Google uses a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three original sources and has been updated over time, with notes where updates have happened. First is the Bloomberg story that broke the news about RankBrain (See also our write-up of it).
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).
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.
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!
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.
People looking for an easier path to integrating with Amazon's Alexa virtual assistant have good news on the horizon. NoHold, a company that builds services for making bots, unveiled a project that seeks to turn a document into an Alexa skill. It's designed for situations like Airbnb hosts who want to give guests a virtual assistant that can answer questions about the home they're renting, or companies that want a talking employee handbook. Bot-builders upload a document to NoHold's Sicura QuickStart service, which then parses the text and turns it into a virtual conversation partner that can answer questions based on the file's contents. Right now, building Alexa skills is a fairly manual process that requires programming prowess and time to figure out Amazon's software development tools for its virtual assistant.
More and more companies are putting drones to work, including tech giants, manufacturers, utilities, and news organizations. With a broad range of practical applications and rapidly evolving technology, drones offer huge untapped potential, but not every market offers equal opportunities for growth. Here are seven facts and forecasts to know before investing. The demand for drones in the U.S. is projected to rise 10% annually to $4.4 billion in 2020, and the number of vehicles sold will more than double to 5.5 million. Drones sold to commercial and consumer users can cost less than $100 on the low end for toy drones to $10,000 or more on the high end for professional drones with sophisticated sensors and controls.
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.
That model is fairly slow. Essentially, that model is trying to pull out all stops to maximize tagger accuracy. Speed consequently suffers due to choices like using 4th order bidirectional tag conditioning. It's nearly as accurate (96.97% accuracy vs. 97.32% on the standard WSJ22-24 test set) and is an order of magnitude faster. Comparing apples-to-apples, the Stanford POS tagger isn't slow.