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Building an #AI #AutonomousAgent using #SupervisedLearning with @DennisMortensen

Posted on March 23rd, 2017

#NYAI

03/21/2017 @ Rise, 43 West 23rd Street, NY, 2nd floor

In mid 2013, dennis@human.x.ai started x.ai to employ machine learning to set up meetings. After an introduction to the software, Dennis talked about the challenges for creating a conversational agent to act as your assistant setting up business meetings.

He talked about the 3 processes within the agent: NLU + reasoning + NLG

Natural Language Understanding needs to define the universe – what is it we can do and what is it that we cannot do and will not do?

Natural Language Understanding (NLU) Challenges

  1. Define intents then hire AI trainers. Need to get the intents right since it’s expensive to change to a different scheme
  2. What data set do we align to? What are the guidelines for labeling? Coders need to learn and remember the rules defining all the intents. Need to keep it compact, but not too much so
  3. They have 101 AI trainers full time. On what software do they label the words? Need a custom-built annotation platform. Spent 2 years building it.

Reasoning challenges

  1. How do people want the agent to behave? Manually determine what is supposed to happen. This will create a new intent, but this often requires changes in the coding of the NLU
  2. Some of the things humans want to do are very complicated. Especially common sense
    1. don’t do meeting after 6:00, but if there is one at 6:15, there is a reason for this happening.
    2. a 6:30 PM call to Singapore might be a good idea.
    3. When to have a meeting and when to have a phone call

Natural Language Generation (NLG) challenges

  1. They have 2 interaction designers
  2. Need to inject empathy if it’s appropriate. For instance if there is a change in schedule, we need to respond appropriately: understanding initially and more assertive if the change needs to be unchanged. Also need to honor requests to speak in a given language.

They evaluate the performance of the software when being used by a client by

  1. customer-centric metrics, such as the number of schedule changes
  2. is the customer happy?

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