A UQ research program seeking to develop computer systems and robots capable of intelligent thought, self-awareness, creativity, learning and emotion has received the first $100,000 donation from Australian entrepreneur Sean Howard.
Mr Howard, founder of Australian Personal Computer magazine and OzEmail, said the funding and future donations would ensure the advancement of the Fluid Analogies Engine (FAE) research program.
In addition to providing financial support for the research, Mr Howard brings considerable personal expertise to the research program and is participating actively in the research.
FAE is a direct extension of the PhD work of senior research fellow and program leader Dr Scott Bolland, and is a multidisciplinary project involving researchers from UQ’s Schools of Psychology, and Information Technology and Electrical Engineering.
According to Mr Howard, it is very rare that technology is developed which has the power to be transformational for humankind.
"I was lucky enough to participate in the early 1990s in the commercialisation of the Internet – something truly revolutionary for us all," Mr Howard said.
"Creating thinking machines has long been a dream of computer science. To-date it has been an unrealised dream.
"But this technology, with its genesis in a little-known research group in the USA some 10 years ago, and whose advancement has been pursued so capably and innovatively by Dr Bolland, may become another of these transformational technologies."
Dr Bolland said the Fluid Analogies project was novel and aimed at investigating and simulating the fundamental mechanisms of the mind, drawing on insights from psychology, biology and neuroscience.
"The approach we are taking differs from typical industry-focused Artificial Intelligence applications that are concerned with solving a particular task, such as face or speech recognition," Dr Bolland said.
Instead, we are interested in identifying what features are shared across a wide range of tasks, from object recognition through to problem-solving and creativity, and implementing systems, like the brain, that can perform well in a wide range of domains.
"Central to our research, and distinct from traditional computer programming, is our belief that intelligent machines of the future will be primarily self-taught.
"To function in the real world requires vast amounts of knowledge, ranging from subtle fine motor commands needed to manipulate objects, through to knowing the consequences that various actions will have on the world.
"It is unrealistic to assume that all this information can be programmed into or taught to a machine. Rather, machines of the future will learn from experience and be driven to actively seek new information and acquire new skills.
"This is similar to how children learn – they are not shown how to walk, but rather discover this and other skills and knowledge through self-guided action, experimentation and play."
Although initially experimental in nature, it is envisaged that the Fluid Analogies Engine program will have long-term implications in a wide range of fields, from defence and scientific discovery through to the development of autonomous robots and interactive toys.
Further into the future however, the advent of true Artificial Intelligence will have wider-reaching implications.
UQ is currently recruiting highly motivated and intelligent PhD students with a strong background in artificial intelligence or machine learning to join the Fluid Analogies research team, with students with an Australian Postgraduate Award (APA) or another scholarship being eligible for top-up funding of $10,000 per annum.
"This is a very exciting time in the evolution of artificial intelligence, and an amazing opportunity for students to work with some of the top researchers in this field," Dr Bolland said.
For further information, and expressions of interest, please email Dr Scott Bolland scottb@psy.uq.edu.au.
More information about the project can be found at http://www.itee.uq.edu.au/~fae
Media: For further information contact Dr Scott Bolland on (07) 3365 1642,
0435 174 589
