Tech News

article-image-microsoft-plans-use-windows-ml-game-development

20 Mar 2018

2 min read

Microsoft plans to use Windows ML for Game development

20 Mar 2018

Microsoft unveiled plans to use Windows ML and DirectX for both game development and game play at the 2018's Game Developers Conference. Announced earlier this month, Windows ML is a runtime framework for neural networks on Windows 10. The new platform enables developers to build machine-learning models, trained in Azure, right into their apps using Visual Studio and run them on their PCs. In this game-oriented reveal, Microsoft Windows ML will primarily be used to enhance the game development process. DirectX Raytracing, a new feature of the DirectX API, aims to make games look more realistic. Custom made Gameplays Microsoft will use Windows ML to help developers leverage deep neural networks (DNN) to enhance their games. They will also make use of ML to naturally adapt a game to a player's gaming style, such as a player’s in-game habits and change things on the fly. As an example, Microsoft says, "If you're someone who likes to find treasures in game but don't care to engage in combat, DNNs could prioritize and amplify those activities while reducing the amount or difficulty of battles." Better Game Development Process Apart from gameplay, Microsoft will also use Windows ML for improving the game development process. This includes using Neural networks to perform some of the more difficult parts of creating assets and graphics, leaving artists and developers free to focus on other areas. Microsoft says. "The time and money that studios could save with tools like these could get passed down to gamers in the form of earlier release dates, more realistic games, or more content to play." Visual Quality Improvements Windows ML will also be used to create and enhance Visuals. So, aliasing around objects in games can be smoothed out by tapping into machine learning models to determine the best color for each pixel. Microsoft also showcased a new part of the DirectX API. The DirectX Raytracing (DXR) will allow developers to use the DXR in DirectX 12 to bring real-time raytracing to their games. At present, it can be used to enhance certain aspects of visual quality, all the while working to be a full replacement of rasterization in the future. The full details are available on the official Microsoft Blog.

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article-image-data-science-news-daily-roundup-20th-march-2018

Packt Editorial Staff

20 Mar 2018

2 min read

Data Science News Daily Roundup – 20th March 2018

Packt Editorial Staff

20 Mar 2018

2 min read

Windows ML for Game development, NASA’s blockchain in deep space, IBM’s new computer : smaller than a grain of salt, and more in today’s top stories around machine learning, deep learning, and data science news Top Data science News Stories of the Day Microsoft plans to use Windows ML for Game development Watson-CoreML: IBM and Apple’s new machine learning collaboration project Other Data Science News at a Glance 1. NASA is using blockchain to help build intelligent computer networks in deep space far from a centralized computer hub. Read more on WOSU Radio 2. IBM has created a computer smaller than a grain of salt. The computer will cost less than ten cents to manufacture, and will also pack several hundred thousand transistors. Read more on Mashable 3. Io-Tahoe recently announced the launch of its smart data discovery platform. The new version includes the addition of Data Catalog, a new feature designed to allow data owners and stewards to use a machine learning-based smart catalog to create, maintain and search business rules. It will allow data-driven enterprises to enhance information about data automatically, regardless of the underlying technology and build a data catalog. Read more on IoT Evolution 4. Splice Machine is launching a connector that aims to boost IoT and machine learning applications. The connector will enable data engineers, data scientists, and developers to directly use Spark without excessive data transfers in and out of Splice Machine. Read more on database trends & applications 5. Datawatch Corporation today announced the general availability of Datawatch Panopticon 16.6. This new version gives users easier data access and faster methods for deploying and using the software’s analytical capabilities. Read more on Globe Newswire

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1615

article-image-ibm-cloud-private-data-ibms-new-machine-learning-data-science-platform-2

