Generative AI will replace majority of jobs, says Sophia robot’s maker

Artificial intelligence scientist Ben Goertzel said that generative AI models and tools like ChatGPT could replace many tasks performed by humans. Most people’s jobs do not require ‘incredible’ creativity or innovation, added Goertzel, who is also the co-developer of Sophia, the humanoid robot. He said that AI could be used for tasks like copy editing, writing sport score summaries, and weather reports, as reported by ZDNet.

He further said with these tools automating major portions of people’s jobs would lead to industry reshuffling and reassigning job duties. He, however, said that AI will not be able to replace jobs that rely on human interaction and the ones that require creativity.

Generative AI replacing or automating tasks has been a hot topic of discussion. OpenAI, ChatGPT’s creator released a study about the impact of large AI models like GPT on the US job market. As per the study, 80% of the US workforce will have at least 10% of their work tasks affected by the introduction of large language models, and 19% of the workers may have 50% of their tasks impacted. The same study also said that jobs that do not require formal educational credentials are safe from ChatGPT but professions that need proficiency in programming and writing are more prone to get automated. 

Further, Goertzel also spoke about artificial general intelligence and about the possibility of generative AI chatbots being conscious. He said that there is no need to ‘resolve the open questions in the philosophy of consciousness to build human-level or even superhuman thinking machines’. He added that developers are closer to achieving AGO than ever before. As per Goertzel, breakthroughs in AGI could occur in the next three to ten years, with a combination of language models, machine reasoning, and evolutionary learning accelerating the progress. 

He said that generative AI today can impersonate general AI by having a broad variety of training data, which is a ‘testament to the power of computer networks and multi-GPU server farms’.