Fact Book


Introduction & FAQs for Artificial Intelligence and Machine Learning

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Artificial Neural Networks (ANNs)In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Deep Learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise, composed of multiple non-linear transformations. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or multisensory integration).


AI and Machine Learning - Detailed Analysis, Facts and Figures An Infographic

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Machine learning is key technology behind use of artificial intelligence applications. We know that AI applications are growing tremendously and businesses are focusing on efficient use of such applications which is becoming mandate for every organization. We are hereby highlighting some viewpoints, facts, figures as findings on AI and machine learning in form of infographic.


Building a Bot to Answer FAQs: Predicting Text Similarity

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We'll conduct a nearest neighbour search in Python, comparing a user input question to a list of FAQs. To do this, we'll use indico's Text Features API to find all the feature vectors for the text data, and calculate the distance between these vectors to those of the user's input question in 300-dimensional space. Add the following code to similarity_text(), just below print t.draw(): If the bot's confidence level meets the threshold, it should return the appropriate FAQ answer. Otherwise, it should notify your customer support manager (you'll have to hook that up based on your messaging app's docs): Update run() one last time and then, well, run the code!


How to build an FAQ Chatbot with API.AI using Node.js -- and PHP

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To start, I've tested many different Machine Learning frameworks out there -- Wit.AI, Microsoft Cognitive services, I had a peek at Luis… for me, it turned out that API.AI was the correct choice. It was totally free with a good response rate, it had a great user interface for development, and it was easy to setup, get started with and expand. Api.ai is pretty smart -- turning it into a conversational agent is just about adding new intents and defining the bot's response. There is a self-explanatory sample on how to do that in the Readme.md Modern Machine Learning frameworks make it very easy to setup conversational agents -- you just witnessed one in less than 4 minutes.


Artificial Intelligence Fact Sheet - Content Science Review

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Content Science is a content strategy and intelligence firm based in Atlanta, GA. Founded in 2010 by Colleen Jones, author of Clout: The Art Science of Influential Web Content, our mission is to transform industries, organizations, and individuals for the better by putting content first. We offer professional services, publications, and software for clients ranging from Fortune 50 companies to nonprofits to government agencies.


Microsoft's new service turns FAQs into bots

PCWorld

The QnA Maker, launched in beta on Tuesday, will let users train an automated conversation partner on existing frequently-asked-questions content. After that information is fed in, the service will create a bot that will respond to customer questions with the content from the knowledge base. Microsoft has been pushing hard to get companies to build intelligent, automated conversation partners, but getting intelligent bots off the ground can take time. NoHold, a company working on customer service bots, recently released Sicura QuickStart, which lets users upload documentation and get it translated into a bot.


FAQ: Analyzing Social Data to Understand the US Electorate

WIRED

Our analytics engine Kairos processes unstructured data from millions of sites, blogs, and social platforms like Twitter and Tumblr. Negativity and Intent are natural language processing classifiers which take advantage of sentence structure as well as keyword matching. Then we modeled the data against survey polls, primary results, and survey pools to obtain weights of influence for each of the social indices. We use a combination of Boolean classifiers, language classifiers (including natural language processing), and machine learning to interpret the true meaning and deeper intent of your audience's conversations.


FAQ: Training Data as a Service (TDaaS)

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We have a complete training data solution, which does include a form of crowdsourcing, but is far more than simply a connection to a crowd. The following paragraphs will explain further, but the short answer is: it's more accurate to call us the world's first Training Data as a Service provider than a crowdsourcing provider. Comparing that to Spare5's TDaaS solution, Mechanical Turk is similar to one part of our workflow: the part where humans label data. In general, you'll find the total cost of ownership (TCO) favorable, given you'll no longer have the expense of employees spending their valuable time labeling data, or paying contractors or a crowdsourcing provider to do it (not to mention the time spent managing the processes and QAing the results).


FAQ: All about the Google RankBrain algorithm

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Google uses a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. As for the second-most important signal, I'd guess that would be "words," where words would encompass everything from the words on the page to how Google's interpreting the words people enter into the search box outside of RankBrain analysis. Things means that instead, Google understands when someone searches for "Obama," they probably mean US President Barack Obama, an actual person with connections to other people, places and things. It's why you can do a search like "when was the wife of obama born" and get an answer about Michele Obama as below, without ever using her name: The methods Google already uses to refine queries generally all flow back to some human being somewhere doing work, either having created stemming lists or synonym lists or making database connections between things.


FAQ: All about the new Google RankBrain algorithm

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Yesterday, news emerged that Google was using a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. As for the second-most important signal, I'd guess that would be "words," where words would encompass everything from the words on the page to how Google's interpreting the words people enter into the search box outside of RankBrain analysis. Things means that instead, Google understands when someone searches for "Obama," they probably mean US President Barack Obama, an actual person with connections to other people, places and things. It's why you can do a search like "when was the wife of obama born" and get an answer about Michele Obama as below, without ever using her name: The methods Google already uses to refine queries generally all flow back to some human being somewhere doing work, either having created stemming lists or synonym lists or making database connections between things.