Search for "chatbot machine learning"
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ChatterBot 0.4.5
An open-source chat bot program written in Python.
ChatterBot is a machine learning, conversational dialog engine.
https://github.com/gunthercox/ChatterBot
https://pypi.python.org/pypi/ChatterBot
This is the documentation. Read this:
http://chatterbot.readthedocs.io/
ChatterBot is a machine-learning based conversational dialog engine build in
Python which makes it possible to generate responses based on collections of
known conversations. The language independent design of ChatterBot allows it
to be trained to speak any language.
Warning
The JsonDatabaseAdapter is not intended for use with large amounts of
data. You may expirience serious performance problems if the size of
this database becomes too large.
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CNN bot at FB Messenger.
Kind of interesting. Enter a word and it shows news.
https://www.messenger.com/t/CNN/
Also, Tech Crunch messenger:
https://www.messenger.com/t/techcrunch/
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http://www.estherbot.com/
Chatbot that tells Esther's life story, bio, resume.
But kind of hard to use.
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Everything you need to know.
https://chatbotsmagazine.com/the-complete-beginner-s-guide-to-chatbots-8280b7b906ca#.ipaapc3aj
Lengthy explanation about how chatbot (messaging) is important for businesses.
Great services you can use to build your bot:
- wit.ai (bought by Facebook)
- Wit is free, including for commercial use. So both private and public Wit apps are free and are governed by our terms.
- Supports Korean and many other languages.
Do you have the ownership on my data?
You have the ownership
of your data but you agree to let us use them to improve Wit. If your
app is private, your data (intents, entities, expressions, logs) will be
accessible only by you and the developers you decide to share your app
with. If your app is open, you agree to share some data. - Seems like a lot of manual work needed?
- howdy’s botkit (raised $1.5+ mil in funding)
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- api.ai (raised $8.6+ mil in funding)
- The leader in conversational voice interfaces for mobile, web and devices.
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- textit.in
- With Flows, anybody can create engaging SMS and voice applications without the need of a programmer or expensive consulting company.
- Motion.ai
- Free for up to 1000 msgs/mo
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- Chatfuel (Ycombinator company)
- I used this to create a bot at Facebook. See ss.
- Chatfuel’s bot-building platform is free
If you plan on hitting more than 100,000 conversations a month, please talk to us at premium@chatfuel.com.
Used by UBER, several pro sports teams
- IBM’s Watson
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- BeepBoopHQ
- Works with Slack
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- Dexter (owned by Betaworks)
- Get a Slack bot that responds with answers from your own Google Sheet.
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- converse.ai
- Gupshup
Other Resources:
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Deep Learning for Chatbots, Part 1 – Introduction
http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/
Skimmed this. Intro stuff.
Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow
But why would you want to build a retrieval-based model if you can
build a generative model? Generative models seem more flexible because
they don’t need this repository of predefined responses, right?
The problem is that generative models don’t work well in practice. At
least not yet. Because they have so much freedom in how they can
respond, generative models tend to make grammatical mistakes and produce
irrelevant, generic or inconsistent responses. They also need huge
amounts of training data and are hard to optimize. The vast majority of
production systems today are retrieval-based, or a combination of
retrieval-based and generative. Google’s
Smart Reply
is a good example. Generative models are an active area of research,
but we’re not quite there yet.
If you want to build a conversational
agent today your best bet is most likely a retrieval-based model.
The Ubuntu Dialog Corpus
In this post we’ll work with the Ubuntu Dialog Corpus (
paper,
github).
The Ubuntu Dialog Corpus (UDC) is one of the largest public dialog
datasets available. It’s based on chat logs from the Ubuntu channels on a
public IRC network. The
paper
goes into detail on how exactly the corpus was created, so I won’t
repeat that here. However, it’s important to understand what kind of
data we’re working with, so let’s do some exploration first.
The training data consists of 1,000,000 examples, 50% positive (label 1) and 50% negative (label 0). Each example consists of a
context, the conversation up to this point, and an
utterance,
a response to the context. A positive label means that an utterance was
an actual response to a context, and a negative label means that the
utterance wasn’t – it was picked randomly from somewhere in the corpus.
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From Quora:
The AI Zone forum at Chatbots.org is a happening place.
There are Quora topics for:
See also my own webpage:
Coursera
Build Intelligent Applications
https://www.coursera.org/specializations/machine-learning?utm_source=gg&utm_medium=sem&utm_campaign=machine_learning_search_intl&campaignid=426787857&device=c&keyword=online%20course%20machine%20learning&matchtype=b&network=g&devicemodel=&adpostion=1t2&hide_mobile_promo&gclid=Cj0KEQjwxLC9BRDb1dP8o7Op68IBEiQAwWggQIYZ0ix9RD4xhPK0B9xe4I_vk5a4ufITLr7uSESFIDEaAjom8P8HAQ
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http://venturebeat.com/2016/07/20/donald-trump-hillary-clinton-chatbot-sapientx/
This Donald Trump chatbot is great… really, really great. It’s unbelievable.
SapientX,
a company that has been in stealth mode for the last year, has created
chatbots for Donald Trump and Hillary Clinton that provide words
directly from the candidate’s mouth on topics ranging from abortion to
taxes and terrorism.
The chatbots are able to answer questions in text or voice
about roughly 100 topics, like: “What do you think about the Black
Lives Matter movement?” or “Do you think that women should be paid as
much as men?” A full list of sample questions and topics is available
here.
The two bots can be found on
AskHillaryandDonald.com, and
they draw on audio clips taken from public media since Clinton and
Trump declared their candidacy for the presidency more than a year ago,
said Jonathan Hirshon, a member of SapientX’s board of advisors.
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