Tag Archives: Business Intelligence

Popular Tweets from January and February 2018

Tweet Activity Analytics

Leveraging Twitter’s Analytics, I’ve extracted the Top Tweets from the last 57 day period (Jan 1 until today).   During that period, there were 46.8K impressions earned.

Summary:

  • 61 Link Clicks
  • 27 Retweets
  • 86 Likes
  • 34 Replies
Top Tweets for January and February 2018
Top Tweets for January and February 2018

Building AI Is Hard—So Facebook Is Building AI That Builds AI

“…companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art [AI] forward, and paying for these top minds is a lot like paying for an NFL quarterback. That’s a bottleneck in the continued progress of artificial intelligence. And it’s not the only one. Even the top researchers can’t build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don’t work, running each one across dozens and potentially hundreds of machines.”


This article represents a true picture of where we are today for the average consumer and producer of information, and the companies that repurpose information, e.g. in the form of advertisements.  
The advancement and current progress of Artificial Intelligence, Machine Learning, analogously paints a picture akin to the 1970s with computers that fill rooms, and accept punch cards as input.
Today’s consumers have mobile computing power that is on par to the whole rooms of the 1970s; however, “more compute power” in a tinier package may not be the path to AI sentience.  How AI algorithm models are computed might need to take an alternate approach.  
In a classical computation system, a bit would have to be in one state or the other. However quantum mechanics allows the qubit to be in a superposition of both states at the same time, a property which is fundamental to quantum computing.
The construction, and validation of Artificial Intelligence, Machine Learning, algorithm models should be engineered on a Quantum Computing framework.

AI Personal Assistants are “Life Partners”

Artificial Intelligent (AI)  “Assistants”, or “Bots” are taken to the ‘next level’ when the assistant becomes a proactive entity based on the input from human intelligent experts that grows with machine learning.

Even the implication of an ‘Assistant’ v.  ‘Life Partner’ implies a greater degree of dynamic, and proactive interaction.   The cross over to becoming ‘Life Partner’ is when we go ‘above and beyond’ to help our partners succeed, or even survive the day to day.

Once we experience our current [digital, mobile] ‘assistants’ positively influencing our lives in a more intelligent, proactive manner, an emotional bond ‘grows’, and the investment in this technology will also expand.

Practical Applications Range:

  • Alcoholics Anonymous Coach , Mentor – enabling the human partner to overcome temporary weakness. Knowledge,  and “triggers” need to be incorporated into the AI ‘Partner’;  “Location / Proximity” reminder if person enters a shopping area that has a liquor store.  [AI] “Partner” help “talk down”
  • Understanding ‘data points’ from multiple sources, such as alarms,  and calendar events,  to derive ‘knowledge’, and create an actionable trigger.
    • e.g. “Did you remember to take your medicine?” unprompted; “There is a new article in N periodical, that pertains to your medicine.  Would you like to read it?”
    • e.g. 2 unprompted, “Weather calls for N inches of Snow.  Did you remember to service your Snow Blower this season?”
  • FinTech – while in department store XYZ looking to purchase Y over a certain amount, unprompted “Your credit score indicates you are ‘most likely’ eligible to ‘sign up’ for a store credit card, and get N percentage off your first purchase”  Multiple input sources used to achieve a potential sales opportunity.

IBM has a cognitive cloud of AI solutions leveraging IBM’s Watson.  Most/All of the 18 web applications they have hosted (with source) are driven by human interactive triggers, as with the “Natural Language Classifier”, which helps build a question-and-answer repository.

There are four bits that need to occur to accelerate adoption of the ‘AI Life Partner’:

  1. Knowledge Experts, or Subject Matter Experts (SME) need to be able to “pass on” their knowledge to build repositories.   IBM Watson Natural Language Classifier may be used.
  2. The integration of this knowledge into an AI medium, such as a ‘Digital Assistant’ needs to occur with corresponding ‘triggers’ 
  3. Our current AI ‘Assistants’ need to become [more] proactive as they integrate into our ‘digital’ lives, such as going beyond the setting of an alarm clock, hands free calling, or checking the sports score.   Our [AI] “Life Partner” needs to ‘act’ like buddy and fan of ‘our’ sports team.  Without prompting, proactively serve up knowledge [based on correlated, multiple sources], and/or take [acceptable] actions.
    1. E.g. FinTech – “Our schedule is open tonight, and there are great seats available, Section N, Seat A for ABC dollars on Stubhub.  Shall I make the purchase?”
      1. Partner with vendors to drive FinTech business rules.
  4. Take ‘advantage’ of more knowledge sources, such as the applications we use that collect our data.  Use multiple knowledge sources in concert, enabling the AI to correlate data and propose ‘complex’ rules of interaction.

Our AI ‘Life Partners’ may grow in knowledge, and mature the relationship between man and machine.   Incorporating derived rules leveraging machine learning, without input of a human expert, will come with risk and reward.

