Tag Archives: IBM

Cloud Serverless Computing: Why? and With Whom?

What is Cloud Serverless Computing?

Based on your application Use Case(s), Cloud Serverless Computing architecture may reduce ongoing costs for application usage, and provide scalability on demand without the Cloud Server Instance management overhead, i.e. costs and effort.
Note: Cloud Serverless Computing is used interchangeability with Functions as a service (FaaS) which makes sense from a developer’s standpoint as they are coding Functions (or Methods), and that’s the level of abstraction.

Microsoft Flow

 

Microsoft Flow Pricing

As listed below, there are three tiers, which includes a free tier for personal use or exploring the platform for your business.  The pay Flow plans seem ridiculously inexpensive based on what business workflow designers receive for the 5 USD or 15 USD per month.  Microsoft Flow has abstracted building workflows so almost anyone can build application workflows or automate business manual workflows leveraging almost any of the popular applications on the market.

It doesn’t seem like 3rd party [data] Connectors and Template creators receive any direct monetary value from the Microsoft Flow platform.  Although workflow designers and business owners may be swayed to purchase 3rd party product licenses for the use of their core technology.

Microsoft Flow Pricing
Microsoft Flow Pricing

Microsoft Azure Functions

Process events with a serverless code architecture.  An event-based serverless compute experience to accelerate development. Scale based on demand and pay only for the resources you consume.

Google Cloud  Serverless

Properly designed microservices have a single responsibility and can independently scale. With traditional applications being broken up into 100s of microservices, traditional platform technologies can lead to significant increase in management and infrastructure costs. Google Cloud Platform’s serverless products mitigates these challenges and help you create cost-effective microservices.

Google Serverless Application Development
Google Serverless Application Development

 

Google Serverless Analytics and Machine Learning
Google Serverless Analytics and Machine Learning

 

Google Serverless Use Cases
Google Serverless Use Cases

 

Amazon AWS  Lambda

AWS provides a set of fully managed services that you can use to build and run serverless applications. You use these services to build serverless applications that don’t require provisioning, maintaining, and administering servers for backend components such as compute, databases, storage, stream processing, message queueing, and more. You also no longer need to worry about ensuring application fault tolerance and availability. Instead, AWS handles all of these capabilities for you, allowing you to focus on product innovation and get faster time-to-market. It’s important to note that Amazon was the first contender in this space with a 2014 product launch.

IBM Bluemix OpenWhisk

Execute code on demand in a highly scalable serverless environment.  Create and run event-driven apps that scale on demand.

  • Focus on essential event-driven logic, not on maintaining servers
  • Integrate with a catalog of services
  • Pay for actual usage rather than projected peaks

The OpenWhisk serverless architecture accelerates development as a set of small, distinct, and independent actions. By abstracting away infrastructure, OpenWhisk frees members of small teams to rapidly work on different pieces of code simultaneously, keeping the overall focus on creating user experiences customers want.

What’s Next?

Serverless Computing is a decision that needs to be made based on the usage profile of your application.  For the right use case, serverless computing is an excellent choice that is ready for prime time and can provide significant cost savings.

There’s an excellent article, recently published July 16th, 2017 by  Moshe Kranc called, “Serverless Computing: Ready for Prime Time” which at a high level can help you determine if your application is a candidate for Serverless Computing.


See Also:
  1. “Serverless computing architecture, microservices boost cloud outlook” by Mike Pfeiffer
  2. “What is serverless computing? A primer from the DevOps point of view” by J Steven Perry

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.

Hey Siri, Ready for an Antitrust Lawsuit Against Apple? Guess Who’s Suing.

The AI personal assistant with the “most usage” spanning  connectivity across all smart devices, will be the anchor upon which users will gravitate to control their ‘automated’ lives.  An Amazon commercial just aired which depicted  a dad with his daughter, and the daughter was crying about her boyfriend who happened to be in the front yard yelling for her.  The dad says to Amazon’s Alexa, sprinklers on, and yes, the boyfriend got soaked.

