Building a High-Quality Machine Learning Model Using Google Cloud AutoML Vision

BUILDING A HIGH-QUALITY
MACHINE LEARNING MODEL USING
GOOGLE CLOUD AUTOML VISION
MARKETING APPLICATIONS & LIMITATIONS
M.S. IN DIGITAL MARKETING
SACRED HEART UNIVERSITY
Bellakarina Solorzano
2018

M.S. IN DIGITAL MARKETING 1
ABSTRACT
This paper aims to demonstrate how easy it is to train a high-quality Machine Learning
model using Google’s Cloud AutoML suite of Artificial Intelligence products. To be more specific,
this paper takes a look at how Google’s AutoML Vision interface can be used to train a custom
visual recognition model that identifies images of popular dishes. To this end, 101,000 images
were labeled, imported and used as the basis for training, validation and testing. Combined with
the power of Machine Learning, Neural Networks and Supervised Training, results were
outstanding. With little to no Machine Learning expertise and minimum effort required, the study
below intents to shed light on the new readily available generation of tools that are changing the
marketing landscape.
This paper takes an in-depth look at how visual recognition technology works and how
Artificial Intelligence, Machine Learning and Deep Learning are changing the way things get done
across industries. The paper illustrates examples of the ways organizations across the world are
taking advantage of these technologies that suddenly seem within their reach. Finally, the paper
touches base on the limitations and expectations of Artificial Intelligence and Machine Learning
models going forward. A detailed account of this work is outlined below.

TABLE OF CONTENTS
Introduction..........................................................................................................................................3
Literature Review.................................................................................................................................4
Methodology .........................................................................................................................................7
The Dataset.........................................................................................................................................8
The Model.........................................................................................................................................12
The Results .......................................................................................................................................18
Discussion............................................................................................................................................21
Artificial Intelligence Marketing......................................................................................................21
Limitations........................................................................................................................................23
Conclusion........................................................................................................................................26
References...........................................................................................................................................27

INTRODUCTION
Over the years, data has become the backbone for many organizations around the world, if not
all of them. Whichever the industry they belong to, or whatever their core objectives are, companies
certainly rely heavily on the data they generate and collect from their customers now more than ever
before. And, whether they are trying to boost revenue, launch new products, make processes more
efficient, diagnose diseases or deliver personalized ads, you can be certain they are looking at their
collective data platforms to make informed decisions.
In fact, data and analytics are deeply embedded in the DNA of many companies, including
industry giants like Amazon, Facebook, Netflix, Google, IBM, and Microsoft. Organizations, from all
shapes and sizes, are actively looking to incorporate advanced technologies to their day-to-day
processes. Advanced technologies that are fueled by Artificial Intelligence that helps make sense of
huge amounts of data, otherwise impossible to process by a human brain. In fact, Forbes, estimates
that 80% of companies are already investing in some form of AI and 30% of companies expect to
expand their AI ventures in the near future (Columbus, 2017a). Companies like IBM and Microsoft
are investing in developing tools that aspire to help other businesses adapt deep-learning applications
like speech-recognition and translation. While others, like Amazon Web Services and Google Cloud
Platform are creating their own open-source software, services and interfaces to make these
technologies custom-made and accessible to the public (Nott, 2018).
According to an article published by Forbes, as of 2017, approximately 53% of companies
have adopted Big Data Analytics in some way. That is a 17% increase from 2015. And, even though,
organizations are using Big Data Analytics for reasons that range from data warehouse optimization to
fraud detection, the common denominator is that organizations are living in a data-first world. That is
where Artificial Intelligence comes in, offering tools that assist in the analysis of big data and deliver
outcomes that surpass the performance of traditional statistical models. One example is the rapidly
growing, highly flexible and cost-effective tool Apache Spark and its Machine Learning Library that
a lot of organizations are investing in to support their efforts to classify, segment and predict
information (Columbus, 2017b).
Throughout the following sections, this paper takes on the task of showing you how to create
a custom visual recognition model using Google Cloud AutoML Vision while shedding new light on
the popularization of Artificial Intelligence tools and how we can confidently expect large amounts of
innovation in the near future, thanks to this technology.