Sugandha Lahoti

19 Mar 2018

2 min read

IBM Cloud Private for Data: IBM’s new machine learning and data science platform

Sugandha Lahoti

19 Mar 2018

2 min read

IBM unveiled a new data science and machine learning platform for accelerating AI adoption. Termed as IBM Cloud Private for Data, it is an integrated data science, data engineering and app building platform which can ingest and analyze massive amounts of data –one million events per second. According to the official press release, “The platform is built to enable users to build and exploit event-driven applications capable of analyzing the torrents of data from things like IoT sensors, online commerce, mobile devices, and more.” The core features of the IBM Cloud Private for Data is an in-memory database, a real-time processing engine and the ability to ingest and analyze massive amounts of data. The platform can ingest up to 1 million rows per second and 250 billion events per day for both transactional and analytical processing. Another feature of the platform is an ML-powered intelligent data catalog that automates the process of creating meta-tags. It also has a real-time ingestion engine based on Apache Spark and the Apache Parquet column-oriented data store. The IBM Cloud Private Data solution also includes key capabilities from IBM’s Data Science Experience, Information Analyzer, Information Governance Catalogue, Data Stage, Db2 and Db2 Warehouse. This new solution provides a data infrastructure layer for AI behind the firewall. Talking about the architecture, IBM Cloud Private for Data is an application layer deployed on the Kubernetes open-source container software. It forms an integrated environment for data science and application development using microservices. In the future, the Cloud Private for Data will run on all cloud and will be available in industry-specific solutions, for financial services, healthcare, manufacturing, and more. In addition, IBM has also announced the formation of the Data Science Elite Team – an elite team of consultants who will help solve real-world data science problems of clients, with no charge. “These two data science efforts,” according to Rob Thomas, General Manager, IBM Analytics, “will bring the AI destination closer and give access to powerful machine learning and data science technologies that can turn data into game-changing insight.” Have a look at the IBM Cloud Private for Data official announcement for further details.

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article-image-apache-ignite-2-4-rolls-machine-learning-spark-dataframes-capabilities

Sugandha Lahoti

19 Mar 2018

2 min read

Apache Ignite 2.4 rolls out with Machine Learning and Spark DataFrames capabilities

Sugandha Lahoti

19 Mar 2018

2 min read

The Apache Ignite community has announced the latest version of Apache Ignite, its open-source distributed database. Apache Ignite 2.4 features new machine learning capabilities, Spark DataFrames support, and the introduction of a low-level binary client protocol. Machine Learning APIs were first teased at the launch of Apache Ignite 2.0, approximately eight months ago. Now with Apache Ignite 2.4, the ML Grid is production ready. With new ML features, Ignite users can deal with fraud detection, predictive analytics, and for building recommendation systems. The ML grid can also solve regression and classification tasks, and avoid ETL from Ignite to other systems. ML Grid in the future releases of Ignite 2.4, will also incorporate a genetic algorithm software, donated by NetMillennium Inc. This software will help in solving optimization problems by simulating the process of biological evolution. These in turn can be applied to real-world applications including automotive design, computer gaming, robotics, investments, traffic/shipment routing and more. There is also a good news for Spark users. Dataframes is now officially supported for Apache Spark. In addition, Apache Ignite can also be installed from the official RPM repository. Apache Ignite 2.4 also has a new low-level binary client protocol. This would allow all developers, including but not limited to Java, C#, and C++ developers, to utilize Ignite APIs in their applications. The protocol communicates with an existing Ignite cluster without starting a full-fledged Ignite node. An application can connect to the cluster through a raw TCP socket from any programming language. Apache Ignite 2.4 took five months in total for development. Normally, a new version is rolled out every three months. You can read the complete list of addition in the release notes.

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2595

article-image-baidu-open-sources-apolloscape-collaborates-berkeley-deepdrive-machine-learning-automotives

Savia Lobo

19 Mar 2018

2 min read

Baidu open sources ApolloScape and collaborates with Berkeley DeepDrive to further machine learning in automotives