What is so special about top spot for the AI Personal Assistant? Controlling the ‘funnel’ upon which all information is accessed, and actions are taken means the intelligent ability to:

  • Serve up content / information, which could then be mixed in with advertisements, or ‘intelligent suggestions’ based on historical data, i.e. machine learning.
  • Proactive, suggestive actions  may lead to sales of goods and services. e.g. AI Personal Assistant flags potential ‘buys’ from eBay based on user profiles.

Three main sources of AI Personal Assistant value add:

  • A portal to the “outside” world; E.g. If I need information, I wouldn’t “surf the web” I would ask Cortana to go “Research” XYZ;   in the Business Intelligence / data warehousing space, a business analyst may need to run a few queries in order to get the information they wanted.  In the same token, Microsoft Cortana may come back to you several times to ask “for your guidance”
  • An abstraction layer between the user and their apps;  The user need not ‘lift a finger’ to any app outside the Personal Assistant with noted exceptions like playing a game for you.
  • User Profiles derived from the first two points; I.e. data collection on everything from spending habits, or other day to day  rituals.

Proactive and chatty assistants may win the “Assistant of Choice” on all platforms.  Being proactive means collecting data more often then when it’s just you asking questions ADHOC.  Proactive AI Personal Assistants that are Geo Aware may may make “timely appropriate interruptions”(notifications) that may be based on time and location.  E.g. “Don’t forget milk” says Siri,  as your passing the grocery store.  Around the time I leave work Google maps tells me if I have traffic and my ETA.

It’s possible for the [non-native] AI Personal Assistant to become the ‘abstract’ layer on top of ANY mobile OS (iOS, Android), and is the funnel by which all actions / requests are triggered.

Microsoft Corona has an iOS app and widget, which is wrapped around the OS.  Tighter integration may be possible but not allowed by the iOS, the iPhone, and the Apple Co. Note: Google’s Allo does not provide an iOS widget at the time of this writing.

Antitrust violation by mobile smartphone maker Apple:  iOS must allow for the ‘substitution’ of a competitive AI Personal Assistant to be triggered in the same manner as the native Siri,  “press and hold home button” capability that launches the default packaged iOS assistant Siri.
Reminiscent of the Microsoft IE Browser / OS antitrust violations in the past.

Holding the iPhone Home button brings up Siri. There should be an OS setting to swap out which Assistant is to be used with the mobile OS as the default.  Today, the iPhone / iPad iOS only supports “Siri” under the Settings menu.

ANY AI Personal assistant should be allowed to replace the default OS Personal assistant from Amazon’s Alexa, Microsoft’s Cortana to any startup company with expertise and resources needed to build, and deploy a Personal Assistant solution.  Has Apple has taken steps to tightly couple Siri with it’s iOS?

AI Personal Assistant ‘Wish” list:

  • Interactive, Voice Menu Driven Dialog; The AI Personal Assistant should know what installed [mobile] apps exist, as well as their actionable, hierarchical taxonomy of feature / functions.   The Assistant should, for example, ask which application the user wants to use, and if not known by the user, the assistant should verbally / visually list the apps.  After the user selects the app, the Assistant should then provide a list of function choices for that application; e.g. “Press 1 for “Play Song”
    • The interactive voice menu should also provide a level of abstraction when available, e.g. User need not select the app, and just say “Create Reminder”.  There may be several applications on the Smartphone that do the same thing, such as Note Taking and Reminders.  In the OS Settings, under the soon to be NEW menu ‘ AI Personal Assistant’, a list of installed system applications compatible with this “AI Personal Assistant” service layer should be listed, and should be grouped by sets of categories defined by the Mobile OS.
  • Capability to interact with IoT using user defined workflows.  Hardware and software may exist in the Cloud.
  • Ever tighter integration with native as well as 3rd party apps, e.g. Google Allo and Google Keep.

Apple could already be making the changes as a natural course of their product evolution.  Even if the ‘big boys’ don’t want to stir up a hornet’s nest, all you need is VC and a few good programmers to pick a fight with Apple.