LITERATURE REVIEW
Artificial Intelligence (AI) is a set of analytic tools that attempt to imitate real life and solve
problems in the most efficient way possible (Gordon, 2011). Artificial Intelligence has become an
essential ingredient to success for many businesses and it is predicted to become key for any business
in the near future. In a recent interview, Google’s CEO Sundar Pichai, defines that moving towards
AI-centric systems is as vital as the discovery of the World Wide Web and the creation of smartphones
(Roettgers, 2016). Evolving into an AI-first ecosphere comes with a multitude of benefits but demands
vast amounts of data collection and processing, as well as, higher levels of investment.
Machine learning (ML) is a field of artificial intelligence that refers to programming computers
by using statistical techniques to learn from data, solve problems, answer questions and yield valuable
insights. Machine learning has become increasingly important over the years, as the amount of
information that humans and computers generate on a daily basis keeps growing exponentially. ML
offers an automated system solution that assists in the analysis of high volumes of data that surpass the
ability of the human brain (Parloff, 2016). Today, we can find Machine Learning pretty much
everywhere, though some places, might not be as obvious. Some examples include, Facebook’s tagging
and face recognition features, Google’s search bar, Spotify’s and Netflix’s recommendations, Tesla’s
self-driving cars and, in the medical field, diagnosis of life-threating diseases such as skin cancer
(Chumley, 2018).
As ML rapidly moves to the center of attention, companies like Google, Amazon and Microsoft
are concentrating their efforts on creating tools, platforms and interfaces that will have these
technologies readily available and accessible to programmers, developers, scientists, government
entities, organizations and the general public. Microsoft has Azure Machine Learning Studio while
Amazon Web Services prides on Sagemaker (Nott, 2018). Just this year, Google launched a set of ML-
powered tools that are extremely easy to use and allow users to build custom ML models without the
need to learn coding or having extensive ML experience. These range of products include AutoML
Vision, AutoML Natural Language and AutoML Translation. And, even though these products are still
on their beta version, they are already changing the technology landscape (Vijayan, 2018).
Companies like Disney, ZSL and Urban Outfitters are already using AutoML’s technology to
build unique ML models that are elevating their competitive advantages, enhancing their customer
experiences, minimizing their overall costs and helping shape their future. For example, Disney is
currently using AutoML Vision’s interface to build models that label Disney products with Disney

characters, categories and colors. These models are fueling Disney’s apps, search features, website
recommendations and are providing visitors with personalized, relevant, insightful, and self-correcting
results and information that are leading to higher levels of satisfaction and conversion (Google Cloud,
2018).
Google’s AutoML Vision interface allows users to categorize images and analyze attributes to
build high quality and accuracy models that fit their needs and demands. AutoML Vision is powered
by Transfer Learning Technology and Neural Architecture Search. Transfer Learning Technology is a
method that encourages machines to transfer knowledge acquired from one task to another related task
with aims of improving learning and making ML as efficient as human learning. For example, Transfer
Learning is widely use when creating and deploying Chatbots. On the other hand, Neural Architecture
Search (NAS) is an algorithm programmed to find the best neural network architecture, saving time
and minimizing costs (Seif, 2018).
Neural Networks are computer systems that are inspired by the way the human brain and
nervous system work and how they connect. The term Deep Neural Networks (DNNs) refers to the use
of multi-layered neural networks (Jouppi et al., 2018). AutoML Vision was built on the premise of
mimicking the way the human eye captures light and color and help computers understand images and
how they are represented. Visual recognition can be complex and challenging. That’s why developers
choose to work with a specific type of DNNs called Convolutional Neural Networks (CNNs). CNNs
make the job much easier by breaking down images into filters, small groups of pixels, and doing a
series of calculations to compare filters against other filters to find and recognize patterns. CNNs
operate in layers, as the layers deepen, and more convolutions are performed, the network starts to
identify more specific patterns and objects within the images (Google Cloud Platform, 2018).
CNNs are trained by using large amounts of labeled images and through error-adjusted
repetitions that run until higher levels of accuracy are met. For more complex models, such as video
recognition, AutoML Vision makes use of another type of DNNs called Recurrent Neural Networks,
also known as RNNs. RNNs are networks that have loops that allow the information to persist. In other
words, RNN technology allows the information to pass from one step to the next without having to
start from scratch, allowing systems to connect previous information to present tasks. Figure 1 shows
an example of an RNN. This particular attribute has made RNNs exceptionally useful for speech
recognition, language modeling, translation, image and video recognition, and so much more (Olah,
2015). Figure 2 illustrates how neural networks make it possible for computer vision to work.

Figure 1. Illustration of a Recurrent Neural Network (Olah, 2015).
Figure 2. How Neural Networks Work (Parloff, 2016).

METHODOLOGY
In this research we will use powerful ML and cloud computing tools to build a high-quality
visual recognition model called Popular Dishes. Figure 3 summarizes these tools.
Figure 3. ML and cloud computing tools used to build the Popular Dishes Model.
Google Cloud AutoML is a set of machine learning products that enables users to perform
difficult and complicated ML-related tasks in an easy and quick manner. One of the greatest perks
offered by Cloud AutoML is that the tool is fully integrated with other Google Cloud services and
provides users with a seamless, consistent and connected experience. Cloud Storage will be used to
store the training dataset. And to labeled images, train the computer, evaluate the model, and generate
predictions, we will be using the recently launched Cloud AutoML Vision interface. Figure 4 illustrates
the three-step guide to create a visual recognition model using AutoML Vision.
Figure 4. Three-step guide to build a visual recognition model using
AutoML Vision (Google Cloud, 2018).
The following sections describe in greater detail how the data was collected, imported and
processed, as well as, how the model was trained and evaluated. In addition, the paper addresses the
validity and the utility of the applications used and relevant indicators for prediction and success.

