background preloader

Framework-AI

Facebook Twitter

Building a Chatbot: analysis & limitations of modern platforms - Tryolabs Blog. The chatbot industry is still in its early days, but growing very fast.

Building a Chatbot: analysis & limitations of modern platforms - Tryolabs Blog

What at first may have looked like a fad or a marketing strategy, is becoming a real need. Would you like to know the movies that are trending in your area, the nearby theaters or maybe watch a trailer? You could use the Fandango bot. Are you a NBA fan trying to get game highlights and updates? Maybe you could try the NBA’s bot.

Marketing motivations cannot be denied, but if chatbots meet the high expectations of the users, they will become indispensable tools for many use cases. To create a chatbot, there is currently an incredible amount of platforms and tools, with different complexity levels, expressive powers and integration capabilities. F5 · Squashing Bugs. Comparison of Bot Frameworks on the Market - Aspect Blogs. Bots are in the spotlight.

Comparison of Bot Frameworks on the Market - Aspect Blogs

Tech superpowers like Microsoft and Facebook released comprehensive frameworks aimed to mass-produce bots. There are numerous startups with their own frameworks and specialized offerings. More established players, including Aspect Software, also joined the race. This post examines some of these frameworks and offerings, based on the first experience. Note that we are not looking at the bot publishing platforms, as this is a different area. Facebook Bot Engine Facebook Bot Engine, released in April 2016, is based on the technology of Wit.ai, acquired by Facebook in early 2015.

Facebook’s strength as a social network is in the number of users and the content they generate, and it is unlikely they will have the motivation to make the bot deployment infrastructure channel-agnostic. Wit.ai is a different story. Wit.ai offers several options: The predefined entities part seems solid. User-defined entities rely on keywords. This seems unpredictable. API.ai. Advanced Natural Language Processing Tools for Bot Makers.

Recent announcements of a bot framework for Skype from Microsoft and Messaging Platform for Messenger from Facebook just heated up the space around chat as a new platform that goes after mobile apps.

Advanced Natural Language Processing Tools for Bot Makers

More and more developers are coming up with an idea to make their own bot for Slack, Telegram, Skype, Kik, Messenger and, probably, several other platforms that might pop up in the next couple of months. Thus, we have a rising interest in the yet to be explored field of making smart bots with AI capabilities and conversational human-computer interaction as the main paradigm. In order to build a good conversational interface we need to look beyond a simple search by a substring or regular expressions that we usually use while dealing with strings.

IBM Watson Developer Cloud. A dialog is a set of conversational nodes that are contained in a workspace.

IBM Watson Developer Cloud

Together the set of nodes makes a dialog tree, on which every branch is a conversation that can be had with a user. Start the dialog First we need to create a starting node for the dialog: IBM Watson Developer Cloud. The dialog component of the Conversation service uses the intents and entities that are identified in the user’s input to gather required information and provide a useful response.

IBM Watson Developer Cloud

Your dialog is represented graphically as a tree; create a branch to process each intent that you define. High-level steps. What is the Best Chatbot Development Site? - NeoSchool. As the explosion in chatbot development continues, more and more educational institutions are switched on by the potential our digital friends can offer.

What is the Best Chatbot Development Site? - NeoSchool

There have been some cool examples of US colleges developing their own chatbots. Including Penn State and Georgia State. So what options exist? Well, if you’re a dev or a software engineer then you’ll likely want to code your own. That’s fine if you cut code for a living. There’s the whole buy vs build discussion up for debate (watch out for that one coming soon). This is one good looking system (if UIs are your ‘thing’). It automatically deals with spelling errors, which you’ll only recognise the value of if you’ve ever programmed a chatbot on a platform that doesn’t offer this (which would be most).

The support information and documentation is excellent, providing a simple to follow format and just the right amount of depth for whatever question you’ve got. Costs: Free to $100 a month. Costs: Free. Costs: Free to $1124 per month. LUIS: Help. Video These 2 videos give an end-to-end example of creating a LUIS application, adding intents, entities, and pre-built entities, labeling utterances, publishing the application and accessing its HTTP endpoint, adding features, and using search and active learning: Overview One of the key problems in human-computer interactions is the ability of the computer to understand what a person wants, and to find the pieces of information that are relevant to their intent.

LUIS: Help

For example, in a news-browsing app, you might say "Get news about virtual reality companies," in which case there is the intention to FindNews, and "virtual reality companies" is the topic. LUIS is designed to enable you to very quickly deploy an http endpoint that will take the sentences you send it, and interpret them in terms of the intention they convey, and the key entities like "virtual reality companies" that are present.