Uncommon Opportunity? R&D Conversational AI Engineer

I had to share this opportunity.  The Conversational AI Engineer role will continue to be in demand for some time.


Title: R&D Conversational AI Engineer
Location: Englewood Cliffs, NJ
Duration: 6+ months Contract(with Possible extension)

Responsibilities:

  • Create Alexa Skills, Google Home Actions, and chatbots for various direct Client’s brands and initiatives.
  • Work with the Digital Enterprises group to create production-ready conversational agents to help Client emerge in the connected life space.
  • Create additional add-ons to the conversational agents
  • Work with new technologies not be fully documented yet
  • Work with startups and their technology emerging in the connected life space.

Quals–
Client is looking for a developer in conversational AI and bot development.

What is Media Labs?   Media Labs is dedicated to driving a collaborative culture of innovation across all of Clients . We serve as an internal incubator and accelerator for emerging technology and are leading the way with fresh ideas to ignite the future of media and storytelling. We are committed to partnering with another telecom giant, startups, research and academic groups, content creators and brands to further innovation at client. One of our main themes is connected life and we are looking for an engineer to lead this development.

Requirements for R&D Engineer: –

  • Bachelor in Computer Science, Engineering, or other related field
  • Experience working with new technologies that may not be fully documented yet
  • Experience communicating technology to non-technical people
  • Experience with AWS (Lambda, CloudWatch, S3, API Gateway, etc)
  • Experience with JavaScript, Node.js
  • Some experience creating Alexa Skills, Google Home Actions, or chatbots

Optional Requirements:

  • Experience creating iOS or Android applications (native or non-native)
  •  Experience with API.AI or another NLP engine (Lex, Watson Conversation)

Amazon’s Alexa vs. Google’s Assistant: Same Questions, Different Answers

Excellent article by  .

Amazon’s Echo and Google’s Home are the two most compelling products in the new smart-speaker market. It’s a fascinating space to watch, for it is of substantial strategic importance to both companies as well as several more that will enter the fray soon. Why is this? Whatever device you outfit your home with will influence many downstream purchasing decisions, from automation hardware to digital media and even to where you order dog food. Because of this strategic importance, the leading players are investing vast amounts of money to make their product the market leader.

These devices have a broad range of functionality, most of which is not discussed in this article. As such, it is a review not of the devices overall, but rather simply their function as answer engines. You can, on a whim, ask them almost any question and they will try to answer it. I have both devices on my desk, and almost immediately I noticed something very puzzling: They often give different answers to the same questions. Not opinion questions, you understand, but factual questions, the kinds of things you would expect them to be in full agreement on, such as the number of seconds in a year.

How can this be? Assuming they correctly understand the words in the question, how can they give different answers to the same straightforward questions? Upon inspection, it turns out there are ten reasons, each of which reveals an inherent limitation of artificial intelligence as we currently know it…


Addendum to the Article:

As someone who has worked with Artificial Intelligence in some shape or form for the last 20 years, I’d like to throw in my commentary on the article.

  1. Human Utterances and their Correlation to Goal / Intent Recognition.  There are innumerable ways to ask for something you want.  The ‘ask’ is a ‘human utterance’ which should trigger the ‘goal / intent’ of what knowledge the person is requesting.  AI Chat Bots, digital agents, have a table of these utterances which all roll up to a single goal.  Hundreds of utterances may be supplied per goal.  In fact, Amazon has a service, Mechanical Turk, the Artificial Artificial Intelligence, which you may “Ask workers to complete HITs – Human Intelligence Tasks – and get results using Mechanical Turk”.   They boast access to a global, on-demand, 24 x 7 workforce to get thousands of HITs completed in minutes.  There are also ways in which the AI Digital Agent may ‘rephrase’ what the AI considers utterances that are closely related.  Companies like IBM look toward human recognition, accuracy of comprehension as 95% of the words in a given conversation.  On March 7, IBM announced it had become the first to hone in on that benchmark, having achieved a 5.5% error rate.
  2. Algorithmic ‘weighted’ Selection verses Curated Content.   It makes sense based on how these two companies ‘grew up’, that Amazon relies on their curated content acquisitions such as Evi,  a technology company which specialises in knowledge base and semantic search engine software. Its first product was an answer engine that aimed to directly answer questions on any subject posed in plain English text, which is accomplished using a database of discrete facts.   “Google, on the other hand, pulls many of its answers straight from the web. In fact, you know how sometimes you do a search in Google and the answer comes up in snippet form at the top of the results? Well, often Google Assistant simply reads those answers.”  Truncated answers equate to incorrect answers.
  3. Instead of a direct Q&A style approach, where a human utterance, question, triggers an intent/goal , a process by which ‘clarifying questions‘ maybe asked by the AI digital agent.  A dialog workflow may disambiguate the goal by narrowing down what the user is looking for.  This disambiguation process is a part of common technique in human interaction, and is represented in a workflow diagram with logic decision paths. It seems this technique may require human guidance, and prone to bias, error and additional overhead for content curation.
  4. Who are the content curators for knowledge, providing ‘factual’ answers, and/or opinions?  Are curators ‘self proclaimed’ Subject Matter Experts (SMEs), people entitled with degrees in History?  or IT / business analysts making the content decisions?
  5. Questions requesting opinionated information may vary greatly between AI platform, and between questions within the same AI knowledge base.  Opinions may offend, be intentionally biased, sour the AI / human experience.

