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How to build smarter chatbots

#artificialintelligence

We're going to be blunt: Chatbots in their current form aren't great. We were promised bots that would change the way we interact with businesses and services, but instead we have interactive bots that perform worse than apps. They are primarily focused on taps or interactive graphical interfaces, and conversing with them using natural language is nearly impossible. Take an example of Poncho Weather on Facebook Messenger. Let's say I'm going to a conference next Monday in San Diego and want to know what the forecast is.


How to build smarter chatbots

#artificialintelligence

We're going to be blunt: Chatbots in their current form aren't great. We were promised bots that would change the way we interact with businesses and services, but instead we have interactive bots that perform worse than apps. They are primarily focused on taps or interactive graphical interfaces, and conversing with them using natural language is nearly impossible. Take an example of Poncho Weather on Facebook Messenger. Let's say I'm going to a conference next Monday in San Diego and want to know what the forecast is.


Deep Learning Startup Maluuba's AI Wants to Talk to You

IEEE Spectrum Robotics

Apple's personal assistant Siri is more of a glorified voice recognition feature of your iPhone than a deep conversation partner. A personal assistant that could truly understand human conversations and written texts might actually represent an artificial intelligence capable of matching or exceeding human intelligence. The Canadian startup Maluuba hopes to help the tech industry achieve such a breakthrough by training AI to become better at understanding languages. The key, according Maluuba's leaders, is building a better way to train AIs. Like humans, AI can only get better at understanding languages by practicing.


Lifelong Learning Dialogue Systems: Chatbots that Self-Learn On the Job

arXiv.org Artificial Intelligence

Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve this situation by endowing the system the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation or "on the job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and improve their conversational skills. A key approach to achieving these is to exploit the multi-user environment of such systems to self-learn through interactions with users via verb and non-verb means. The paper discusses not only key challenges and promising directions to learn from users during conversation but also how to ensure the correctness of the learned knowledge.


5 Models for Conversational AI

#artificialintelligence

How can chatbots become truly intelligent by combining five different models of conversation? Conversational AI is all about making machines communicate with us in natural language. They are called using various names -- chatbots, voice bots, virtual assistants, etc. In reality, they may be slightly different to each other. However one key feature that ties them all together is their ability to understand natural language commands and requests from us-human users. In the back-end, these agents will have to deal with carrying out the request and engage in a conversation.