Research from the aKin Team

Research output from aKin

Robotics adoption matters: combining artificial and natural intelligence
In the last day of the Robotics adoption matters series, Alexander Hadjiivanov discusses the case for AI in human-robot collaboration.
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.

Prior research by aKin team members

Singularity University keynote

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

Publised: SingularityU Australia Summit 2018

Chatbots

Invention patent for variable-based conversational chatbot operation.

Published: U.S. Patent Office.

Cyberpersonalities in artificial reality

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

Published: U.S. Patent Office

Chatbots

Invention patent for variable-based conversational chatbot operation.

Published: U.S. Patent Office

Cyberpersonalities in artificial reality

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

Published: U.S. Patent Office.

Social Intelligence

Invention patent concerning social intelligence, that is artificial intelligence about the behaviour of individuals or groups, and in particular a method for operating a computer interface to analyse unstructured online content generated by a user to produce a profile of a user.

Published: Google Patents

CLIVE-An Artificially Intelligent Chat Robot for Conversational Language Practice

Paper presenting artificially intelligent chat robot, for foreign language acquisition, CLIVE.

Published: SETN: Hellenic Conference on Artificial Intelligence conference

Computer network search engine

Patent for a computer network search engine which anaylzes search results according to one or more themes.

Published: U.S. Patent Office.

Search Beyond Google

Citied: Techonlology Review

Performance Analysis of GAME: A Generic Automated Marking Environment

This paper describes the Generic Automated Marking Environment (GAME) and provides a detailed analysis of its performance in assessing student programming projects and exercises.

Published: Computers and Education, Vol. 50

A Comparison of Neural-based Techniques Investigating Rotational Invariance for Upright People Detection in Low Resolution Imagery

This paper describes a neural-based technique for detecting upright persons in low-resolution beach imagery in order to predict trends of tourist activities at beach sites.

Published: Australasian Joint Conference on Artificial Intelligence

Extensions to Generic Automated Marking Environment: Game-2+

This paper describes the Generic Automated Marking Environment (GAME-2+), which is the extension of GAME-2 and provides an analysis of its performance in assessing student programming projects.

Published: Proceedings of the Interactive Computer Aided Learning Conference (ICL 2009)

An Enhanced Generic Automated Marking Environment: GAME-2

In this paper we describe an extension of the Generic Automated Marking Environment (GAME-2) and provide an analysis of its performance in assessing student programming projects.

Published: IEEE Multidisciplinary Engineering Education Magazine, Vol. 2.

The detection and quantification of persons in cluttered beach scenes using neural network-based classification

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

An Exhaustive Search Strategy for Detecting Persons in Beach Scenes using Digital Video Imagery and Neural Network-based Classification

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 of Persons in Cluttered Beach Scenes using Digital Video Imagery and Neural Network-based Classification

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

Investigation of a Classification-based Technique to Detect Illicit Objects for Aviation Security

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).

Intelligent Illicit Object Detection System For Enhanced Aviation Security

A proposed intelligent security technology system that  provides the civil aviation authority with maximum security  whilst minimising adverse impacts on airlines and airport operations.

Published: 5th International Conference on Simulated Evolution And Learning (SEAL 04)

An Experimental Analysis of GAME: A Generic Automated Marking Environment

This paper describes the Generic Automated Marking Environment (GAME) and provides a detailed analysis of its performance in assessing student programming projects and exercises.

Published: 9th Annual Conference on Innovation and Technology in Computer Science Education