Digital communication (v. 2021 ITA)

Frieda Brioschi - frieda.brioschi@gmail.com
Emma Tracanella - emma.tracanella@gmail.com
DIGITAL COMMUNICATION
LESSON 9 - 2021

LESSON 11
THE COURSE
1. Introduction. What are data and information, why they matter
2. How to collect and organize data
3. Information classiﬁcation
4. Data lingo
5. Around Data Science
6. How we perceive information
7. Visual communication of quantitative data
8. Visual communication of qualitative data
9. Storytelling with data
10.Effective content
11.AI demythologized
12.Tools for analysis and data visualization
2

INTO YOUR DATA PROJECT
LET’S JUMP

LESSON 9
4
UPDATES ON YOUR PROJECT
FOR THE FINAL EXAMINATION
Photo by William Iven on Unsplash

A POWERFUL GLOBAL CONVERSATION HAS BEGUN. THROUGH
THE INTERNET, PEOPLE ARE DISCOVERING AND INVENTING NEW
WAYS TO SHARE RELEVANT KNOWLEDGE WITH BLINDING SPEED.
AS A DIRECT RESULT, MARKETS ARE GETTING SMARTER—AND
GETTING SMARTER FASTER THAN MOST COMPANIES
Cluetrain Manifesto, 1999
LESSON 9
7

LESSON 9
SOCIAL MEDIA
Social media are online technologies and practices that people use to share text, image, video
and audio.
Andreas Kaplan and Michael Haenlein deﬁne social media as "a group of Internet-based
applications that build on the ideological and technological foundations of Web 2.0, and that
allow the creation and exchange of user-generated content.”
They represented a change in how people learn, read and share information and contents: a
blend between sociology and technology takes place and it tranforms a monologue (1-to-many)
into a dialogue (many-to-many) and information result democratized, transforming persons from
users to editors.
▸ https://web.archive.org/web/20111124233421/michaelhaenlein.eu/Publications/Kaplan,%20Andreas%20-%20Users%20of%20the%20world,
%20unite.pdf
8

LESSON 9
SOCIAL NETWORKS
9
Danah Boyd and Nicole Ellison deﬁne a social network as a web-based services that
allow individuals to:
▸ construct a public or semi-public proﬁle within a bounded system,
▸ articulate a list of other users with whom they share a connection, and
▸ view and traverse their list of connections and those made by others within the
system.
The nature and nomenclature of these connections may vary from site to site.
▸ http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html

LESSON 9
NETWORKED PUBLICS
10
According to Danah Boyd, social network sites can be understood as networked
publics which are simultaneously:
▸ the space constructed through networked technologies and
▸ the imagined community that emerges as a result of the intersection of
people, technology, and practice
▸ http://www.danah.org/papers/TakenOutOfContext.pdf

LESSON 9
PROPERTIES & DINAMICS
11
Four properties:
1. persistence
2. searchability
3. replicability
4. scalability
▸ http://www.danah.org/papers/TakenOutOfContext.pdf
Three dynamics:
1. invisible audiences
2. collapsed contexts
3. the blurring of public and private

LESSON 9
LUMASCAPES
14https://lumapartners.com/luma-content/

LESSON 9
18
https://www.youtube.com/watch?v=OQwHNqMapiE

LESSON 9
19
https://www.brandz.com/admin/uploads/ﬁles/BZ_Global_2019_WPP.pdf

MARKETS ARE CONVERSATIONS.
Cluetrain Manifesto, 1999
LESSON 9
21

LESSON 9
CONTENT STRATEGY
22
What do you want to communicate?
3 steps:
1. what
2. where
3. how

LESSON 10
CONTENT STRATEGY
23
What do you want to communicate?
3 steps:
1. what
2. where
3. how
https://www.techsoup.org/support/articles-and-how-tos/5-steps-to-an-effective-content-strategy-for-your-nonproﬁt

GOOD CONTENT IS
WRITTEN FOR A
SPECIFIC AUDIENCE
https://medium.com/strategic-content-marketing/the-7-signs-of-truly-effective-content-270b50b8c6ae

IT’S OPTIMIZED FOR
SEARCH AND SOCIAL,
TOO.

GOOD CONTENT
PROVIDES VALUE.

YOUR CONTENT SHOULD
INFORM.

GOOD CONTENT
CONVERTS.

