SAP Leonardo Machine Learning Foundational APIs have been recently made available in the trial landscape. Anyone can register for a trial account and test drive these ML APIs. In this blog, I want to quickly show you how you get started using the ML APIs to works with the pre-trained models. At SAPPHIRE, the Machine Learning team also announced a set of new pre-trained and customizable services for Face detection, Scene text recognition etc. In the blog, I would like to focus on Scene text recognition which will enable to read text from natural images/scenes.

The rise of the machines is coming. By that, I don't mean that someone is about to break through a singularity and create a rapidly self-teaching AI that will enslave all of humanity. I mean, that's probably on the cards too, but it's not really what we're talking about today. Smart machines performing roles traditionally held by human beings. They're already used today in medicine, robotics, remote sensors, and even in ATMs.

More than 80 Amazon scientists and engineers will attend this year's International Conference on Machine Learning (ICML) in Stockholm, Sweden, with 11 papers co-authored by Amazonians being presented. "ICML is one of the leading outlets for machine learning research," says Neil Lawrence, director of machine learning for Amazon's Supply Chain Optimization Technologies program. "It's a great opportunity to find out what other researchers have been up to and share some of our own learnings." At ICML, members of Lawrence's team will present a paper titled "Structured Variationally Auto-encoded Optimization," which describes a machine-learning approach to optimization, or choosing the values for variables in some process that maximize a particular outcome. The first author on the paper is Xiaoyu Lu, a graduate student at the University of Oxford who worked on the project as an intern at Amazon last summer, then returned in January to do some follow-up work.

The study focuses on linguistic aspects such as word choice for machine translation, parts of speech-tagging and word-sense disambiguation. The study's research paper considers the language learning process as a disambiguation problem and applies the linear separator technique. A formal definition of the disambiguation problem is defined in terms such as different word predicates, their classifications and features for the learning problem. In addition, various disambiguation methods are also emphasised for using them as linear separators.

As Data Science (DS) continues to be a growing field with promising prospects [1]-[3], it is attracting significant attention from many including learners of different learning backgrounds and applications areas. From a DS educator's perspective, the result is a very diverse cohort of learners. This typically includes (in no order) mathematicians, statisticians, operations researchers, computer scientists of all their colours, other scientists (e.g.

We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.

Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it and addresses challenges related to sources with diverse labeling volume and diverse reliability. The first challenge is combining domain similarity and source reliability by proposing a new transfer learning method that utilizes both source-target similarities and inter-source relationships. The second challenge involves pool-based active learning where the oracle is only available in source domains, resulting in an integrated active transfer learning framework that incorporates distribution matching and uncertainty sampling. Extensive experiments on synthetic and two real-world datasets clearly demonstrate the superiority of our proposed methods over several baselines including state-of-the-art transfer learning methods.

Self-paced learning (SPL) mimics the cognitive process of humans, who generally learn from easy samples to hard ones. One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset. In this paper, we reformulate the self-paced learning problem into a distributed setting and propose a novel Distributed Self-Paced Learning method (DSPL) to handle large-scale datasets. Specifically, both the model and instance weights can be optimized in parallel for each batch based on a consensus alternating direction method of multipliers. We also prove the convergence of our algorithm under mild conditions. Extensive experiments on both synthetic and real datasets demonstrate that our approach is superior to those of existing methods.

Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becomes difficult as the number of users and items grows. We develop a novel approach to generate all latent variables on demand from the ratings matrix itself and a fixed pool of parameters. We estimate missing ratings using chains of evidence that link them to a small set of prototypical users and items. Our model automatically addresses the cold-start and online learning problems by combining information across both users and items. We investigate the scaling behavior of this model, and demonstrate competitive results with respect to current matrix factorization techniques in terms of accuracy and convergence speed.

Forty percent of HR leaders believe artificial intelligence will help fill the skills gap. That's according to a new study by Learning House and Future Workplace, which surveyed 600 U.S. HR leaders. More than half of those surveyed acknowledged the skills gap and more than a third believe it's harder to fill open positions now than it was in 2017, but some critics say companies are not doing much to fix the problem. The study found that 74 percent of companies are only investing $500 per employee on learning and development. Jeremy Walsh, senior vice president of enterprise learning solutions at Learning House, said he was shocked by the low amount of money being spent on L&D.