Robot Makes Scientific Discovery (Mostly) On Its Own 250
Hugh Pickens writes "A science-savvy robot called Adam has successfully developed and tested its first scientific hypothesis, discovering that certain genes in baker's yeast code for specific enzymes which encourage biochemical reactions in yeast, then ran an experiment with its lab hardware to test its predictions, and analyzed the results, all without human intervention. Adam was equipped with a database on genes that are known to be present in bacteria, mice and people, so it knew roughly where it should search in the genetic material for the lysine gene in baker's yeast, Saccharomyces cerevisiae. Ross King, a computer scientist and biologist at Aberystwyth University, first created a computer that could generate hypotheses and perform experiments five years ago. 'This is one of the first systems to get [artificial intelligence] to try and control laboratory automation,' King says. '[Current robots] tend to do one thing or a sequence of things. The complexity of Adam is that it has cycles.' Adam has cost roughly $1 million to develop and the software that drives Adam's thought process sits on three computers, allowing Adam to investigate a thousand experiments a day and still keep track of all the results better than humans can. King's group has also created another robot scientist called Eve dedicated to screening chemical compounds for new pharmaceutical drugs that could combat diseases such as malaria.
Re:But... (Score:5, Informative)
You joke but really undergrads are cheaper than graduate students... At least from my experience working in a biology lab in college. It was/is common practice to recruit undergrads to do free work for the labs. The undergrad gets some experience in the field and the lab gets free labor in exchange for dealing with the inexperience of the average undergrad.
Re:But... (Score:5, Informative)
The case of electronics assembly is arguably analogous. Humans are cheap; but (quite expensive) pick and place machines are ubiquitous. Why? Because they are faster, more precise, and more consistent than humans.
It is already starting. This piece [wired.com] describes a massive robot setup for processing brain samples(cue: whatcouldpossiblygowrong). In high volume gene sequencing, automated equipment is common enough to essentially be a stock photo cliche by now.
Robot or automated lab? (Score:3, Informative)
I used to work with Motoman K6's a few years back. Using these robots, we performed plasma cutting, arc welding, material handling, etc... Just looking at the K6, you knew it was a robot. Watching a robot work in a cell after you've trained it to do it's job is a very rewarding experience. Of course we also had other machines that were also very complex in their tasks, but we didn't consider them robots. CNC mills and lathes, pipe benders, other machines that ran autonomously that also had to be programmed and synchronized with the flow of production. Sometimes the line resembled a kind of demented Rube Goldberg contraption, but we were somewhat strict to define only the articulated manipulators themselves as robots.
So when I saw this pile of servos in a glass cleanroom set to the over-dramatic theme of "Bonanza Reloaded", I thought, "Yeah, that's nice, but... It just doesn't strike me as a 'robot' so much as it does an automated bio lab."
And yes, I realize there were clearly robots within the cell, but calling the unit as a whole a "robot" just irks me a little.
Of course in the spirit of all the other bad jokes I've seen posted, do you think this "robot" will use it's genetic findings with the yeast cells to perfect the most delicious and moist cake recipe ever?
Re:Eh hehh... (Score:3, Informative)
Re:Robot discovers Humans "unnecessary"... (Score:2, Informative)
Awwwww. I've got mod points but I've already posted above. That really made me chuckle.
Your description above of guessing and stats is a really good non-technical description of how the system works. The first step is to actually analyse what is already known about the problem domain, then some guesswork is applied about how to improve that knowledge. The nice thing about King's work is those guesses translate directly into automated experiments, and then the system can close the loop - the results can be automatically analysed and integrated with the pre-existing knowledge in the system.
I saw King give a talk about the system last year and it is really impressive work. It looks like the first tentative steps towards building a functioning AI for a non-trivial domain. I haven't read the article so I don't know if they tossed in the future plans with the lasers, but that is just too cool for words.