THE DATASET
The dataset was obtained via Kaggle. Kaggle is an online platform owned by Google. This
platform hosts a community of scientists, developers and machine-learning enthusiasts who share data
and host competitions on an ongoing basis with various purposes, all of which are linked to data science
and machine learning. Kaggle makes it easy for registered users to access its public data platform and
thrives on the share of resources and education (2018).
The dataset of choice comprised 101 categories of popular dishes. Examples of categories
include Pancakes, Pizza and Ravioli. Each category contained 1,000 real world photographs totaling
101,000 files, making it the perfect diverse and exciting dataset to use for vision analysis. Being in
possession of such a varied group of images highly benefited machine learning efforts, as it provided
the AutoML tool with different angles, resolutions and backgrounds. Table 1 illustrates examples of
the images and categories found in the dataset.
Pancakes
Pizza
Ravioli
Table 1. Examples of the images and categories found in the dataset of choice
for the Popular Dishes AutoML Vision Model.

The AutoML Vision tool highly recommends providing about 1,000 training images per label
or category to obtain a more accurate model. For regular models, the tool requires a minimum of 10
images per label and for advanced models, a minimum of 50 images per label. In addition, several
image formats are supported by AutoML Vision, including but not limited to the three most common
image file formats PNG, JPEG and GIF. The maximum allowed file size is 30MB. All images that
were part of the dataset of choice were downloaded, stored and imported as JPEG file formats. For
simplicity, Figure 5 summarizes the details of the dataset.
Figure 5. Details of the dataset of choice for the
Popular Dishes AutoML Vision Model.
To be able to properly use and implement the AutoML Vision tool, steps like opening a Google
Cloud account, creating a new project, enabling billing, activating APIs, creating a storage bucket, and
last but not least, allowing the AutoML Vision service account to access the Google Cloud project
were indispensable.
Once the dataset was ready for import, all 101,000 images were processed and labeled. Because
of the number of files and the size of the import, images were processed directly into the AutoML
Vision console in batches of 500. Figure 6 illustrates a view of the images tab in the AutoML Vision
console. AutoML Vision automatically assist users with the detection of duplicated files. One to three
images per category, totaling 80 files, were removed from the dataset of choice during the data
cleansing stage. Table 2 provides a detailed account of labels and images.
Food
Images
Dataset
101 Categories
1,000 Real
World Images
per Category
101,000 JPEG
files
Size: 5.32GB

Figure 6. View of the Images Tab of the AutoML Vision console.
Notice that information on the number of labeled and unlabeled images,
label names and number of images per label can be located to the left.
Actual images and filtering options are located to the right.
# Label / Dish Name Images # Label / Dish Name Images
1 Apple_Pie 999 52 Guacamole 1000
2 Baby_Back_Ribs 997 53 Gyoza 1000
3 Baklava 1000 54 Hamburger 999
4 Beef_Carpaccio 999 55 Hot_Dog 1000
5 Beef_Tartare 999 56 Hot_and_Sour_Soup 1000
6 Beet_Salad 999 57 Huevos_Rancheros 1000
7 Beignets 999 58 Hummus 999
8 Bibimbap 1000 59 Ice_Cream 1000
9 Bread_Pudding 1000 60 Lasagna 997
10 Breakfast_Burrito 998 61 Lobster_Bisque 1000
11 Bruschetta 1000 62 Lobster_Roll_Sandwich 1000
12 Caesar_Salad 999 63 Macaroni_and_Cheese 1000
13 Cannoli 1000 64 Macarons 999
14 Caprese_Salad 999 65 Miso_Soup 1000
15 Carrot_Cake 1000 66 Mussels 1000
16 Ceviche 1000 67 Nachos 999
17 Cheese_Plate 1000 68 Omelette 999
18 Cheesecake 1000 69 Onion_Rings 999
19 Chicken_Curry 1000 70 Oysters 1000
20 Chicken_Quesadilla 999 71 Pad_Thai 999
21 Chicken_Wings 999 72 Paella 999
22 Chocolate_Cake 998 73 Pancakes 999

