How GPUs, AI and Deep
Learning Make Smart
By Alianna J. Maren, Ph.D.
AI vs. Standard Computing
Advances in artificial intelligence (AI), advanced computing
architectures, and low-cost, high-performance sensors
are enabling the development of a growing number of
commercial applications for autonomous vehicles, drones,
robots and other so-called “autonomous edge devices”.
Since AI combined with deep learning (DL) is a relatively new
field, we need to first understand how AI/DL systems differ
from conventional embedded systems.
Jensen Huang, co-founder & CEO of NVIDIA, summarizes
the difference between AI and standard computing with this
phrase: “AI is just the modern way of doing software.” Since
AI-based systems are, at their core, still digital computers,
the tools used to develop AI/DL applications have much in
common with those used to program conventional computing
applications. There are still functions and many aspects that
programmers will recognize.
However, there some aspects of how AIs work, and how
they are prepared to support a specific application, that
will seem strange, if not downright alien, to the average
code-slinger. These differences arise from some basic
characteristics of an AI:
(1) A world model that provides the AI with a “machine’s-
eye view” of the world it is operating in.
( 2) The AI’s ability to learn from new information and
update its world model.
( 3) The AI’s ability to make inferences that allow it to deal
with situations that it has not specifically observed or learned
These unique characteristics are responsible for one
of the most fundamental differences between creating
traditional software and AI development. When programming
a conventional computer, every response has to be prespecified by the programmer. In contrast, a good deal
of programming an AI involves allowing it to “learn” from
examples of situations it will encounter, and training it to
produce the responses it is expected to give (Figure 1).
The ability to learn and adapt without additional
programming enables AI-based systems to do many things
that were difficult or impossible to do with conventional
computing. It will also become apparent that AI systems
require much more powerful computing elements and
present the developer with a number of new and complex
Figure 1: Simulation-based training allows robots and other
AI-based applications to build deep, complex, knowledge
bases that include worst-case conditions before they are
deployed in the real world. Image source: NVIDIA