Savia Lobo

19 Mar 2018

2 min read

Baidu has dual announcements to make! Firstly, the launch of ApolloScape, a massive self-driving dataset for autonomous industry. Secondly, its collaboration with Berkeley DeepDrive (BDD) Industry Consortium, a research alliance exploring state-of-the-art technologies in computer vision and machine learning for automotive applications. ApolloScape : Largest Self-driving Dataset for autonomous driving technology The ApolloScape dataset is released under Baidu’s autonomous driving platform, Apollo. This dataset eliminates the time taken for manual data collection. Also, because it is open sourced, developers can now use ApolloScape as a base for building self-driving vehicles. Let’s take a quick look at some of the striking features of the ApolloScape dataset: Its data volume is 10 times greater than any other open-source autonomous driving dataset, including Kitti and CityScapes. This data could be further used for perception, simulation scenes, road networks etc., as well as enabling autonomous driving vehicles to be trained in more complex environments, weather and traffic conditions. It defines 26 different semantic items — eg. cars, bicycles, pedestrians, buildings, streetlights, etc. — with pixel-by-pixel semantic segmentation technique. It can also save researchers and developers a huge amount of time on real-world sensor data collection. Beyond data, ApolloScape will also facilitate advanced research on cutting-edge simulation technology aiming to create a simulation platform that aligns with real-world experience. Baidu’s collaboration with Berkeley DeepDrive Prior to Baidu, Berkeley DeepDrive (BDD) Industry Consortium, has partnered with many other famous brands including, Ford, Nvidia, Qualcomm, and General Motors (GM). The key research focus of BDD include, deep reinforcement learning, cross-modal transfer learning, and clockwork FCNs for fast video processing. This collaboration between Baidu and BDD would incorporate Apollo’s industrial resources and Berkeley’s top academic team to ramp up the innovation of theoretical research, applied technology, and commercial applications. Also, Apollo Open Platform and BDD will also jointly conduct a Workshop on Autonomous Driving at CVPR 2018 (IEEE International Conference on Computer Vision and Pattern Recognition) this June in Salt Lake City where they will organize task competitions based on ApolloScape. To know more about ApolloScape, visit the official website.

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article-image-data-science-news-daily-roundup-19th-march-2018

Packt Editorial Staff

19 Mar 2018

2 min read

Data Science News Daily Roundup – 19th March 2018

Packt Editorial Staff

19 Mar 2018

2 min read

Baidu open sources ApolloScape and collaborates with Berkeley DeepDrive, Uber AI Labs launch VINE, Skope-rules, a Python machine learning module built on top of scikit-learn and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day Baidu open sources ApolloScape and collaborates with Berkeley DeepDrive to further machine learning in automotives Apache Ignite 2.4 rolls out with Machine Learning and Spark DataFrames capabilities IBM Cloud Private for Data: IBM’s new machine learning and data science platform Other Data Science News at a Glance 1. Uber AI Labs launch VINE, an Open Source Interactive Data Visualization Tool for Neuroevolution. VINE can illuminate both evolution strategies (ES) and genetic algorithms (GA) style approaches, which help to train deep neural networks to solve difficult reinforcement learning (RL) problems. Read more on UBER Engineering blog. 2. XMPro to participate in the Industrial Internet Consortium (IIC) Smart Factory Machine Learning for Predictive Maintenance Testbed. The testbed aims to evaluate and validate machine learning techniques for predictive maintenance on high volume production machinery. Read more on prnewswire. 3. Introducing Skope-rules, a Python machine learning module built on top of scikit-learn and distributed under the 3-Clause BSD license. Skope-rules aims at learning logical, interpretable rules for "scoping" a target class, i.e. detecting with high precision instances of this class. Read more on Sebastian Raschka’s Twitter post 4. Fimmic Launches Aiforia Cloud, Bringing Self-Service Deep Learning AI to Digital Pathology. The platform’s new Aiforia Create tools bring unique self-service capabilities by allowing users to generate their own deep learning algorithms by training convolutional neural networks (CNN) to learn, detect and quantify specific features of interest in tissue images. Read more on WLNS.com

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article-image-amoebanets-googles-new-evolutionary-automl

