank. The chatbot, based on Kasisto's Kai Banking platform, can answer customers' questions via the bank's mobile app or its website. The new solution is set to allow retail customers to get account information, make payments, transfer money, track expenses and analyze their spending.

Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences. Standard supervised learning algorithms assume accurate instance-level labeling of training data. Multiple instance learning is a paradigm for handling such labeling ambiguities. However, the widely used large-margin classification methods for multiple instance learning are heuristic in nature with high computational requirements. In this paper, we present stochastic sub-gradient optimization large margin algorithms for multiple instance classification and ranking, and provide them in a software suite called pyLEMMINGS. Results: We have tested pyLEMMINGS on a number of bioinformatics problems as well as benchmark datasets. pyLEMMINGS has successfully been able to identify functionally important segments of proteins: binding sites in Calmodulin binding proteins, prion forming regions, and amyloid cores. pyLEMMINGS achieves state-of-the-art performance in all these tasks, demonstrating the value of multiple instance learning. Furthermore, our method has shown more than 100-fold improvement in terms of running time as compared to heuristic solutions with improved accuracy over benchmark datasets. Availability and Implementation: pyLEMMINGS python package is available for download at: http://faculty.pieas.edu.pk/fayyaz/software.html#pylemmings.

Stein variational gradient descent (SVGD) is a nonparametric inference method, which iteratively transports a set of randomly initialized particles to approximate a differentiable target distribution, along the direction that maximally decreases the KL divergence within a vector-valued reproducing kernel Hilbert space (RKHS). Compared to Monte Carlo methods, SVGD is particle-efficient because of the repulsive force induced by kernels. In this paper, we develop the first analysis about the high dimensional performance of SVGD and emonstrate that the repulsive force drops at least polynomially with increasing dimensions, which results in poor marginal approximation. To improve the marginal inference of SVGD, we propose Marginal SVGD (M-SVGD), which incorporates structural information described by a Markov random field (MRF) into kernels. M-SVGD inherits the particle efficiency of SVGD and can be used as a general purpose marginal inference tool for MRFs. Experimental results on grid based Markov random fields show the effectiveness of our methods.

This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the issue of learning CBM from labeled data. CBM is of high knowledge interpretability but na\"{i}ve learning of it requires exponentially large computation time with respect to data dimension and sample size. To overcome this computational difficulty, we propose an algorithm GRAB (GRAfting for Boolean datasets), which efficiently learns CBM within the $L_1$-regularized loss minimization framework. The key idea of GRAB is to reduce the loss minimization problem to the weighted frequent itemset mining, in which frequent patterns are efficiently computable. We employ benchmark datasets to empirically demonstrate that GRAB is effective in terms of computational efficiency, prediction accuracy and knowledge discovery.

In this work, we analyze the problem of adoption of mobile money in Pakistan by using the call detail records of a major telecom company as our input. Our results highlight the fact that different sections of the society have different patterns of adoption of digital financial services but user mobility related features are the most important one when it comes to adopting and using mobile money services.

The biggest acquisition in the history of technology has been tabled. Broadcom, which itself was purchased by Singapore's Avago Technologies in 2016, has made a $130 billion bid for rival chipmaker Qualcomm. If it goes through (and that's a big if), Broadcom would be paying 20 times the amount Candy Crush-maker King was purchased for, or more than 130 times the amount it cost Facebook to buy Instagram. It could even get the equivalent of five LinkedIns for the price. The proposed deal is so big it's nearly double the biggest tech buyout of all time, Dell's $67bn buyout of EMC in 2015. Broadcom's purchase of Qualcomm would make the company dominant in the chipmaking industry.

A chatbot programmed to be a seven-year-old boy has become the first AI bot to be granted official residence in Tokyo, Japan. Shibuya Mirai is the latest resident of Shibuya, a Tokyo ward with a population of around 224,000 people, despite only existing as a chatbot on the Line messaging app. The ward's decision to make Mirai--meaning'future' in Japanese--an official resident is part of a project aimed at making the local government more familiar and accessible to locals. The chatty seven-year-old is designed to listen to the opinions of Shibuya residents. "His hobbies are taking pictures and observing people," Shibuya Ward said in a statement seen by the AFP news agency.

AI market revenue is forecasted to grow 487% between 2016 and 2020, and employees need to adapt to the potential threat AI poses to their jobs. Still, the Philippines believe coupling the tech with their English skill set will be a "game changer" to the industry, according to the report. Some are hopeful that the tech will strengthen its BPO services. But AI is not necessarily the job threat to the U.S. market that many fear. The technology is expected to cultivate an additional 2.3 million jobs by 2020, eliminating only 1.8 million jobs. Still, U.S. companies that use foreign countries as a means to reduce costs now have the option of weakening those ties.

She's a robot who uses artificial intelligence to write and share hair-raising horror stories. Named after famed "Frankenstein" writer Mary Shelley, the bot is the brainchild of MIT postdoctoral candidate Pinar Yanardag and research scientist Manuel Cebrian. They claim that Shelley is the world's first collaborative AI horror writer. On Oct. 27, Shelley came to life. But, before she did, co-creators Pinar Yanardag and Manuel Cebrian spent a lot of time figuring out where to obtain enough data to power Shelley.

Come see Anshuman Guha, Data Scientist from Spark Cognition Speak at ODSC West. In machine learning, the most traditional and popular methods of clustering are hierarchical clustering (similarity-based clustering) and k-means clustering (feature-based clustering). Hierarchical clustering, put simply, is grouping together points in a vector space that are closest in distance from each other. Hierarchical clustering works great on small datasets. A major advantage of this method is the user does not need to know anything about the dataset in advance and specify any hyper-parameters (like number of clusters).