23 Chocolate_Mousse 1000 74 Panna_Cotta 998
24 Churros 999 75 Peking_Duck 1000
25 Clam_Chowder 999 76 Pho 998
26 Club_Sandwich 1000 77 Pizza 1000
27 Crab_Cakes 1000 78 Pork_Chop 1000
28 Creme_Brulee 1000 79 Poutine 1000
29 Croque_Madame 1000 80 Prime_Rib 997
30 Cupcakes 999 81 Pulled_Pork_Sandwich 1000
31 Deviled_Eggs 998 82 Ramen 1000
32 Donuts 1000 83 Ravioli 1000
33 Dumplings 999 84 Red_Velvet_Cake 1000
34 Edamame 999 85 Risotto 1000
35 Eggs_Benedict 1000 86 Samosa 1000
36 Escargots 1000 87 Sashimi 1000
37 Falafel 999 88 Scallops 1000
38 Filet_Mignon 1000 89 Seaweed_Salad 1000
39 Fish_and_Chips 1000 90 Shrimp_and_Grits 1000
40 Foie_Gras 999 91 Spaghetti_Bolognese 999
41 French_Fries 1000 92 Spaghetti_Carbonara 997
42 French_Onion_Soup 998 93 Spring_Rolls 1000
43 French_Toast 1000 94 Steak 1000
44 Fried_Calamari 999 95 Strawberry_Shortcake 998
45 Fried_Rice 999 96 Sushi 1000
46 Frozen_Yogurt 1000 97 Tacos 1000
47 Garlic_Bread 999 98 Takoyaki 1000
48 Gnocchi 999 99 Tiramisu 1000
49 Greek_Salad 999 100 Tuna_Tartare 1000
50 Grilled_Cheese 1000 101 Waffles 999
51 Grilled_Salmon 998 Total 100,940
Table 2. Detailed account of the labels and number of images used to train the model.
When the importing and labeling tasks were completed, the tool was ready to proceed to the
next step: training. In this stage, AutoML Vision randomly divided the dataset into three datasets, one
dataset for training, one for validation and one for testing. Figure 7 provides a visual representation of
how and in what percentages AutoML Vision separated the Popular Dishes dataset. Because AutoML
Vision randomly splits the dataset, individuals need to be mindful as very similar images might end up
in the train and validation sets and could result into overfitting and poor performance on the test dataset.
Luckily, AutoML Vision lets users customize percentages to better fit their needs. However, the default
percentages were maintained to simplify this study.

Figure 7. Visual representation of how AutoML Vision
separates the dataset while training a model.
AutoML Vision used the training dataset to identify patterns and try various algorithms. The
tool then used the validation dataset to examine those patterns and algorithms and selected the best
performers. As a final step, AutoML Vision used the test dataset to determine the error rate, as well as,
to provide an unbiassed assessment of the quality and accuracy of the model.
THE MODEL
The model was trained for one computer hour, it analyzed 101 labels, 100,940 images, from
which a total of 10,277 images served as test images. Results indicated that the model has a precision
of 86.31% and a recall of 68.40%. Figure 8 summarizes these results. Let’s use the “Beignets” label as
an example to explain these two metrics. Precision indicates that from all the test images that were
assigned the “Beignets” label, 86.31% were supposed to be labeled as “Beignets.” On the other hand,
recall indicates that from all the test examples that should have had the label “Beignets” assigned,
64.40% were actually assigned the “Beignets” label. In other words, a higher precision model produces
a fewer number of false positives and a high recall model produces a fewer number of false negatives.
These two metrics help us evaluate the effectiveness of the model, how well the model captured the
information and how much of the information was left behind. In addition, the model’s average
precision is 83.10%, this metric indicates how well the model is performing throughout all the score
thresholds. The closer to 100% the average precision is, the better the model performed on the test
dataset.
Popular Dishes Model
10% Test
10%
Validation
80%
Training

Figure 8. Summary of the model’s main metrics.
As part of the model’s evaluation, AutoML Vision platform also provides a score threshold
slider tool and precision-recall curves that help further assess the effectiveness of the model under
different levels of confidence. The score threshold indicates the level of confidence the model requires
when assigning a label to an item from the test dataset. The ability to move this score threshold up and
down helps users examine the effect of different thresholds for all labels together and also for all the
individual labels found in the dataset. Which, in return, can help find a suitable balance between false
positives and false negatives. Figure 9 illustrates the resulting metrics at a .5 confidence level for all
labels. These three curves represent the model as a whole, including all labels, the AutoML Vision tool
used the top-scored label to calculate these metrics.
Figure 9. Graphical representation of the relationship between precision
and recall at a .5 confidence level.

In a low confidence level scenario, the model classifies a higher number of images, but presents
a higher risk of misclassification. On the contrary, when the confidence level is high, the model
classifies a lower number of images, but presents a lower risk of misclassification. Figure 10 illustrates
metrics at a .35 confidence level for all labels and Figure 11 illustrates the metrics at a .65 confidence
level for all labels. As evident by these graphic illustrations, at a lower confidence level the recall rate
of the model improves while the precision rate worsens. Inversely, at a higher confidence level
precision gets better while the recall is compromised.
Figure 10. Graphical representation of the relationship between precision and recall
at a .35 confidence level.