Amazon and Microsoft Drinking their own AI Chatbot Champagne?

A relatively new medium of support for businesses small to global conglomerates becomes available based on the exciting yet  embryonic [Chabot] / Digital Agent services.   Amazon and Microsoft, among others, are diving into this transforming space.  The coat of paint is still wet on Amazon Lex and Microsoft Cortana Skills.   MSFT Cortana Skills Kit is not yet available to any/all developers, but has been opened to a select set of partners, enabling them to expand Cortana’s core knowledge set.  Microsoft’s Bot Framework is in “Preview”  phase.  However, the possibilities are extensive, such as another tier of support for both of these companies, if they turn on their own knowledge repositories using their respective Digital Agents [Chabot]  platforms.

Approach from Inception to Deployment

  • The curation and creation of knowledge content may occur with the definition of ‘Goals/Intents’ and their correlated human utterances which trigger the Goal Question and Answer (Q&A) dialog format.  Classic Use Case.  The question may provide an answer with text, images, and video.
  • Taking Goals/Intents and Utterances to ‘the next level’ involves creating / implementing Process Workflows (PW).    A workflow may contain many possibilities for the user to reach their goal with a single utterance triggered.  Workflows look very similar to what you might see in a Visio diagram, with multiple logical paths. Instead of presenting users with the answer based upon the single human utterance, the question, the workflow navigates the users through a narrative to:
    • disambiguate the initial human utterance, and get a better understanding of the specific user goal/intention.  The user’s question to the Digital Agent may have a degree of ambiguity, and workflows enable the AI Digital Agent to determine the goal through an interactive dialog/inspection.   The larger the volume of knowledge, and the closer the goals/intentions, the implementation would require disambiguation.
    • interactive conversation / dialog with the AI Digital Agent, to walk through a process step by step, including text, images, and Video inline with the conversation.  The AI chat agent may pause the ‘directions’ waiting for the human counterpart to proceed.

Future  Opportunities:

  • Amazon to provide billing and implementation / technical support for AWS services through a customized version of their own AWS Lex service?   All the code used to provide this Digital Agent / Chabot maybe ‘open source’ for those looking to implement similar [enterprise] services.
  • Digital Agent may allow the user to share their screen, OCR the current section of code from an IDE, and perform a code review on the functions / methods.
  • Microsoft has an ‘Online Chat’ capability for MSDN.  Not sure how extensive the capability is, and if its a true 1:1 chat, which they claim is a 24/7 service. Microsoft has libraries of content from Microsoft Docs, MSDN, and TechNet.  If the MSFT Bot framework has the capability to ingest their own articles,  users may be able to trigger these goals/intents from utterances, similar to searching for knowledge base articles today.
  • Abstraction, Abstraction, Abstraction.  These AI Chatbot/Digital Agents must float toward Wizards to build and deploy, and attempt to stay away from coding.  Elevating this technology to be configurable by a business user.  Solutions have significant possibilities for small companies, and this technology needs to reach their hands.  It seems that Amazon Lex is well on their way to achieving the wizard driven creation / distribution, but have ways to go.  I’m not sure if the back end process execution, e.g. Amazon Lambda, will be abstracted any time soon.

Evaluating fobi.io Chatbot Powered By Google Forms: AI Digital Agent?

Interesting approach to an AI Chatbot implementation.  The business process owner creates one or more Google Forms containing questions and answers, and converts/deploys to a chatbot using fobi.io.  All the questions for [potential] customers/users are captured in a multitude of forms.  Without any code, and within minutes, an interactive chatbot can be produced and deployed for client use.

The trade off for rapid deployment and without coding is a rigid approach of triggering user desired “Goal/Intents”.  It seems a single goal/intent is mapped to a single Google Form.  As opposed to a digital agent, which leverages utterances to trigger the user’s intended goal/intent.  Before starting the chat, the user must select the appropriate Google Form, with the guidance of the content curator.