TO BE MOST EFFECTIVE,
CONTENT MUST ALSO
BE STRUCTURED WELL.

GOOD CONTENT SHOULD
BE PROPERLY
DISTRIBUTED AND
PROMOTED.

LESSON 9
MOST COMMON CONTENT TYPES
31
1. Blog Posts
2. Long Form Articles
3. Original Research
4. Video
5. Infographics
6. Images
7. Case Studies
8. White Papers/Reports
9. Ebooks
10.Presentations
11.Webinars
12.Quizzes and Polls
13.Podcasts
14.Checklists
15.Email Newsletters

LESSON 9
VISUALIZATION OF FIRE IN AUSTRALIA
36

LESSON 9
37https://www.instagram.com/p/B67bRtPnVzR/

LESSON 9
38
https://ﬁrms.modaps.eosdis.nasa.gov/map/#z:5;c:130.2,-27.3;t:adv-points;d:2019-12-05..2020-01-05;l:ﬁrms_viirs

LESSON 9
39https://www.statista.com/chart/20387/recent-wildﬁre-events-by-acreage-burned/?fbclid=IwAR07k1uGsQleU1aQU37YGg0LPfWZjx-t3H9Zh6Q0ZZTgSF4_h3_az2tPhvc

AI IS THE PART OF COMPUTER SCIENCE WHICH STUDIES THEORIES,
METHODOLOGIES AND TECHNIQUES THAT ENABLE TO DESIGN
HARDWARE AND SOFTWARE SYSTEMS THAT GIVE TO COMPUTERS
SOME SKILL WHOSE SCOPE WOULD SEEM, TO A COMMON
OBSERVER, TO BE EXCLUSIVELY POSSIBLE TO HUMAN INTELLIGENCE.
Marco Somalvico
LESSON 9
41

LESSON 9
FOUR APPROACHES
The ﬁeld of AI:
1. Thinking humanly: cognitive modeling. Systems should solve problems the
same way humans do.
2. Thinking rationally: the use of logic. Need to worry about modeling uncertainty
and dealing with complexity.
3. Acting humanly: the Turing Test approach.
4. Acting rationally: the study of rational agents: agents that maximize the
expected value of their performance measure given what they currently know.
42
https://cs.lmu.edu/~ray/notes/introai/

LESSON 10
43
https://towardsdatascience.com/patenting-ai-lets-start-with-a-history-lesson-af2cbc73a024

LESSON 9
THE TURING TEST: CAN A COMPUTER PASS FOR A HUMAN?
45
https://www.youtube.com/watch?v=3wLqsRLvV-c

LESSON 9
46https://atos.net/en/artiﬁcial-intelligence#1529915993356-51db395f-6082

ARTIFICIAL
INTELLIGENCE
THE AGE OF

data & content design
LESSON 9
48
https://hai.stanford.edu/news/infographic-age-artiﬁcial-intelligence

49
LESSON 9

50
LESSON 9

CLASSIFYING AI
51
▸ Machine Learning
▸ Deep Learning
▸ Reinforcement Learning: the use of rewarding systems that achieve objectives in order
to strengthen (or weaken) speciﬁc outcomes. This is frequently used with agent systems.
▸ Agent Systems: systems in which autonomous agents interact within a given
environment in order to simulate emergent or crowd based behavior. Used more and
more frequently with games in particular, but is also used with other forms of
simulations.
▸ https://www.forbes.com/sites/cognitiveworld/2019/08/20/what-is-artiﬁcial-intelligence/#66ebdad9306f
LESSON 9

CLASSIFYING AI
52
▸ Non-Linear Grid Systems: a variation of agented systems in which cells in n-
dimensional grids maintain internal state but also receive stimulae from
adjacent cells and generate output to those cells. The distant ancestor of
most of these is Conway's Game of Life, but the idea is used to a much
higher degree of complexity with most weather and stock modeling systems
that are fundamentally recursive.
▸ Self-Modifying Graph Systems: these include knowledge bases and so forth
in which the state of the system changes due to system contingent
heuristics.
LESSON 9

