Tag Archives: SME

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.

People Turn Toward “Data Banks” to Commoditize Purchase and User Behavior Profiles

Anyone who is anti “Big Brother”, this may not be the article for you, in fact, skip it. 🙂

The Pendulum Swings Away from GDPR

In the not so distant future, “Data Bank” companies consisting of Subject Matter Experts (SME) across all verticals,  may process your data feeds collected from your purchase , and user behavior profiles.  Consumers will be encouraged to submit their data profiles into a Data Bank who will offer incentives such as a reduction of insurance premiums to cash back rewards.

 

Everything from activity trackers, home automation, to vehicular automation data may be captured and aggregated.    The data collected can then be sliced and diced to provide macro and micro views of the information.    On the abstract, macro level the information may allow for demographic, statistical correlations, which may contribute to corporate strategy.

On a granular view, the data will provide “data banks” the opportunity to sift through data to perform analysis and correlations that lead to actionable information.

 

Is it secure?  Do you care if a hacker steals your weight loss information? May not be an issue if collected Purchase and Use Behavior Profiles aggregate into a Blockchain general ledger.  Data Curators and Aggregators work with SMEs to correlate the data into:

  • Canned, ‘intelligent’ reports targeted to specific subject matter, or across silos of data types
  • ‘Universes’ (i.e.  Business Objects) of data that may be ‘mined’ by consumer approved, ‘trusted’ third party companies, e.g. your insurance companies.
  • Actionable information based on AI subject matter rules engines

 

Consumers may have the option of sharing their personal data with specific companies by proxy, through a ‘data bank’ granular to the data point collected.  Sharing of Purchase and User Behavior Profiles:

  1. may lower [or raise] your insurance premiums
  2. provide discounts on preventive health care products and services, e.g. vitamins to yoga classes
  3. Targeted, affordable,  medicine that may redirect the choice of the doctor to an alternate.  The MD would be contacted to validate the alternate.

The curriated data collected may be harnessed by thousands of affinity groups to offer very discrete products and services.  Purchase and User Behavior Profiles,  correlated information stretches beyond any consumer relationship experienced today.

 

At some point, health insurance companies may require you to wear a tracker to increase or slash premiums.  Auto Insurance companies may offer discounts for access to car smart data to make sure suggested maintenance guidelines for service are met.

You may approve your “data bank” to give access to specific soliciting government agencies or private research firms looking to analyze data for their studies. You may qualify based on the demographic, abstracted data points collected for incentives provided may be tax credits, or paying studies.

 

Purchase and User Behavior Profiles:  Adoption and Affordability

If ‘Data Banks’ are able to collect Internet of Things (IoT) enabled, are cost inhibiting.  here are a few ways to increase their adoption:

  1.  [US] tax coupons to enable the buyer, at the time of purchase, to save money.  For example, a 100 USD discount applied at the time of purchase of an Activity Tracker, with the stipulation that you may agree,  at some point, to participate in a study.
  2. Government subsidies: the cost of aggregating and archiving Purchase and Behavioral profiles through annual tax deductions.  Today, tax incentives may allow you to purchase an IoT device if the cost is an itemized medical tax deduction, such as an Activity Tracker that monitors your heart rate, if your medical condition requires it.
  3. Auto, Life, Homeowners, and Health policyholders may qualify for additional insurance deductions
  4. Affinity branded IoT devices, such as American Lung Association may sell a logo branded Activity Tracker.  People may sponsor the owner of the tracking pedometer to raise funds for the cause.

The World Bank has a repository of data, World DataBank, which seems to store a large depth of information:

World Bank Open Data: free and open access to data about development in countries around the globe.”

Here is the article that inspired me to write this article:

http://www.marketwatch.com/story/you-might-be-wearing-a-health-tracker-at-work-one-day-2015-03-11

Privacy and Data Protection Creates Data Markets

Initiatives such as General Data Protection Regulation (GDPR) and other privacy initiatives which seek to constrict access to your data to you as the “owner”, as a byproduct, create opportunities for you to sell your data.  

Blockchain: Purchase, and User Behavior Profiles

As your “vault”, “Data Banks” will collect and maintain your two primary datasets:

  1. As a consumer of goods and services, a Purchase Profile is established and evolves over time.  Online purchases are automatically collected, curated, appended with metadata, and stored in a data vault [Blockchain].  “Offline” purchases at some point, may become a hybrid [on/off] line purchase, with advances in traditional monetary exchanges, and would follow the online transaction model.
  2. User Behavior (UB)  profiles, both on and offline will be collected and stored for analytical purposes.  A user behavior “session” is a use case of activity where YOU are the prime actor.  Each session would create a single UB transaction and are also stored in  a “Data Vault”.   UB use cases may not lead to any purchases.

These datasets wholly owned by the consumer, are safely stored, propagated, and immutable with a solution such as with a Blockchain general ledger.