Figure 11. Graphical representation of the relationship between precision and recall at a .65
confidence level.
Additionally, the AutoML platform lets users modify the confidence level on a per label basis
providing performance metrics for each and every label. Figure 12 shows the evaluation summary of
the label Fried Rice. Figures 13, 14 and 15, exemplify true positive, false negative and false positive
results for the label Fried Rice. For simplicity, the model was set to maintained .5 level of confidence,
as this level generates satisfactory performance metrics across all 101 categories.
Figure 12. Evaluation summary of the label “Fried Rice” at a .5 confidence level.

Figure 13. True positive results of the label Fried Rice.
Figure 14. False negative results for the label Fried Rice.

Figure 15. False positive results for the label Fried Rice.
Notice that the AutoML allows users to switch between the images
of the labels that should have been predicted instead: Paella, Ceviche,
Risotto, Bibimbap, Greek Salad, Pad Thai and Escargots.
The AutoML Vision platform also provides users with a Confusion Matrix, which serves as
another tactic to assess the model’s performance. The Confusion Matrix compares the performance of
each label and indicates how often the model classifies the label correctly. The Confusion Matrix then
lists the labels that were most often confused for that label. Figure 16 illustrates the Confusion Matrix
for this model. Ideally, the percentages on the diagonal will be higher than all the other percentages.
This shows that the labels are being identified correctly. However, if values in the surrounding areas
are high, the model is misclassifying test images. Notice that the diagonal of the model presents higher
percentages compare to the surrounding areas and that the most common confusions derive from dishes
that are quite similar to the human eye, such as Filet Mignon and Steak.

Figure 16. Confusion Matrix. This table shows how often the model classified each label correctly
(in blue), and which labels were most often confused for that label (in orange).
THE RESULTS
Once the evaluation metrics of the model deem reasonable, a fresh new set of images can be
uploaded to the “Predict” page of the AutoML Vision platform. This practice allows users to mimic
real life, set up a scenario that is outside of the dataset, and assess whether or not the model meets
expectations. The “Predict” tool is very easy to use and provides the top five label picks for every
image. Figure 17 illustrates the resulting labels for a real-life photograph of a waffle and ice cream
topped with cereal crumbs and chocolate syrup. Waffles and ice creams are part of the model’s training.
Thus, this choice of photograph captures the diversity of the dataset and, at the same time, represents
a challenge for this model.

Figure 17. Prediction results for a real-life photograph of a waffle and ice cream topped
with cereal crumbs and chocolate syrup.
The model picked five labels for this photograph including waffles, pancakes, ice cream, apple
pie and strawberry shortcake. The numbers that show to the right indicate how certain the model is that
those labels are the correct ones. In this case, the model is 98.2% confident that the item in the picture
is a waffle, 1.13% it is a pancake, .3% it is ice cream, .2% it is an apple pie and .0% it is a strawberry
shortcake. Evidently, the waffle label is on point. Pancakes and waffles are very similar in nature, so
it makes sense that it shows as a top label but with much lower certainty. Moreover, it is not surprising
that the model had a hard time recognizing ice cream as a label, since the components of the photograph
(i.e. cereal crumbs and chocolate syrup) limit the view.
Now, let’s try to predict labels for a picture of an item that has not being part of the model’s
training. Figure 18 illustrates the prediction results of an image of a bottle of Coca-Cola. As expected,
the model is uncertain of the label selections, with levels of confidence under 30%. Notice that the
labels of choice are dishes that are often accompanied by a Coca-Cola, so some of the dataset pictures
might have included Coca-Cola products in the background.
Finally, let’s examine the results for a popular dessert item that was part of the original dataset
for this model. Figure 19 shows the prediction results of a real-life photograph of an apple pie. The
model clearly identifies the item as apple pie with a confidence rate of 88.2% and while other four

labels are present in the results, they have a very low confidence rate. As clearly seen by these
examples, the popular dishes model is meeting expectations.
Figure 18. Prediction results for a Coca-Cola bottle.
Figure 19. Prediction results for an apple pie picture.

DISCUSSION
The purpose of this study was to create a high-quality ML model for visual recognition using
readily available and user-friendly AI tools that take care of the hard parts. This study demonstrated
how quickly and easy it was to accomplish it by using the Google Cloud AutoML Vision platform.
The resulting model, Popular Dishes, generates accurate predictions that exceed expectations, and even
when errors become present, they seem within reason. With high levels of accuracy and a range of
analytical tools, AutoML Vision is changing the technology landscape for marketing and all other
industries, offering an easy-to-access and easy-to-use ML platform without requiring advanced
knowledge or previous experience in ML.
ARTIFICIAL INTELLIGENCE MARKETING
Over the years, an increasing amount of companies have welcome AI into their day to day
operations, resulting in remarkable value and the discovery of new approaches to problem-solving.
Marketing is just one of the disciplines that have greatly improved thanks to the use of these
technologies. In fact, a recent study conducted by Harvard Business Review, shows that even though
the IT discipline is where most of AI efforts are being implemented, marketing is increasingly using
AI to anticipate consumer behavior, improve media buying, monitor social channels and personalize
promotions (Ramaswamy, 2018). Figure 20 illustrates how companies around the world are using AI.

