Australia is lagging on regulation of artificial intelligence, writes legal commentator Morry Bailes, who sets out the challenges posed by the technology.

We will remember 2023 as the year when Artificial Intelligence (AI) reached out to touch all of us in a direct way when OpenAI launched Chat GPT4, following picture and imagery AI, DALL-E, released in late 2022).

While it is not the only big tech company in the market, Opan AI’s ChatGPT4 marked a turning point. We could now all start directly using the technology in our own homes, even though AI had indirectly been a part of our lives for at least a decade before.

In just one instant, all the talk, all the hypothesising, was seemingly swept aside and we took a giant step toward realising the true potential of AI. That potential sent a shiver down the spine of collective humanity. Was this the death of literature, music and arts as we know it, or a great leap into the unknown that would bring us prosperity and improved lives forever?

Stephen Hawking said of AI: ‘The development of full artificial intelligence could spell the end of the human race… It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”

A year on, we are more reflective. AI surely has a major role to play in business, and the legal profession is one of many industries that must grapple both with its upside and downside. However, its wide-reaching implications are as sobering as they are exciting. The starting point is to imagine, if we can, the possible power of generative AI.

Generative AI grew from earlier models of AIs trained for specific functions and given specific data. An example in the law is a search-specific AI to assist in the litigious process of discovery. Required of every party to litigation, this is the process of identifying and listing all documents and evidence held by a party, relevant to the case. This process was made faster and easier by specifically trained AIs.

This type of AI was first described by academics at Stanford University as “foundation models”. These models were ultimately joined together, fed terabytes of data, and could be used in many different applications. This next step was termed generative AI, because the AIs became able to generate a new idea, drawing from the vast amounts of data input to them. Through a process of tuning generative AI, the generative model could turn itself toward specific functions for the purpose, say of assisting a business with a particular task. Tuning is the adding of a small amount of additional data in order to have the generative AI perform a specific task.

Photo: Florence Lo/Reuters

In short, the performance power of having many foundation AIs connected to produce generative AI vastly outperforms a single model designed for a single task. Additionally, the productive power of generative AI, when only a small amount of additional data is required to achieve a specific gain of function, is huge. It harnesses that productivity power from the vast sum of the data earlier collected.

However, there are clear downsides to generative AI.........

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