One of these
days I read about Dr. Andrew Ng.
When he was
a kid, he dreamed of building machines that could think like people, but when
he got to college and came face-to-face with the Artificial Intelligence (or
simply AI) research of the day, he gave up. Later, as a professor, he would
actively discourage his students from pursuing the same dream. Later on,
fortunately Professor Ng changed his mind back to his primary visions of AI. He
reportedly claims his 180o navigation course correction took place
when he ran into the “one algorithm” hypothesis, popularized by Jeff Hawkins,
an AI entrepreneur who’d dabbled in neuroscience research. And the dream
returned.
Well, Andrew
Ng (born 1976, Chinese: 吳恩達) is an associate professor in the Department
of Computer Science and the Department of Electrical Engineering by courtesy at
Stanford University, and he works as the Director of the Stanford Artificial
Intelligence Lab. He also co-founded Coursera, an online education platform,
with Daphne Koller. He researches primarily in artificial intelligence machine
learning, and deep learning. His early work includes the Stanford Autonomous
Helicopter project, which developed one of the most capable autonomous helicopters
in the world, and the STAIR (STanford Artificial Intelligence Robot) project,
which resulted in a Robot Operation System (ROS), a widely used open-source
robotics software platform. Ng is also the author or co-author of over 100
published papers in machine learning, robotics and related fields, and some of
his work in computer vision has been featured in a series of press releases and
reviews. In 2008, he was named to the MIT Technology Review TR35 as one
of the top 35 innovators in the world under the age of 35. In 2007, Ng was
awarded a Sloan Fellowship. For his work in Artificial Intelligence, he is also
a recipient of the Computers and Thought Aw (Wikipedia, 2013).Well at this time
and place I think of my friend Alicia Benjamin and try to explain what is an
algorithm.
In mathematics
and computer science, an algorithm is a step-by-step procedure, to be used for calculations as much as a cake recipe is worth for
making cakes. Algorithms
are used for calculation, data processing, and automated reasoning. It is an effective
method expressed as a finite, concise list or set of well-defined
instructions for calculating, for example, the value of a mathematical function.
Starting from an initial state and an initial “input”, the instructions work as
a combined set of actions designed to stimulate the whole process to proceed
through a finite number of well-defined successive states, eventually producing
an “output” and terminating at a final ending state.
According to
Ng, in the early days of AI, the prevailing opinion was that human intelligence
derived from thousands of simple agents working in concert, what MIT’s Marvin
Minsky called “The Society of Mind.”In his book Minsky brilliantly portrays the
mind as a "society" of tiny components that are themselves mindless.
To achieve
AI, engineers believed, they would have to build and combine thousands of
individual computing modules. One agent, or algorithm, would mimic language.
Another would handle speech. And so on. In short, they believed the brains
disposed of one algorithm to be used at a time and each one would deal specifically
with the task that fits its particular nature. Well, to “reproduce” such a
machine seemed an insurmountable feat.
The good
news as seen by Ng is the solidity of the concept introduced by Minsky aside
with the concept of “one algorithm” hypothesis popularized by Jeff Hawkins.
Deep
Learning is a first step in this new direction. Basically, it involves building
neural networks — networks that mimic the behavior of the human brain.
Much like the brain, these multi-layered computer networks can gather
information and react to it. They can build up an understanding of what objects
look or sound like.
Now it is
time to raise a simple question: What are the primary ideas behind the quest of
men after a society that offer more time to have fun and less time to spend
with bothering tasks of a mechanical daily life?
First, it was the Industrial
Revolution that brought about the transition to new manufacturing processes.
It occurred in the period from about 1760 to sometime between 1820 and 1840.
This transition included going from hand production methods to machines, new
chemical manufacturing and iron production processes, improved efficiency of water
power, the increasing use of steam power and development of machine tools. The
transition also included the change from wood and other bio-fuels to coal. The
Industrial Revolution began in Great Britain and within a few decades had
spread to Western Europe and the United States.
Then the Information Age, that I
will refer to by the acronym “IA” was advanced by a society marked by the
capitalization on the computer microminiaturization advances, with a transition
spanning from the advent of the personal computer in the late 1970s, to the
Internet's reaching a critical mass in the early 1990s, and the adoption of
such technology by the public in the two decades after 1990. Bringing about a
fast evolution of technology in daily life, as well as of educational life
style, the Information Age has allowed rapid global communications and
networking to shape modern society.
Next will be
the Artificial Intelligence “AI” Revolution with its machines or software, and is also a branch of computer
science that studies and develops intelligent machines and software. As we have
seen by the topics we discussed above, the central problems (or goals) of AI
research include reasoning, knowledge, planning, learning, communication, perception
and the ability to move and manipulate objects.
At the end
of the day, industry is becoming more information-intensive and less labor and
capital-intensive. This trend has important implications for the workforce;
workers are becoming increasingly productive as the value of their labor
decreases. However, there are also important implications for capitalism
itself; not only is the value of labor decreased, the value of capital is also
diminished. In the classical model, investments in human capital and financial
capital are important predictors of the performance of a new venture. However,
as demonstrated by Mark Zuckerberg and Facebook, it now seems possible for a
group of relatively inexperienced people with limited capital to succeed on a
large scale.
References:
1. A Glimpse into Google´s Brain Hidden
in a spreadsheet app; Access: http://www.buzzfeed.com/justinesharrock/a-glimpse-into-googles-brain-hidden-in-a-spreadsheet-app (accessed on: May 28, 2013);
2. Artificial Intelligence; Access: http://en.wikipedia.org/wiki/Artificial_intelligence
(accessed on: May 28, 2013)
3. Information Age; Access: http://en.wikipedia.org/wiki/Information_Age
(accessed on: May 28, 2013);
4. Marvin Minsk’s Homepage; Access: http://web.media.mit.edu/~minsky (accessed on: May 28, 2013);
5. Image: http://www.academicearth.org/courses/machine-learning/
(accessed on: May 28, 2013);