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!
Natural Language Understanding: Taking sequences of words and determining the intended meaning. One of the key applications of AI is to combine these technologies -- speech recognition, natural language understanding, dialog management and so on -- to create Intelligent Assistants. Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond.
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.
NoHold, a company that builds services for making bots, unveiled a project that seeks to turn a document into an Alexa skill. In contrast, a bot made with QuickStart can be changed by uploading a new version of the document that spawned it. However, NoHold's work still requires Amazon to make some changes to its development tools before the new capabilities can be made broadly available. In the meantime, people can still try out QuickStart's text bot creation capabilities for free.
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Comparing apples-to-apples, the Stanford POS tagger isn't slow. Compared to MXPOST, the Stanford POS Tagger with this model is both more accurate and considerably faster. This can be done by using a cheaper conditioning model class (you can get another 50% speed up in the Stanford POS tagger, with still little accuracy loss), using some other classifier type (an HMM-based tagger is just going to be faster than a discriminative, feature-based model like our maxent tagger), or doing more code optimization (probably more to be done here, but the current state is not so bad). It's a quite accurate POS tagger, and so this is okay if you don't care about speed.
Machines Who Think was conceived as a history of artificial intelligence, beginning with the first dreams of the classical Greek poets (and the nightmares of the Hebrew prophets), up through its realization as twentieth-century science. The interviews with AI's pioneer scientists took place when the field was young and generally unknown. From the 30,000-word afterword, that summarizes the field since the original was published: "In the late 1970s and early 1980s, artificial intelligence moved from the fringes to become a celebrity science. The new edition also has two separate time-lines, one tracing the evolution of AI in its narrowest sense, and a second one taking a much broader view of intellectual history, and placing AI in the context of all human information gathering, organizing, propagation, and discovery, a central place for AI that has only become apparent with the development of the second generation World Wide Web, which will depend deeply on AI techniques for finding, shaping and inventing knowledge.
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.