The document provides an overview of artificial intelligence (AI) and machine learning (ML), emphasizing their impact on businesses and daily life through applications like autonomous driving and smart homes. It includes a glossary of essential ML terms such as classification, annotation, image tagging, and natural language processing, highlighting their significance in developing intelligent technologies. CloudFactory is introduced as a company that helps businesses by providing training data and organizing datasets to enhance AI and ML innovations.

1. AI, Machine Learning & Data
What Businesses Need to Know

2. Artificial intelligence and machine
learning are at the forefront of technical
innovation.

3. From autonomous driving to
predictive analytics, robotic
manufacturing to smart
homes, how we live, work
and play is impacted in
profound ways.

4. As talk of AI and machine
learning permeates our
world, the benefits for
businesses are getting lost
in a sea of technical jargon
and prognostication by
experts from all walks of life.

5. At the end of the day, data
is the fuel for any machine
learning algorithm and it’s
common for the terms
used to describe how that
data is used to become
confusing.

6. As folks who love data, machines, and especially
people, we decided to put together a helpful
guide to machine learning and the terms that
businesses should know as they look to
navigate this exciting and complex frontier of
technological advancement.

7. Machine Learning 101
A Glossary of Machine Learning Data Terms

8. Artificial intelligence is building
technology that behaves like a human.
Self-driving cars, Siri, smart homes, and
many other emerging technologies are
examples of AI.
Artificial Intelligence

9. Machine learning is a subset of AI that
uses algorithms to learn from data sets.
Using data and algorithms, machine
learning technologies make intelligent
predictions, similar to that of a human,
but at scale.
Machine Learning

10. In machine learning, the
framework is developed
and data is provided, so
that machines can teach
themselves what they
need to know and
understand what to do.

11. Classification is when a set of data
trains a machine to identify certain
inputs or observations, and based on
those, assign them to certain outputs
or categories.
Classification

12. Classification is an example of supervised
learning, because it uses a set of labeled data
examples and classifies any new data into
those sets.

13. Annotation
In the traditional sense, an annotation
is when we reference a piece of
information back to the original source
or to additional, relevant data. In
machine learning, it’s very similar.

14. In order to be able to perform sophisticated functions like
understanding and responding to human language and verbal
commands, computers need to be able to find patterns and make
inferences from the data. This is done by creating annotated examples
that are in the background of datasets.

15. Image Tagging
Image tagging is the simple act of
identifying objects, categories, or even
human gestures to help organize data
for ground truth data sets and ongoing
machine learning applications.

16. Facial Recognition
Facial recognition has primarily been
used for security purposes, but new
possibilities in marketing and
enhanced UX have made facial
recognition an important aspect of
machine learning.

17. Natural Language Processing
Natural language processing or NLP is
the ability for computers to interact in
dialogue with humans, either audibly or
through text. NLP machine learning
technologies are built to understand
and communicate with human
languages...think of your favorite
chatbot :-)

18. Communication is one of the most natural
functions for humans, so it’s crucial to develop
natural language processing in AI.
Technologies like Siri, Amazon Echo, and
Google Home operate using NLP-based
algorithms.

19. Sentiment Analysis
Sentiment analysis, also called opinion
mining, is the process of studying
words and determining their emotional
meaning. In other words, you track
what people say about you online and
determine if those statements are
positive, negative, or neutral.

20. Landmarking
Landmark recognition is the ability for
technology to identify a building,
physical object or landmark. To do this,
technology relies on large amounts of
image data and smart algorithms.

21. Machine learning is revolutionizing
how we live, work, play, and interact
with the world around us. It holds the
potential to augment human
capabilities to make us smarter and
more productive than ever.

22. CloudFactory is helping companies by providing human
intelligence to create accurate training data sets that
improve machine learning algorithms. Whether that’s in
the form of annotating images, identifying classifiers,
drawing bounding boxes, labeling objects or analyzing
text for NLP, we’ve made it super easy to offload that
work, by spinning up a WorkStream, which empowers
data teams and engineers to focus on innovating the next
generation of AI that very well may change the world.

23. About
CloudFactory CloudFactory makes it super EASY to offload data
work so our customers can focus on innovation and
growth. We specialize in preparing and organizing
data sets and work with companies like Microsoft,
Embark, Drive.ai, FaceTec to implement them into
building innovative AI, ML and other complex
technologies.
www.cloudfactory.com
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