AI Personal Assistant Needs Remedial Guidance for their Users

Providing Intelligent ‘Code’ Completion

At this stage in the application platform growth and maturity of the AI Personal Assistant, there are many commands and options that common users cannot formulate due to a lack of knowledge and experience.  Using Natural Language to formulate questions has gotten better over the years, but assistance / guidance formulating the requests would maximize intent / goal accuracy.

A key usability feature for many integrated development environments (IDE) are their capability to use “Intelligent Code Completion” to guide their programmers to produce correct, functional syntax. This feature also enables the programmer to be unburdened by the need to look up syntax for each command reference, saving significant time.  As the usage of the AI Personal Assistant grows, and their capabilities along with it, the amount of commands and their parameters required to use the AI Personal Assistant will also increase.

AI Leveraging Intelligent Command Completion

For each command parameter [level\tree], a drop down list may appear giving users a set of options to select for the next parameter. A delimiter such as a period(.) indicates to the AI Parser another set of command options must be presented to the person entering the command. These options are typically in the form of drop down lists concatenated to the right of the formulated commands.  Vocally, parent / child commands and parameters may be supplied in a similar fashion.

AI Personal Assistant Language Syntax

Adding another AI parser on top of the existing syntax parser may allow commands like these to be executed:

  • Abstraction (e.g. no application specified)
    • Order.Food.Focacceria.List123
    • Order.Food.FavoriteItalianRestaurant.FavoriteLunchSpecial
  • Application Parser
    • Seamless.Order.Food.Focacceria.Large Pizza

These AI command examples uses a hierarchy of commands and parameters to perform the function. One of the above commands leverages one of my contacts, and a ‘List123’ object.  The ‘List123’ parameter may be a ‘note’ on my Smartphone that contains a list of food we would like to order. The command may place the order either through my contact’s email address, fax number, or calling the business main number and using AI Text to Speech functionality.

All personal data, such as Favorite Italian Restaurant,  and Favorite Lunch Special could be placed in the AI Personal Assistant ‘Settings’.  A group of settings may be listed as Key-Value pairs,  that may be considered short hand for conversations involving the AI Assistant.

A majority of users are most likely unsure of many of the options available within the AI Personal assistant command structure. Intelligent command [code] completion empowers users with visibility into the available commands, and parameters.

For those without a programming background, Intelligent “Command” Completion is slightly similar to the autocomplete in Google’s Search text box, predicting possible choices as the user types. In the case of the guidance provided by an AI Personal Assistant the user is guided to their desired command; however, the Google autocomplete requires some level or sense of the end result command. Intelligent code completion typically displays all possible commands in a drop down list next to the constructor period (.). In this case the user may have no knowledge of the next parameter without the drop down choice list.  An addition feature enables the AI Personal Assistant to hover over one of the commands\parameters to show a brief ‘help text’ popup.

Note, Microsoft’s Cortana AI assistant provides a text box in addition to speech input.  Adding another syntax parser could be allowed and enabled through the existing User Interface.  However, Siri seems to only have voice recognition input, and no text input.

Is Siri handling the iOS ‘Global Search’ requests ‘behind the scenes’?  If so, the textual parsing, i.e. the period(.) separator would work. Siri does provide some cursory guidance on what information the AI may be able to provide,  “Some things you can ask me:”

With only voice recognition input, use the Voice Driven Menu Navigation & Selection approach as described below.

Voice Driven, Menu Navigation and Selection

The current AI personal assistant, abstraction layer may be too abstract for some users.  The difference between these two commands:

  • Play The Rolling Stones song Sympathy for the Devil.
    • Has the benefit of natural language, and can handle simple tasks, like “Call Mom”
    • However, there may be many commands that can be performed by a multitude of installed platform applications.

Verse

  • Spotify.Song.Sympathy for the Devil
    • Enables the user to select the specific application they would like a task to be performed by.
  • Spotify Help
    • A voice driven menu will enable users to understand the capabilities of the AI Assistant.    Through the use of a voice interactive menu, users may ‘drill down’ to the action they desire to be performed. e.g. “Press # or say XYZ”
    • Optionally, the voice menu, depending upon the application, may have a customer service feature, and forward the interaction to the proper [calling or chat] queue.

