Generative AI: game-changer that society, industry need to be ready for VIDEO

While generative AI has people excited about a new wave of creativity, there are concerns about the impact of these models on society. Digital artist Greg Rutkowski fears that the internet will be flooded with artwork that is indistinguishable from his own, simply by telling the system to reproduce an artwork in his unique style. Professor of art Carson Grubaugh shares this concern and predicts that large parts of the creative workforce, including commercial artists working in entertainment, video games, advertising, and publishing, could lose their jobs because of generative AI models.

Besides profound effects on tasks and jobs, generative AI models and associated externalities have raised alarm in the AI governance community. One of the problems with large language models is their ability to generate false and misleading content. Meta’s Galactica – a model trained on 48 million science articles with claims to summarize academic papers, solve math problems, and write scientific code – was taken down after less than three days of being online as the scientific community found it was producing incorrect results after misconstruing scientific facts and knowledge.

This is even more alarming when seen in the context of automated troll bots, with capabilities advanced enough to render obsolete, The Turing Test – which tests a machine’s ability to exhibit intelligent behaviour similar to or indistinguishable from a human. Such capabilities can be misused to generate fake news and disinformation across platforms and ecosystems.

Large models continue to be trained on massive datasets represented in books, articles and websites that may be biased in ways that can be hard to filter completely. Despite substantial reductions in harmful and untruthful outputs achieved by the use of reinforcement learning from human feedback (RLHF) in the case of ChatGPT, OpenAI acknowledges that their models can still generate toxic and biased outputs.

In the private sector, two approaches to the governance of generative AI models are currently emerging. In one camp, companies such as OpenAI are self-governing the space through limited release strategies, monitored use of models, and controlled access via API’s for their commercial products like DALL-E2. In the other camp, newer organizations, such as Stability AI, believe that these models should be openly released to democratize access and create the greatest possible impact on society and the economy. Stability AI open sourced the weights of its model – as a result, developers can essentially plug it into everything to create a host of novel visual effects with little or no controls placed on the diffusion process.

In the public sector, little or no regulation governs the rapidly evolving landscape of generative AI. In a recent letter to the White House, US Congresswoman Anna Eshoo highlighted "grave concerns about the recent unsafe release of the Stable Diffusion model by Stability AI”, including generation of violent and sexual imagery.

Other issues surround intellectual property and copyright. The datasets behind generative AI models are generally scraped from the internet without seeking consent from living artists or work still under copyright. “If these models have been trained on the styles of living artists without licensing that work, there are copyright implications,” according to Daniela Braga, who sits on the White House Task Force for AI Policy.