*Contemporary science sure does have some contemporary problems.
Nobel winner Venki Ramakrishnan
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If someone were to describe what I became well known for, it would be like describing someone who developed the Betamax. There’s a famous book on computers called Soul of a New Machine by Tracy Kidder, which is about a Data General computer that was going to be a step above anything that existed at the time. Ironically, that computer never really took off. A few years after it came out, it was superseded by the VAX series of computers.
This is a little bit like that: just 15 years after the first ribosome structures were cracked by crystallography, now no one would use crystallography to do it. I'm not going to give back the Nobel, of course, because the Nobel was not for using crystallography, it was for the discovery of the atomic structure of the ribosome and the functional implications of it.
There are broader aspects that I think about now, partly because a year ago I became president of the Royal Society. That has led me to think about science in a broader context.
There are a few things that I worry about, one of which is that science has always succeeded because it's evidence-based, which has led to public trust. The public believes that when scientists say something, it's based on hard evidence that they've looked at critically. More importantly, when one scientist claims something based on evidence, other scientists—his/her competitors—check it out carefully because they don't want to let someone get away with something if it isn't sound. That's led to an enormous trust in scientists.
If you look at public opinion polls, scientists are among the most trusted professions, certainly they are in the UK and probably in the US as well. But we're getting to a stage where that's at risk for a variety of reasons. Some of them are technical reasons and some of them are cultural reasons. I'll get to the technical part first.
We're now accumulating data at an incredible rate. I mentioned electron microscopy to study the ribosome—each experiment generates several terabytes of data, which is then massaged, analyzed, and reduced, and finally you get a structure. At least in this data analysis, we believe we know what's happening. We know what the programs are doing, we know what the algorithms are, we know how they come up with the result, and so we feel that intellectually we understand the result.
What is now happening in a lot of fields is that you have machine learning, where computers are essentially taught to recognize patterns with deep neural networks. They're formulating rules based on patterns. There are are statistical algorithms that allow them to give weights to various things, and eventually they come up with conclusions.
When they come up with these conclusions, we have no idea how; we just know the general process.
(((You know what? This is really, really a problem. It's like UFOs arrive and give you cargo-cult solutions, and you're like, "What the heck, I dunno what a steel axe is, but I gotta admit it works on these coconut trees.")))
If there's a relationship, we don't understand that relationship in the same way that we would if we came up with it ourselves or came up with it based on an intellectual algorithm. So we're in a situation where we're asking, how do we understand results that come from this analysis? This is going to happen more and more as datasets get bigger, as we have genome-wide studies, population studies, and all sorts of things.
There are so many large-scale problems dependent on large datasets that we're getting more divorced from the data. There's this intermediary doing the analysis for us. To me, that is a change in our way of understanding it. When someone asks how we know, we say that the system analyzed it and came up with these relationships—maybe it means this or maybe it means that. That is philosophically slightly different from the way we've been doing it. (((It's more than "slightly," it's more like a complete abdication of moral responsibility. It's like you had a deep-learner that could verbally fuse the King James Bible, the Koran and Mao's Little Red Book, and you read the resulting text and it sounded really majestic, and you're like: "Hey! These are received gospels! Let's live like this and disrupt all the heretics!")))
The other reason to worry is a cultural reason. The Internet and the World Wide Web have been a tremendous boon to scientists. It's made communication far easier among scientists. It's in many ways leveled the playing field.
I remember when I grew up in India, if you wanted to get a book, it would show up six months or a year after it had already come out in the West, sometimes two years. Journals would arrive by surface mail a few months later. I didn't have to deal with it because I left India when I was nineteen, but I know Indian scientists had to deal with it. Today, they have access to information at the click of a button. More importantly, they have access to lectures. They can listen to Richard Feynman. That would have been a dream of mine when I was growing up. They can just watch Richard Feynman on the Web. That's a big leveling in the field.
Along with the benefits, what has happened is a huge amount of noise. You have all of these people spouting pseudoscientific jargon and pushing their own ideas as if they were science. They couch all their stuff in technical jargon....
