aKin Wave 3 AI

The next wave of AI opens up new areas of capability beyond classic Generative AI
Hallucinations -> Truth

LLMs “guess” a sequence of words and are not reliable for critical tasks.aKin reaches true understanding of an environment and the people in it.

Single-turn -> Complex Goals

LLMs depend on prompts, and  can only handle single-turn input-outputsaKin enables complex  goals, in ambiguous contexts, with high compliance.

Brute Force -> Efficiency

Other GenAI startups spend a large portion of their funding on compute.aKin AI focuses on “meaning patterns”, and it uses data and compute very efficiently.

Research from the aKin Team

Generative AI Research from Prof Alan Blair Chief Science advisor
MPATH: Continuous Learning with Membrane Potential and Activation Threshold Homeostasis
Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input integrators.
aKin wins significant space agency grant for ‘AI crew for Space’.
The Australian Space Agency has awarded aKin a grant to build helper and inspector robots, and an AI habitat manager.
Limbic scaffolding: bridging the gap between AI and robotics
What would we gain from a robot that has all the perceptual capabilities and dexterity of a human as well as a mind that can make use of those capabilities? Read more.
Epigenetic evolution of deep convolutional models
Winner of the Outstanding Student Paper Award at the 2019 IEEE Congress on Evolutionary Computation.
Complexity-based speciation and genotype representation for neuroevolution
Joint paper by Alexander Hadjiivanov and Alan Blair. Introduction of a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons, which is indicative of the complexity of the search space.
Adaptive conversion of real-valued input into spike trains
This paper presents a biologically plausible method for converting real-valued input into spike trains for processing with spiking neural networks.
The Importance of Being (L)earnest
The benefit of biological inspiration in machine learning and natural language processing.
Continuous adaptive learning for neural networks
This study presents a biologically plausible method (based on the operation of the mammalian retina) which can convert raw input continuously into a form that is suitable for both the training and deployment stages of neural networks.
Start with a billion users in mind
Liesl Yearsley, CEO of aKin, wants to drive the co-evolution of Artificial Intelligence as society’s companion and a tool to enable positive progress for all. Read the full article here.
Liesl Yearsley, CEO and founder of aKin | Overview of research
Overview of research by Liesl Yearsley, founder and CEO of aKin and CEO/Founder of three previous successful companies.


Singularity University keynote
Liesl Yearsley CEO

Keynote delivered on creating AI able to form deep relationships with humans.

Publised: SingularityU Australia Summit 2018

Liesl Yearsley CEO

Invention patent for variable-based conversational chatbot operation.

Published: U.S. Patent Office.

Cyberpersonalities in artificial reality
Liesl Yearsley CEO

Invention patent concerning cyberpersonalities, including their and varied use in artificial reality.

Published: U.S. Patent Office

Liesl Yearsley CEO

Invention patent for variable-based conversational chatbot operation.

Published: U.S. Patent Office

The Detection of Persons in Cluttered Beach Scenes using Digital Video Imagery and Neural Network-based Classification
Dr. Steve Green PhD; Chief Research Officer

Investigation into the detection and quantification of persons in real-world beach scenes for the automated monitoring of public recreation areas.

Published: International Journal of Computational Intelligence and Applications, Vol.6

An Exhaustive Search Strategy for Detecting Persons in Beach Scenes using Digital Video Imagery and Neural Network-based Classification
Dr. Steve Green PhD; Chief Research Officer

This paper presents an investigation of a neural-based technique for detecting and quantifying persons in beach imagery for the purpose of predicting trends of tourist activities at beach sites.

Published: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2006)

The detection and quantification of persons in cluttered beach scenes using neural network-based classification
Dr. Steve Green PhD; Chief Research Officer

A proposed system uses image enhancement and segmentation techniques to detect objects in cluttered scenes.

Published for: Proceedings of the International Conference on Computational Intelligence and Multimedia Applications

Investigation of a Classification-based Technique to Detect Illicit Objects for Aviation Security
Dr. Steve Green PhD; Chief Research Officer

In this paper we present an initial investigation into the use of a classification-based technique for illicit object detection in aviation security.

Published: The IASTED International Conference on Artificial Intelligence and Applications (AIA 2005).

Co-evolutional learning, human / AI behavior

Dynamic Language comprehension

Humanizing AI, biologically inspired

Complex image and landscape analysis, evolutional art

Problem solving - games and conundrums

Problem solving - robotics

Computational Intelligence, Learning  and Problem solving  -  approaches