Posts by Category: Artificial Intelligence Resources

Microsoft Azure Cognitive Services

November 03, 2018

Microsoft Azure Cognitive Services
https://azure.microsoft.com/en-in/services/cognitive-services/

Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today. Use AI to solve business problems: 1) Vision – Image-processing algorithms to smartly identify, caption and moderate your pictures; 2) Speech – Convert spoken audio into text, use voice for verification or add speaker recognition to your app; 3) Knowledge – Map complex information and data in order to solve tasks such as intelligent recommendations and semantic search; 4) Search – Add Bing Search APIs to your apps and harness the ability to comb billions of webpages, images, videos and news with a single API call; 5) Language – Allow your apps to process natural language with pre-built scripts, evaluate sentiment and learn how to recognise what users want. Engage your customers through chat … Bring together Cognitive Service APIs and Bot Framework to engage your audience on a whole new level. Build a bot that embodies your brand, addresses your customers’ main questions and escalates to a human operator if needed. This will be added to ChatterBots Subject Tracer™. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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Rasa NLU – Language Understanding for ChatBots and AI Assistants

November 03, 2018

Rasa NLU – Language Understanding for ChatBots and AI Assistants
https://rasa.com/docs/nlu/

Rasa NLU is an open-source natural language processing tool for intent classification and entity extraction in chatbots. The target audience is developers building chatbots and voice apps. The main reasons for using open source NLU are that: a) You don’t have to hand over all your training data to Google, Microsoft, Amazon, or Facebook; b) Machine Learning is not one-size-fits all. You can tweak and customize models for your training data; and c) Rasa NLU runs wherever you want, so you don’t have to make an extra network request for every message that comes in. You can read about the advantages of using open source NLU in this blog post . You can see an independent benchmark comparing Rasa NLU to closed source alternatives here. This will be added to ChatterBots Subject Tracer™. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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WIT.ai – Natural Language for Developers

November 03, 2018

WIT.ai – Natural Language for Developers
https://wit.ai/

Wit.ai makes it easy for developers to build applications and devices that you can talk or text to. Our vision is to empower developers with an open and extensible natural language platform. Wit.ai learns human language from every interaction, and leverages the community: what’s learned is shared across developers. Used by over 160,000 developers! This will be added to ChatterBots Subject Tracer™. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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Dialogflow – Build Natural and Rich Conversational Experiences

November 02, 2018

Dialogflow – Build Natural and Rich Conversational Experiences
https://dialogflow.com/

Give users new ways to interact with your product by building engaging voice and text-based conversational interfaces, such as voice apps and chatbots, powered by AI. Connect with users on your website, mobile app, the Google Assistant, Amazon Alexa, Facebook Messenger, and other popular platforms and devices. Features include: a) Powered by Google’s machine learning – Dialogflow incorporates Google’s machine learning expertise and products such as Google Cloud Speech-to-Text; b) Built on Google infrastructure – Dialogflow is backed by Google and runs on Google Cloud Platform, letting you scale to hundreds of millions of users; c) Optimized for the Google Assistant – Dialogflow is the most widely used tool to build Actions for more than 400M+ Google Assistant devices; d) On any platform – Build Actions, Skills, bots, and apps for the Google Assistant, Alexa, Cortana, Facebook Messenger and other platforms your users are on; e) Across devices – Whether your users are on-the-go or at home, engage with them through wearables, phones, cars, speakers and other smart devices; and f) Around the world – Broaden your reach globally with 20+ supported languages including Spanish, French, and Japanese. This will be added to ChatterBots Subject Tracer™. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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ML Kit – Machine Learning for Mobile Developers

November 02, 2018

ML Kit – Machine Learning for Mobile Developers
https://developers.google.com/ml-kit/