Savia Lobo

16 Mar 2018

2 min read

AmoebaNets: Google’s new evolutionary AutoML

Savia Lobo

16 Mar 2018

2 min read

In order to detect objects within an image, artificial neural networks require careful design by experts over years of difficult research. They later address one specific task, such as to find what's in a photograph, to call a genetic variant, or to help diagnose a disease. Google believes one approach to generate these ANN architectures is through the use of evolutionary algorithms. So, today Google introduced AmoebaNets, an evolutionary algorithm that achieves state-of-the-art results for datasets such as ImageNet and CIFAR-10. Google offers AmoebaNets as an answer to questions such as, By using the computational resources to programmatically evolve image classifiers at unprecedented scale, can one achieve solutions with minimal expert participation? How good can today's artificially-evolved neural networks be? These questions were addressed through the two papers: Large-Scale Evolution of Image Classifiers,” presented at ICML 2017. In this paper, the authors have set up an evolutionary process with simple building blocks and trivial initial conditions. The idea was to "sit back" and let evolution at scale do the work of constructing the architecture. Regularized Evolution for Image Classifier Architecture Search (2018). This paper includes a scaled up computation using Google's new TPUv2 chips. This combination of modern hardware, expert knowledge, and evolution worked together to produce state-of-the-art models on CIFAR-10 and ImageNet, two popular benchmarks for image classification. One important feature of the evolutionary algorithm (AmoebaNets) that the team used in their second paper is a form of regularization, which means: Instead of letting the worst neural networks die, they remove the oldest ones — regardless of how good they are. This improves robustness to changes in the task being optimized and tends to produce more accurate networks in the end. Since weight inheritance is not allowed, all networks must train from scratch. Therefore, this form of regularization selects for networks that remain good when they are re-trained. These models achieve state-of-the-art results for CIFAR-10 (mean test error = 2.13%), mobile-size ImageNet (top-1 accuracy = 75.1% with 5.1 M parameters) and ImageNet (top-1 accuracy = 83.1%). Read more about AmoebaNets on Google Research Blog

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21470

article-image-unity-releases-ml-agents-v0-3-imitation-learning-memory-enhanced-agents

Sugandha Lahoti

16 Mar 2018

2 min read

Unity releases ML-Agents v0.3: Imitation Learning, Memory-Enhanced Agents and more

Sugandha Lahoti

16 Mar 2018

2 min read

The Unity team has released the version 0.3 of their anticipated toolkit ML-Agents. The new release is jam-packed with features on the likes of Imitation Learning, Multi-Brain Training, On-Demand Decision-Making, and Memory-Enhanced Agents. Here’s a quick look at what each of these features brings to the table: Behavioral cloning, an imitation learning algorithm ML-Agents v0.3 uses imitation learning for training agents. Imitation Learning uses demonstrations of the desired behavior in order to provide a learning signal to the agents. For v0.3, the team uses Behavioral Cloning as the choice of imitation learning algorithm. This works by collecting training data from a teacher agent, and then simply using it to directly learn a behavior. Multi-Brain training Using Multi-Brain Training, one can train more than one brain at a time, with their separate observation and action space. At the end of training, there is only one binary (.bytes) file, which contains one neural network model per brain. On-Demand Decision-Making Agents ask for decisions in an on-demand fashion, rather than making decisions every step or every few steps of the engine. Users can enable and disable On-Demand Decision-Making for each agent independently with the click of a button! Learning under partial observability The unity team has included two methods for dealing with partial observability within learning environments through Memory-Enhanced Agents. The first memory enhancement is Observation-Stacking. This allows an agent to keep track of up to the past ten previous observations within an episode, and to feed them all to the brain for decision-making. The second form of memory is the inclusion of an optional recurrent layer for the neural network being trained. These Recurrent Neural Networks (RNNs) have the ability to learn to keep track of important information over time in a hidden state. Apart from these features, there is an addition of a Docker-Image, changes to API Semantics and a major revamp of the documentation. All this to make setup and usage simpler and more intuitive. Users can check the GitHub page to download the new version and learn all the details on the release page.

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article-image-data-science-news-daily-roundup-16th-march-2018

Packt Editorial Staff

16 Mar 2018

2 min read

Data Science News Daily Roundup – 16th March 2018

Packt Editorial Staff

16 Mar 2018

2 min read

Unity releases ML-Agents v0.3, Magenta introduces MusicVAE, CockroachDB’s new enterprise feature named geo-partitioning, and more in today’s top stories around machine learning, deep learning, and data science news Top Data science News Stories of the Day Unity releases ML-Agents v0.3: Imitation Learning, Memory-Enhanced Agents and more AmoebaNets: Google’s new evolutionary AutoML Other Data Science News at a Glance 1. AI chip startup SambaNova raises $56M from Google Ventures. SambaNova is a startup that makes chips specifically for artificial intelligence. The Series A funding round was led by Walden International and GV, Alphabet Inc.’s venture capital arm, and it also included participation from Redline Capital and Atlantic Bridge Ventures. Read more on SiliconAngle. 2. Magenta introduces MusicVAE, a machine learning model that lets us create palettes for blending and exploring musical scores. The technical goal of this class of models is to represent the variation in a high-dimensional dataset using a lower-dimensional code, making it easier to explore and manipulate intuitive characteristics of the data. Read more on the Magenta Blog. 3. CockroachDB introduces a new enterprise feature named geo-partitioning which aims at improving performance by reducing latency. Geo-partitioning grants developers row-level replication control. Read more on Cockroach Labs blog. 4. Princeton announces blockchain analysis tool, BlockSci 0.4.5. BlockSci is a fast and expressive tool to analyze public blockchains. The 0.4.5 version has a large number of feature enhancements and bug fixes including a 5x speed improvement over the initial version. Read more on Freedom to Tinker. 5. Earnix Ltd announced the introduction of its Integrated Machine Learning technology, as an enhancement to the existing insurance software suite. This new capability is designed for demanding, high-performance real-time enterprise production systems, and will deliver a new level of market responsiveness and analytical sophistication to insurers. Read more on digitaljournal.