Update – 9/11/16

  • I just installed Microsoft Cortana for iOS, and at a glance, the application has a leg up on the competition
    • The Help menu gives a fair number of examples by category.  Much better guidance that iOS / Siri 
    • The ability to enter\type or speak commands provides the needed flexibility for user input.
      • Some people are uncomfortable ‘talking’ to their Smartphones.  Awkward talking to a machine.
      • The ability to type in commands may alleviate voice command entry errors, speech to text translation.
      • Opportunity to expand the AI Syntax Parser to include ‘programmatic’ type commands allows the user a more granular command set,  e.g. “Intelligent Command Completion”.  As the capabilities of the platform grow, it will be a challenge to interface and maximize AI Personal Assistant capabilities.

AI Assistant Summarizing Email Threads and Complex Documents

“Give me the 50k foot level on that topic.”
“Just give us the cliff notes.”
“Please give me the bird’s eye view.”

AI Email Thread Abstraction and Summarization

A daunting, and highly public email has landed in your lap..top to respond.  The email thread goes between over a dozen people all across the globe.  All of the people on the TO list, and some on the CC list, have expressed their points about … something.  There are junior technical and very senior business staff on the email.  I’ll need to understand the email thread content from the perspective of each person that replied to the thread.  That may involve sifting through each of the emails on the thread.  Even though the people on the emails are English fluent, their response styles may be different based on culture, or seniority of staff (e.g. abstractly written).  Also, the technical folks might want to keep the conversation of the email granular and succinct.
Let’s throw a bit of [AI] automation at this problem.
Another step in our AI personal assistant evolution, email thread aggregation and summarization utilizing cognitive APIs | tools such as what IBM Watson has implemented with their Language APIs.  Based on the documentation provided by their APIs, the above challenges can be resolved for the reader.   A suggestion to an IBM partner for the Watson Cognitive cloud, build an ’email plugin’ if the email product exposes their solution to customization.
A plugin built on top of an email application, flexible enough to allow customization, may be a candidate for Email Thread aggregation and summarization.  Email clients may include IBM Notes, Gmail, (Apple) Mail, Microsoft Outlook, Yahoo! Mail, and OpenText FirstClass.
Add this capability to the job description of AI assistants, such as Cortana, Echo, Siri, and Google Now.   In fact, this plug-in may not need the connectivity and usage of an AI assistant, just the email plug-in interacting with a suite of cognitive cloud API calls.

AI Document Abstraction and Summarization

A plug in may also be created for word processors such as Microsoft Word.   Once activated within a document, a summary page may be created and prefixed to the existing document. There are several use cases, such as a synopsis of the document.
With minimal effort from human input, marking up the content, we would still be able to derive the  contextual metadata, and leverage it to create new sentences, paragraphs of sentences.
Update:
I’ve not seen an AI Outlook integration in the list of MS Outlook Add-ins that would bring this functionality to users.

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.

The Race Is On to Control Artificial Intelligence, and Tech’s Future

Amazon, Google, IBM and Microsoft are using high salaries and games pitting humans against computers to try to claim the standard on which all companies will build their A.I. technology.

In this fight — no doubt in its early stages — the big tech companies are engaged in tit-for-tat publicity stunts, circling the same start-ups that could provide the technology pieces they are missing and, perhaps most important, trying to hire the same brains.

For years, tech companies have used man-versus-machine competitions to show they are making progress on A.I. In 1997, an IBM computer beat the chess champion Garry Kasparov. Five years ago, IBM went even further when its Watson system won a three-day match on the television trivia show “Jeopardy!” Today, Watson is the centerpiece of IBM’s A.I. efforts.

Today, only about 1 percent of all software apps have A.I. features, IDC estimates. By 2018, IDC predicts, at least 50 percent of developers will include A.I. features in what they create.

