The social enterprise is on the rise--a signal of a broader shift in consumer culture and a growing expectation that private companies will make a positive impact on the world. But, how do we measure impact? For years, business leaders have clamored for actionable, often automated, data to measure the return on investment (ROI) of their initiatives. As a result, the business world has become infused with the buzzword, "data-driven." Measuring the effectiveness of solutions on outcomes is important for all businesses, but it is particularly critical for companies whose outcomes are intended to improve our society.

Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the heterogeneous sequence characteristics, AFPs are found to demonstrate a high degree of diversity, which makes their prediction a challenging task. Herein, we propose a machine learning framework to deal with this vigorous and diverse prediction problem using the manifolding learning through composition of k-spaced amino acid pairs. We propose to use the deep neural network with skipped connection and ReLU non-linearity to learn the non-linear mapping of protein sequence descriptor and class label. The proposed antifreeze protein prediction method called AFP-CKSAAP has shown to outperform the contemporary methods, achieving excellent prediction scores on standard dataset. The main evaluater for the performance of the proposed method in this study is Youden's index whose high value is dependent on both sensitivity and specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on the independent dataset, which is better than previous methods.

Forums play an important role in providing a platform for community interaction. The introduction of irrelevant content or spam by individuals for commercial and social gains tends to degrade the professional experience presented to the forum users. Automated moderation of the relevancy of posted content is desired. Machine learning is used for text classification and finds applications in spam email detection, fraudulent transaction detection etc. The balance of classes in training data is essential in the case of classification algorithms to make the learning efficient and accurate. However, in the case of forums, the spam content is sparse compared to the relevant content giving rise to a bias towards the latter while training. A model trained on such biased data will fail to classify a spam sample. An approach based on Synthetic Minority Over-sampling Technique(SMOTE) is presented in this paper to tackle imbalanced training data. It involves synthetically creating new minority class samples from the existing ones until balance in data is achieved. The enhanced data is then passed through various classifiers for which the performance is recorded. The results were analyzed on the data of forums of Spoken Tutorial, IIT Bombay over standard performance metrics and revealed that models trained after Synthetic Minority oversampling outperform the ones trained on imbalanced data by substantial margins. An empirical comparison of the results obtained by both SMOTE and without SMOTE for various supervised classification algorithms have been presented in this paper. Synthetic oversampling proves to be a critical technique for achieving uniform class distribution which in turn yields commendable results in text classification. The presented approach can be further extended to content categorization on educational websites thus helping to improve the overall digital learning experience.

Most deep latent factor models choose simple priors for simplicity, tractability or not knowing what prior to use. Recent studies show that the choice of the prior may have a profound effect on the expressiveness of the model,especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders(AAEs). We introduce the notion of code generators to transform manually selected simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than AAEs in both supervised and unsupervised settings. Lastly, we present its ability to do cross-domain translation in a text-to-image synthesis task.

We introduce WIQA, the first large-scale dataset of "What if..." questions over procedural text. WIQA contains three parts: a collection of paragraphs each describing a process, e.g., beach erosion; a set of crowdsourced influence graphs for each paragraph, describing how one change a ffects another; and a large (40k) collection of "What if...?" multiple-choice questions derived from the graphs. For example, given a paragraph about beach erosion, would stormy weather result in more or less erosion (or have no e ff ect)? The task is to answer the questions, given their associated paragraph. WIQA contains three kinds of questions: perturbations to steps mentioned in the paragraph; external (out-of-paragraph) perturbations requiring commonsense knowledge; and irrelevant (no e ff ect) perturbations. We find that state-of-the-art models achieve 73.8% accuracy, well below the human performance of 96.3%. We analyze the challenges, in particular tracking chains of influences, and present the dataset as an open challenge to the community.

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific models are learned in the inner-loop, by using only a small amount of data from the current task. A key challenge in scaling these approaches is the need to differentiate through the inner loop learning process, which can impose considerable computational and memory burdens. By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer. This effectively decouples the meta-gradient computation from the choice of inner loop optimizer. As a result, our approach is agnostic to the choice of inner loop optimizer and can gracefully handle many gradient steps without vanishing gradients or memory constraints. Theoretically, we prove that implicit MAML can compute accurate meta-gradients with a memory footprint that is, up to small constant factors, no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost. Experimentally, we show that these benefits of implicit MAML translate into empirical gains on few-shot image recognition benchmarks.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

This is the next blog in my random series on better understanding some of these advanced Artificial Intelligence and Deep Learning algorithms. This "episode" takes on Generative Adversarial Networks (GANs). Hope you enjoy my "Deep Learning" learning journey. I originally wrote in "Transforming from Autonomous to Smart: Reinforcement Learning Basics" how Reinforcement Learning was creating learning agents to beat games such as Chess, Go and Mario Bros. Reinforcement learning creates intelligent agents that learn via trial-and-error how to map situations to actions so as to maximize rewards. Reinforcement Learning is one of the more powerful Artificial Intelligence (AI) concepts because it is designed to learn and circumnavigate "situations" where you don't have data sets with explicit known outcomes (which represents most real-life situations, like operating an autonomous vehicle).

PLYMOUTH, Mass., September 9, 2019 -- Microway, a leading provider of computational clusters, servers, and workstations for AI and HPC applications, announces it has provided Oregon State University with six NVIDIA DGX-2 supercomputer systems, deployment services, and bringup expertise. Each DGX-2 packs 16 fully connected Tesla V100 GPUs, giving Oregon State a linked network of the world's most powerful AI systems powered by 96 GPU accelerators. The new, massively increased computing capabilities at the College of Engineering resolved a significant campus hardware gap and helped support cutting-edge research on medical imaging, nuclear science, bridge construction, robotics, and driverless vehicles. When planning expanded capability, university faculty and administrators determined they needed enough GPU capacity to serve the diverse needs of undergraduate classes and research workloads, plus lightning-fast storage. The University selected the NVIDIA DGX-2 platform for its immense power, technical support services, and the Docker images with NVIDIA's NGC containerized software.

The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning. You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers. You will be able to scale Machine Learning on Big Data using Apache Spark.