Artificial Intelligence Tutorial

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Make AI Work for You
Key Terms and Facts for the AI Journey
Artificial IntelligenceTutorial

What is Artificial Intelligence?
Artificial Intelligence allows for large or complex bodies of
data to be analyzed quickly and more effectively than humans
can, due to superior processing speed and scalability.
 
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Today, AI extends our own cognitive capabilities  
and lowers the cost and time it takes to gain insights
from large data sets. In the future, it will perform, and
potentially out-perform, some human cognitive
functions altogether.
Artificial Intelligence is derived by
the use of Cognitive Engines. 
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“The more specialized the engine,
the higher the level of accuracy.”
A Cognitive Engine is a specialized machine learning algorithm
trained to do certain things, such as transcribe spoken language
from audio files or recognize faces and objects in video files.
What are Cognitive Engines?
“No one engine can do everything well.”
Make AI Work for You !4

How Cognitive Engines Work
Notice that while Siri is effective in handling daily language,
it doesn’t support a strong medical vocabulary.The wider
the scope of needs, the lower the accuracy of results.
An engine can be trained to recognize spoken English
speech, but that same engine would be incapable of
recognizing French.
Over 10 thousand engines have been developed, and
that number is growing exponentially.
An Engine’s task needs to be defined narrowly and then trained,
in order for it to produce accurate and effective results.
!5Make AI Work for You

How Cognitive Engines Work
“Cognitive Engines alone are not the answer.
An upper layer of AI is required to conduct,
orchestrate, and learn which Cognitive
Engines are the best to be deployed for the
task, at the right time, to yield the most
accurate result.”
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Cognitive Engine Limitations
For most tasks, no single engine is capable of producing the
required analysis at a sufficient level of accuracy.
For example:
๏ a law enforcement user may need to search surveillance
footage for both faces and tattoos.
๏ a conversation being transcribed, may shift between
English and Spanish.
๏ a user may want to search for a specific individual
speaking certain words.

Multiple cognitive engines are often needed, across
multiple classes to provide differing outputs that
can be combined to reach the most accurate result.
Applications can then be introduced to customize
the output for the use case.
Therefore, to solve this technical problem, a
Cognitive Operating System is needed.
Cognitive Engine Limitations

An Operating System which orchestrates multiple cognitive engines to normalize
and correlate the data supplied to it so an integrated application can interpret and
leverage the resulting information.
Engine 01
TRANSCRIPTION
Engine 02
SENTIMENT
Engine 02
Engine 01
FACE 
RECOGNITION
OBJECT
RECOGNITION
Engine 02
Engine 01
LOGO
RECOGNITION
Engine 01
MORE
Engine 02
Engine 02
Engine 02
Operating System
Engine 01
Engine 01
Cognitive Operating System
Similar in approach to a computational
operating system (like the one on your
computer) that exists so that the
computer’s processors, peripherals and
applications can work together.
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Why an Operating System?
For business users
It solves the problem of being able to quickly
and accurately analyze massive amounts of
information in all its primary forms — including
audio, video and text — and use the results to
meet their objectives.
For IT decision-makers
With the fast pace of technological
development, implementation of any single
technology eventually becomes dated. In this
case, an AI operating system accommodates a
broad range and constantly growing ecosystem
of intelligent applications and cognitive engines,
essentially future-prooﬁng investment.
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Getting Started
Organizations can begin their AI journey
with a single project and expand to other
activity domains, as the business value
becomes evident.
Veritone’s offering 
aiWARE Operating System
Why an Operating System?
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A layer of AI on top of the Cognitive Engines in
the Operating System can be used to intelligently
orchestrate multiple engines simultaneously.
Machine learning is leveraged to record the
strengths and weaknesses of each engine in
real-time, delivering the highest value and most
accurate results.
This approach delivers superior analysis of the
provided data, outperforming the results any one
engine could otherwise supply.
Applying AI to the Operating System
aiWARE Conductor
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Developers of Cognitive Engines, applications,
data products, as well as system integrators,
unified to bring the strongest results and
future-proof offerings.
An Ecosystem Delivers the Best Results
Veritone Ecosystem
!13

