Decisive Analytics Corporation is focused on developing and implementing advanced algorithms for managing large amounts of unstructured and structured data. Whether this data is in the form of text, imagery, video, or audio, we build the machine learning software that enables our customers to extract information from the sea of data produced from a multitude of sources. We are seeking applicants with strong software development skills, and a keen interest in machine learning to join our team. In this role you will have the opportunity to develop state of the art machine learning algorithms and deploy them in the big data stack of Hadoop, MapReduce, Spark, Storm, Accumulo, and Mongo DB. In this role, the successful candidate will have the opportunity to work at an employee-owned company that is consistently rated as one of the best places to work by Washingtonian Magazine and the Washington Business Journal.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Maurice Fallon and Ioannis Havoutis from the Dynamic Robot Systems Group at the University of Oxford have a shiny new ANYmal that they've been putting to work doing useful stuff: I know Boston Dynamics gets all the attention, but in terms of practical quadrupeds, ANYmal is getting a lot of solid work done. Might want to teach it not to impale itself through the neck with traffic cones, though.

Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties. How can a human train an intelligent agent to understand natural language instructions? We believe that this research question is important from both technological and scientific perspectives. No matter how advanced AI technology becomes, human users may want to customize their intelligent helpers to be able to better understand their desires and needs.

Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental data does not suffer from confounding but is usually limited in both scope and scale. We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data. Our method makes strictly weaker assumptions than existing approaches, and we prove conditions under which it yields a consistent estimator. We demonstrate our method's efficacy using real-world data from a large educational experiment.

Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmenta- tion. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising.

GANs involve training two networks in an adversarial game, where each network's task depends on its adversary. Recently, several works have framed GAN training as an online or continual learning problem [1-6]. We focus on the discriminator, which must perform classification under an (adversarially) shifting data distribution. When trained on sequential tasks, neural networks exhibit forgetting. For GANs, discriminator forgetting leads to training instability [1]. To counter forgetting, we encourage the discriminator to maintain useful representations by adding a self-supervision. Conditional GANs have a similar effect using labels. However, our self-supervised GAN does not require labels, and closes the performance gap between conditional and unconditional models. We show that, in doing so, the self-supervised discriminator learns better representations than regular GANs.

There is among K-12 schools a veritable on-rush to put a device in the hands of every student; a laudable and necessary ambition. However, while there are certainly successes, very many teachers and superintendents report that 1:1 programs are not correctly integrated, that the necessary training is not available, and that the focus is very much more on the "device" than what the device can do in terms of enhanced learning opportunities and outcomes. A 2015 study by the Organization for Economic Co-operation and Development found that unless students achieve recognized base-lines of proficiency in reading and numeracy/math prior to engaging in a 1:1 program the results are negligible. They also found that although a vast majority of students, from across all social backgrounds, have access to computers, the pre-existing gaps between advantaged and disadvantaged students are not immediately or thoroughly addressed by the simple provision of technology. Which helps us conclude that technology is not "the silver bullet" that will immediately create equitable educational outcomes.

Machine learning now powers a huge range of applications, from speech recognition systems to search engines, self-driving cars, and prison-sentencing systems. Many applications that were once designed and programmed by humans now combine human-written components with behaviors learned from data. This shift presents new challenges to computer science (CS) practitioners and educators. In this column, we consider how machine learning might change what we consider to be core CS knowledge and skills, and how this should impact the design of both machine learning courses and the broader CS university curriculum. Computing educators1,6 have historically considered the core of CS to be a collection of human-comprehensible abstractions in the form of data structures and algorithms.

NEW DELHI: A Netherlands-based start-up has developed an artificial intelligence (AI) powered smartphone app for deaf and mute people, which it says offers a low-cost and superior approach to translating sign language into text and speech in real time. The easy-to-use innovative digital interpreter dubbed as "Google translator for the deaf and mute" works by placing a smartphone in front of the user while the app translates gestures or sign language into text and speech. The app, called GnoSys, uses neural networks and computer vision to recognise the video of sign language speaker, and then smart algorithms translate it into speech. Affordable and always available interpreter services are in huge demand in the deaf community. Every day thousands of local businesses around the globe face problems with providing their services to deaf, said Konstantin Bondar, Co-Founder & CTO of Evalk, the company which developed the app. According to the National Deaf Association (NAD), 18 million people are estimated to be deaf in India.

Are you looking for Python NLP Libraries? I know it really confusing to find the best one . Usually when we search it on internet, we find a big list of framework . Do not worry, This article will not overload you with tons of information . Here I will list only which are the most useful and easy to learn and implement .All you need to read this article till end for understanding Pros and Cons for each NLP frameworks .