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1155

article-image-tensorflow-1-7-0-rc0-arrives-close-heels-tensorflow-1-6-0

Sugandha Lahoti

15 Mar 2018

2 min read

Tensorflow 1.7.0-rc0 arrives close on the heels of Tensorflow 1.6.0!

Sugandha Lahoti

15 Mar 2018

2 min read

It’s only been a few days since we witnessed the release of Tensorflow 1.6.0, and now the first release candidate of Tensorflow 1.7.0 is already here! There are quite a few major features and improvements in this new release candidate. However, no breaking changes are unveiled as such. With Tensorflow 1.7.0-rc0, TensorBoard Debugger Plugin, the graphical user interface (GUI) of TensorFlow Debugger (tfdbg), is now in alpha. Also, Eager mode is moving out of contrib. Other additional major features include: EGraph rewrites emulating fixed-point quantization compatible with TensorFlow Lite are now supported by new tf.contrib.quantize package. Easily customize gradient computation available with tf.custom_gradient. New tf.contrib.data.SqlDataset provides an experimental support for reading a sqlite database as a Dataset Distributed Mutex / CriticalSection added to tf.contrib.framework.CriticalSection. Better text processing with tf.regex_replace. Easy, efficient sequence input with tf.contrib.data.bucket_by_sequence_length Apart from these, there is a myriad of bug fixes and small changes. Some of these include: MaxPoolGradGrad support is added for Accelerated Linear Algebra (XLA). CSE pass from Tensorflow is now disabled. tf.py_func now reports the full stack trace if an exception occurs. TPUClusterResolver now integrated with GKE's integration for Cloud TPUs. A new library added for statistical testing of samplers. Helpers added to stream data from the GCE VM to a Cloud TPU. ClusterResolvers are integrated with TPUEstimator. Metropolis_hastings interface unified with HMC kernel. LIBXSMM convolutions moved to a separate --define flag so that they are disabled by default. MomentumOptimizer lambda fixed. tfp.layers boilerplate reduced via programmable docstrings. auc_with_confidence_intervals, a method for computing the AUC and confidence interval with linearithmic time complexity added. regression_head now accepts customized link function, to satisfy the usage that user can define their own link function if the array_ops.identity does not meet the requirement. initialized_value and initial_value behaviors fixed for ResourceVariables created from VariableDef protos. TensorSpec added to represent the specification of Tensors. Constant folding pass is now deterministic. To know about other bug-fixes and changes visit the Tensorflow 1.7.0-rc0 Github Repo.

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9361

article-image-data-science-news-daily-roundup-15th-march-2018

Packt Editorial Staff

15 Mar 2018

2 min read

Data Science News Daily Roundup – 15th March 2018

Packt Editorial Staff

15 Mar 2018

2 min read

Tensorflow 1.7.0-rc0, Google’s NSynth Super, Microsoft’s Machine translation system, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day Tensorflow 1.7.0-rc0 arrives close on the heels of Tensorflow 1.6.0! Google launches Nsynth Super, a hardware companion to its NSynth AI tool to algorithmically create new sounds. Microsoft releases Windows 10 SDK Preview Build 17115 with Machine Learning APIs. Other Data Science News at a Glance Microsoft researchers have created the first machine translation system that they claim can translate sentences of news articles from Chinese to English with the same quality and accuracy as a real person. Read more on the Windows blog. NetBase unveiled next-gen artificial intelligence with image analysis capabilities. This AI can analyze visual posts to identify brand logos and keywords, classify images by facial emotions, and measure the impact of images on Instagram and other visual channels. Read more on MarTech Series. Elasticsearch has updated the Elastic Dotnet versions 2.x, 5.x & 6.x clients to use JSON.NET 11.0.1. Read more on Github release notes for versions 2.5.8, 5.6.1, and 6.0.1. Intel has issued the latest version of its Math Kernel Library in an effort to help developers leverage instruction sets, improve hardware or software performance, and democratize data science tools. Read more on SiliconANGLE. ObjectRocket For MongoDB Is Now On Microsoft Azure Cloud. With this new global offering via Microsoft Azure Cloud, businesses of all sizes can get their data as close to their application as possible, reducing latency and improving data performance. Read more on ObjectRocket.

