Supersized Science: A Q&A with Theoretical Physicist Sean Carroll on How Memory-Driven Computing Will Change Our Understanding of the Universe
Hewlett Packard Labs recently announced that it has successfully demonstrated Memory-Driven Computing for the first time. Enabling one of the most significant shifts in computing architecture in more than 60 years, this milestone for The Machine research program promises enormous potential in the realms of technology and beyond.
A handful of observers inside and outside the tech industry have had a preview of the technology, including Sean Carroll, a theoretical physicist at the California Institute of Technology. Sean is not only a deep thinker and prolific author but also a frequent contributor to television and film, translating science for the big screen, including serving as a consultant to the TV scientists of the CBS hit comedy The Big Bang Theory. Below he paints a picture of how science and research can change when no longer limited by today’s computing technology.
Q: Tell us about your background and the work that you’re doing at Caltech. What role, if any, does technology play in your field?
A: My research focuses on fundamental physics and cosmology, quantum gravity and spacetime, and the evolution of entropy and complexity. At Caltech, we often use supercomputers and sophisticated software to test theories and try simulations. A recent project from my colleagues involved simulating the formation of the Milky Way Galaxy.
Q: What are your initial impressions of Memory-Driven Computing and The Machine research project? Can you liken it to any other breakthroughs you've seen over the years?
A: Every scientist knows that the amount of data we have to deal with has been exploding exponentially – and that computational speeds are no longer keeping up. We need to be imaginative in thinking of ways to extract meaningful information from mountains of data. In that sense, Memory-Driven Computing may end up being an advance much like parallel processing – not merely increasing speed, but also putting a whole class of problems within reach that had previously been intractable.
Q: How does it spark your imagination? How do you think it changes the field of theoretical physics and related fields?
A: As soon as you hear about Memory-Driven Computing, you start thinking about two kinds of issues: data analysis and numerical simulations. Having too much data doesn't sound like a problem, but it's a real issue faced by modern scientists. We have experiments like the Large Hadron Collider, where we’re looking at colliding particles; the Laser Interferometric Gravitational-Wave Observatory, in which we study gravitational waves in spacetime; and the Large Synoptic Survey Telescope, in which we’re monitoring the sky in real time. Each of these projects produces far more data than we can thoroughly analyze.
On the simulation side, scientists are building better models of everything from galaxy formation to the Earth's climate. In every case, the ability to manipulate large amounts of data directly in memory will allow us to boost our simulations to unprecedented levels of detail and accuracy. Taken together, these advances will help us understand the universe at a deeper level than ever before.
Q: Take us forward 20 years. What do you expect you and your colleagues will be able to do when the computing paradigm shifts and exponential processing power is unleashed?
A: We might plausibly be discovering natural phenomena that would have completely escaped our notice. With modern, ultra-large data sets, there is a large cost associated with simply looking through them for new signals. If we know what we're looking for, we do a pretty good job; but what you really want to do is probe the completely unknown. Memory-Driven Computing will help us find new surprises in how nature works.
Q: What are the first problems you'd work to solve when having access to Memory-Driven Computing system?
A: As one of many possible examples: we know we live in a universe that has more invisible "dark matter" than ordinary matter (the atoms and particles we examine in the lab). How does that dark matter interact, and how does it influence the structure of galaxies and the universe? To answer these questions we need to be able to simulate different theoretical models in extraordinary detail, examining how dark matter and ordinary matter work together to make stars and galaxies. By doing so, we might just discover entirely new laws of physics.
Q: What are the long-term benefits and consequences of putting compute with orders of magnitude more power to work in the world? What do you expect it to mean for science, entertainment, everyday life?
A: Big data doesn't just come from scientific instruments; it's generated by ordinary human beings going through their day, using their phones and interacting online. I'm very excited by the prospects of artificial intelligence and brain/computer interfaces. Imagine a search engine that you can talk with like an ordinary person, one that understands what you're really after. We'll be connected not just through devices, but through virtual-reality environments and perhaps even direct interfaces with our brains. The idea of checking facts on Wikipedia is just the very beginning, and will look incredibly primitive in comparison with next-generation ways of connecting human intelligence to the world of information. To make that happen we need the kind of vast improvements in processing power that Memory-Driven Computation can enable.
Sean Carroll is a Research Professor of theoretical physics at the California Institute of Technology.
He received his Ph.D. in 1993 from Harvard University. His research focuses on fundamental physics and cosmology, quantum gravity and spacetime, and the evolution of entropy and complexity. He is the author of The Big Picture: On the Origins of Life, Meaning, and the Universe Itself; The Particle at the End of the Universe: How the Hunt for the Higgs Boson Leads Us to the Edge of a New World; From Eternity to Here: The Quest for the Ultimate Theory of Time; and the textbook Spacetime and Geometry: An Introduction to General Relativity.
He has been awarded prizes and fellowships by the National Science Foundation, NASA, the Sloan Foundation, the Packard Foundation, the American Physical Society, the American Institute of Physics, the Freedom From Religion Foundation, the Royal Society of London, and the Guggenheim Foundation. He frequently consults for film and television, and has been featured on shows such as The Colbert Report, PBS’s NOVA, and Through the Wormhole with Morgan Freeman.