Figure 20. How Companies Around the World are Using Artificial Intelligence
(Ramaswamy, 2018).
Companies like Amazon have become more efficient and effective in their operations, with the
company’s revenue growing as much as 10 times, from the time it started keeping track of the value of
AI in 2004 to 2013. Likewise, Microsoft Corporation has long be known for investing in new
technologies, and back in 2008 the company incorporated ML to improve the performance of its search
engine Bing. Not long after in 2015, Bing’s market share had grown by 20% and was generating over
$1 billion in revenue per quarter (Ramaswamy, 2018). Now, Microsoft Corporation and other tech
giants like Google, are making AI accessible to everyone by eliminating the main pain points that, in
most cases, have kept other companies from implementing these tools in the past.
AI-powered assistants have become widely popular. Alexa, Siri and Cortana are great
examples of how AI is allowing virtual assistants to effectively perform simple and complex tasks in
user-friendly interfaces. Chatbots are becoming more effective at taking care of customers while saving
companies millions of dollars. Digital guides like Waze, are improving their performance by the
second, learning from the users’ past behaviors and suggesting the best alternatives to reach the
selected destination (Schrage, 2017).
American Express and Procter & Gamble are no different, these companies have been in the
market for over 150 years and have become experts at introducing new technologies and adapting to
imminent change. American Express, introduced a virtual assistant in the 1980s, a system powered by

AI that assisted human employees with the approval of large transactions by Amex cardholders.
Similarly, P&G built a coffee blender expert machine that saved the company approximately $20
million in costs. Both companies have a history of focusing their efforts on hiring talented individuals
and building their technology and skills in-house, as part of their cost-efficient approaches. Moreover,
both companies are consumer-focused and are constantly developing tools to attend to consumer needs
and increase satisfaction. For example, P&G recently deployed an ML-based app that allows Olay
Skincare consumers to upload pictures and receive personalized product recommendations based on
their unique skin type. In addition, ML has optimized expenditures, improved supply chain
management and trade promotions between P&G and retailers, allowing the company to become even
more profitable (Davenport & Bean, 2017).
Even though, AI has proven its effectiveness over the years, highly successful companies still
believe that humans are irreplaceable and that despite machines being able to automate certain
processes, a human touch still matters. In fact, companies believe that AI is making jobs easier and
more productive, while empowering humans to become smarter and more knowledgeable as the
technology advances. Humans and machines must work together to get the job done and to foster a
culture of growth and innovation (Enkel, 2017).
LIMITATIONS
As AI is still developing, and for most companies, still in the phases of early adoption, there
are a lot of limitations that need to be taken into consideration when implementing AI-powered tools.
Throughout the next paragraphs we discuss these limitations and provide ideas of actions that can be
taken to overcome them. Figure 21 summarizes these limitations.

Figure 21. Summary of the study’s limitations.
Data is the main ingredient required by AI and ML to be able to effectively train machines. As
most of these models are built using supervised learning, large amounts of structured, labeled and high-
quality data are critical for success. Unfortunately, not all companies have access to this type of data
and, in some industries, data is just not available, which can make it extremely difficult to benefit from
AI. Therefore, it is extremely important for companies to consciously take action to create a plan of
action that ensures the collection of data in a structured way. In this way, companies won’t need to
invest millions of dollars hiring personnel to take care of this task and risking getting high human error
rates. In addition, companies can consider investing in different specialized tools and resources that
are available in the market, like outsourcing data houses management to third-party companies or
signing up for Google’s human labeling service or Vision API (Chui, et al., 2018). The truth is that
sufficiently large datasets with high variety, velocity, veracity and value are essential for any AI and
ML-based model to work.
While a lot of progress has been made when it comes to AI, there is still a lot that needs to be
done for improvement. For example, sometimes machines can come up with models that are just too
complex for humans to understand, which have led to the limited adoption of these technologies.
Unlike humans, machines learn information in different ways, and sometimes machines find it difficult
to connect with information gathered on previous cases. Some of the technical solutions that are a
“work in progress” at the moment, include transfer learning techniques that allow machines to transfer
the knowledge acquired in one task to a different task (Chui, et al., 2018).
Acquisition & Availability of Large-Scale Data
Extremely Complex Models
Learning & Machines
AI Bias & Societal Consequences
Privacy Concerns
Ethical Concerns

