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insideBIGDATA Latest News – 12/29/2019

In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.

Innodata Launches docAnalytics™, a Web-Based, AI-Enabled Document-to-Data Transformation Engine & Document Analytics Platform

Innodata Inc. (NASDAQ: INOD) announced the launch of docAnalytics™, a web-based document analytics platform that enables companies to seamlessly analyze and manage complex documents without the drudgery and expense of traditional human review. docAnalytics is built to work across multiple industries, including financial services, where complex, lengthy documents often require expensive and exhaustive expert review. With docAnalytics, even the most complex documents are turned into rich data points that conform to industry or proprietary data sets. The data points are then made available within an easy-to-use application enabling complex analysis across a large document portfolio and the ability to downstream data to other systems or stakeholders, including regulators.

The platform is designed for managing complex financing agreements (such as mortgage agreements and leases), trade documents (including shipping orders, bills of lading, and letters of credit), transactional agreements (spanning ISDA, IFXCO, GMRA, prime brokerage, securities lending, investment management and 23 other document types), and fixed income documents (including bonds, prospectuses and indentures). To date, more than 23,000 documents have been digitized, analyzed and ingested in the platform.

docAnalytics’ AI-driven data extraction is trained and maintained by Innodata’s in-house experts or can be managed by an organization’s internal specialists. Furthermore, the entire workflow is easily integrated with an API.

“The ability to transform complex documents into normalized, computer-addressable data is going to empower companies to react quickly and appropriately to an ever-changing landscape,” said Jack Abuhoff, Chief Executive Officer at Innodata. “While market participants often invest significant resources in the lengthy process of document creation, typically involving negotiators, lawyers, tax experts, credit officers and compliance officers, they have lacked the right tools to make it work. The result is that when something happens requiring quick thinking – such as a bankruptcy, default, acquisition, a ratings downgrade, or a net asset value decline – firms are faced with slow and cumbersome expert reviews. With our approach, the experts are augmented with true digital data and can react quickly to market events and manage their portfolios of documents proactively.”

Dotscience Gains Momentum in the MLOps Ecosystem and Accelerates Deployment of Machine Learning Models into Production with New Technology Partnerships and Product Innovations

Delivering on its vision that ML engineering should be just as easy, fast and safe as modern software engineering when using DevOps techniques, Dotscience, a leader in DevOps for Machine Learning (MLOps), announced new partnerships with GitLab and Grafana Labs; deep integrations to include Scikit-learn, H2O.ai and TensorFlow; expanded multi-cloud support with Amazon Web Services (AWS) and Microsoft Azure; and a joint collaboration with global enterprises to develop an industry benchmark for helping enterprises get maximum ROI out of their AI initiatives.

“MLOps is poised to dominate the enterprise AI conversation in 2020, as it will directly address the challenges enterprises face when looking to create business value with AI,” said Luke Marsden, CEO and founder at Dotscience. “Through new partnerships, expanded multi-cloud support, and collaborations with MLOps pioneers at global organizations in the Fortune 500, we are setting the bar for MLOps best practices for building production ML pipelines today.”

Traffic4cast Competition  Reveals Novel Way to Predict  Traffic Flow using AI

The Institute for Advanced Research in Artificial Intelligence (IARAI), an independent global machine-learning research institute established by HERE Technologies, announced the results and winners of its traffic prediction competition, which aimed to solve mobility challenges using artificial intelligence (AI). Traffic4cast, a unique competition merging movie-prediction machine learning with traffic research, challenged competitors to understand complex traffic systems and make predictions about how they would flow in the future.

The results show how AI can effectively uncover insights to solve traffic gridlock through trial and error of industrial geospatial data from HERE, a leader in mapping and location-based services. Traffic comes about when drivers make simple decisions that lead to complex behavior patterns. These patterns depend on various factors, such as time of day, the road network, congestion situations, holidays, weather conditions and day of the week. Effectively identifying and analyzing traffic patterns lead to more accurate predictions of how traffic would move on given roads at given times of day.

AI, and more specifically neural networks-computer systems modeled on the human brain and nervous system-can help to solve this problem because they are very good at spotting patterns. Neural networks “learn” to do tasks by considering examples, such as data sets, usually without being programmed with task-specific rules. This ability to learn without being programmed means that although neural networks are good at identifying patterns, why they are good at it is unclear. Their inner workings are one of the mysteries of machine learning, the so-called “black box” AI, meaning that the processes cannot be easily understood or tested by programmers.

The Traffic4cast results show that neural networks were the most effective method used at predicting traffic and came closest to simulating the exact traffic flow. All the top entrants used neural networks instead of “non-black box” solutions, such as support vector machines, Bayesian networks and other fixed algorithms. Winners from South Korea, Oxford/Zurich and Toronto were among more than 40 teams from around the world who submitted over 4,000 entries. 

Working with HERE, IARAI provided participants with traffic movie clips based on a year’s worth of industrial-scale, real-world data for three diverse cities: Berlin, Istanbul and Moscow. The clips were created using data based on an unprecedented number of over 100 billion probe points from positions reported by a large fleet of probe vehicles. They captured morning, evening and rush-hour traffic. Each movie frame summarized GPS trajectories mapped to spatio-temporal cells. The movies showed multiple color channels characterizing traffic volume, speed and direction.

“This competition is special alone because of the sheer scope and size of the data,” said Sepp Hochreiter, a founding co-director of IARAI and an artificial intelligence pioneer (he invented the long short-term memory (LSTM) neural network framework).

Entrants had to forecast the traffic by completing the next part of each movie clip for all three cities. Contestants were given 285 full training days (full movie for the entire day) and 72 testing days (containing five blocks of 12 consecutive images with at least 30 frames between each such block); the rest were marked out validation sets. Each contestant then had to produce the three consecutive images following each given block of 12 images in each movie file for each day in the test set for each city.

“This competition brought together diverse groups to tackle a fundamental problem-predicting geospatial processes-that lies at the heart of sustainable mass mobility,” said Michael Kopp, head of research at HERE and founding co-director of IARAI. “Guiding the AI revolution to this problem using an interdisciplinary approach via billions of real-life data points is both novel and a paradigm shift that will be reflected in many applied scientific disciplines. The results seem to prove that ‘black box’ machine learning is most effective at solving predictive problems. This gives us a jumping-off point for further research into how AI learns.”

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