A new paper in PLoS Computational Biology is getting a lot of attention (which event, while not trying to be snarky about it, is not something that happens every day). Here’s the press release, which I can guarantee that most of the articles written about this work will be based on. That’s because the paper itself becomes heavy going after a bit – the authors (from Tufts) have applied machine learning to the various biochemical pathways involved in flatworm regeneration.
That in itself sounds somewhat interesting, but not likely to attract the attention of the newspapers. But here’s the claim being made for it:
An artificial intelligence system has for the first time reverse-engineered the regeneration mechanism of planaria–the small worms whose extraordinary power to regrow body parts has made them a research model in human regenerative medicine.
The discovery by Tufts University biologists presents the first model of regeneration discovered by a non-human intelligence and the first comprehensive model of planarian regeneration, which had eluded human scientists for over 100 years.
The “100 years” part is hyperbole, because it’s not like people have been doing a detailed mechanistic search for that amount of time. Biology wasn’t up to the job, as the earlier biologists well knew. But is the artificial intelligence part hyperbole, or not? As the many enzymes and other proteins involved in planarians have been worked out, it has definitely been a challenge to figure out what’s doing what to what else for which reasons, and when. (That’s the shortest description of pathway elucidation that I can come up with!) The questions about this work are (1) is the model proposed correct (or at least plausibly correct)? (2) Was it truly worked out by a computational process? And (3) does this process rise to the level of “artificial intelligence”?
We’ll take those in order. I’m actually willing to stipulate the first point, pending the planarian people. There are a lot of researchers in the regeneration field who will be able to render a more meaningful opinion than mine, and I’ll wait for them to weigh in. I can look at the proposed pathways and say things like “Yeah, beta-catenin would probably have to be involved, damn thing is everywhere. . .yeah, don’t see how you can leave Wnt out of it. . .” and other such useful comments, but that doesn’t help us much.
What about the second point? What the authors have done is apply evolutionary algorithms to a modeled version of the various pathways involved, and let it rip, rearranging and tweaking the orders and relationships until it recapitulates the experimental data. It is interesting that this process didn’t spit out a wooly Ptolemaic scheme full of epicycles and special pleading, but rather a reasonably streamlined account of what could be going on. The former is always what you have to guard against with machine-learning systems – overfitting. You can make any model work if you’re willing to accept sufficient wheels within wheels, but at some point you have to wonder if you’re optimizing towards reality.
How close is the proposed scheme to what people already might have been thinking (or might have already proposed themselves?) In other words, did we need a ghost come from the grave to tell us this? I am not up on the planarian stem-cell literature, but my impression is that this new model really is more comprehensive than anything that’s been proposed before. It provides testable hypotheses. For example, it interprets the results of some experiments as inferring the existence of (yet unknown) regulatory molecules and genes. (The authors present candidates for two of these, and I would guess that experimental evidence in this area will be coming soon).
It’s also important to note, as the authors do, that this model is not comprehensive. It only takes into account 2-D morphology, and has nothing to say about (for example) the arrangement of planarian internal organs. This, though, seems to be a matter of degree, only – if you’re willing to collect more data, code it up, and run the model for longer after doing some more coding on it, its successor should presumably be able to deal with this sort of thing.
And that brings us to point three: is this a discovery made via artificial intelligence? Here we get into the sticky swamp of defining intelligence, there to recognize the artificial variety. The arguments here have not ceased, and probably won’t cease until an AI hosts its own late-night talk show. Is the Siri software artificial intelligence? Are the directions you get from Google Maps? A search done through the chemical literature on SciFinder or the like? An earlier age would have probably answered “yes” (and an even earlier age would have fled in terror) but we’ve become more used to this sort of thing.
I think that one big problem in this area is that the word “intelligence” is often taken (consciously or not) to mean “human intelligence”. That doesn’t have to be true, but it does move the argument to whether border collies or African grey parrots demonstrate intelligence. (Personally, I think they do, just at a lower level and in different ways than humans). Is Google Maps as smart, in its own field, as a border collie? As a hamster? As a fire ant, or a planarian? Tough question, and part of the toughness is that we expect intelligence to be able to handle more than one particular problem. Ants are very good at what they do, but they seem to me clearly to be bundles of algorithms, and is a computer program any different, fundamentally? (Is a border collie merely a larger bundle of more complex algorithms? Are we? I will defer discussion of this disturbing question, because I see no way to answer it).
One of the hardest parts of the work in this current paper, I think, was the formalization step, where the existing phenomena from the experimental literature were coded into a computable framework. Now that took intelligence. Designing all the experiments (decades worth) that went into this hopper took quite a bit of it, too. Banging through it all, though, to come up with a model that fit the data, tweaking and prodding and adjusting and starting all over when it didn’t work – which is what the evolutionary algorithms did – takes something else: inhuman patience and focus. That’s what computers are really good at, relentless grinding. I can’t call it intelligence, and I can call it artificial intelligence only in the sense that an inflatable palm is an artificial tree. I realize that we do have to call it something, though, but the term “artificial intelligence” probably confuses more than it illuminates.