Bias in data and algorithms is a complex subject that continues to spark controversy and to
challenge AI efforts. Bias can generate harmful consequences for society. As Google summarizes it,
the bias present in ML models can cause representational harm, opportunity denial and
disproportionate product failure that create disadvantages for minority groups (Google, 2018). There
is an increasing risk of AI tools amplifying unjust biases. If there is a pattern in the data we input into
the system or the model that we are training, AI will magnify that pattern. Remember the quality of
your model or system is only going to be as good as the quality of the data you use to train it. Thus, AI
systems that are intended to approve or deny loan or insurance applications, for example, could present
high denial rates for minority groups based on their race, gender, address, income level and other socio-
economic factors. AI does not have the ability to correct patterns of discrimination.
Organizations and institutions around the world are collecting data on individuals and
combining it with AI to power multiple services like personalizing ads, populating social feeds,
creating customized health plans and even determining court rulings. But the reality is that more often
than not, individuals do not know how their personal information is being used and how decisions are
being made. In fact, most people do not consent to giving away their information in the first place,
which raises ethical concerns and poses moral questions. Individuals simply do not know how these
tools work and how they arrive to decisions that, in some cases, are life-altering. Responsibilities are
being so widely spread when it comes to AI that if something goes wrong, with an algorithm for
example, nobody really is at fault (Gordon-Murnane, 2018). Without being able to question or validate
AI decisions, the ability of individuals and organizations to trust AI systems is jeopardized and AI
tools become these mysterious black boxes. Privacy is definitely a core issue here as well. Should
companies that collect your DNA to give you information about your family tree be allowed to sell
your DNA data to save lives, for example? (Miller, 2018).
Fortunately, companies like Google are creating and implementing fairness practices and
ethical principles into the tools they create and deploy. Tech leaders and giants, like Amazon, IBM and
Apple, are increasingly allocating resources into the investigation of these issues and are developing
their own ethical principles to prevent and eliminate them. However, as companies continue to create
and customize models in-house, bias and ethics continue to be core issues and central topics in AI
(Gordon-Murnane, 2018). Therefore, it is becoming increasingly important to create AI models that
are not just powerful and accessible, but also transparent and ethical.
Other significant issues that could affect a marketers’ work on AI and big data marketing are
data breaches and data monetization. Disastrous data breaches like in the case of Target, Equifax and
Yahoo (over 3 billion of accounts compromised) have negatively impacted AI and big data marketing

efforts, exposing vulnerability and destroying brand reputations, forcing many companies, researchers
and prestigious entities to take immediate actions and work on new internal and external policies that
promote more transparent and secure processes (Hardekopf, 2017). Organizations are having difficulty
determining how much work they should let machines do and are just starting to realize how difficult
it is to enforce universal principles in a globalized world (Miller, 2018).
CONCLUSION
Designing ML models is not nearly as difficult as it sounds anymore. The Google team has
created Cloud AutoML to automate the design of neural networks, a machine-learning approach that
is making companies more efficient, enhancing consumers’ lifestyles, automating processes, freeing
time for humans to work on relevant and rewarding tasks, solving complex problems, expanding
creativity and benefiting disciplines across industries (Forbes, 2018). AutoML Vision, as demonstrated
above, assists with the classification of large-scale datasets and allows individuals to create their own
custom models designed to fit their needs. This technology now performs as well as humans do, and it
is expected to surpass human capabilities and produce even better results in the near future. Cloud
AutoML suite of AI tools presents marketing with unlimited opportunities and opens the door to a new
world of possibilities.
Google’s approach to AI is so effective because the company understands the importance of
these technologies, has a broad portfolio of tools that boost the power of AI and has been working
towards an AI-first architecture for a while now. Visual recognition models have a lot of potential and
present unlimited opportunities for multiple industries. For example, in the education industry, AI
allows students with learning difficulties and special needs to receive the education they need, in the
form they need it. In the healthcare industry, medical teams can get assistance from AI-powered
systems that allow them to diagnose diseases and create a plan of action in real-time. In the marketing
industry, AI can be used to improve user experience, optimize spend and amplify reach. These are just
a few of the examples we have discussed throughout this paper that give a basic idea of the power of
AI architectures. The truth is that predicting the future is not considered magic anymore, it is AI. Are
you ready for it?

