Macro detail of some cables.
It is now clear that the data centers of tomorrow will need a lot of energy to operate.
I have witnessed these very sudden initiatives for safe and common American nuclear power, with the reopening of Three Mile Island by Microsoft and Constellation, or groundbreaking work on small local nuclear power plants.
There is also quite a bit of research on renewable energy and how it works. I even wrote about these new projects to produce energy and perform AI operations on satellites orbiting the Earth, to harvest energy in a different way.
But let’s look at some of the underlying challenges and our history of solving similar problems.
Electrical engineering as a guide
In a recent presentation, Deborah Douglas points out that as we seek to solve the problem of the power of AI, we already have some historical examples that can inform our research. Douglas is senior collections director and curator of science and technology at the MIT Museum.
She talks about early electrical systems and how the grid evolved from disparate networks.
I would also add that we can also make an analogy with the Internet. You can also talk about decentralized Internet. But Douglas also mentions the tools that professionals used for engineering long before our modern electrical systems became the norm.
(To clarify: sure, there was electricity, but it just wasn’t in the modern systems and formats we see today.)
Douglas takes us back to the 1940s, where many changes were happening – not only in power grids, but also in society.
A woman’s story
She cites the example of Phyllis Fox, who was working on a master’s thesis right here at MIT and submitted it in 1949. The title? “The solution to power grid problems on large-scale digital computers.”
It’s a story of the glass ceiling, tenacity and the power to change the world.
Douglas also mentions Vannevar Bush, who is also popular at MIT and who taught electrical engineering in 1919. He had a machine called a differential analyzer, and Fox was interested in it.
A hard day at work
Douglas presents us with a sort of visual picture of how Fox worked at GE as an assistant engineer.
“She was placed in an office with a Marchant calculator and given sets of problems to calculate, on average she did one equation per minute. So 60 per hour, you know, 300, 400 per day, if you want. And they were supposed to stay there. And there was a supervisor, a man who walked around making sure they were doing their work at that time, and no talking was allowed. Well, Phyllis was one of those adventurous souls, and she took a walk around campus and she discovered during her lunch hour that there was an office on the lower level that had a calculator. And they did the solutions by hand upstairs. So she came down at lunchtime and spent a whole day working on the calculator. then she would go back upstairs and pretend to be working, which was very clever in my opinion. But she also, in this meandering, discovered that GE had purchased a differential analyzer, just like the one I just showed you in the MIT photo.
Eventually, she notes, Fox got to work on the differential analyzer.
This calculator thing reminds me of what I wrote the other day, with MIT Professor Ethan Mollick’s idea of ”expectation calculus” – that you don’t need to do all kinds of manual work painful if you can wait. so that modern tools are available to automate this same work.
Regardless, in this case her adventurous nature was rewarded and she was able to participate in the evolution of a fairly important field.
However, Douglas notes that Fox was later laid off when the men returned from World War II – and later continued his master’s degree.
She then found another employer.
His name, Douglas adds, was Jay Forrester.
“He was ecumenical in his recruitment,” she said. “So he hired women, he hired men. He hired the first black computer operator in the United States. He hired a blind computer operator. He hired people of Japanese origin, which was very controversial in the middle of World War II. He embraced (hired) Phyllis Fox, who he thought was very brilliant.
So what did these pioneers do with differential analyzers?
Some big machines
I went to the MIT library site and took a look at some of these early machines, including something called the Rockefeller Differential Analyzer.
These are behemoths the size of a washer-dryer system, with large gears and wheels that will perform the calculations on a differential equation.
These are truly inspiring examples of analog computing.
Anyway, Fox ended up teaching at MIT and showing more of how process calculations worked, using flowcharts, block diagrams and that sort of thing.
She got a job at the Atomic Energy Commission’s computer center and eventually developed something called Dynamo, one of the first computer simulation languages. Fox also reportedly wrote the first LISP manual, worked at Newark College of Engineering (now the Jersey Institute of Technology), and obtained his position in 1972.
This is all incredibly impressive, and Douglas ended his presentation by hypothesizing that there may be someone around today who will play this kind of role in solving our bottlenecks and challenges. current.
For my part, I certainly hope so. But just thinking about the history of differential analyzers and analog equipment shows me that we have the ability to create new tools to do what we need in pioneering AI power solutions.
In other words, it may seem daunting to think that data centers will need X gigawatts and terawatts of power. But who knows how we will see this in 10 or 20 years? Or even earlier?
Think about these inspiring examples and do yourself a favor: go back and look at some of these now-obsolete machines. Consider that in their day, less than a century ago, they were at the cutting edge of technology.