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article-image-microsoft-releases-windows-10-sdk-preview-build-17115-machine-learning-apis

Sugandha Lahoti

15 Mar 2018

2 min read

Microsoft releases Windows 10 SDK Preview Build 17115 with Machine Learning APIs

Sugandha Lahoti

15 Mar 2018

2 min read

The Windows 10 SDK Preview Build 17115 is out now. With this preview, Microsoft has added Windows Machine Learning APIs, Gaze Input API improvements, bug fixes and other development changes to the API surface area. These API updates and additions are adaptive to run correctly on the widest number of Windows 10 devices. The entire list of API additions in the Windows 10 SDK Preview Build 17115 is available on the Windows Blogs. In addition to these machine learning APIs, this release also includes the C++/WinRT headers and cppwinrt compiler (cppwinrt.exe). This compiler comes in handy when a user wants to add a third-party WinRT component or if they need to author their own WinRT components with C++/WinRT. However, the authoring support is currently experimental and subject to change. The easiest way to get working with it is to start the Visual Studio Developer Command Prompt and run the compiler in that environment, after installing the Windows Insider Preview SDK. Another exciting feature of this build is the addition of new MIDL keywords.These keywords are added to the midlrt tool as a part of the “modernizing IDL” effort. The new keywords are: event set get partial unsealed overridable protected importwinmd If any of these keywords is used as an identifier, it will generate a build failure indicating a syntax error. To fix this, you can modify the identifier in error to an “@” prefix in front of the identifier. That will cause MIDL to treat the offending element as an identifier instead of a keyword. The Windows 10 SDK Preview Build 17115 can be downloaded from developer section on Windows Insider. More information about this release is available on the Windows blog.

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11077

article-image-google-launches-nsynth-super-hardware-companion-nsynth-ai-tool-algorithmically-create-new-sounds

Savia Lobo

15 Mar 2018

2 min read

Google launches Nsynth Super, a hardware companion to its NSynth AI tool to algorithmically create new sounds

Savia Lobo

15 Mar 2018

2 min read

Magenta, a research project by Google Inc., which creates music using machine learning, has launched a new instrument - NSynth Super. It is an open source experimental physical interface for the NSynth or Neural Synthesizer machine learning algorithm. About NSyth NSyth (Natural Synthesizer) is an algorithm that generates new sounds by combining the features of existing sounds. This is done by taking different sounds as an input, use a deep neural network to learn the characteristics of the input sounds, and then create a completely new sound based on these characteristics. Magenta developed NSynth by using WaveNet, a neural network developed by Google’s own DeepMind to make artificial speech sound more natural. WaveNet allows NSynth to simulate musical instruments that would be impossible in the real world. Watch this video to listen to some cool music created by NSynth Super. [embed]https://www.youtube.com/watch?time_continue=3&v=iTXU9Z0NYoU[/embed] NSynth provides artists with intuitive control over timbre and dynamics, and the ability to explore new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer by learning directly from the data. About NSynth Super NSynth Super is an open source experimental instrument which gives musicians the ability to make music using completely new sounds generated by the NSynth algorithm from 4 different source sounds. It features touch screen and dial controls, along with an OLED display and custom-designed printed circuit board. Source : NSynth Super Website NSynth Super is built using open source libraries, which includes TensorFlow and openFrameworks. This allows a wider community of artists, coders, and researchers to experiment with machine learning within their creative sphere. The open source version of the NSynth Super prototype, which includes all of the source code, schematics, and design templates, is available for download on GitHub. Read more about this exciting project on NSynth Super’s official website.