ML Kit beta brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. Features include: a) Optimized for mobile – Machine learning can make your apps more engaging, personalized, and helpful, and provides solutions that are optimized to run on-device; b) Built with Google expertise – ML Kit offers the technologies that have long powered Google’s own experiences on mobile; and c) Approachable and comprehensive – Use out-of-the-box solutions (base APIs) or custom models, running on-device or in the Cloud, depending on your specific needs. Their ultimate goal is to reduce idea–to–implementation cycles and make AI an essential and intuitive part of a developer’s toolkit. We will do so by continuing to add new Base APIs that leverage Google’s machine learning expertise. Base APIs will ultimately cover significantly more use cases in the vision, speech, and text fields. We will also continue to simplify use of custom models, adding tools to deploy, compress, and create them. We hope you will find ML Kit useful! This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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Core ML – Integrate Machine Learning Models Into Your App

November 02, 2018

Core ML – Integrate Machine Learning Models Into Your App
https://developer.apple.com/documentation/coreml

A trained model is the result of applying a machine learning algorithm to a set of training data. The model makes predictions based on new input data. For example, a model that’s been trained on a region’s historical house prices may be able to predict a house’s price when given the number of bedrooms and bathrooms. Core ML is the foundation for domain-specific frameworks and functionality. Core ML supports Vision for image analysis, Natural Language for natural language processing, and GameplayKit for evaluating learned decision trees. Core ML itself builds on top of low-level primitives like Accelerate and BNNS, as well as Metal Performance Shaders. Core ML is optimized for on-device performance, which minimizes memory footprint and power consumption. Running strictly on the device ensures the privacy of user data and guarantees that your app remains functional and responsive when a network connection is unavailable. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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Plickers

October 30, 2018

Plickers
https://play.google.com/store/apps/details?id=com.plickers.client.android&hl=en

Plickers lets you poll your class for free, without the need for student devices. Just give each student a card (a “paper clicker”), and use your Android smartphone or tablet to scan them to do instant checks-for-understanding, exit tickets, and impromptu polls. Best of all, your data is automatically saved, student-by-student, at plickers.com. This will be added to Education and Academic Resources Subject Tracer™. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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OpenCV (Open Source Computer Vision Library)

October 30, 2018

OpenCV (Open Source Computer Vision Library)
https://opencv.org/

OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform. Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 14 million. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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Caffe2 – New Lightweight, Modular, and Scalable Deep Learning Framework

October 30, 2018

Caffe2 – New Lightweight, Modular, and Scalable Deep Learning Framework
https://caffe2.ai/

CODE ONCE, RUN ANYWHERE … Your favorite deep learning technology, now from zero to scale, cloud to mobile. affe2 aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. ry out their quickstart tutorials or jump in and start developing. Caffe2 comes with Python & C++ APIs so you can prototype now, easily optimize later. Use cloud services, Docker, or install on Mac, Windows, or Ubuntu. It integrates with Visual Studio, Android Studio, and Xcode for mobile development. You can use pre-trained models to quickly build demo applications and explore deep learning capabilities without doing any time-consuming and resource-intensive training. You can recreate and evaluate the results from others’ projects, hack together new uses, or improve upon the previously posted models. This will be added to Artificial Intelligence Resources 2019 Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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Quantum Cloud Services (QCS)

October 08, 2018

Quantum Cloud Services (QCS)
https://www.rigetti.com/qcs

The quantum-first cloud platform designed to accelerate your research in quantum computing and its applications. Develop and execute quantum-classical programs in a virtual, classical compute environment that is side-by-side with our real quantum hardware. Features include: a) Low Latency – With co-located classical and quantum hosts, jobs that once took seconds now take milliseconds. This low-latency access to hardware makes QCS their fastest quantum computing platform ever; b) Pre-Configured Environment – Every user has a dedicated Quantum Machine Image that comes pre-configured with our Forest SDK and serves as a single access point to our QVM and QPU backends. No request forms. No waiting in line; and c) Parametric Programs – Further reduce quantum-classical feedback time by taking compilation out of the loop. Their custom control electronics open the possibility to have compiled program binaries that allow for dynamic inputs at run-time. Faster iteration means getting to your solution sooner. This will be added to Grid, Distributed and Cloud Computing Resources Subject Tracer™. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Script Resources Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

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