Academia is supposed to be a place where creative types can be free, and with that freedom accomplish great things, whether it be new art, breakthrough treatises, scientific discoveries, or feats of engineering. But academia isn’t what it used to be, and to provide some insights into some of its problems, I compared notes with friend and former colleague, Nick Cassimatis, who is associate professor in the Department of Cognitive Science at Rensselaer. In our own ways, he and I have found severe limitations in academia today, limitations that led to my leaving academia to co-found a research institute, 2AI to be funded by intellectual property, and that led Nick to start his own company outside academia, SkyPhrase in order to achieve his ambitions in artificial intelligence.

Nick’s romantic ambitions started early – he began research into artificial intelligence and natural language at the precocious age of fifteen, and wrote a French-to-English translation program that helped put him on the Top-20 High School Students List by USA Today. More than simply artificial intelligence, his aim is to understand human-level intelligence, and how it can come about via many unintelligent parts. “Language is good piece of that problem to work on because human language can do so much more than any other animal language, and because humans can talk about almost anything, it must be a really general capacity,” wrote Nick to me in our discussion.

At home in philosophy as well as computer science, mathematics, linguistics and cognitive science, Nick has his own broad philosophical approach driving his AI, one he calls his Cognitive Substrate Hypothesis, that “a relatively small set of properly integrated data structures and algorithms can underlie the whole range of cognition required for human-level intelligence” (consonant with my own approach to understanding human intelligence in Harnessed). With this theoretical backbone in hand, Nick has for the last decade been building his own master solution to human-level AI, called Polyscheme, and he’s used traditional academic funding mechanisms.

But these traditional mechanisms aren’t enough, not for achieving human-level AI which is what he describes as “extraordinary science,” more akin to achieving immortality in medicine rather than treatment. Because of its “extraordinary” status, he believes there are fundamental differences in how scientists must behave in order to achieve it. AI that adopts traditional, incremental scientific methods is doomed to failure. ...doomed, as Nick says, to climbing mountains to reach the moon.

And this is, in part, what has led Nick to go beyond academia in his quest to achieve his goals. Lucky for him, achieving human-level intelligence is not only extraordinary, but extraordinarily useful. “The technological applications are vast, as well. There are so many things that people can formulate easily in language, but which are very difficult to do with computers. If you could bridge that gap, the pace of progress in many fields that depend on computers, in both science and business, would accelerate significantly.”

Nick’s approach to AI and natural language differs from mainstream computational linguistics in his focus on developing algorithms that fuse the properties of logical reasoning with those of more neutrally-inspired forms of processing. “Some aspects of language are best dealt with using logical structures and reasoning, while other aspects of language require the more statistically oriented algorithms one often sees in models of neural processing.”

Enter SkyPhrase ( ), a new “natural language technology” start-up company he started with two of his students, letting users do complicated things simply using natural language. At the moment it is up and running for intelligent search within Gmail and Twitter, but it will soon be extended into many other domains, including product and flight searches, and eventually to longer-term applications such as, say, data analysis.

In fact, the technology underlying SkyPhrase makes moving to new applications relatively easy. “Our approach is based on the belief that both logical and statistical information are important One result is that we are developing an API, which we are using internally now, that makes it very simple to build language models for specific domains. For example, the linguistic part of our Twitter service took one developer only about two or three weeks of effort.”

Although we all take it for granted, our everyday ability to communicate with natural language allows us to state very complicated and precise things, and what we’d like is technology that actually understands what you mean when you speak as you normally do. “This increased complexity makes all kinds of new applications and use cases possible. Our long term goal is to get all the data and services on the web surfaced in natural language so that consumers, businesses and scientists can do a lot more of what they want with devices and the internet much more simply and quickly.”

For example, Siri can understand something like “tweets from markchangizi”, but not something like "tweets from markchangizi tagged 2ai that mention blood flow". And Nick provided me with the following list of Twitter and Gmail queries that SkyPhrase understands but Siri doesn’t: [Twitter] “tweets from nasa with pictures”, “tweets about romney last week”, “yesterday's tweets about the debate”, “search twitter for pictures of nasa”, “tweets about nasa or debate”, [Gmail] “emails that jane sent me during the holidays containing pictures”, “emails tagged receipts from Southwest the last three months”, “find pictures from jerry regarding italy”, “PDFs john sent with subject ‘resume’ ”.

Now that you know something about Professor Nick Cassimattis – his research, his start-up – here are our thoughts on several questions about entrepreneurism and the troubles with academia.


1. What is broken about academia?

Nick Cassimatis:

My research has been aimed at addressing the gaps between human and computer languages. Human language is often vague, ambiguous, ill-formed and incomplete. Computer languages are specifically designed to not have those properties, and they must because of the limited intelligence of computers. My research approach was aimed at bridging this gap by enabling linguistic processing to be integrated with general reasoning abilities. This way, language processing can rely on general reasoning to use context and background knowledge to deal with all the messiness of human language. This approach is consistent with my Cognitive Substrate Theory (and the approach you lay out in Harnessed) which claims that language processing occurs using domain-general reasoning abilities.

Since our goal is to actually identify mechanisms that are powerful enough to achieve human-level intelligence, the best way we have of proving that our theory is correct is to actually implement it and show it actually understands language at a human level. It's actually surprisingly difficult to get research like this published and supported within normal academic communities because they are more interested with smaller, incremental results that can be precisely quantified. It is very difficult to get academic papers about complex systems published in the quantities you need to thrive in academia.

The entrepreneurial endeavor helps out here. There is a great premium put on working systems that are actually useful in industry, and that is what we are trying to deliver with SkyPhrase. What we have achieved so far is still a long way from human-level language understanding, but also a significant advance over the past state of the art.

Me (Mark Changizi):

One of the problems with academia has nothing to do with any particular kind of research. Rather, one of the problems with academia today is that one’s career progress is disproportionately linked to bringing in money (almost always government money). When one asks oneself how to best ensure getting a grant, the answer is invariably, “Keep doing more of whatever got money before.” That means doing whatever one’s graduate student or postdoc advisor did. And once one gets that first grant, the next grant will tend to stay in the same area – too risky to move far from there. A couple decades can flash by, and one has had a “successful” career of many grants all within this area, an area they often only haphazardly landed in by virtue of whose lab they found a place in back in graduate school. (Of course, the scientist often feels that he or she is moving dynamically to new areas, but that’s usually only because he or she is so steeped in the issues that the tiny shifts seem mighty. They’re usually not.) The freedom to truly move laterally in order to try one’s head in unrelated areas where one might have some insight and interest is stunted.