Heatseeker

Video above: Here you'll see the neurons self-organize to dynamically solve exponentially hard cluster optimization problems with dynamic time-evolving variables, providing immediate structure to unstructured big data. Heatseeker connectionless neurons feature self-programmed interactions with instantaneous machine learning without training, and data is self-encoding.

As CogWorld's evolves into a knowledge-rich hub, and our AI ecosystem contributes extensive knowledge, content and more, Heatseeker will enable self-organized optimization of content and engagement. Right knowledge; right time; right place.. CogWorld helps to optimize today's enterprises, and Heatseeker simplifies big data analytics.


A peek into Heatseeker

The video above demonstrates one instance of this novel self-organizing AI. Explanation coming soon.

1. Connectionless architecture with self-encoded data

2. ANN self-organization into evolving deep networks

3. Each neuron is capable of real-time instantaneous unsupervised learning

4. Each neuron is capable of creating long-term memories 


Click to expand slides below



 

Dynamic Self-Organized Structure from Unstructured Big Data

Unified Intelligence Heatseeker is a connectionless unsupervised deep learning system composed of intelligent self-organizing neurons, each capable of learning complete memories persistently in real-time. 

Video on left: Here you see the neurons self-organize to dynamically solve exponentially hard cluster optimization problems with dynamic time-evolving variables, providing immediate structure to unstructured big data. Heatseeker connectionless neurons feature self-programmed interactions with instantaneous machine learning without training, and data is self-encoding.

As CogWorld's evolves into a knowledge-rich hub, and our AI ecosystem contributes extensive knowledge, content and more, Heatseeker will enable self-organized optimization of content and engagement. Right knowledge; right time; right place.. CogWorld helps to optimize today's enterprises, and Heatseeker simplifies big data analytics.


Machine Learning (ML) algorithms, all current connectionist models of knowledge representation, such as current Artificial Neural Networks (ANN's), whose algorithms have not changed fundamentally in decades. These are brittle systems - IBM's Watson and hybrids using Monte Carlo brute force, and the technology hyped by Google's AlphaGo team at the Alphabet corporation. All non-scalable narrow systems no different than those we pioneered thirty years ago for the DoD. The difference now is the application processing speed. All could be accomplished with digital Matched filter technologies. The only difference between a Matched filter and the current AI applications is you trade off one form of programming for another. We were faced with this question decades ago while working with GTE (now Verizon). When faced with this question by senior mathematicians and engineers we tasked a group of several scientists to answer this question with hard facts and the answer was the above statement and it's still true today.

We've studied the problem of machine intelligence for decades and spent a couple decades developing systems to test out different hypotheses in fields ranging from fusion physics to smart molecules, which use molecular recognition to control their own therapeutic biological activity through molecular inter/intra particle supramolecular solicitations. 

Heatseeker Unified Intelligence (UI) Algorithm Triad

First it is important to understand that this is not conjecture. The Heatseeker algorithm has been implemented and refined for two decades. You can only refine something by putting it into practice. Therefore the Heatseeker was applied to orthogonal applications. The final Heatseeker implementation has the following characteristics, i.e. the UI triad.

1.  Efficient, nonlinear and dynamic solution to NP-Complete optimization problems in four dimensions (three degrees of freedom plus time). This is independent of computer processing speed. The NP-Complete solution generator functions nonlinearly to provide hyper-linear solution convergence. Hyper-linear convergence refers to NP solution convergence that is independent of NP-Complete optimization problem complexity.

2.  Connectionless machine learning where the current archaic and wrong connectionist models of thought are dispensed with completely. The human brain only appears to be a connectionist system. When viewed in the genetic lens of billions of years it is dynamic and connectionless. Connectionist models slow down machine learning convergence with their relatively static structures. Connectionless neurons have only temporal receiver based virtual interactions in uniform signaling space, i.e. no hard wires.

3.  Self-Programming and encoding neurons that learn instantaneously, with absolutely no training and where each neuron capable of learning complete memories. The idea of neurons as simple connectionist processing elements is fundamentally incorrect. Self-programming and encoding are neurons that do not require data encoding to process information and re-write their interactions as independent semi-autonomous agents.

Heatseeker Unified Intelligence (UI) NP-Complete n-Partition Problem Example

All AI problems are reducible to NP-Complete optimization problems, which the UI Heatseeker solves geometrically in four dimensions (three degrees of freedom plus time). NP-Complete problems are mathematically equivalent and translatable into one another. NP-Complete problems are challenging to solve even by approximate methods. Here the UI Heatseeker solves an n-partition problem, which is strongly NP-Complete.

The n-partition problem is a generalization of the NP-Complete 3-partition problem, applicable to solving all problems where optimal clustering and organization is required. We start with a discussion of the 3-partition problem. The 3-partition problem is to decide whether a given multiset of integers can be partitioned into triples that all have the same sum. More precisely, given a multiset S of n = 3 m positive integers, can S be partitioned into m triplets S1, S2, …, Sm such that the sum of the numbers in each subset is equal. The subsets S1, S2, …, Sm must form a partition of S in the sense that they are disjoint and they cover S. Let B denote the (desired) sum of each subset S of i, or equivalently, let the total sum of the numbers in S be m B. The 3-partition problem remains NP-complete when every integer in S is strictly between B/4 and B/2. In this case, each subset S of i is forced to consist of exactly three elements (a triple).

The n-partition problem is the generalized analog of the 3-partition in which the goal is to partition a given set S into n-tuples all with the same sum: precisely, the difference is that S now consists of n = N m integers, each held strictly between B/N and B/N-2.

The above is a screen captured video of Heatseeker dynamically solving the n-partition problem where the value of n that solves the problem is discovered dynamically as the variables change in real-time. The elements in the video are Heatseeker connectionless neurons, which are self-programming to discover a solution to the problem as the given multiset S of integers that define the problem are randomly changed in real-time where the goal is to partition a given set S into n-tuples all with the same sum. The clusters of neurons are the n-partition solutions to this highly challenging time varying problem.

The Heatseeker neurons exist in four dimensions composed of a three dimensional coordinate and a length varying parameter, which is their time coordinate. When the neurons elongate (more specifically jump to a distant spatial coordinate in 3D space) they are breaking the symmetry of a local minima (n-partition false solution) to derive the global solution to the current n-partition problem, which rapidly changes over time.

The neurons never stop moving because the n-partition problem is constantly changing. The Heatseeker neurons all exist in a connectionless environment where they each must individually choose what other neurons to communicate with to solve the problem. Each of the Heatseeker neurons is a real-time machine learning element requiring no training. Note each Heatseeker neuron controls its own real-time learning, when and how it does.


Heatseeker

Contact:

Lisa Wood
lisa@cognitiveworld.com
206 452 3054