Tag Archives: Business Intelligence

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!

Companies Turn Toward “Data Sifters” & “Data Banks” to Commoditize on ‘Smart Object’ Data

Anyone who is anti “Big Brother”, this may not be the article for you, in fact skip it. 🙂
In the not so distant future, “Data Sifter” companies consisting of Subject Matter Experts (SME) across all verticals,  may process your data feeds collected from ‘smart objects’.   Consumers will be encouraged to submit their Smart data to ‘data sifters’ who will offer incentives such as a reduction of insurance premiums.
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, or 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 sifters” the opportunity to sift through ‘smart’ object 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?  In fact, you might feel more nervous if only the intended parties are allowed to collect the information. Collected ‘Smart Object’ data enables SMEs to correlate the data into:
  • Canned, ‘intelligent’ reports targeted to specific subject matter, or across silos of data
  • ‘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, people, may have the option of sharing their personal data with specific companies  by proxy, through a ‘data bank’ down to the data point collected   The sharing of personal data or information:

  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 ‘smart object’ data collected may be harnessed by thousands of affinity groups to provide very discrete products and services.  The power of this collected ‘smart data’ and 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.   Incentives provided may be tax credits, or paying studies.

‘Smart Object’ Adoption and Affordability

If ‘Smart Objects’, 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 ‘Smart Objects’ through annual tax deductions.  Today, tax incentives may allow you to purchase a ‘Smart Object’ 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 policy holders may qualify for additional insurance deductions
  4. Affinity branded ‘Smart Objects’ , 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:
Smart Object Data Ecosystem
Smart Object Data Ecosystem

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.

BI Applied to YouTube Yields Value for Advertising, Marketing, & Sales

Google has scanned and indexed books, and showed the most commonly used words, and added meta data, based on book publishing year, genre, and so on.  It would be great to see that functionality come to YouTube.  Business Intelligence (BI) applied to video libraries yields profit for advertising, sales, and marketing.

Every video, in a batch process, gets analyzed for words used, as well as a word count per word, and there is meta data regarding the video, such as genre, and any other user provided information.  Then as videos get processed, a tag bubble cloud as the high level view shows dynamically the most commonly used words get bigger, and the relatively less appearing / said in the view get smaller.  Someone can then click on that word, and drill down to other information about the word, in a sense business intelligence about the word.  Advertisers, for example, may target certain classes, or word tags for their advertisements to appear.  Another profitable model may be to use this business intelligence for business presentations to understand what are the most frequently used words in business speeches, or presentations, based on a current time period, x and y dates, to track what are the buzz words, and even more granular, the business current buzz words of a specific sector, and global region.

Even the music industry can get into the act by getting ‘current phrases’ and incorporate them into their songs.

Microsoft Business Intelligence Whiteboard Prototype Potential

I just read an interesting article in BBC News – Microsoft unveils self-sketching whiteboard prototype, progressing Microsoft Business Intelligence, and I must concur and disagree with Brian Blau, a consultant analyst at Gartner, and is quoted in the article:

“The metrics have to be tailored for each one of these circumstances and companies are still likely to need experts in their own business to be able to run something like this if it’s to be useful.”

There are already Business Intelligence (BI) Analysts in most mid size to large firms across almost all sectors. These analysts already are creating custom reports using business intelligence tools, and then displaying these reports on multiple mediums.  The prototype, it seems, Microsoft is proposing should expand BI to C suite executives, and also enhance the existing abilities of BI analysts, that is the hope.  It does not seem the article is suggesting the removal of the Business Analysts; however, the hope in the BI world has been to try to get more executives to create dynamic reports, and visualizations.  Although, typically C Suite executives ask their BI Analysts to generate `canned`, defined reports executives can export to their Powerpoint presentations. If this new prototype medium is similar to the intuitiveness Microsoft has recently brought to the table with the Surface, it`s possible we may see more adventurous executives trying to create reports; however, that may create new problems.

Business Analysis typically use a language called SQL, or Structured Query Language, although some BI tools try to abstract that layer, and provide the senior BI analysts or execs the layer of abstraction they can use, and more easily understand.  Unfortunately, in many cases, a performance degredation occurs in generating the reports for any number of reasons, e.g. the executes create reports, which are well-meaning, but the non skilled execs not familiar with the intricacies of analytics, may create non-sensical reports, ie, ask the wrong questions, wastes database, or data warehouse resources, and eventually they get an answer to a question, which they didn`t mean to ask.

So in regards to Brian Blau`s quote, I would tend to agree with him after all, although the question C-Suite executives ask typically: I have all this information and technology, why can`t I access it directly.  Answer: the way the data is stored is typically in a way which requires SQL,  a skill set the C-Suite executives may not have or be very skilled at using.  It`s not the sexyness of the medium that will eventually make this work, although it will make the execs salivate, it`s a layer of abstraction from the complexity of the data, which is easy for anyone to use, and is extremely efficient.  Additionally, are advances in data warehouses which are making the analytical processing faster. We may be there, only time will tell.

Digital Media Platforms: BI Competitive Edge for Businesses & Consumers

This post applies to any digital media platform that distributes news articles, books, music, movies, and more.

As I was looking online at a New York Times article, when I scrolled to the bottom of the screen, a popup appeared and told me I had 9 of 10 free articles left for the month, and I thought that was brilliant.  As digital media becomes more competitive, and the content on the platform varies, regardless if it’s the pay as you go model; trial, with unlimited after trial;  or free until max per month or week as the lure; all companies need to allow their clients or potential clients to see how they are using the digital media platform’s products.

As an example, I would like to see what percentage of Technology articles I am viewing per day, week, or month verses Business articles for a certain periodical, and then I can make an informed decision regarding which periodicals I choose to subscribe to for business and also for Technology.  Maybe digital media companies will evolve to have mixed business models, such as, pay per consumption option for all articles after free until max, then for select sections, such as Business or Technology, they may offer unlimited option for the Business, and eventually even a particular editor of Op-Ed pieces.  It could be a price that is significantly less then getting the whole periodical, but at least you are able to attract consumers that have been less willing to go for the full paper, and don’t want the hassle of a pay per go, or monthly chargeback per use model.

If I want to choose a magazine for photography, and I am into archeology from a specific region, as a perspective buyer, I might want to know from the publisher’s entire content, and not just what I have read, a drill down pie chart of subject matters for all photos, and then after I selected Archeology, what percentage of those articles are from a particular region, a subject, and then a photographer.  This is also a powerful business intelligence tool for existing consumers, and may give you a competitive edge.  Also, alliances, that are able to partner for other content, index, and transform that content, say using NewsMLG2, and then perform sharing margin and chargeback.  The lure to their portal would be the driver for the competition as well as the vast of content, and partnerships.

A Note for Advertisers

There are other forms of Business Intelligence for your digital medial consumption that can be offered, such as indexed content, text, images, and video.  You can not only capture image descriptions, and objects within a video to be indexed, which can be used for advertisers to see what the demographics of consumers are watching videos with the most  sneakers, or smartphones, and descriptions that may include dancing clowns.  This may assist the small to mid side startup digital advertiser to understand the consumers in their target markets, and abstract the data.