Businesses already exist which have developed and sell Virtual Receptionist, that handle many caller needs (e.g. call routing).
However, AI Digital Assistants such as Alexa, Cortana, Google Now, and Siri have an opportunity to stretch their capabilities even further. Leveraging technologies such as Natural language processing (NLP) and Speech recognition (SR), as well as APIs into the Smartphone’s OS answer/calling capabilities, functionality can be expanded to include:
- Call Screening – The digital executive assistant asks for the name of the caller, purpose of the call, and if the matter is “Urgent”
- A generic “purpose” response or a list of caller purpose items can be supplied to the caller, e.g. 1) Schedule an Appointment
- The smartphone’s user would receive the caller’s name, and the purpose as a message back to the UI from the call, currently in a ‘hold’ state,
- The smartphone user may decide to accept the call, or reject the call and send the caller to voicemail.
- Call / Digital Assistant Capabilities
- The digital executive assistant may schedule a ‘tentative’ appointment within the user’s calendar. The caller may ask to schedule a meeting, the digital executive assistant would access the user’s calendar to determine availability. If calendar indicates availability, a ‘tentative’ meeting will be entered. The smartphone user would have a list of tasks from the assistant, and one of the tasks is to ‘affirm’ availability of the meetings scheduled.
- Allow recall of ‘generally available’ information. If a caller would like to know the address of the smartphone user’s office, the Digital Assistant may access a database of generally available information, and provide it. The Smartphone user may use applications like Google Keep, and any notes tagged with a label “Open Access” may be accessible to any caller.
- Join the smartphone user’s social network, such as LinkedIn. If the caller knows the phone number of the person but is unable to find the user through the social network directory, an invite may be requested by the caller.
- Custom business workflows may also be triggered by the smartphone, such as “Pay by Phone”.
The Digital Executive Assistant capabilities:
- Able to gain control of your Smartphone’s incoming phone calls
- Able to interact with the 3rd party, dial in caller, on a set of business dialog workflows defined by you, the executive.
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)
- 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.
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)
- Some experience creating Alexa Skills, Google Home Actions, or chatbots
- Experience creating iOS or Android applications (native or non-native)
- Experience with API.AI or another NLP engine (Lex, Watson Conversation)
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.
- 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.
- 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.
- 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.
- 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?
- 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.