Imagine a team member who remembers every scrap of information he or she ever encounters, learns from it, shows up for work everyday, and performs with unerring accuracy, reliability, and integrity.
Such a worker may sound like the intended outcome of Australia's impending industrial relations laws, but you are more likely to find one being developed by artificial intelligence (AI) researchers and software developers around Australia and the world.
These researchers are not, however, attempting to replicate human intelligence -- that is a task that would require work by dozens of scientific disciplines and is currently beyond them all. The main obstacle to replicating human intelligence -- ie, AI doing all the things a real person does -- is the human brain's complexity. There are 10 to the power of 15 connections between the synapses inside our heads, a number that is all-but-impossible to replicate.
Another issue is that while we understand the anatomy of the brain in broad brush strokes, the precise electrochemical mechanisms that drive thoughts and deeds are poorly understood. Finding ways to program them into a machine is an almost literally mind-boggling task.
Rather, because building a brain is so hard, most AI researchers instead work to replicate the way humans think in software, a goal worth pursuing as people are pretty good at solving problems.
Take this example: ask a human to recognise a dog, and most will succeed even if their previous experience of canines includes only a single chihuahua and a single great dane. Ask a computer the same question, that also was only offered a data set that described both breeds as a dog, and it would struggle to classify a labrador as also belonging to the canine family and would probably mis-classify a sheep as a dog as well.
The human ability to make classifications and judgements that let us recognise dogs is not supported by most common data processing tools. A database knows where data is stored, not what it is or how to classify it. Yet for the contents of a database to accrue value, it is necessary to classify what it contains so different data can be treated differently. However, using actual humans to classify data is immensely expensive, making AI a worthy aim.
The earliest attempts at baking AI techniques and custom reasoning into code to improve performance and imbue it with abilities that simpler software cannot match were "expert systems". Popular in the 1980s, these aimed to reduce experts' knowledge of reasonably limited problem domains to a knowledge base that was subjected to a series of Boolean rules and other logical processes to extract insights that replicated human thought processes. Users were typically led through a questionnaire to use an expert system.
The technique quickly gathered a reputation as being problematic because, as the numbers of rules in an expert system grew, the potential for conflicts between them escalated. Many expert systems ground to a halt due to many problems requiring lengthy and costly debugging. Since then Moore's Law has been kind to expert systems by offering them sheer processing power to make the technique more viable, but other more sophisticated techniques have since come to the fore.
3%
2%
