i3 - January/February 2018 - 43

MACHINE LEARNING
Systems that train algorithms to
perform tasks by learning from
previous data and examples
rather than explicit commands
programmed by humans.
NEURAL NETWORKS
Machine learning algorithms and
computational models designed
to function like neurons in the
human brain. They progressively
"learn" from data without being
explicitly programmed.

PREDICTIVE ANALYTICS
Programs that combine
techniques from data science,
statistics and AI to analyze
sets of structured and unstructured data, uncover patterns and
relationships, and use them to
make predictions about probable
future outcomes and events and
identify risks and opportunities.

micro-scopic images. An Entrupy device
takes microscopic photographs of different areas of a high-value item and
runs them through a computer that uses
proprietary algorithms. It claims
authentication accuracy above 96.4 percent, valuable to merchants and resellers of luxury products.
The proliferating efforts to use AI
tools are reaching into more industries
and the daily lives of more individuals.
Software developer SAS is putting visual
data mining, machine learning and
visual text analytics into its SAS
Platform and SAS Viya products, which
embed AI capabilities into products
used by its customers.
For example, SciSports finds football
stars via AI. The Netherlands-based
company uses SAS Viya to identify the
influence of individual players on team
results, track player development, determine potential market value for a player
and predict game results. It's a codified
version of the human expertise of the
Moneyball book and movie - but useable on a much wider scale. The AI process is used in conjunction with SciSport's
BallJames camera system for object
detection to access player movements

2018

AI AT
2018 CES

The AI opportunity is certainly big enough for
all kinds of aspirations, from smart cars, robots
and drones to medical diagnostics and sophisticated personal digital assistants. In CTA's
member roster, more than two dozen young
companies identify themselves as "artificial

C TA . t e c h / i 3

PRESCRIPTIVE ANALYTICS
Produce actionable data
and a feedback system that
tracks outcomes.
RECOMMENDATION ENGINES
OR RECOMMENDER SYSTEMS
AI-driven information filtering
systems that predict user preferences and responses to queries
based on past behavior, relationships to other users, similarity
among items being compared
and context.

VOICE-ENABLED DIGITAL
ASSISTANTS (or Intelligent
Agents, Virtual Personal Assistants, Automated Assistants
or Virtual Agents) Programs that
can organize, store and output
information that helps them
conduct a conversation with
a human. They answer voice
queries with information from
a multitude of online sources.
Examples include Apple's Siri,
Google Now, Amazon's Alexa
and Microsoft's Cortana.

through 3D images. The company says that the system can identify rising
stars or undervalued players compared to others in their league.
At the Massachusetts Institute of Technology's Media Lab, an AI program
called "Shelley" (a paean to the author of Frankenstein) recently wrote
a horror story that was deemed sufficiently scary. The Lab's deep learning
algorithm read many terror stories and trained itself to write horror fiction.

FINDING AI EXPERTS

These and dozens of other business and consumer projects illustrate the
potential range of AI projects. While customer experiences with AI are
proliferating, thanks to Alexa, Google Home, drones and self-driving cars,
experts agree that the AI juggernaut is just beginning. Aside from the inevitable debates about privacy and employment impact, AI is facing another
rollout challenge.
"The biggest barriers to the successful and wide adoption of AI will be finding talent who know how to build these algorithms and managers who know
how to ask the right questions," says Zodiac's Fader, who cites computers' ability to make more accurate diagnostic decisions. "We've seen a reluctance with
some businesses to trust the algorithms and the data in the face of lots of 'noise'
[about] changes in the economy and new competitors in the landscape."
Meanwhile experts are upbeat that AI is entering an era of explosive
growth, which will trigger even more innovations. For example, Intel is
deeply involved in "neuromorphic computing," which will mimic the way
the human brain works rather than rely on transistor-based circuits that
process data. In addition to faster processing, Intel Circuit Research Labs
expects that neuromorphic designs can cut energy consumption by up to
300 times compared to today's circuits. Although there's no timetable yet,
it's easy to imagine how such digital brainpower will be used in wearables,
sensors and the Internet of Things without requiring continual network
connections. Think about that.
intelligence" providers. They have plunged
into a field where venerated technology
firms such as Intel, NVIDIA, IBM and Google
plus countless academic institutions are
accelerating their work.
At CES 2018 the new Artificial Intelligence
Marketplace is showcasing about a dozen
companies, presenting products ranging from
automotive to smart home to smart cities
applications. AI Marketplace exhibitors range
from NovuMind, which is working on AI

projects and heterogeneous computing
for the IoT and what it calls "the Intelligent
Internet of Things (I²oT)," to Backyard Brains,
which makes neuroscience experiment kits for
students (elementary to grad school) who want
a hands-on education in electrophysiology.
Another three dozen exhibitors are in the
Robotics Marketplace, with an expanding
array of personal, education, healthcare and
recreational robots; the Drones Marketplace is
home to about 50 unmanned aerial vehicles.
JANUARY/FEBRUARY 2018

43


https://www.cta.tech/ http://www.cta.tech

i3 - January/February 2018

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