The AI Journey is Just Starting

Gating Factors to AI Adoption
Technology and
Deployment Decisions 
AI represents change  
to an organization.
Deployment strategy,
organizational ownership,
technology and  
vendor choices need  
to be evaluated.
Scar Tissue from
Previous False Starts 
Many organizations are
hesitant to make
signiﬁcant investments
in AI if they have
experienced obstacles
in past implementation
efforts.
Trust 
Humans are not ready  
to fully trust computers
alone. Augmented
intelligence solutions that
extend users’ cognitive
capabilities rather than
replace them are much
easier to accept today.
1 2 3
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The Veritone Strategy
๏ Deliver an Operating System which uniﬁes the best AI cognitive engines in the
market and orchestrates them via Conductor technology to deliver the highest
value results. Veritone aiWARE delivers better insight than the sum of its parts.
๏ Bring the Operating System for AI to market, powered by an ecosystem of
cognitive engine developers, application developers and system integrators.
๏ Deliver solutions that offer future-proof investment in technology.
๏ Offer the marketplace for Trusted AI, which augments human capabilities with
computing-based cognitive capabilities.
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Glossary of terms
Artificial Intelligence | Computer science techniques that enable computers to mimic human intelligence (decision making and
problem solving) by using logic, if-then rules, decision trees, and Machine Learning.
AI Operating System | A computer's operating system (OS) manages all of the software and hardware that is installed on it. Veritone
has created aiWARE, the world’s first operating system for Artificial Intelligence. Like a traditional operating system, developers can
build both applications and engines on aiWARE. These can be produced by partners and made commercially available to the Veritone
customer-base, or by customers themselves to deploy internally for their own proprietary benefit.
aiWARETM
| Veritone has created aiWARE, the world’s first operating system for AI. An operating system is required to manage the
multitude of AI point solutions and provide a software layer that solves real-world challenges. aiWARE unlocks the power of cognitive
computing to automatically generate actionable insights, previously inaccessible to organizations in their audio, video and other data
sources. With thousands of AI engines developed by technology titans to niche players and customers, aiWARE uses proprietary AI
technology to orchestrate these engines delivering the most accurate result no single engine can produce on its own. Customers gain
timely access to the latest AI advances in an easy, unified offering, providing infrastructure software to jump start their AI journey while
future-proofing their technology investment. aiWARE is offered in flexible deployment models, typically as a SaaS offering, to meet a
range of customer requirements.
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Glossary of terms
Artificial Narrow Intelligence (ANI) | AI techniques that meet or surpass human level capabilities for a single, cognitive function.
Artificial General Intelligence (AGI) | AI techniques that meet human-like capabilities across all categories of cognition, rather than
a specific one, adapting to different environments and tasks, transferring knowledge between them. Once reached, AGI will be able
to think, learn, and reason holistically, developing a “common sense” of its own to come up with new creative ways and solutions.
Artificial Super Intelligence (ASI) | AI techniques that result in performance that is superior to human level, across all categories of
cognition. Once reached, ASI will be able to think, learn, and reason, holistically, developing a “common sense” of its own to come up
with new creative ways and solutions.
Cognitive Computing | Algorithms that simulate human thought processes in a computerized model by learning at scale, reasoning
with purpose, and interacting with humans in a natural style. Cognitive computing involves self-learning systems that use data
mining, pattern recognition and Natural Language Processing to handle context and considerations of human nuances, such as
language or behavior.
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Glossary of terms
Cognitive Engine | Software that facilitates the processing of a data file or data stream via a Machine Learning Model to either: 
(i) produce associated metadata in a specific category of cognition; or (ii) transform the source data into a new format or create
derivative data.
Cognitive Engine Deployment Models | Veritone offers different deployment models for its Cognitive Engines, depending on the type of
network access that the cognitive engine requires, as well as the location and security level of the data. The options are (1) Network
Isolated: where the engine is fully contained and runs within Veritone's infrastructure, not requiring Internet access; (2) Semi-
Containerized: where the engine is mostly contained and runs within Veritone's infrastructure, requiring Internet access for tasks such
as license checks and database updates; (3) Non-Containerized: where the engine runs outside of Veritone's infrastructure. The code
executed within Veritone's infrastructure makes API calls to an external service that is running the core engine logic; and Human review:
where the code passes data outside of Veritone's infrastructure for human review, for example, a human labeling service.
ConductorTM
| aiWARE incorporates Veritone’s proprietary Conductor technology, which uses Machine Learning to intelligently route data
