When OpenAI introduced ChatGPT to the world in 2022, it brought generative artificial intelligence into the mainstream and commenced a snowball effect that led to its rapid integration into industry, scientific research, health care, and the on a regular basis lives of people that use the technology.
What comes next for this powerful but imperfect tool?
With that query in mind, a whole lot of researchers, business leaders, educators, and students gathered at MIT’s Kresge Auditorium for the inaugural MIT Generative AI Impact Consortium (MGAIC) Symposium on Sept. 17 to share insights and discuss the potential way forward for generative AI.
“It is a pivotal moment — generative AI is moving fast. It’s our job to ensure that that, because the technology keeps advancing, our collective wisdom keeps pace,” said MIT Provost Anantha Chandrakasan to kick off this primary symposium of the MGAIC, a consortium of industry leaders and MIT researchers launched in February to harness the ability of generative AI for the nice of society.
Underscoring the critical need for this collaborative effort, MIT President Sally Kornbluth said that the world is counting on faculty, researchers, and business leaders like those in MGAIC to tackle the technological and ethical challenges of generative AI because the technology advances.
“A part of MIT’s responsibility is to maintain these advances coming for the world. … How can we manage the magic [of generative AI] so that every one of us can confidently depend on it for critical applications in the actual world?” Kornbluth said.
To keynote speaker Yann LeCun, chief AI scientist at Meta, probably the most exciting and significant advances in generative AI will most probably not come from continued improvements or expansions of enormous language models like Llama, GPT, and Claude. Through training, these enormous generative models learn patterns in huge datasets to supply recent outputs.
As an alternative, LuCun and others are working on the event of “world models” that learn the identical way an infant does — by seeing and interacting with the world around them through sensory input.
“A 4-year-old has seen as much data through vision as the most important LLM. … The world model goes to turn out to be the important thing component of future AI systems,” he said.
A robot with any such world model could learn to finish a brand new task by itself with no training. LeCun sees world models as the perfect approach for corporations to make robots smart enough to be generally useful in the actual world.
But even when future generative AI systems do get smarter and more human-like through the incorporation of world models, LeCun doesn’t worry about robots escaping from human control.
Scientists and engineers might want to design guardrails to maintain future AI systems heading in the right direction, but as a society, we’ve got already been doing this for millennia by designing rules to align human behavior with the common good, he said.
“We’re going to must design these guardrails, but by construction, the system is not going to give you the chance to flee those guardrails,” LeCun said.
Keynote speaker Tye Brady, chief technologist at Amazon Robotics, also discussed how generative AI could impact the longer term of robotics.
As an illustration, Amazon has already incorporated generative AI technology into lots of its warehouses to optimize how robots travel and move material to streamline order processing.
He expects many future innovations will concentrate on using generative AI in collaborative robotics by constructing machines that allow humans to turn out to be more efficient.
“GenAI might be probably the most impactful technology I even have witnessed throughout my whole robotics profession,” he said.
Other presenters and panelists discussed the impacts of generative AI in businesses, from largescale enterprises like Coca-Cola and Analog Devices to startups like health care AI company Abridge.
Several MIT faculty members also spoke about their latest research projects, including using AI to cut back noise in ecological image data, designing recent AI systems that mitigate bias and hallucinations, and enabling LLMs to learn more concerning the visual world.
After a day spent exploring recent generative AI technology and discussing its implications for the longer term, MGAIC faculty co-lead Vivek Farias, the Patrick J. McGovern Professor at MIT Sloan School of Management, said he hoped attendees left with “a way of possibility, and urgency to make that possibility real.”
