Chatbots are a hot topic. Conversational commerce and Artificial Intelligence are at the peak; if you haven’t already experimented with chatbots for your customer services, now’s the time. But how do you create a chatbot yourself? Let’s review the options from the simplest to the most complicated.
Added by Olha Zhydik on September 23, 2019 at 4:00am — No Comments
Summary: 99% of our application of NLP has to do with chatbots or translation. This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels. The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.
Added by William Vorhies on September 3, 2019 at 7:35am — No Comments
Summary: Move over RNN/LSTM, there’s a new algorithm called Calibrated Quantum Mesh that promises to bring new levels of accuracy to natural language search and without labeled training data.
Added by William Vorhies on July 29, 2019 at 8:02am — No Comments
Articulate is an open source project that will allow you to take control of you conversational interfaces, without being worried where and how your data is stored. Also, Articulate is built with an user-centered design where the main goal is to make experts and beginners feel comfortable when building their intelligent agents.
The main features of Articulate are:
Added by Daniel Calvo-Marin on July 2, 2018 at 7:00pm — No Comments
Summary: Our starting assumption that sequence problems (language, speech, and others) are the natural domain of RNNs is being challenged. Temporal Convolutional Nets (TCNs) which are our workhorse CNNs with a few new features are outperforming RNNs on major applications today. Looks like RNNs may well be history.
This is a continuation of my previous blog, “Natural Language Understanding – Application Notes with Context Discriminant”.
Natural Language Understanding (NLU) is a subtopic of Natural Language Processing (NLP). Successful implementations of NLU are difficult because of limitations in prevailing technology. SiteFocus solved these limitations with a new approach to NLU. This approach has been successfully…Continue
Summary: This is the second in our chatbot series. Here we explore Natural Language Understanding (NLU), the front end of all chatbots. We’ll discuss the programming necessary to build rules based chatbots and then look at the use of deep learning algorithms that are the basis for AI enabled chatbots.
Summary: This is the first in a series about Chatbots. In this first installment we cover the basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. In subsequent articles we’ll describe in more detail about how they are actually programmed and best practice dos and don’ts.
Business ventures based on existing or disruptive business models taking on the route of Initial Public Offering are always a challenge to investors who want to profit from early investment into those would be “unicorn IPO”. A good investment may get worse before it gets better. Others may get worse and never recover. Aside from the macroeconomics and consumer trends that could affect the outcome of such investments, the fundamentals of these new public offerings…Continue
Added by Sing Koo on October 31, 2017 at 2:30am — No Comments
Deep Learning can be used to automate just about every repetitive task that is currently or formerly performed by humans. Factory robots, autonomous cars, Internet of Things are example of these automations. Yet, mentally challenging tasks such as conducting research or strategic planning with natural language textual documents remain a daunting task for automation. We look into the root cause of this challenge and have implemented a solution to automate these…Continue
Added by Sing Koo on October 6, 2017 at 1:00pm — No Comments