Source: The Race Is On to Control Artificial Intelligence, and Tech’s Future – The New York Times

The next “tit-for-tat” publicity stunt should most definitely be a battle with robots, exactly like BattleBots, except…

  1. Use A.I. to consume vast amounts of video footage from previous bot battles, while identifying key elements of bot design that gave a bot the ‘upper hand’.  From a human cognition perspective, this exercise may be subjective. The BattleBot scoring process can play a factor in 1) conceiving designs, and 2) defining ‘rules’ of engagement.
  2. Use A.I. to produce BattleBot designs for humans to assemble.
  3. Autonomous battles, bot on bot, based on Artificial Intelligence battle ‘rules’ acquired from the input and analysis of video footage.

As a Data Deluge Grows, Companies Rethink Storage

At Pure Storage, a device introduced on Monday holds five times as much data as a conventional unit.

  • IBM estimates that by 2020 we will have 44 zettabytes — the thousandfold number next up from exabytes — generated by all those devices. It is so much information that Big Blue is staking its future on so-called machine learning and artificial intelligence, two kinds of pattern-finding software built to cope with all that information.
  • Pure Storage chief executive, Scott Dietzen, “No one can look at all their data anymore; they need algorithms just to decide what to look at,”

Source: As a Data Deluge Grows, Companies Rethink Storage – The New York Times

Additional Editorial:

Pure Storage is looking to “compress” the amount of data that can be stored in a Storage Array using Flash Memory, “Flashblade”.   They are also tuning the capabilities of the solution for higher I/O throughput, and optimized, addressable storage.

Several companies with large and growing storage footprints have already begin to customize their storage solutions to accommodate the void in this space.

Building more storage arrays is a temporary measure while the masses of people, or fleets of cars turn on their IoT enabled devices.

Data is flooding the Internet, and innumerable, duplicate ‘objects’  of information, requiring redundant storage, are prevalent conditions. A registry, or public ‘records’ may be maintained.   Based on security measures, and the public’s appetite determine what “information objects” may be centrally located.  As intermediaries, registrars may build open source repositories, as an example, using Google Drive, or Microsoft Azure based on the data types of ‘Information Objects”

  • Information object registrars may contain all different types of objects, which indicate where data resides on the Internet.
    • vaguely similar to Domain name registrar hierarchy
    • another example, Domain Name System (DNS) is the best example of the registration process I am suggesting to clone and leverage for all types of data ranging from entertainment to medical records.
  • Medical “Records”, or Medical “Information Objects”
    • X-ray images, everything from dental to medical, and correlating to other medical information object(s),
  • Official ‘Education’ records from K-12 and beyond, e.g. degrees and certifications achieved;
  • Secure, easy access to ‘public’ ‘information objects’ by the owner, and creator.  Central portal(s) driving user traffic.  Enables ‘owner’ of records to take ‘ownership’ of their health, for example

Note: there are already ‘open’ platforms being developed and used for several industries including medical; with limed access.  However, the changes I’m proposing imposes a ‘registrar’ process whereby portals of information are registered, and are interwoven, linking to one another.

It’s an issue of excess weight upon the “Internet”, and not just the ‘weight’ of unnecessary storage, the throughput within a weaved set of networks as well.

Think of it in terms of opportunity cost.  First quantify what an ‘information object’, or ‘block of data’ equates to in cost.  It seems there must already be a measurement in existence, a medium amount to charge / cost per “information object”.  Finally, for each information object type, e.g. song, movie, news story, technical specifications, etc. identify how many times this exact object is perpetuated in the Internet.

Steps on reducing  data waste:

  • Without exception, each ‘information object’ contains an (XML) meta data file.
  • Each of the attributes describing information objects are built out as these assets are being used; e.g. proactive autopopulate search, and using an AI Induction engine
  • X out of Y metadata type and values are equivalent
    • the more attributes correlate to one or more objects, the more likely these objects are
      • related on some level, e.g. sibling, cousin
      • or identical objects, and may need meta relationship update
    • the metadata encapsulates the ‘information object’

Another opportunity to organize “Information Asset Objects” would be to leverage the existing DNS platform for managing “Information Asset Repositories”.   This additional Internet DNS structure would enable queries across information asset repositories.   Please see “So Much Streaming Music, Just Not in One Place”  for more details.

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!