People’s opinions of AI vary widely from the possibility of creating a utopian machine-assisted world or precipitate a dystopic machine-dominated future á la SkyNet from the Terminator movie series, with millions of possibilities and potential in between. But two AI pioneers made one thing clear at the “Great Minds, Bold Visions: What’s Next for AI” conference session moderated by Rajeev Chand, partner and head of research at Wing Venture Capital: AI is here to stay and will only become what humans make of it.
“AI is a general-purpose technology, meaning that it’s not useful for one thing. It’s kind of like electricity, another general-purpose technology,” explained Andrew Ng, AI Fund managing general partner, the founder of DeepLearning.AI, co-founder and chairman of Coursera, an adjunct professor at Stanford University, and was the founding lead of the Google Brain team. “If I ask you what electricity is for, it’s useful for so many different things. AI is like that, too. There are so many use cases all around the world to be identified and built out.”
AI is “a deep-end horizontal technology,” agreed fellow Stanford professor Fei-Fei Li, co-director of Stanford Institute’s Human-Centered Artificial Intelligence, co-founder of AI4ALL, and often referred to as the godmother of AI. “It is becoming a true transformative driving force of the next digital revolution or industrial revolution. In terms of public media coverage, it’s going to just go in waves, and that’s not very relevant. What is relevant is that this technology is here to stay, is here to be deep end into all vertical businesses and consumer experiences, and is changing the very fabric of our societal, economic, and political landscape. And that is just a fact.”
Both Li and Ng recognized and tried to allay the many fears AI engenders, especially the potential for AI to steal jobs from humans, especially creators.
“We have to be careful about [talking about] replacing jobs versus replacing tasks,” Li insisted. “Every given human job is actually a suite of multiple tasks. A nurse works an eight-hour shift, but that actually comprises hundreds of tasks. So, I do see that AI agents help them, being assistive and augmentative on many tasks. You can actually discern repeatable patterns out of the data, and that’s where you can start. Where the patterns of data prove to be valuable and actionable in your business is where one should be looking.”
“It turns out that there’s a recipe for businesses to figure out what tasks you should try to use AI to augment,” Ng advised. “Jobs are made out of tasks, and even artists’ jobs are made of many tasks. AI may automate 20-30 percent of someone’s tasks, but that’s still a lot of other tasks that we need people to do and maybe they could be more efficient and actually even make more money. But there is this fear that I think is challenging.”
