@_faezahmed
www.youtube.com MIT Challenges | Designing Solutions with AI: Challenge Launch with... MIT Mechanical Engineering Professor Faez Ahmed challenges you to u... Instead of continuing to emphasize automation, a human-centric approach to the next generation of #AI technologies in #manufacturing could enhance workers' skills and boost productivity.
mit-genai.pubpub.org/pub/9s6690gd/r…
@AustinLentsch @DAcemogluMIT @baselinescene @_faezahmed @MITMechEView quoted post on X
AI models that prioritize similarity falter when asked to design something completely new. A study by Prof. Faez Ahmed and his team makes the case that generative AI must learn to innovate to excel at engineering design.
mitsha.re/LqHo50PZ9y3View quoted post on X
I get asked a lot: why stay in academia, all the excitement in AI is happening in industry with massive compute. And I am seeing some profs leaving academia, but also seeing lots of researchers in industry looking to go back to academia, especially those who don’t work on LLMs.View quoted post on X
I have spent some time and actively working with industry, and it has been a great experience, but for me the answer is simple:
1. Students: Nothing can replace working with really smart & amazing students.
2. Academic freedom: In general, industry will one way or another dictate what you should work on. Today it is LLMs, tomorrow it is something else. The good old days of "here is a cool idea, let me investigate and publish" are pretty much over. But as an academic, I can work on whatever I want: I can start working on "black holes" tomorrow if I choose to.
3. And my favourite one: No reorgs -- big tech really loves reorgs that happen every few months.
Finally, as an academic, I can always take some time off, work in industry or start-up, and come back if I want to. Going into industry is usually a one-way street: It is far easier to go to industry from academia and much harder the other way around.