Another trade off is, it seems, no integration on the backend to execute a business process, essential to many chatbot workflows. For example, given an Invoice ID, the chatbot may search in a transactional database, then retrieve and display the full invoice.  Actually, I may be incorrect. On the Google Forms side, there is a Script Editor. Seems powerful and scary all at the same time.

Another trade off that seems to exist, more on the Google Forms side, is building not just a Form with a list of Questions, but a Consumer Process Workflow, that allows the business to provide an interactive dialog based on answers users provide.  For example, a Yes/No or multichoice answer may lead to alternate sets of questions [and actions].  It doesn’t appear there is any workflow tool provided to structure the Google Forms / fobi.io chatbot Q&A.

However, there are still many business cases for the product, especially for small to mid size organizations.

* Business Estimates – although there is no logic workflow to guide the Q&A sessions with [prospective] customers, the business still may derive the initial information they require to make an initial assessment.  It seems a Web form, and this fobi.io / Google Forms solution seems very comparable in capability, its just a change in the median in which the user interacts to collect the information.

One additional note, Google Forms is not a free product.  Looks like it’s a part of the G Suite. Free two week trial, then the basic plan is $5 per month, which comes with other products as well.  Click here for pricing details.

Although this “chatbot” tries to quickly provide a mechanism to turn a form to a chatbot, it seems it’s still just a form at the end of the day.  I’m interested to see more products from Zoi.ai soon

Evaluating Amazon Lex – AI Digital Agent / Assistant Implementation

Evaluating AI chatbot solutions for:

  • Simple to Configure – e.g. Wizard Walkthrough
  • Flexible, and Mature Platform e.g. Executing backend processes
  • Cost Effective and Competitive Solutions
  • Rapid Deployment to XYZ platforms

The idea is almost anyone can build and deploy a chat bot for your business, small to midsize organizations.

Amazon Lex

Going through the Amazon Lex build chat process, and configuration of the Digital Assistant was a breeze.  AWS employs a ‘wizard’ style interface to help the user build the Chatbot / Digital Agent.  The wizard guides you through defining Intents, Utterances, Slots, and Fulfillment.

  • Intents – A particular goal that the user wants to achieve (e.g. book an airline reservation)
  •  Utterances – Spoken or typed phrases that invoke your intent
  • Slots – Data the user must provide to fulfill the intent
  • Prompts – Questions that ask the user to input data
  • Fulfillment – The business logic required to fulfill the user’s intent (i.e. backend call to another system, e.g. SAP)
Amazon Lex Chabot
Amazon Lex Chabot

The Amazon Lex Chatbot editor is also extremely easy to use, and to update / republish any changes.

Amazon Chat Bot Editor
Amazon Chat Bot Editor

The challenge with Amazon Lex appears to be a very limiting ability for chatbot distribution / deployment.  Your Amazon Lex Chatbot is required to use one of three methods to deploy: Facebook, Slack, or Twilio SMS.  Facebook is limiting in a sense if you do not want to engage your customers on this platform.   Slack is a ‘closed’ framework, whereby the user of the chat bot must belong to a Slack team in order to communicate.  Finally, Twilio SMS implies use of your chat bot though a mobile phone SMS.

Amazon Chatbot Channels
Amazon Chatbot Channels

 

I’ve reached out to AWS Support regarding any other options for Amazon Lex chatbot deployment.  Just in case I missed something.

Amazon Chatbot Support
Amazon Chatbot Support

There is a “Test Bot” in the lower right corner of the Amazon Lex, Intents menu.  The author of the business process can, in real-time, make changes to the bot, and test them all on the same page.

Amazon Chatbot, Test Bot
Amazon Chatbot, Test Bot

 

Key Followups

  • Is there a way to leverage the “Test Bot” as a “no frills” Chatbot UI,  and embed it in an existing web page?  Question to AWS Support.
  • One concern is for large volumes of utterances / Intents and slots. An ideal suggestion would allow the user a bulk upload through an Excel spreadsheet, for example.
  • I’ve not been able to utilize the Amazon Lambda to trigger server side processing.
  • Note: there seem to be several ‘quirky’ bugs in the Amazon Lex UI, so it may take one or two tries to workaround the UI issue.

IBM Watson Conversation also contends for this Digital Agent / Assistant space, and have a very interesting offering including dialog / workflow definition.

Both Amazon Lex and IBM Watson Conversation are FREE to try, and in minutes, you could have your bots created and deployed. Please see sites for pricing details.