Douglas Adams was right – knowledge without understanding is meaningless | John Naughton

Using supercomputers to explain life, the universe and everything takes us into territory previously only laughed at

Fans of Douglas Adams’s Hitchhiker’s Guide to the Galaxy treasure the bit where a group of hyper-dimensional beings demand that a supercomputer tells them the secret to life, the universe and everything. The machine, which has been constructed specifically for this purpose, takes 7.5m years to compute the answer, which famously comes out as 42. The computer helpfully points out that the answer seems meaningless because the beings who instructed it never knew what the question was. And the name of the supercomputer? Why, Deep Thought, of course.

It’s years since I read Adams’s wonderful novel, but an article published in Nature last month brought it vividly to mind. The article was about the contemporary search for the secret to life and the role of a supercomputer in helping to answer it. The question is how to predict the three-dimensional structures of proteins from their amino-acid sequences. The computer is a machine called AlphaFold. And the company that created it? You guessed it – DeepMind.

Proteins are large biomolecules constructed from amino acid residues and are fundamental to all animal life. They are, says one expert, “the most spectacular machines ever created for moving atoms at the nanoscale and often do chemistry orders of magnitude more efficiently than anything that we’ve built”.

But these vital biomachines are also inscrutable because they assemble themselves into structures of astonishing complexity and beauty. (Illustrations of them make one think of what can go wrong when trying to wrap Christmas presents with those nice ribbons that only shop assistants can manage.) Understanding this “folding” process is one of the key challenges in biochemistry, partly because proteins are necessary for virtually every cell in a body and partly because it’s suspected that mis-folding may help to explain diseases such as diabetes, Alzheimer’s and Parkinson’s.

A representation of folding proteins as predicted by the DeepMind AlphaFold computer.
A representation of folding proteins as predicted by the DeepMind AlphaFold computer. Photograph: PR Handout

So the question “How do proteins fold?” is definitely worth asking. The traditional way of answering it was by lab-based x-ray crystallography, which is expensive and slow. So researchers have turned to building computer models that simulate the folding process and predict protein structures. For some years, specialists in the field have run a biennial competition in critical assessment of protein structure prediction (CASP), where teams are challenged to design computer programs that predict protein structures from amino sequences.

Two years ago, DeepMind, having conquered the board game Go, decided to take on the challenge, using the deep-learning technology it had developed for Go. The resulting machine was, predictably, named AlphaFold. At the CASP meeting last December, it unveiled the results. Its machine was, on average, more accurate than the other teams and by some criteria it was significantly ahead of the others. For protein sequences modelled from scratch – 43 of the 90 – AlphaFold made the most accurate prediction for 25 proteins. Its nearest rival only managed three.

These results seem to have had a seismic impact on many of the researchers present. The atmosphere and the implications were summed up in a remarkable blog post entitled “What Just Happened?” by Harvard’s Mohammed AlQuraishi, a world expert in the field. On the one hand, he was judiciously cautious about the contribution of the DeepMind team. It represented “substantial progress, more so than usual”. But does that mean the problem is solved or nearly so? “The answer right now,” he concludes, “is no. We are not there yet. However, if the [AlphaFold-adjusted] trend… were to continue, then perhaps in two CASPs, ie four years, we’ll actually get to a point where the problem can be called solved.”

On the other hand, AlQuraishi also discussed the existential angst generated by AlphaFold in the young scientists present at the event. Their underlying concern, he says, was “whether protein structure prediction as an academic field has a future, or whether… the best research will from here on out get done in industrial labs, with mere breadcrumbs left for academic groups”. Young biochemists will have to decide whether it’s good for their careers to continue working on structure prediction. For some (many?) of them, it may make sense to go into industrial labs, while for others it will mean staying in academia but shifting to entirely new problems that avoid head-on competition with DeepMind.

