Tag Archives: AI

Alzheimer’s Inflicted: Technology to Help Remember Habitual Activities  

Anyone ever walk into a room and forget why on Earth you were there?  Were you about to get a cup of coffee, or get your car keys?  Wonderful!  It’s frustrating on my level of distraction, now magnify that to the Nth degree, Alzheimer’s.  Apply a rules and Induction engine, and poof!  A step further away from a managed care facility.

Teaching the AI Induction and rules engine may require the help of your 10 year old grandson.  Relatively easy,  you might need your grandson to sleep over for a day or two.

It’s all about variations of the same theme, tag a location, a room in an apartment, also action tag, such as getting a cup of coffee from the kitchen.  The repetitive nature of the activities with a location tag draws conclusions based on historical behavior.  The more variations of action and coinciding location tags, will begin to become ‘smarter’ about your habitual activities.  In addition, the calculations create a bell curve, a way to prioritize the most probable Location/Action tags used for the suggested behavior.    The ‘outliers’ on the bell curve will have the lowest probability of occurrence.

In addition, RFID tags installed in your apartment will increase the effectiveness of the ‘advice’ engine by adding more granular location tags.

Microchip RFID compared to the size of a grain of rice.
Beyond this ‘black box’ small, lightweight computer (smartphone) integrate a Bluetooth, NFC, WiFi antenna, a mobile application and you’re set.  A small, high quality Bluetooth microphone to interact with the app.  There’s also potential for exploring beyond the home.

Kidding, you don’t need that Grandson to help.  Speak into the mic, “Train” go into the room and say your activity, coffee.  This app will correlate your location, and action.  Everyone loves to be included in the Internet of Things, so app features like alerts for deviation from the location ‘map’ are possible.

In earnest, I am mostly certain that this type of solution exists.  Barriers to adoption could be computer/ smartphone generational gap.  Otherwise, someone is already producing the solution, and I just wasted a bus ride home.

Additionally, this software may be integrated with Apple’s Siri, Google Now,  Yahoo Index, Microsoft Cortana,  an extension of the Personal Assistant.

Google Acquisition of Machine Learning Co. has a big impact on your future

Machine learning or AI induction,  proactively learns by correlating data points, and then makes a proactive decision.  Typically, the AI engine needs the data or in this case, web sites, blogs, etc. to have consistent meta data, information that describes the information.  The data is collected & processed. 

Instead of an enforced meta schema across the internet, which is difficult, needs to be enforced by browsers, and a standards body with a large set of Internet stakeholders needs to decide and implement it, e.g. NewsML-G2.

This technology seems to be able to collect Internet assets, parse them, create meta data on the fly, then, where possible, correlate data points, and in the exact format the AI engine needs.

This tech may be used for anything, and I mean, anything or anyone.   A machine learning engine can be fed any subject matter, a database of images, audio, or text from Google Plus Posts, Profiles, Android objects or any Google product,  and then once a schema is in place for the meta data, the process above begins. This AI enging processing is ongoing to keep refining the predictiveness of the AI engine. The process of Induction needs a large data set to be more accurate, or else the AI engine projections may include outlier behaviors. The induction engine needs to be able to filter out the outliers, and use what is within the bell curve of behaviors, thus eliminating false positive trends. Google wants to, at a minimum, project predictes trends, output in Google Plus.

Google may also skew the data by purposely picking items within the bell, but not on top of the bell, the most common range, to project what they want as the trends. E.g. for advertising.

It can even be applied to computer recognized objects in images, perhaps you see a friend once a week, every week on Thursday at or around 3. If you use Google Glass and forget to see someone, your Android might ask you are you going to see Sally today, it is not in your calendar, and she is not in your proximity when you ‘normally’ see that person.

Another case is when images are posted to Google via Glass, once the user publishes the post, AI could analyze clothing, or jewelry objects it ‘sees’, perform induction on every object in Google Plus public or private photos, and predict fashion trends.

Google has a privacy policy that may abstract the user specific data, and is able to then classify users into groups or types of people, then they are able to proactively publish trends before they occur, or are noticed by the human mind. Trends may also be geo specific, which don’t seem to appear yet in G+.


Samsung and Cambridge to Produce Interactive Avatar

BBC News – Is this interactive avatar the face of the future?.

I read this article, and instantly saw a logical progression of taking the eye & facial tracking software, such as built in Samsung S4, and integrating that feature with a cost effective version of the Cambridge project.  There are many applications:

  • The S Voice Drive, or another voice recognition component driving smartphone features may display, instead of the typical microphone, a ‘friendly’ avatar, such as one of several choices, e.g. a famous star, a comedian  an actress, or sports athlete.   Then the eye and facial tracking software may ask you what you want smartphone functions you want to perform.
  • An AI induction engine, i.e. an learning rules engine, may record your facial gestures, eye movements, as well as sounds, even inflection, as data points to correlate, so now the responses can be proactive, not reactive, e.g. the avatar would say, “Should I call your wife?   You seem tense, and you may want to call her to relax you.”
  • This is a slippery slope with respect to an AI providing advice on how to react to human output, such as eye movements and facial gestures.  It seems people are, at present, more comfortable with integrating mechanical AI induction engines, such as an eye movement to turn a page, read mail or make a phone call.  These very mechanical processes and allow people to feel more comfortable with the technology.

Editorial: 4D printed objects that make themselves

BBC News – TED 2013: 4D printed objects make themselves.

A simple rules engine that has the ability to create itself, is a slippery slope, perhaps, one of dramatic proportions such as “4D” printing. The concepts are clear from the article, as well as the experiments at MIT.

The value of combining a rules engine to printed or created object, that can transform itself, or evolve has vast applications, such as the simple scope, as defined in the article, to the more dramatic concepts we can only imagine in what was once Science Fiction.  The applications vary from the horrific to the wondrous.  I will leave it to the readers imagination.  Although there are significant revenue opportunities in 4D printed objects, objects that make themselves, there may be a question of ethics, and governance, now that the genie is out of the bottle.

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.

Companies’ Fiber Infra Perform Quantum Computing Prior to Cloud, Elastic Computing Hub

A Qubit, a base unit in Quantum computing, representing several states simultaneously, as opposed to a bit, on or off, two states, got me thinking based on the New York Times article on the Australians developing a new class of Quantum Computer, plus the article on Google’s Fiber Project, and my conversation with a Verizon FiOS engineer regarding their experimentation with alternate [color] light and their fiber applications got me thinking:
– The delivery system itself would process data, in addition to the EC hub, similar to the human nervous system.
– The alternating colors / frequencies represent multiple ‘applications’ overlapped within the fiber, and could provide the ability to throttle frequencies?

Note: However, Optical cables transfer data at the speed of light in glass (slower than vacuum). This is typically around 180,000 to 200,000 km/s, resulting in 5.0 to 5.5 microseconds of latency per km.

Cable Companies’ Fiber Gives an Expanded Scope to Cloud, Elastic Computing.  Leased lines may form a whole new battleground of opportunity, or at least reignite the battle for those familiar with the break up of the “Baby Bells”.


Facebook Gifts Modified: ‘Like’ a Pic with caption ‘Nice Dress’, AI suggests, Buy Now, and presents vendors.

If Facebook uses facial recognition, why not expand to cover vendor / partner library catalogs, use the AI Image Recognition to identify objects, and ‘read’ and recognize simple phrases from the captions or comments of pictures.  If the caption says ‘nice dress’, you can use the AI image recognition rules engine to suggest N list of vendors, local and web, with the lowest price, and ‘Buy Now’ if you ‘Like’ the picture.