Slashdot Mirror
Robot Makes Scientific Discovery (Mostly) On Its Own
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.
I knew that Ross was up to something bigger than protein secondary structure prediction when I met him 15 years ago at ICRF. He was a great Prolog fan then. Now he has probably bunch of graduate students coding for him.
I do not believe in karma. "Funny"=-6. Do good and forbid evil. Yours, Oft-Offtopic Flamebaiting Troll.
Yes, but there are no ethical rules against watching your two lab robots fuck each other.
I'm sure with the right thesis, you can get away with watching student volunteers fucking each other.
greed@All_Evils:~#
This reminds me of the Automated Mathematician (AM) program I read about in an AI course (or was it an old Byte magazine?). This program was programmed with a bunch of axioms, and basic strategies. It looked for "interesting things", like what happens when you apply identical arguments to a two argument function. As I recall, it discovered for itself the concept of prime numbers. It applied what it learned and came up with the theorem that all angles can be expressed as the sum of two prime angles (or something like that).
This seems to be doing the same thing: mixing and matching patterns, looking for interesting coincidences, and then testing for them. The only difference is that this is doing it with real world biological samples, and not abstract mathematical constructs.
When our name is on the back of your car, we're behind you all the way!
Not necessarily. The least elegant way to create strong AI is probably to brute force simulate a whole brain down to nearly every neurotransmitter molecule, something which futurists argue will be doable by supercomputers around 2020.
This is a worst case solution since it would imply that the brain is not understood yet and instead of having a simpler model that can provide the same level of strong AI we just throw raw power at it.
In this case, the AI would theoritically emerge out of the complexity of the system and although malicious intent wouldn't be programmed in (neither would anything else) the system might learn it by himself.
What's far more fascinating and promising is the development of hardware neural nets. To put it into perspective:
Since the neurons are so small, the system runs 100,000 times faster than the biological equivalent and 10 million times faster than a software simulation. "We can simulate a day in one second," Meier notes.
10 million times faster than software? That's like jumping from an abacus to a Pentium.
I just hope these folks continue to receive the funding they need.