Underpinning all this, though, is a deeper question. Reaching a scientific explanation of how protein folding works will be a gigantic intellectual task. (In 1969, the molecular biologist Cyrus Levinthal formulated a famous paradox: as any protein can fold in an astronomically large number of ways it would take longer than the universe has existed for every configuration to be tested, yet most small proteins fold spontaneously in milliseconds. Nobody knows how.)

It’s conceivable that a machine-learning approach will soon enable us to make accurate predictions of how a protein will fold and this may be very useful to know. But it won’t be scientific knowledge. After all, AlphaFold knows nothing about biochemistry. We’re heading into uncharted territory.

The Hitchhiker’s Guide to the Galaxy: 42.

What I’m reading

Sale of the century
Machine learning used to be an exotic technology. Now Timothy B Lee argues in Vox that it’s being commoditised. That may not be as good as it sounds.

What’s your poison?
If some algorithms can have harmful psychological effects on users, shouldn’t they be regulated like pharmacological drugs? There’s an interesting argument about it in Wired’s opinion section.

Progressive learning
Should “progress” be an academic subject? Read Diane Coyle’s thoughtful essay on the matter on the Project Syndicate website.

Contributor

John Naughton

The GuardianTramp

Related Content

Article image
No one can read what’s on the cards for AI’s future | John Naughton
AI is now beating us at poker, but not even Google co-founder Sergey Brin can say with any certainty what the next steps for machine learning are

John Naughton

29, Jan, 2017 @7:00 AM

Article image
Can Google’s AlphaGo really feel it in its algorithms? | John Naughton
When game-playing system AlphaGo defeated a master of the Chinese game go, its creators could not explain why. Is this a sign of intuitive AI?

John Naughton

31, Jan, 2016 @9:00 AM

Article image
The science stories that shook 2018
Our guest scientists pick the breakthroughs and discoveries that defined their year, from insights into human evolution to our first trip aboard an asteroid

Adam Rutherford, Jim Al-Khalili, Pete Etchells, Sheena Cruickshank, Callum Roberts, Julia Jones, Mark Miodownik, Athene Donald, Mark Jobling, Anil Seth, Jon Butterworth

23, Dec, 2018 @7:00 AM

Article image
Laughing parrots, backflipping robots and saviour viruses: science stories of 2017
Leading scientists pick the dozen most significant discoveries and developments of 2017

Liz Sockett, Sophie Scott, Adam Rutherford, Jim Al-Khalili, Amy Dickman, Beverley Glover, Callum Roberts, Jan Zalasiewicz, Danielle George, Mark Miodownik, Helen Czerski and Kevin Fong

24, Dec, 2017 @9:15 AM

Article image
The truth about artificial intelligence? It isn't that honest | John Naughton
Tests of natural language processing models show that the bigger they are, the bigger liars they are. Should we be worried?

John Naughton

02, Oct, 2021 @3:00 PM

Article image
Yes, DeepMind crunches the numbers – but is it really a magic bullet? | John Naughton
The machine learning outfit’s foray into pharmaceuticals could be very useful, but its grand claims should be taken with a pinch of salt

John Naughton

13, Nov, 2021 @4:00 PM

Article image
GPT-3: an AI game-changer or an environmental disaster? | John Naughton
The tech giants’ latest machine-learning system comes with both ethical and environmental costs

John Naughton

01, Aug, 2020 @3:00 PM

You won’t believe what the smarter robot is reading these days…
AI techies have discovered that the distinctive structure of the Mail Online is ideal for teaching machines intuitive language skills. Should we be worried?

John Naughton

21, Jun, 2015 @8:30 AM

Article image
How Amazon puts misinformation on your reading list | John Naughton
Algorithms routinely come up with ‘recommendations’ for anti-vax ‘bestsellers’ or juices that can cure cancer

John Naughton

08, Aug, 2020 @4:00 PM

Article image
Don’t believe the hype: the media are unwittingly selling us an AI fantasy | John Naughton
Journalists need to stop parroting the industry line when it comes to artificial intelligence

John Naughton

13, Jan, 2019 @7:00 AM