One of the latest was last week's AWS re:Invent, where Amazon announced a new suite of AI-enabled technology designed with businesses in mind. And while these new products, features, and tools come with a host of opportunities for the developers, marketers, and others who plan to use them, they also raise a few questions. What are they designed to do? How can they help you? Let's take a look at some of these AI announcements from AWS re:Invent and dig deeper into just what they mean.
Apple TV has grown up a lot since its iTV days. It's not just for iTunes rentals anymore--Apple TV handles just about anything we watch, from House of Cards to Game of Thrones and Major League Baseball games, and now that includes 4K content. Since Apple opened up its tiny streaming box to developers, it's gone from fun to indispensable. With an extensive library of apps, Siri support, and a drop-dead simple interface, Apple TV is one of the underrated players in Apple's lineup. Just like all those years ago, it's still the device that "completes the story" of Apple's entertainment ecosystem, and even without some of the bells and whistles of its competitors, Apple TV is still one of the best streaming boxes you can buy--from SD to HD to brilliant 4K.
Chatbot could be utilized to automate business-to-client interactions such as for creating customer service application. Not every task could be handled by the bot though, but it could handle a lot of tasks before some of the more complex ones are delegated to human. However, a lot of heavy-lifting is required to create the AI the bot, such as processing the input of the users, implementing the language understanding, training the machine, testing it, etc. Sometimes, we just want a simple bot that answers to frequently asked questions (FAQs). But different people ask differently, right? And maybe we are wondering how are we able to make our bot understand different questions that might have the same meaning and context.
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!
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.