Posts Tagged ‘AI’
In what has become a bad couple of months for computer science, John McCarthy, the father of artificial intelligence, died in late October at the age of 84.
McCarthy was a giant in the field of computer science and a seminal figure in artificial intelligence, defining the field for more than five decades. After studying at Caltech and Princeton, with brief professorships at Stanford and Dartmouth College, he went to MIT where he, along with his colleague Marvin Minsky, founded the MIT Artificial Intelligence Project. In 1962, he transferred to Stanford’s newly formed Department of Computer Science, where he founded and led the Stanford Artificial Intelligence Laboratory (SAIL). McCarthy was known for bringing scientific rigor to every aspect of life and for his wry, often self-deprecating sense of humor. This was best exemplified in a personal philosophy he termed “radical optimism” –- a positive outlook so strong that he said “Everything will be OK even if people don’t take my advice.“. Even by the end of his early days at MIT, he was already affectionately referred to as “Uncle John” by his students.
Most remarkable about his contributions are their diversity, their depth, and how they span both theory and practice: logical AI, advances in lambda calculus and the theory of computation, the Lisp programming language, garbage collection, design of the ALGOL programming language, popularising time-sharing, modern program verification (and with the benefit of hindsight it seems that he came remarkably close to denotational semantics), circumscription logic for non-monotonic reasoning and computer chess. He won the ACM Turing Award in 1971, the Kyoto Prize in 1988 and the US National Medal of Science in 1990.
There have been a huge number of excellent obituaries to John McCarthy, for example by SAIL, Stanford, CACM, The New York Times, The Guardian, as well as an excellent article in Nature by Vladimir Lifschitz. There is also much to be mined from his SAIL web pages.
As soon as it works, no one calls it AI any more.
John McCarthy (in response to the over-promises of AI in the late 1970s and early 1980s)
Turning seemingly innocuous comments into sexual innuendo by adding the words “That’s what she said” (TWSS) has become a (chiefly American, occasionally annoying) cultural phenomenon. Unfortunately, identifying humour and double entendre through software is hard. This is interesting to me from a research perspective: I am interested in the wider area of knowledge representation and reasoning, particularly declarative problem-solving. It is hard to perform sentiment analysis and infer meaning from human language statements that can have non-standard structures, particularly if you want to do it with large-scale datasets (think Twitter, et al.).
For many years, artificial intelligence researchers have been trying to solve the natural language processing (NLP) problem. This field bridges computer science and linguistics and aims to build software that can analyse, understand and generate languages that humans use naturally, so that eventually you will be able to address your computer as though you were addressing another person. Natural language understanding is sometimes referred to as an AI-complete problem, because it requires extensive world knowledge and the ability to manipulate it; to call a problem AI-complete reflects an attitude that it cannot be solved by a simple algorithm. In NLP, the meaning of a sentence will often vary based on the context in which it is presented (since text can contain information at many different granularities), and this is something that is difficult to represent in software. When you add humour, puns and double entendre, this can get substantially harder.
But maybe the first steps have been made: a recent paper (That’s What She Said: Double Entendre Identiﬁcation) by Chloe Kiddon and Yuriy Brun, computer scientists from the University of Washington, presents a software program capable of understanding a specific type of humour, the TWSS problem: “Double Entendre via Noun Transfer” or DEviaNT for short.
Kiddon and Brun’s approach consists of three functions that are used to score words based on a number of sample sentences sourced from either an erotic corpus or from the Brown corpus, the standard used in this field. And this was the part that caught my geek attention: the noun sexiness function, NS(n), rates nouns based on their relative frequencies and whether they are euphemisms for sexually explicit nouns. For example, words with high NS(n) scores include “rod” and “meat”. The two other functions are the adjective sexiness function, AS(a), which detects adjectives such as “hot” and “wet”, and the verb sexiness function, VS(v).
These three functions are used to score sentences for noun euphemisms i.e. does a test sentence include a word likely to be used in an erotic sentence. Other scoring elements include the presence of adjectives and verbs combinations more likely to be used in erotic literature. Finally, they use information such as the number of punctuation and non-punctuation items in sentences. These scores were used to train the WEKA toolkit, an open source collection of machine learning algorithms for data mining tasks. Using this test set they were able to show a high level (around 72% accuracy) of identification of sentences which were suitable for TWSS-style jokes, while keeping false negatives to a minimum: the authors flagging that making the joke when the sentence is not appropriate is much worse than not making the joke when it is appropriate.
While this is preliminary work (the authors will be presenting it at the 49th Annual Meeting of the Association for Computation Linguistics: Human Language Technologies in June), the technique of metaphorical mapping may be generalised to identify other types of double entendres and other forms of humour.
Or maybe it’s just far too big to get a grip on…