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1991

article-image-data-science-news-daily-roundup-14th-march-2018

Packt Editorial Staff

14 Mar 2018

2 min read

Data Science News Daily Roundup – 14th March 2018

Packt Editorial Staff

14 Mar 2018

2 min read

Big Squid, Inc. releases Kraken platform, Baidu’s Machine Reading Comprehension Challenge, Apache Kylin new version supports SparkSQL, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day Anaconda version 5.1.1 released! AWS releases image recognition AI for Asia-Pacific developers Other Data Science News at a Glance 1. “Released the Kraken”, announces Big Squid, Inc.. The Kraken platform, through self-service machine learning, aims to bring powerful analytical insights to customers, which will further extend the ability of organizations to gain insight into future trends affecting their business and to make decisions on those forecasts with greater certainty, maximizing their data and business intelligence investment. Read more on PRWeb. 2. Baidu Research launches a Machine Reading Comprehension Challenge to advance the state of the art in Natural Language Processing (NLP). Contestants will have access to the world's largest Chinese MRC dataset and a chance to win 100k RMB. Read more on Baidu’s Challenge Page. 3. Progress Launches AI-Driven Chatbot, Progress NativeChat. It is an artificial intelligence-driven platform for creating and deploying chatbots. NativeChat is based on patent pending CognitiveFlow technology that can be trained with goals, examples and data from existing backend systems, similar to the process used for training new customer service agents. Read more on Digital Journal. 4. Apache Kylin has been updated with a new version that supports SparkSQL in building intermediate flat Hive tables. Kylin is an open source distributed analytics engine designed to provide a SQL interface and multi-dimensional analysis (OLAP) on Apache. Read more on I Programmer blog. 5. Google Open Sources its Exoplanet-Hunting AI, Kepler. Google is saving everyone the time of training a neural network on Kepler data by releasing its code freely. One can get the TensorFlow code on GitHub. Read more on Extreme Tech.

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2434

article-image-aws-makes-amazon-rekognition-image-recognition-ai-available-asia-pacific-developers

Savia Lobo

14 Mar 2018

1 min read

AWS makes Amazon Rekognition, its image recognition AI, available for Asia-Pacific developers

Savia Lobo

14 Mar 2018

1 min read

Amazon Rekognition, one of AWS’ Artificial Intelligence (AI) services, is now available in the AWS Asia Pacific (Sydney) Region. With this provision, Australian developers can add visual analysis and recognition to their applications. Amazon Rekognition is a deep learning-based service which easily add images and analyzes video for your applications. Rekognition Image API allows you to detect objects, scenes, faces and inappropriate content, extract text, search and compare faces within images, and so on. One can also use Rekognition Video to detect objects, scenes, activities and inappropriate content, and also search faces in video stored in Amazon S3 in the AWS Asia Pacific (Sydney) region. With Rekognition API, developers can easily: Build an application that measures the likelihood that faces in two images are of the same person, thereby being able to verify a user against a reference photo in near real-time. Also, developers can create collections of millions of faces (detected in images) and can search for a face similar to their reference image in the collection. Amazon Rekognition has no minimum fees or upfront commitment and works on a pay-per-usage model. To know more in detail and other regions where these APIs are available, read the Amazon documentation.

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14041

Microsoft plans to use Windows ML for Game development

Data Science News Daily Roundup – 20th March 2018

IBM Cloud Private for Data: IBM’s new machine learning and data science platform

Apache Ignite 2.4 rolls out with Machine Learning and Spark DataFrames capabilities

Baidu open sources ApolloScape and collaborates with Berkeley DeepDrive to further machine learning in automotives

Data Science News Daily Roundup – 19th March 2018

AmoebaNets: Google’s new evolutionary AutoML

Unity releases ML-Agents v0.3: Imitation Learning, Memory-Enhanced Agents and more

Data Science News Daily Roundup – 16th March 2018

Tensorflow 1.7.0-rc0 arrives close on the heels of Tensorflow 1.6.0!

Trending Topics

Data Science News Daily Roundup – 15th March 2018

Microsoft releases Windows 10 SDK Preview Build 17115 with Machine Learning APIs

Google launches Nsynth Super, a hardware companion to its NSynth AI tool to algorithmically create new sounds

Data Science News Daily Roundup – 14th March 2018

AWS makes Amazon Rekognition, its image recognition AI, available for Asia-Pacific developers

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