Building the AI Bridge
A lot has changed in the 15 years since I was a PhD student. When I was in my PhD stage, there was a high wall between different disciplines and subjects, and even a high wall within computer science. The guy sitting next to me could be doing things that I completely couldn’t understand.
The AI Revolution
In the seven months since I joined the MIT Schwarzman College of Computing as the Douglas Ross (1954) Career Development Professor of Software Technology in the Department of Electrical Engineering and Computer Science, I am experiencing something that I believe is "very rare in human scientific history" – a lowering of the walls that expands across different scientific disciplines.
This shift began in 2012 with the "deep learning revolution," when it was realized that machine-learning methods based on neural networks were so powerful that they could be put to greater use. Computer vision, in particular, began growing rapidly, as it turned out that the same methodology could be applied to many different problems and many different areas.
The Future of AI in Science
Since coming to MIT, I have talked to professors in almost every department. Some days I find myself in conversation with two or more professors from very different backgrounds. I certainly don’t fully understand their area of research, but they will just introduce some context and then we can start to talk about deep learning, machine learning, and neural network models in their problems. In this sense, these AI tools are like a common language between these scientific areas: the machine learning tools "translate" their terminology and concepts into terms that I can understand, and then I can learn their problems and share my experience, and sometimes propose solutions or opportunities for them to explore.
The Reciprocal Effect
While AI tools provide a clear benefit to the work of my scientist colleagues, I also note the reciprocal effect they can have, and have had, on the creation and advancement of AI. Scientists provide new problems and challenges that help us continue to evolve these tools. But it is also important to remember that many of today’s AI tools stem from earlier scientific areas – for example, artificial neural networks were inspired by biological observations; diffusion models for image generation were motivated from the physics term.
Conclusion
In conclusion, I believe that AI is transforming the way scientists work, and will continue to do so in the future. As AI tools become more widely adopted, we will see a greater convergence of different scientific disciplines, leading to new breakthroughs and innovations.
Frequently Asked Questions
Q: What is the impact of AI on science?
A: AI tools are providing a common language between different scientific disciplines, allowing researchers to share ideas and collaborate more effectively.
Q: What are the benefits of AI in science?
A: AI tools are helping to accelerate the research cycle, reduce costs, and provide new insights and discoveries.
Q: What is the future of AI in science?
A: I believe that AI will continue to play a key role in scientific research, driving breakthroughs and innovations in a wide range of fields.
