Astute followers of artificial intelligence may recall a
moment from three years ago, when Google announced it had birthed unto
the world a computer able to recognize cats using only videos uploaded by YouTube users. At the time, this represented something of a high water mark in AI. To get an idea for how far we have come since then, one has only to reflect on recent advances in the RoboWatch project, an endeavor that is teaching computers to learn complex tasks using instructional videos posted on YouTube.
That innocent “learn to play guitar” clip you posted on your
YouTube video feed last week? It may someday contribute to putting
Carlos Santana out of a job. That’s probably pushing it; it’s more
likely that thousands of home nurses and domestic staff will be axed
long before guitar gods have to compete with robots. A recent
groundswell of interest in bringing robots into the marketplace as
caregivers for the elderly and infirm, in part fueled by graying
population bases throughout the developed world, has created the
necessity for teaching robots simple household tasks. Enter the
RoboWatch project.
Most advanced forms of AI currently in use rely upon a branch of supervised machine learning, which
requires large datasets to be “trained” on. The basic idea is that when
provided with a sufficiently large database of labeled examples, the
computer can learn to recognize what differentiates the items within the
training set, and later apply that classifying ability to new instances
it encounters. The one drawback to this form of artificial intelligence
is that it requires large databases of labeled examples, which are not
always available or require much human curation to create.
RoboWatch is taking a different tack, using what’s called
unsupervised learning to discover the important steps in YouTube
instructional videos without any previous labeling of data. Take for
instance a YouTube video on omelet making. Using the RoboWatch method,
the computer successfully parsed the video on omelet creation and
catalog the important steps without having first been trained with
labeled examples.
Color code activity steps and automatically generated captions, all created by the RoboWatch algorithm for making an omelet.
It was able to do this by looking at a large amount of
instructional omelet-making videos on YouTube and creating a universal
storyline from their audio and video signals. As it turns out, most of
these videos will contain certain identical steps, such as cracking the
eggs, whisking them in a bowl, and so on. When presented with enough
video footage, the RoboWatch algorithm can tease out what the essential
parts of the process are and what is arbitrary, creating a kind of
archetypal omelet formula. It’s easy to see how unsupervised learning
could quickly enable a robot to gain a vast assortment of practical
household know-how while keeping human instruction to a minimum.
The RoboWatch project follows similar advances in video
captioning pioneered at Carnegie Mellon University. Earlier this year,
we reported on a project headed by Dr. Eric Xing, which seeks to use real-time video summarization
to detect unusual activity in video feeds. This could lead to
surveillance cameras with the built-in ability to detect suspicious
activity. Putting these developments together, it’s clear unsupervised
learning models using video footage are likely to pave the way for the
next breakthrough in artificial intelligence, one that will see robots
entering our lives in ways that are likely to both scare and fascinate
us.
Post a Comment