Artificially Intelligent Computer Outperforms Humans on IQ Test
Artificially Intelligent Computer Outperforms Humans on
IQ Test
The deep learning machine can reach the intelligence
level between people with bachelor degrees and people with master degrees
By Sage Lazzaro | 06/15/15 11:46am
For decades, the Intelligence Quotient (IQ) has had
numerous uses for humans but little importance when it comes to computers. With
the focus and importance of AI research increasing, it was only a matter of
time before we used this measure of intelligence to compare humans and
machines.
Researchers from Microsoft and the University of Science
and Technology of China built a deep learning machine that outperforms the
average human on the types of problems that have always been toughest for
computers, according to their study.
The test contains three categories of questions: logic
questions (patterns in sequences of images); mathematical questions (patterns
in sequences of numbers); and verbal reasoning questions (questions dealing
with analogies, classifications, synonyms and antonyms). Computers have never
been too successful at solving problems belonging to the final category, verbal
reasoning, but the machine built for this study actually outperformed the
average human on these questions.
In the past, computer scientists have used data mining
techniques to analyze sets of texts to find links between words they contain
and determine how those words relate to each other. This method has worked
successfully for translating and other tasks, but it functions by assuming each
word has a single meaning. Verbal tests as well as other tasks computer
scientists are looking to accomplish with machines tend to focus on words with
more than one meaning.
The researchers found it necessary to go beyond existing
technologies to automatically solve verbal comprehension questions, so they
created a framework consisting of three components.
The first element is a classifier that recognizes the
specific type of a verbal question (Is it an analogy, classification, synonym
or antonym problem?) The second prong involves leveraging a word embedding
method that considers the multi-sense nature of words and the relational
knowledge among words contained in dictionaries. Lastly, for each specific type
of question, they developed a simple yet effective solver based on the obtained
distributed word representations and relation representations. The overall
result is a technique for recognizing the different meanings a word can have.
The researchers had the deep learning machine and 200
human subjects at Amazon’s Mechanical Turk crowdsourcing facility answer the
same verbal questions. The result: their system performed better than the
average human.
“Our RK model can reach the intelligence level between
the people with the bachelor degrees and those with the master degrees, which
also implies the great potential of the word embedding to comprehend human
knowledge and form up certain intelligence,” the report states, based on the
education levels of the human participants.
It looks like old Atari games aren’t the only thing
computers are dominating these days.
Comments
Post a Comment