Reclaim the Lost Promise of the Semantic Web

I was eager to learn about the latest developments in the Semantic Web through the lens of a "new kind of semantics" as Abraham Bernstein et al. explored in their Viewpoint "A New Look at the Semantic Web" (Sept. 2016), but by the end I had the impression the entire vision of a Semantic Web was somehow at risk.

If I understand it correctly, semantics is a mapping function that leads from manifest expressions to elements in a given arbitrary domain. Based on set theory, logicians have developed a framework to set up such mapping for formal languages like mathematics, provided one can fix an interpretation function. On the other hand, 20^th-century logicians (notably Alfred Tarski) warned of the limits of the framework when applied to human languages. Now, to the extent it embraces a set-theoretic semantics (as in the W3C’s Ontology Web Language), the Semantic Web seems to be facing exactly such limitations or experiencing, dealing with, and suffering them.

Most Web content is expressed as natural language, and it is not easy for programmers to bring it into clean logical form; meanwhile, Percy Liang’s article "Learning Executable Semantic Parsers for Natural Language Understanding" (also Sept. 2016) gave an idea of the early stage of "semantic parsing," or the task of obtaining a formal representation of the meaning of a given text. It seems the "new semantics" in Bernstein et al., albeit not formally characterized, was an attempt to outline a better approach to tapping the linguistic nature of the Web, which is indeed remarkable.

In taking a language-oriented view, however, Bernstein et al. seemed to neglect a key feature of formal semantics—transparency. They seem comfortable with the relaxation of logic as a conceptual framework for the Semantic Web, which is typical of modern Knowledge Graphs (such as the one Google uses). But one of the consequences of such relaxation is that part of data semantics ends up being embedded in algorithms. Not only practitioners but also common users are aware that algorithms that work on Web data are embedded in only a few monolithic, private platforms that are far from open, transparent, and auditable.

Isn’t keeping meanings in a handful of proprietary algorithms exactly the opposite of what the Semantic Web was meant to be?

Guido Vetere, Rome, Italy

Back to Top

Authors Respond:

As we mentioned in the Viewpoint, the Semantic Web is not just about texts but also about myriad data, images, video, and other Web resources. While a formal logic that could be both transparent enough for all such resources and yet usable by Web developers is a noble ambition, current logics are simply not up to the task. The transparency of "some semantics" is the best to hope for and would allow all potential developers to build Web-scale, best-effort applications.

Abraham Bernstein, Zürich, Switzerland, James Hendler, Troy, NY, and Natalya Noy, Mountain View, CA

Back to Top

More Than a Red Flag for AIs

Toby Walsh’s Viewpoint "Turing’s Red Flag" (July 2016) proposed a legislative remedy for various potential threats posed by software robots that humans might mistake for fellow humans. Such an approach seems doomed to fail. First, unless a "red flag" law would be adopted by all countries, we humans would have the same problems identifying and holding accountable violators we have with cybercrime generally. Second, though Walsh acknowledged it would take a team of experts to devise an effective law, it would likely be impossible to devise one that would address all possible interactions with non-humans or not lead to patently silly regulations, as with the original 19^th-century Red Flag Act. How would a law handle algorithm-based securities trading? How about if one human is dealing with another human, but that human has an AI whispering in his or her ear (or implanted in his or her brain) telling them what to say or do?

More important, the most significant potential harms from bots or sophisticated AIs generally would not be mitigated by just knowing when we are dealing with an AI. The harm Walsh proposed to address seemed more aimed at the "creep factor" of mistaking AIs for humans. We have been learning to deal with that since we first encountered a voicemail tree or political robocall. Apart from suffering less emotional shock, what advantage might we gain from knowing we are not dealing with a fellow human?

Learning to live with AIs will involve plenty of consequential challenges. Will they wipe us out? Should an AI that behaves exactly like a human—emotional responses and all—have the legal rights of a human? If AIs can do all the work humans do, but better, how could we change the economic system to provide some of the benefits of abundance made possible by AI-based automation to the 99% whose jobs might be eliminated? Moreover, what will we humans do with our time? How will we even justify our existence to ourselves? These sci-fi questions are quickly becoming real-life questions, requiring we have more than a red flag to address them.

Martin Smith, McLean, VA

Back to Top

Author Responds:

This critique introduces many wider and orthogonal issues like existential risk and technological unemployment. Yes, it will be difficult to devise a law to cover every situation. But that is true of most laws and does not mean we should have no law. However, actions speak loudest, and the New South Wales parliament has just recommended such a law in Australia; for more, see http://tinyurl.com/redflaglaw.

Toby Walsh, Berlin, Germany

Back to Top