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ARTIFICIAL INTELLIGENCE: THE TYPES, VALUE AND APPLICATIONS

Does Google’s Language Model for Dialogue Applications, or LaMDA, have
consciousness? Is their artificial intelligence-driven conversational agent
(commonly known as a chatbot) sentient? Google says, “no.” An employee who
published a transcript of his exchanges with LaMDA and was fired for violating
the company’s confidentiality policy disagrees.

What I find intriguing about the situation is the advanced degree of machine
learning LaMDA demonstrates, and how chatbots as well as other use cases enabled
by artificial intelligence (AI) have become part of our lives. Furthermore, AI
has significantly impacted business by helping to optimize daily operations,
elevate customer experiences and ignite new revenue opportunities. It’s quickly
becoming an essential part of any business strategy.

While it’s interesting to learn about amazing AI-powered use cases, let’s take a
step back to AI basics, a move that can help take some of the “fuzziness” out of
an increasingly complex technology.

ARTIFICIAL INTELLIGENCE 101: THE BASICS

Artificial intelligence involves algorithms, or sets of programmable
instructions, to automate tasks and problem-solve. Many functions that would
normally require human intelligence are now executed by smart machines. These
machines don’t have I.Q.s like humans, so there are many aspects that AI cannot
master, such as multi-tasking, social interaction, self-awareness and
relationships. However, that’s not to say computer scientists cannot come close.

AI technologies are categorized by their capacity to mimic human
characteristics, the technology they use to do this, their real-world
applications and the theory of mind.1 Real or hypothetical, all AI systems fall
into one of three categories:

1. Artificial narrow intelligence (ANI) – Machines complete specific, singular
tasks like recommendation engines, chatbots or voice assistants. These
machines can’t mimic human intelligence, but they simulate behavior within a
narrow set of constraints.
2. Artificial general intelligence (AGI) – AGI is the AI ability concept
closest to human capabilities in that machines think, understand and act
like a human. This has not yet been achieved because machines would need a
full set of cognitive and experiential knowledge.
3. Artificial superintelligence (ASI) – which is a theoretical system in which
machines are more capable than humans as they evolve, not only to become
self-aware but also to act upon wants and needs on their own. This is the
stuff you read about in science fiction. Is it a human or is it a
machine?

Fortunately, for the time being, we are saved from the unknown consequences
resulting from machines being humanoid or even far more “intelligent” than
people, as AGI and ASI are currently not possible. And while AI is still in its
infancy, practices like machine learning and deep learning are inching us closer
to that reality.

MACHINE LEARNING AND DEEP LEARNING DRIVE DATA OUTCOMES

Machine learning (ML) is an AI technique that uses data, algorithms and models
to understand and learn from experience over time. Essentially, it finds
insights from data, adjusts itself based on the data being processed and
continues to improve. With machine learning, AI systems get better at predicting
and computing without being programmed to do so.

As an example of machine learning, streaming music services learn from the
songs, artists and albums that you’ve previously listened to and suggest music
you might like. The algorithms simply determine the genre(s) of music you are
most likely to enjoy and provide similar options.

Deep learning, a subfield of machine learning, processes large amounts of data
with complex patterns through neural network architectures. Without getting into
the weeds, that just means the program can utilize a decision tree built from
all the deeply hidden data it is storing and self-correct. Deep learning is far
more advanced than traditional machine learning.

Deep learning requires large amounts of data to return the right results.
Translation services, for example, rely on this type of learning to quickly and
accurately translate while ensuring proper grammar.

LEARNING AT THE DATA CENTER, THE CLOUD AND THE EDGE

Today, all of us are walking around with not-exactly-humanoid smart devices in
our hand. Our houses have smart thermostats and doorbells, our cars can predict
where we are driving and our computers can recognize our face to log in. No
matter the technology, AI has slowly crept into our everyday lives without us
even realizing.

Supply chains, factories, connected cars and smart homes all deploy millions of
sensors that require actions based on data. Many of these devices input
information, transmit it to the cloud, execute instructions and return the
decision to the device. Because of the number of parallel operations required
for most machine learning models, they typically live in data centers that have
the processing capability required to better learn and reflect behavior.

Now, many applications need to gather, process and analyze the data closest to
where it’s been generated to reduce the time it takes from data to be ingested
to the response. Enter decision-making at the edge.

An edge computing ecosystem to the centralized cloud. The hierarchy is broken
into four primary tiers.3

Edge data centers can enable AI decision-making at the edge, thanks to their
proximity to the application. For example, healthcare IoT systems can apply
machine learning to analyze data, identify issues and alert caregivers. To be
effective, the latency needs to be in the sub-10 millisecond range.

AI-assisted cybersecurity is another application. Part of that includes using AI
to monitor web traffic behavior for bots, and then take action to protect mobile
apps, networks and infrastructure in clouds. In this instance, the AI system can
reside in a data center that’s not on the edge. However, that data is sent back
to the cloud or data center to be aggregated for further analysis, continuous
learning and even smarter decision making.

This combination speeds up data processes, allows the most critical data to
drive intelligent real-time decisions and delivers better real-world outcomes.
It’s no wonder that businesses see edge computing as key to driving success. In
2018, only 10% of data was created and processed outside the data center, but
this number is expected to rise to 75% by 2025.2

ARTIFICIAL INTELLIGENCE POWERS DIGITAL SERVICES

In this world of ever-changing technology, AI will continue to reshape much of
the world’s businesses. The rapid growth of knowledge and learning within
machines powers new digital services that can bring this technology up to its
highest potential. It’s not exactly science fiction anymore, and the
possibilities are infinite.

When you are ready to discuss how colocation data centers can help you when
adopting or optimizing AI in your business, get in touch. We would welcome the
opportunity to learn about your goals and exchange thoughts on how we can help.

1. What are the 3 types of AI? A guide to narrow, general, and super artificial
intelligence
2. The promise of edge computing comes down to data
3. The Edge Computing Ecosystem: From Sensors to the Centralized Cloud

TAGS
industry trends
Artificial Intelligence

Gerry Fassig

Vice President of Sales, West

Gerry has more than 25 years of experience in the tech industry in both
enterprise and start-up leadership roles.

Read more from this author
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