REFERENCES
Chumley, C. K. (2018, February 20). Artificial intelligence is everywhere, watching almost all
modern-day human behaviors. Retrieved November 15, 2018, from
https://www.washingtontimes.com/news/2018/feb/20/artificial-intelligence-everywhere-
watching-almost/
Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your
business. McKinsey Quarterly, (1), 96–108. Retrieved from
https://sacredheart.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=tru
e&db=buh&AN=128677101&site=eds-live&scope=site
Columbus, L. (2017a, October 16). 80% Of Enterprises Are Investing in AI Today. Retrieved
November 15, 2018, from https://www.forbes.com/sites/louiscolumbus/2017/10/16/80-of-
enterprises-are-investing-in-ai-today/#1e4de6ca4d8e
Columbus, L. (2017b, December 25). 53% Of Companies Are Adopting Big Data Analytics.
Retrieved October 26, 2018, from
https://www.forbes.com/sites/louiscolumbus/2017/12/24/53-of-companies-are-adopting-big-
data-analytics/#69d1526c39a1
Davenport, T. H., & Bean, R. (2017, March 31). How P&G and American Express Are Approaching
AI. Retrieved November 10, 2018, from https://hbr.org/2017/03/how-pg-and-american-
express-are-approaching-ai
Enkel, E. (2017, April 17). To Get Consumers to Trust AI, Show Them Its Benefits. Retrieved
November 15, 2018, from https://hbr.org/2017/04/to-get-consumers-to-trust-ai-show-them-
its-benefits
Forbes. (2018, March 01). 14 Ways AI Will Benefit or Harm Society. Retrieved November 10, 2018,
from https://www.forbes.com/sites/forbestechcouncil/2018/03/01/14-ways-ai-will-benefit-or-
harm-society/#4c72b9b84ef0
Google. (2018). Responsible AI Practices – Google AI. Retrieved November 10, 2018, from
https://ai.google/education/responsible-ai-practices?category=fairness
Google Cloud. (2018). Vision API - Image Content Analysis | Cloud Vision API | Google Cloud.
Retrieved from https://cloud.google.com/vision/
Google Cloud Platform. (2018, April 19). Retrieved November 22, 2018, from
https://www.youtube.com/watch?v=OcycT1Jwsns

Gordon, B. M. (2011). Artificial Intelligence: Approaches, Tools, and Applications. New York: Nova
Science Publishers, Inc. Retrieved from
e&db=e000xna&AN=440805&site=eds-live&scope=site
Gordon-Murnane, L. (2018). Ethical, Explainable Artificial Intelligence: Bias and Principles. Online
Searcher, 42(2), 22–44. Retrieved from
e&db=tfh&AN=128582745&site=eds-live&scope=site
Hardekopf, B. (2017, October 06). This Week in Credit Card News: 1 Worker Caused Equifax
Breach; Yahoo Hack Hit All 3 Billion Accounts. Retrieved April 8, 2018, from
https://www.forbes.com/sites/billhardekopf/2017/10/06/this-week-in-credit-card-news-1-
worker-caused-equifax-breach-yahoo-hack-hit-all-3-billion-accounts/#5469c0661e4c
Jouppi, N. P., Young, C., Patil, N., & Patterson, D. (2018). A Domain-Specific Architecture for Deep
Neural Networks. Communications of the ACM, 61(9), 50–59. https://doi-
org.sacredheart.idm.oclc.org/10.1145/3154484
Kaggle. (2018). Food Images (Food-101). Retrieved September 28, 2018, from
https://www.kaggle.com/kmader/food41
Miller, J. W. (2018). The Creeping Ethical Challenges of Artificial Intelligence. America, 219(11),
20. Retrieved from
e&db=ulh&AN=132839449&site=eds-live&scope=site
Nott, G. (2018, January 18). Google launches AutoML Vision in bid to 'democratize AI'. Retrieved
October 26, 2018, from https://www.computerworld.com.au/article/632309/google-launches-
automl-vision-bid-democratise-ai/
Olah, C. (2015, August 27). Understanding LSTM Networks. Retrieved November 15, 2018, from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Parloff, R. (2016, September 28). Why Deep Learning Is Suddenly Changing Your Life. Retrieved
October 27, 2018, from http://fortune.com/ai-artificial-intelligence-deep-machine-learning/
Ramaswamy, S. (2018, July 24). How Companies Are Already Using AI. Retrieved November 10,
2018, from https://hbr.org/2017/04/how-companies-are-already-using-ai
Roettgers, J. (2016). How artificial intelligence is changing media: companies like News Corp.,
YouTube, and VUDU use AI to help their services connect better with consumers. Variety,
(1), 50. Retrieved from

e&db=edsgao&AN=edsgcl.472988749&site=eds-live&scope=site
Schrage, M. (2017, July 20). AI Won't Change Companies Without Great UX. Retrieved November
12, 2018, from https://hbr.org/2017/04/ai-wont-change-companies-without-great-ux
Seif, G. (2018, August 21). Everything you need to know about AutoML and Neural Architecture
Search. Retrieved November 17, 2018, from https://towardsdatascience.com/everything-you-
need-to-know-about-automl-and-neural-architecture-search-8db1863682bf
Vijayan, J. (2018). Google Starts Beta Evaluation of New AI Developer Tools. EWeek, 1. Retrieved
from
e&db=f5h&AN=131234709&site=eds-live&scope=site

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