And it’s that grunt lab work that takes away from what I think is the most important part of the scientific process, and that’s the creative process. If technology allows us to free scientists from the day to day grind by letting robots do the bulk work of data collection, then a scientific culture can be built that’s more attuned to the big picture of analyzing data, formulating new hypotheses, and connecting disparate ideas. In other words, scientists can cultivate those skills that spur new thinking and innovation, rather than worrying about whether they properly accounted for the meniscus when pouring fluid into an Erlenmeyer flask.
Indeed, one ancillary benefit of automating science might be a rise of more cross-disciplinary scientists, who are expert in more than one area and able to program robots to test their hypotheses for them while they move on to devising other experiments. That ability to work across disciplines could lead to ideas and discoveries more quickly by virtue of scientists seeing connections that they simply didn’t have time to see before.
Advanced Chess As a Model For Science
Everyone remembers the famous chess match between Garry Kasparov and Deep Blue, in which a computer first beat a human chess master. Fewer people are aware of the work he’s done since — not in working against computers in chess, but working with computers to produce a style of play better than computers or humans can produce by themselves. This has become known as Advanced Chess, and Kasparov himself has noted its superiority in real competition:
In 2005, the online chess-playing site Playchess.com hosted what it called a “freestyle” chess tournament in which anyone could compete in teams with other players or computers. Normally, “anti-cheating” algorithms are employed by online sites to prevent, or at least discourage, players from cheating with computer assistance. (I wonder if these detection algorithms, which employ diagnostic analysis of moves and calculate probabilities, are any less “intelligent” than the playing programs they detect.)
Lured by the substantial prize money, several groups of strong grandmasters working with several computers at the same time entered the competition. At first, the results seemed predictable. The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.
I think that a similar human-computer partnership could achieve similar results in science. Let computers and robots handle “tactics” — the day to day grind of ensuring that experiments are property set up and managed, while humans handle the “strategy” — interpreting data, developing hypotheses, and making big picture connections. As Dr. Coolon notes regarding his own research, ”There are some things I wouldn’t have caught that a computer might have caught, but there are some things that a computer would have missed, like big picture things that just ‘don’t look right.’”
Let the strengths of computers compensate for the weaknesses of humans, and vice-versa, and you have a formula for more reliable results and more creative thinking. It will lead to better science just like letting computers focus on tactics and humans on strategy leads to better chess.
The Art of Science
Another positive aspect of using robots and computers to free humans from the day to day drudgery of science is, I hope, an ability to re-energize the ideal of the scientist as Renaissance Man. Thanks to advances in information technology, it’s become easier for cross-disciplinary studies to emerge, and automation of experimentation should push this ever forward, encouraging scientists to think and analyze data from different perspectives, particularly from an artistic one.
As Steve Martin ably dramatized in his great play Picasso at the Lapin Agile, there is an incredible overlap between great scientific discoveries and the arts, as well as the passion and thinking process that leads to great works in both. Leonardo da Vinci is, of course, who most minds turn to when thinking about Renaissance men, but the fact of the matter is that there is a great deal of overlap to this very day. Robert Root-Bernstein recently noted in his blog this little tidbit:
In fact, I’ve just published a study that shows that almost all Nobel laureates in the sciences are actively engaged in arts as adults. They are twenty-five times as likely as average scientist to sing, dance, or act; seventeen times as likely to be an artist; twelve times more likely to write poetry and literature; eight times more likely to do woodworking or some other craft; four times as likely to be a musician; and twice as likely to be a photographer. Many connect their art with their scientific creativity.
Again, more spare time in the day and more time devoted to be able to think and create might help give more scientists the time to delve into the arts and explore the connections between the two disciplines, catapulting their creativity into even greater areas of discovery.
I’m hopeful that the formalization and automation of scientific experimentation leads to a future of science where the strengths of humans and computers can be combined to produce greater discoveries, and where scientists are free to stretch their creative wings into more and more areas. I can’t even fathom what discoveries and innovations might be made in a world where scientists have more free time to unleash their imaginations, but the prospect is exhilarating.