AI research continues to break new
ground and, with the help of HPE
supercomputing power, another
breakthrough has occurred.
AI Background
Artificial Intelligence (AI) has been broadly defined as intelligence exhibited by
machines and a branch of AI called Deep Learning (DL) has garnered a lot of
attention in the past few years. DL, or hierarchical learning, is a subset of
Machine Learning that mimics the function of the human neocortex. The neo-
cortex is the part of the brain involved in complex brain functions such as sen-
sory perception, motor commands, spatial reasoning, language and cognition.
A company called DeepMind, which was acquired by Google in 2014, has been
driving many of the breakthroughs in DL, fueled by a lot of the data Google has
access to. Note that DL algorithms traditionally “learn” by digesting large
amounts of data. As we will see, there are new ways in which DL is starting to
learn.
The most recent breakthrough was achieved by Professor Tuomas Sandholm
and a team of his graduate students at Carnegie Mellon University running on
supercomputing hardware from HPE.
We will look at the three main breakthroughs that have occurred in the past 2+
decades, explain at a high level the different approaches that were used and
discuss some exciting possibilities for how this emerging technology might be
used in the future.
The First Breakthrough
Dial back to 1996-97 when a system called Deep Blue became the first AI pro-
gram to beat a reigning world chess champion. This system was originally
developed by Feng-hsiung Hsu at Carnegie Mellon University, but was com-
pleted after he joined IBM along with fellow team members Thomas Anantha-
raman and Murray Campbell. Chess has a relatively small playing board of 8x8
squares and while there are many possible moves at any point in a game, the
problem could be solved by brute-force computing power, which was provided
by IBM hardware at the time. That system was capable of evaluating 200 mil-
lion positions per second.
The basic approach created a generalized algorithm with a lot of parameters.
The system then calculated the optimal values of these parameters by analyz-
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