The concept of machine learning was brought to the forefront for the general masses when IBM’s Watson computer appeared on Jeopardy and wiped the floor with humanity. For those same masses, machine learning quickly faded from view as Watson moved out of the spotlight … or so they may think.
Machine learning is slowly and quietly becoming democratized. Goodreads, for instance, recently purchased Discovereads.com, presumably to make use of its machine learning algorithms to make book recommendations.
To find out more about what’s happening in this rapidly advancing field, I turned to Alasdair Allan, an author and senior research fellow in Astronomy at the University of Exeter. In an email interview, he talked about how machine learning is being used behind the scenes in everyday applications. He also discussed his current eSTAR intelligent robotic telescope network project and how that machine learning-based system could be used in other applications.
In what ways is machine learning being used?
Alasdair Allan: Machine learning is quietly taking over in the mainstream. Orbitz, for instance, is using it behind the scenes to optimize caching of hotel prices, and Google is going to roll out smarter advertisements — much of the machine learning that consumers are seeing and using every day is invisible to them.
The interesting thing about machine learning right now is that research in the field is going on quietly as well because large corporations are tied up in non-disclosure agreements. While there is a large amount of academic literature on the subject, it’s actually hard to tell whether this open research is actually current.
Oddly, machine learning research mirrors the way cryptography research developed around the middle of the 20th century. Much of the cutting edge research was done in secret, and we’re only finding out now, 40 or 50 years later, what GCHQ or the NSA was doing back then. I’m hopeful that it won’t take quite that long for Amazon or Google to tell us what they’re thinking about today.
How does your eSTAR intelligent robotic telescope network work?
Alasdair Allan: My work has focused on applying intelligent agent architectures and techniques to astronomy for telescope control and scheduling, and also for data mining. I’m currently leading the work at Exeter building a peer-to-peer distributed network of telescopes that, acting entirely autonomously, can reactively schedule observations of time-critical transient events in real-time. Notable successes include contributing to the detection of the most distant object yet discovered, a gamma-ray burster at a redshift of 8.2.
A diagram showing how the eSTAR network operates. The Intelligent Agents access telescopes and existing astronomical databases through the Grid. CREDIT: Joint Astronomy Centre. Eta Carinae image courtesy of N. Smith (U. Colorado), J. Morse (Arizona State U.), and NASA.
All the components of the system are thought of as agents — effectively “smart” pieces of software. Negotiation takes place between the agents in the system. each of the resources bids to carry out the work, with the science agent scheduling the work with the agent embedded at the resource that promises to return the best result.
This architectural distinction of viewing both sides of the negotiation as agents — and as equals — is crucial. Importantly, this preserves the autonomy of individual resources to implement observation scheduling at their facilities as they see fit, and it offers increased adaptability in the face of asynchronously arriving data.
The system is a meta-network that layers communication, negotiation, and real-time analysis software on top of existing telescopes, allowing scheduling and prioritization of observations to be done locally. It is flat, peer-to-peer, and owned and operated by disparate groups with their own goals and priorities. There is no central master-scheduler overseeing the network — optimization arises through emerging complexity and social convention.
How could the ideas behind eSTAR be applied elsewhere?
Alasdair Allan: Essentially what I’ve built is a geographically distributed sensor architecture. The actual architectures I’ve used to do this are entirely generic — fundamentally, it’s just a peer-to-peer distributed system for optimizing scarce resources in real-time in the face of a constantly changing environment.
The architectures are therefore equally applicable to other systems. The most obvious use case is sensor motes. Cheap, possibly even disposable, single-use, mesh-networked sensor bundles could be distributed over a large geographic area to get situational awareness quickly and easily. Despite the underlying hardware differences, the same distributed machine learning-based architectures can be used.
At February’s Strata conference, Alasdair Allan discussed the ambiguity surrounding a formal definition of machine learning:
This interview was edited and condensed.