Tag Archives: Programming

Google Plus…Beta in Progress

Last night I was driving my daughter and her guy friend home from an after school program, and I really like this kid, he’s a little tech geek like me.  I spoke to this computer savvy twelve year old kid, and was asking him all these questions from a kid’s perspective about all these new technologies.  He was articulating and rattling off thought provoking and meaningful information.  It was like I was talking to an industry analyst, bright fun, good kid.  He and I talked non-stop, and after we dropped him off, and I realized we monopolized the conversation, and my daughter might have wanted to get in a word, so I apologized.  Forgot what it was like to be teen, a girl no less.

Anyway, in the mist of our discussion the kid said he uses a gmail account instead of his default ISP.  I asked him about what he thought of Google Plus.  He said he did some exploring of it. “Yeah, Google + was created to compete with Facebook, but it’s not really that great.”   I asked him if he knew about a few features I thought were cool, and his response was “No, not really, Instagram,” he said “was ‘killin it’ though.” We went onto more market analysis of the space.  I was amazed.  Kids.

It was then I realized why the kid didn’t get past the first page.  Appeal and usability.  These are concepts in User Interface design and are essential in attracting users. These types of features are usually added later on.  The standard technology mantra, is “Make it work, then make it work [faster, refine UI, etc].  If I was trying to really be unbiased, Google Plus is tantamount to a Beta product.  As an example, the “Your Circle” buttons truncate the Circle name, are square shaped, and don’t have an appeal.  In fact, many of the user interface features feel canned.  The user interface is not the focus initially when putting out a product, especially when you are in a rapid mode of delivering, and are certain your product may change drastically, i.e. based on your road map, user feedback, and so on.  Although I really like the baseline platform, and am trying not to be, I am a bit biased in favor of Google.  Google Plus looks like they are using the Agile methodology with Scrum Sprints and constant releases. To be clear, I am using their own, Google’s latest browser Chrome, on Windows 7 with a powerful computer.

So, what does this teach us?  Well, in Project Management, sometimes you can add all the resources in the world to a project, but at some point you get diminishing returns, and there is a limit to delivery capability even using Agile and Scrum methodologies, especially if Social Networking is high on Google’s agenda.  Agile requires user feedback, hence the release, and user response cyclical feedback loop.

Mobile Memory Management Advantage: Multithreading in Android verse Apple iOS

I walked into an electronics store today in Athens where they proudly displayed the latest Mobile technology like the iPads (iOS), Android and Windows OS tablets, and as you enter the store, I believe roughly in that order. However they had iOS in front of the store, but MSFT translation terminals for inventory, transaction execution, etc., which is interesting politics, in itself.

Anyway, as I was leaving the store, collecting my US power converter, I noticed something I’ve seen dozens of times before especially in previous iOS versions, but the latest iOS still has its multithreading implementation with applications exiting to the main menu, but not quitting, therefore leaving a memory footprint, and the application at the very least, is in a low memory, idle CPU, and at the most, a potential for issues with consumption of the resources mentioned.  I’d suspect the application is still running in memory for notifications.

To the contrary, in the Android OS, you will typically have a quit application menu option; however, I don’t remember how they perform the notification process, think it runs as a separate thread, as a service, not Y application, which one may argue may take up more memory.  However not all applications are designed to provide notifications to the user, therefore not a one to one correlation of Android application to a service.  Apple iOS forces a memory footprint regardless. Inefficient resource management on a relatively resource constrained device.

Twitter Client to Sort by Score and Time: Floating Your Best Tweets to the Top

I seem to have used a tool to add Twitter users with criteria tweets.  I now have to go back and read through all the tweets and deside which users to keep and which to ditch.

Once that is done, there are still some Twitter users I value more then others, as they provide ‘better’ tweets with information such that I consistently click on their links and want to know their information first.

It would be ideal to have a Twitter client that allows the sorting of tweets by score, then by time, adding a metadata score value to each Twitter user followed.  I click the user’s tweet link their score goes up by X amount.  If I favorite their tweet, the user’s score goes up by Y amount, and if I re-tweets another user, the followed user’s score goes up by Z amount.  X, Y, Z are configurable.  If this tool exists, please give me a shout.

Disseminate from the noise of social media.

Today, an AI Application Can Take Action on Your Behalf: Good or Bad?

A neat idea, is if you give permission to a user for a specific action, an AI voice/text recognition programs could:

  • If your tagged ‘best friend’ texts you, let’s go out tonight, and your work calendar setup for parameters 8 AM to 7 PM, and both you and your ‘best friend’ are tagged as liking a restaurant, or have a frequent geography tag to a specific location, given permission to a calendar, AI may automatically call ahead a restaurant, and book a reservation, or email the resturant with the reservation.  Given permission, the AI, would also provide a preferred credit card to hold the reservation.
  • If you give the voice/text AI recognition program a spending limit, and you have a profile of a person on file, it’s their birthday, the AI program can automatically purchase something for the person, e.g. an e-card from a ‘preferred store’, or ‘reading’ the person’s profile, may dynamically send a gift using their preferences, using your preferred credit card on file, and get it to the person on time.
  • Based on your current Geo-tagged location, AI can suggest you attend a bar/restaurant based on you’re bosses, or on your pursued romantic interlude checkins; to increases your odds of promotion or romantic interlude, or both.

