Given the choice between a flesh-and-blood doctor and an artificial intelligence system for diagnosing diseases, Pedro Domingos is willing to stake his life on AI. “I’d trust the machine more than I’d trust the doctor,” says Domingos, a computer scientist at the University of Washington, Seattle.

Lying close to the heart of AI’s revival is a technique called probabilistic programming, which combines the logical underpinnings of the old AI with the power of statistics and probability.

Bayesian networks aren’t enough by themselves because they don’t allow you to build arbitrarily complex constructions out of simple pieces. Instead it is the synthesis of logic programming and Bayesian networks into the field of probabilistic programming that is creating a buzz.

At the forefront of this new AI are a handful of computer languages that incorporate both elements, all still research tools. There’s Church, developed by Goodman, Tenenbaum and colleagues, and named after Alonzo Church who pioneered a form of logic for computer programming. Domingos’s team has developed Markov Logic Network, combining Markov networks – similar to Bayesian networks – with logic. Russell and his colleagues have the straightforwardly named Bayesian Logic (BLOG).

Russell demonstrated the expressive power of such languages at a recent meeting of the UN’s Comprehensive Test Ban Treaty Organization (CTBTO) in Vienna, Austria. The CTBTO invited Russell on a hunch that the new AI techniques might help with the problem of detecting nuclear explosions. After a morning listening to presenters speak about the challenge of detecting the seismic signatures of far-off nuclear explosions amidst the background of earthquakes, the vagaries of signal propagation through the Earth, and noisy detectors at seismic stations worldwide, Russell sat down to model the problem using probabilistic programming (Advances in Neural Information Processing Systems, vol 23, MIT Press). “And in the lunch hour I was able to write a complete model of the whole thing,” says Russell. It was half a page long.

Prior knowledge can be incorporated into this kind of model, such as the probability of an earthquake occurring in Sumatra, Indonesia, versus Birmingham, UK. The CTBTO also requires that any system assumes that a nuclear detonation occurs with equal probability anywhere on Earth. Then there is real data – signals received at CTBTO’s monitoring stations. The job of the AI system is to take all of this data and infer the most likely explanation for each set of signals.

Therein lies the challenge. Languages like BLOG are equipped with so-called generic inference engines. Given a model of some real-world problem, with a host of variables and probability distributions, the inference engine has to calculate the likelihood of, say, a nuclear explosion in the Middle East, given prior probabilities of expected events and new seismic data. But change the variables to represent symptoms and disease and it then must be capable of medical diagnosis. In other words its algorithms must be very general. That means they will be extremely inefficient.

The result is that these algorithms have to be customised for each new challenge. But you can’t hire a PhD student to improve the algorithm every time a new problem comes along, says Russell.

Daphne Koller of Stanford University, for instance, is attacking very specific problems using probabilistic programming and has much to show for it. Along with neonatologist Anna Penn, also at Stanford, and colleagues, Koller has developed a system called PhysiScore for predicting whether a premature baby will have any health problems – a notoriously difficult task. Doctors are unable to predict this with any certainty.

PhysiScore takes into account factors such as gestational age and weight at birth, along with real-time data collected in the hours after birth, including heart rate, respiratory rate and oxygen saturation (Science Translation Medicine, DOI: 10.1126/scitranslmed.3001304). “We are able to tell within the first 3 hours which babies are likely to be healthy and which are much more likely to suffer severe complications, even if the complications manifest after 2 weeks,” says Koller.

“Neonatologists are excited about PhysiScore,” says Penn. As a doctor, Penn is especially pleased about the ability of AI systems to deal with hundreds, if not thousands, of variables while making a decision. This could make them even better than their human counterparts. “These tools make sense of signals in the data that we doctors and nurses can’t even see,” says Penn.

This is why Domingos places such faith in automated medical diagnosis. One of the best known is the Quick Medical Reference, Decision Theoretic (QMR-DT), a Bayesian network which models 600 significant diseases and 4000 related symptoms. Its goal is to infer a probability distribution for diseases given some symptoms. Researchers have fine-tuned the inference algorithms of QMR-DT for specific diseases, and taught it using patients’ records. “People have done comparisons of these systems with human doctors and the [systems] tend to win,” says Domingos. “Humans are very inconsistent in their judgements, including diagnosis. The only reason these systems aren’t more widely used is that doctors don’t want to let go of the interesting parts of their jobs.”

There are other successes for such techniques in AI, one of the most notable being speech recognition, which has gone from being laughably error-prone to impressively precise (New Scientist, 27 April 2006, p26). Doctors can now dictate patient records and speech recognition software turns them into electronic documents, limiting the use of manual transcription. Language translation is also beginning to replicate the success of speech recognition.

Besides developing inference algorithms that are flexible and fast, researchers must also improve the ability of AI systems to learn, whether from existing data or from the real world using sensors.

FURTHER READING

Wikipedia – A Markov logic network (or MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all unsatisfiable statements have a probability of zero, and the set of all entailed formulas have probability one.

Bayesian logic (BLOG) is a first-order probabilistic modeling language under development at MIT and UC Berkeley. It is designed for making inferences about real-world objects that underlie some observed data: for instance, tracking multiple people in a video sequence, or identifying repeated mentions of people and organizations in a set of text documents. BLOG makes it (relatively) easy to represent uncertainty about the number of underlying objects and the mapping between objects and observations.

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