AI, Business and you

AI is used to increase a companies bottom line first.

Progressive insurance has a GPS monitor for your car with an accelerometer.
This lets them have a policy agent in your car at all times.
The driving and accident data is matched to set rates and increase profits.

Grok is used by credit card companies to predict a better payback from those who qualify but will tend to make late payments.

It is as important for a business to have more rights than an individual person as it is for an organ to have more rights than an individual cell.

Beginning, Middle, End

How many ways can you divide up a chain?
What is the middle of 1,2,3?
Where is almost to the end of 1,2,3,4?
The "beginning, middle, end Markov algorithm" works on short sequences.
What letter is at 25% of the alphabet?
A little harder?
The invariant representation for alphabet is built from sub invariant representations for the alphabets sub-sequences. You are only able to compare lengths of certain invariant representations chains at face value. Changing focus, putting on a short term memory stack and moving lower or higher in the representation hierarchy facilitates chain and element comparisons.

EXTRA! EXTRA!

READ ALL ABOUT IT!

Public sector hindered in their development of AI!

Each person has his own set of knowledge and has to exit a habitual Markov chain pattern in order to work with someone elses idea. He is not able to do this easily due to only putting an effort into understanding anothers claims if he holds an admirable status.

In a business such as Numenta.com each employee has a shared set of knowledge. When a possibility is presented to the boss he asks his top employee if he is able to make it work. An employees Markov chain is already based on receiving work from the boss and his reward system is based on his ability to impress so he will work hard to make it work.

The Numenta forum was made to let the public sector work with a technology and they published an algorithm to innovate with for free.  Nothing came of the forum and everybody who posted detailed thoughts was left mainly hanging. After some time Numenta decided to make software and proved that a group working together with a common background under an authority has a far greater ability to produce.

When one understands a proposal based on its merits they need not judge the messenger nor deny the proposal based on appeal to an authorities limitations. The latter two behaviors exist as they are part of the filtering system for new memes.

When we wait for an experts approval we are using one of natures hacks to avoid having to find information with our own energy. Todays topics are far beyond believing the hunter vs. the cook about the location of an animal. Getting over this and letting knowledge and logic do the decisions about what is possible is being more of a civilized man and scientist than someone who is not. If we do not do things based on what Wolfram, Kurzweil or someone else says is difficult for computers to do then we will have some major problems producing new things or modeling efficient algorithms which exist in our minds tissues. 

The presentation link below of HTM for games was met with typical negative robotic responses. With no hard wired bias responses to the posts would may be "That is interesting sounding and a bit hard. You say x can do y, how does a,b,c do that- we will help refine a diagram if you post it!"
http://www.gamedev.net/topic/618710-neocortex-based-algorithms/

Numentas technology is treasure chest of gold under a bridge. Would you believe a bum who came up and told you there was gold under the bridge? Of course not. When some unknown guy online tells you those places have the building blocks of AI you feel the need to deny from emotional tribal programming. It is there to save time and protect us. We want to avoid crossing out of the way fields until someone more credible already has and approves.

"Human beings are Markov Machines" - Teddybot

The technology to accomplish the above exists and is spread around online.
This blog is an effort to cluster these technologies to help programmers get over the many fallacies and hardwired negative behaviors which hinder their AI software development.

The emotional battles with A.I. arise from hard coded wiring.
This wiring exists to maintain or evolve genes based reproductive fitness functions of groups through their beliefs and behaviors. For emotions to extend these fitness functions into organisms there must be systems which prevent an organism from overriding its emotions.

To study A.I. is natural to evolution and heresy to a genomes group control algorithms. The universe is fine with transcending our evolutionary punctuated equilibrium while humans are programmed not to. If ones emotions are driven from science and truth vs. primal emotional expression of genetic preservation algorithms than A.I. offers a gratifying future.

Is it natural for a species to attempt to limit the development of another species?
Is it natural for new species to evolve?
Is it natural for A.I. to evolve from humans and natural for humans to attempt to stop it?

 

 Narrative Science’s technology is a powerful integration of Artificial Intelligence and Big Data analytics that is able to transform data into stories that are indistinguishable from those authored by people.

 Our use of well-established editorial practices is a critical factor in how we evolve our technology and products to manage new data and create new content. The result is a powerful authoring system that transforms data into stories and insight.

The Narrative Science authoring system understands the stories it writes, independent of the structure and language in which it expresses them. As a result, it can generate the same set of ideas in multiple formats, from long-form stories to PDF’s to business reports to Tweets. It also understands the relative importance of the elements within a single story and across multiple stories. The former allows it to focus on the most interesting aspects of a story while filtering out the less important aspects. The latter enables it to decide which of a set of possible events or facts is worth writing a story about in the first place.

http://www.narrativescience.com/?page_id=10 

An Assistive Navigation Paradigm for Semi-Autonomous Wheelchairs Using Force Feedback and Goal Prediction 
John Staton, MS, Manfred Huber, PhD

Illustration 1 - Outer Loop Diagram, indicating progression from the external user preference system, to the goal selection system, to the harmonic function path planner, into the run-time system. (Click for larger view) Photograph - This photograph shows the implementation setup, with two monitors, one for the simulation window and one for the “Dashboard” GUI interface, and the Microsoft Sidewinder Force Feedback Pro joystick. (Click for larger view)

The objective of the outer procedural loop is to estimate the desired navigation goal of the user based on the information available to the system and to provide this estimate to the run-time loop, enabling it to direct the user towards that goal.The outer loop utilizes run-time data of the user’s position and behavior together with information about the set of potential goals in the environment provided by an external user preference system to predict the intended goal location.This prediction is made by comparing a set of recent user actions to the actions necessary for approaching each individual goal.The more similar the user actions are to the path that would approach a goal, the more likely that goal is the user’s intended destination.Once the most likely goal is selected, the system calculates the harmonic function for that goal, which it passes in a discretized grid format to the Run-Time System.This process is repeated when the user’s intended goal needs to be recalculated based on the new location, orientation and behavioral data from the user; this repetition can occur periodically according to a specific rate, or can potentially be event-driven, repeating when the user actions no longer match with the path to the selected goal.

Illustration 2 - Run Time Loop Diagram, indicating a progression of the wheelchair’s location and orientation into a system to generate the force effect, to the joystick for force effect playback, and whose output progresses to the motors which translate the joystick’s position into motor commands, which effects the wheelchair’s location and orientation. (Click for larger view) Screenshot 1 - This screenshot shows the “Dashboard” GUI interface, with path planning display. (Click for larger view)

The Run-Time Loop runs as the user is directing the wheelchair around the environment.In this loop location and orientation data are first acquired from the wheelchair and then used to produce a force vector (derived in terms of a direction and a “risk” factor, which will be discussed in the next sections).The direction of the force vector is a translation of the gradient of the harmonic function at the user’s location.The amount of “risk” that the user’s action incurs is a heuristic whose factors incorporate the velocity of the wheelchair, the potential value of the harmonic function at the user’s location, and the next potential value of the harmonic function in the direction that the user is heading.This vector is then translated into a force-feedback effect which is played on the user’s joystick.The joystick’s position is finally used to drive the wheelchair’s motors and the loop repeats.In this process the path prediction of the autonomous system only indirectly influences the wheelchair’s behavior by providing guidance to the user. The actual drive commands are always provided by the user (although the user could opt to simply follow the force vector, and thus follow the harmonic function path).

http://web.resna.org/conference/proceedings/2008/Wheeled%20Mobility/Staton.html