The ideas are endless what AI can do, the technology is out there, the rules, APIs, and applications are to be built on top of the AI APIs.  We live in an incredible, yet scary time. It’s important to set parameters, but one rule, one order of precedence, one parameter can make or break careers, relationships, and so on.  Tread carefully.  We are only human.

Artificial Intelligence: Tuning with a Content Index And Predictive Models

As I was reading an All Things Digital article, Artificial-Intelligence Professor Makes a Search App to Outsmart Siri, there was a statement that made:

“We memorize the dictionary to read the Library of Congress,” he said. “Siri is trying to memorize the Library of Congress.”

 

A tool more commonly used in the past, in books, at the end of a book, an index of where the words appeared in the book was noted with page numbers.  Classic rules engines is ‘data in a black box’, searchable within the context they appear. The more put into the black box, users can search on ‘rules’ or content, and in precedence, an action occurs, or the content that is searched appears.  If there are cross-references with an associated category or tag with ‘each line of data’ or ‘rule’ that will enable the Artificial Intelligence engine to be more efficient.  Therefore the correlations of ‘data to other data’ with similar or like tags enables an Artificial Intelligence will be more intelligent.  In theory, categorized or tagged content indexed to references of the data points should fine tune the engine.

An addition theory, allows for predictive models to produce refined searches, or rules.  You can make a predictive model, where the intelligence of the user actually refines the engine. The user can ask a question, and as they refine their question, a predictive model,  may allow for refined user output.  If a user is allowed to participate and tag data search output, the search output could be more granular, like a refined Business Intelligence drill down.  The output of a search, for example, can contain a title, brief summary, and tags that can be added or removed (by the user), which allows for a more robust search, and predictive model; however, you are relying on the user to a) not be malicious, and b) have understanding of what information he is search for within the data.  If web crawlers, or if the webmaster submits URLs with tags, the meta data tags of the page, the black box or Artificial Intelligence rules engine will, if properly submitted, or indexed, correlate the data.  To most people, this is AI or Search Engine 101.  Some people cheat, and add pages with false meta data tags because they want their site to appear in a higher order, or precedence and they may make more revenue with advertising dollars.

There are multiple ways around trying to cheat an Artificial Intelligence Content Index:

  • Hit Ratio: People searching on the same question over and over increase the ‘score’ ratio, thus pushing the false results downward on the list, or removing them entirely.
  • Enlist ‘quality’ users, who are known quantities, such as like Twitter ‘certifies’ certain users.  You may apply for ‘relatively’ unbiased, certification status, such as people who have reputations and certifications in the field, are qualified to ‘enhance’ tags, and improve upon your result outputs. e.g Professors, Statisticians,
  • Enlist users who will actually derive revenue, if their ‘hit ratio’ score delta increases exponentially some N number.  These tags are classified as unverified, however, the people are monetarily motivated to increase peoples’ probably of success to find what people are looking for when other users search the tags become qualified as the results of the tags attract users to their content.  If they are using, let’s say, a browser, which the search engine company owns, such as Chrome, a little plug in can appear and say, was this what you were looking to find, yes or no.

Categorizing and Prioritizing Notifications : Get the Noise Out of Google Plus and Twitter

I really like the spinning red wheel, especially when it gets to 99+, but when you select ‘View Notifications’, you get this droned list of notifications, there has to be some way, in this case to accentuate some of the notifications, maybe +1, are another color than some you’ve commented on.  There is so much information, that  a classification, or a taxonomy would be helpful.  Even some of the comments I’ve put on someone’s Post, I am not interested in being notified if someone adds additional comments, so maybe, a ‘don’t notify’ on a particular post, does that exist, in truth, haven’t checked.  Anyway, it seems like all tweet notifications, or DMs can be easily differentiated, but all tweet notifications as well as Google Plus, or FB notifications seem to be treated equal, it’s NOISE, and we will drown in notifications and start to ignore them, and let some of the good ones pass us by.  I would say an API that allows you to filter notifications by key words, and then that central program can handle your notifications.  If there is an API for notifications, which we know Twitter has, not sure about Google Plus, but there has got to be a 3rd party tool already out there that does this  Simple rules engine. Get the noise out of your notifications and filter and/or prioritize the tweets and Google Plus notifications to want to see first.  It looks like Google Plus did some color alternations between alternate types of notifications, but its not as intuitive as it could be.

I would also like to see the Android Operating System and the Apple iOS expand out their icons to include variation of the application notification.  I don’t think this is available with the 4.01 Android OS, variants of the tweet icon, e.g., still awaiting my update from Samsung for my S3. 🙁