through the most effective workflows of cognitive engines. Conductor leverages algorithms to match media with the most appropriate
processing path across individual or multiple engine classes, to meet cost, accuracy, and speed requirements.
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Glossary of terms
Cognitive Learning | An AI model that extracts the accurate parts of the output and recycles the remainder through a process of
transformation that reroutes the data to the relevant engine or engines. The ongoing learning process happens as the conductor gets
better and better at inferring patterns and building a topography by using multiple cognitive engines simultaneously. This dramatically
improves accuracy and performance. Conducted learning further overcomes the limitations of narrow AI through two methods:
intraclass learning and interclass learning. A generative model, it can also generate another engine.
Deep Learning | A subfield of Machine Learning that filters data through self-adjusting Neural Networks to recognize complex patterns
in datasets and make predictions. Pioneered and perfected by several AI researchers, including Geoff Hinton, Yann LeCun and Yoshua
Bengio, this technique involves multiple layers of processing that gradually refine results. It dates back to the 1960s, 1970s and 1980s,
but regained popularity when Big Data and GPUs became available to reveal its power.
Intra-class | Data processing that uses multiple algorithms (i.e. Cognitive Engines) that perform the same function (i.e. are in the same
class), for example, translation. Intraclass learning is optimal where accuracy is a paramount concern because it uses Cognitive Engines
of the same type to refine data processing and reach the highest accuracy rates, which go beyond the single capabilities of one
Cognitive Engine, even a highly trained one.
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Glossary of terms
Inter-class | Data processing that employs several algorithms (i.e. Cognitive Engines) that perform different function across different
Cognitive Classes, for example visual and auditory algorithms, to work in concert. This allows AI to use multiple “senses” / cognitive
capabilities to process complex data.
Machine Learning | A field in Computer Science that enables algorithms and software to use example data, experience, learning or
adapting instead of explicitly being programming to complete a task.
Neural Networks (ANN) | A computing system built from a collection of connected units called Artificial Neurons, which are vaguely
inspired by biological neural networks. These systems continuously improve performance ("learning") in the absence of task-specific
programming.
Natural Language Processing (NLP) | The capability of algorithms and software to structure, interpret, understand, and generate
human languages.
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Glossary of terms
Orchestration | Proprietary Veritone technology that optimizes the Cognitive Engine selection process based on the media being
processed by periodically evaluating the cost, speed and accuracy of each engine, improving results and achieve higher levels of
cognition. The data is intelligently routed to the most appropriate cognitive engines within chosen engine classes, working in concert
by having specialized engines work at specific points to generate the overall most accurate result with the most effective runtime
and at the lowest cost.
Supervised Learning | A set of pattern-seeking algorithms trained with labeled example data, such as images, teaching a computer
what to do by using methods such as classification, regression, prediction, and gradient-boosting prediction. Algorithms in this
category include: linear and logistic regression, ensemble techniques (including random forests), deep and shallow Neural Networks,
decision trees or support vector machines.
Unstructured Data | Also known as Unlabeled Data, it refers to information that is not organized in a predefined data model. It is
most often in text form but can also include images, audio, and video files. Unstructured text is generated and collected in a wide
range of forms, including email messages, documents, transcripts, social media posts, presentations, survey responses, transcripts
of call center interactions, etc.
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Glossary of terms
Unsupervised Learning | A set of algorithms that learns without annotated examples, just from experience of data or the world. It
infers a conclusion or hidden structure used with Unstructured Data to reach an unknown yet-to-be-identified conclusion.
Veritone | Veritone (NASDAQ: VERI) has created the world’s first operating system for Artificial Intelligence. Veritone’s aiWARE
operating system unlocks the power of cognitive computing to transform and analyze audio, video and other data sources in an
automated manner to generate actionable insights. The Veritone platform provides customers ease, speed and accuracy at low cost.
Veritone has been among the first to be recognized by AWS for Machine Learning Expertise, and has been recognized by Oracle for
Excellence in Application Development.
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๏ Borealis AI: borealisai.com
๏ Center for Human-Compatible AI: humancompatible.ai
๏ USC Center for Artificial Intelligence Society: cais.usc.edu
๏ Carnegie Mellon Artificial Intelligence: ai.cs.cmu.edu
๏ MIT Computer Science and Artificial Intelligence Lab: csail.mit.edu
๏ Veritone: veritone.com
Additional Resources
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