Financial Technology – Categories of FinTech Solutions

FinTech refers to new solutions which demonstrate an incremental or radical / disruptive innovation development of applications, processes, products or business models in the financial services industry. These solutions can be differentiated in at least five areas.

  1. First, the banking or insurance sector are distinguished as potential business sectors. Solutions for the insurance industry are often more specifically named “InsurTech”.
  2. Second, the solutions differ with regard to their supported business processes such as financial information, payments, investments, financing, advisory and cross-process support.[4] An example is mobile payment solutions.
  3. Third, the targeted customer segment distinguishes between retail, private and corporate banking as well as life and non-life insurance. An example are telematics-based insurances that calculate the fees based on customer behaviour in the area of non-life insurances.
  4. Fourth, the interaction form can either be business-to-business (B2B), business-to-consumer (B2C) or consumer-to-consumer (C2C). An example are social trading solutions for C2C.
  5. Fifth, the solutions vary with regard to their market position. Some for example provide complementary services such as personal finance management systems, others focus on competitive solutions such as e.g. peer-to-peer lending.

Global investment in financial technology increased more than twelvefold from $930 million in 2008 to more than $12 billion in 2014

Source: Financial technology – Wikipedia, the free encyclopedia

Building Apps Incorporating the AI Power of IBM Watson’s Cognative Computing Cloud

IBM Watson’s APIs are available today so teams may ramp up quickly and use IBM’s cognitive computing engine.  From IBM Watson’s site, it seems like anyone may build against their cognitive computing platform.  In addition,  your team may submit to be ‘Featured’ in their application Gallery.  Explore the library of featured applications produced by this partnership.  At the time of this writing, there were 14 applications.
Several of these apps have been created by IBM to showcase their technology.  IBM Watson APIs are categorized into ‘Services’ used:
  • Dialog
  • Natural Language Classifier
  • AlchemyData News
  • Personality Insights
  • Tradeoff Analytics
  • Speech to Text
  • Language Translation
  • Text to Speech
  • Visual Recognition
  • Concept Insights
  • Relationship Extraction

They sound like AI,BI comprehensive services, but in full disclosure, I’ve not read though the API docs available by IBM.  It can be found here, grouped by IBM Watson’s Cognitive Services.

One of the applications powered by IBM Watson in their gallery is a “News Explorer”, which leverages the Service ‘AlchemyData News’.
The app runs in a browser, and consists of 5 main User Interface components.  The centrally placed, “News Network” widget similar to a mind map, correlates articles, companies, organizations, and people.  Visually it displays these components and their relationships in groupings similar to a relationship tree.
 News Network
The left side of the screen has a table called ‘Details’, one column with short descriptions of the stories.  From the UI perspective, it enables users to follow the data from left to right, from details to graphical representations.
Details
The right most side of the screen contains a world map leveraged as a heat map in which all the News is derived.
Locations
Right under the ‘Locations’ widget, there is a ‘Topics’ tag cloud.
TopicsTags
I encourage you to check out the News application, click here.
In addition to UI drill down within the widgets, there is a comprehensive search capacity.
Are you ready to compete with Siri, or Cortana, or build your own Expert solution?  Looks like IBM is empowering you to do so!

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.

Searching Big Data for ‘Digital Smoke Signals’ – NYTimes.com

Searching Big Data for ‘Digital Smoke Signals’ – NYTimes.com.

Excellent Article how the Public Sector is transforming by the private sector.  Good read.  Article frames out the group structure, and the team, but doesn’t go into the output of the statistics, i.e. group output, which is disappointing.  Gives you the sense the team is new, and is still coming to grips with what to output, whom to present it, and the advantages presented and opportunities taken as a result of the data. It could be this new, dynamic group within the United Nations is still trying to integrate with the rest of the organization, they are still wresting with the data, or the data draws dangerous conclusions that are not for public distribution.  Give the article a run through, and you will see the subtext is predicting world economies, and that is confidential to the people being analyzed, and also to the people who invest.

Yahoo’s Opportunity for Success: Entertainment & News Streams

Taking on Tumblir, showing top tweets, trends currently active, is Yahoo going to provide Business Intelligence (BI) for entertainment and news?  I see a paradigm emerging where there are one of several BI widgets to express a visual depliction of real time news and entertainment streams from several sources including active searches, and the popular tags of the moment.

It’s possible Yahoo becomes a dashboard of BI news and entertainment widgets allowing drilling into a deeper level.  One widget might be a pie chart, showing a dynamic view of categories of tags for feeds, e.g. world news.  Then the user can click and drill into that category showing by regional news.  Cloud tags could be another graphical representation deplicting entertainment categories by the celebrity name or topic.

Other BI widgets such as a stop light or rpm speedometer may show hot topics, such as immediate news where the threshold of tweets, searches, posts, exceeds a certain level, the odometer goes from green to red on alert news, tornado warning in your area, or hot topics of any kind. Yahoo needs to evolve their old jump page, and instead of a hogpog of old and new paradigms, proceed with a news and entertainment business intelligence dashboard, default and configurable.

In addition, BI widget streams may be customized based upon a user’s subscription feeds.