CLASSIFYING AI
53
▸ Knowledge Bases, Business Intelligence Systems and Expert Systems: these often form a
spectrum from traditional data systems to aggregate semantic knowledge graphs. To a
certain extent they are human curated, but some of this curation is increasingly switching
over to machine learning for both classiﬁcation, categorization and abstraction.
▸ Chatbots and Intelligent Agents: this differs from agent systems. Agents in general are
computer systems that are able to parse written or spoken text, use it to retrieve certain
content or perform certain actions, and the respond using appropriately constructed
content. The earliest such system, Eliza, dates back to the mid-1960s, but was very
primitive. Today's agents and chatbots, on the other hand, use a combination of
semantics, Bayesian analysis and machine learning to both build up the appropriate
information and learn about the user.
LESSON 9

CLASSIFYING AI
54
▸ Visual/Audio Recognition Systems: in most cases V/A systems work by converting the
media in question to an encoded compressed form, then algorithms look via either
indexes or machine learning systems for the closest matches. This is often enhanced
with Bayesian Analysis, where speciﬁc patterns are analysed based upon their
frequency of occurrence relative to one another, and are also often tied in with
semantic systems that provide relationship information.
▸ Fractal Visualization: the connection between fractals and AI runs deep, and not
surprisingly one of the biggest areas for AI is in the development of parameterized
natural rendering - the movement of water, the roar of ﬁre, the coarseness of rock, the
effects of smoke in the air, all of which have become standard fare in big Hollywood
blockbusters.
LESSON 9

NOT PROPERLY AI
55
▸ Autonomous Vehicles: these make use of visual recognition systems and real time modeling in
order to both anticipate obstacles (static and moving) and to determine actions based upon
objectives.
▸ Drones: a drone is an autonomous vehicle without a passenger, and can be as small as a
dragonﬂy or as large as a jet. Drones can also act in a coordinated fashion, either by following
swarm behavior (an agent system) or by following preprogrammed instructions.
▸ Data Science / Data Analytics: this is the use of data to identify patterns or predict behavior.
This uses a combination of machine learning techniques and numeric statistical analysis, along
with an increasingly large roll for non-linear differential equations. The primary distinction is
that most data scientist does not make heavy use of higher order functions or recursion,
though again, this is changing.
LESSON 9

NOT PROPERLY AI
56
▸ Blockchain and Distributed Ledgers: distributed ledger technology underlies electronic coinage, but
it is also playing a bigger and bigger role in tracking resources and transactions. One aspect of such
systems is that they make it possible to bind virtual objects as if they were unique physical objects, in
effect making intellectual property exchangeable. This has application throughout the AI space,
especially in the realm of agented systems, even if it is not AI per se.i
▸ Internet of Things / Robotics: internet of things is intended to provide network connectivity to devices
so that they can communicate with other devices. Robotics involves creating autonomous physical
agents capable of movement. In that both of these may end up managing their own state, relies upon
AI-based systems for identifying signals and determining response, they use AI, but aren't directly AI.
▸ GPUs: the Central Processing Unit is so last century. Artiﬁcial intelligence is taking advantage of Graph
Processing Units in a big way, as their structure makes them ideal for both semantic analysis and
recursive ﬁlter applications.
LESSON 9

MACHINE LEARNING -
DEEP LEARNING
ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE
58
On a lower level, an AI can be only a programmed rule that determines the
machine to behave in a certain way in certain situations.
So basically Artiﬁcial Intelligence can be nothing more than just a bunch of if-
else statements. An if-else statement is a simple rule explicitly programmed by a
human.
LESSON 9

59https://www.bbc.com/news/technology-50779761
LESSON 9

MACHINE LEARNING
60
Algorithms that analyze data, learn from it and make informed decisions based on
the learned insights.
Machine Learning algorithms must be trained on data. The more data you provide
to your algorithm, the better it gets.
The “training” part of a Machine Learning model means that this model tries to
optimize along a certain dimension. The Machine Learning models try to minimize the
error between their predictions and the actual ground truth values.
In short machine learning models are optimization algorithms. If you tune them right,
they minimize their error by guessing and guessing and guessing again.
LESSON 9

DEEP LEARNING
61
Deep Learning uses a multi-layered structure of algorithms called the neural
network.
LESSON 9

FEATURE EXTRACTION
62
The models of deep learning require little to no manual effort to perform and
optimize the feature extraction process.
LESSON 9

EXAMPLE
63
If you want to use a machine learning model to determine whether a particular
image shows a car or not, we humans first need to identify the unique features of
a car (shape, size, windows, wheels, etc.), extract these features and give them to
the algorithm as input data. This way, the machine learning algorithm would
perform a classification of the image. That is, in machine learning, a programmer
must intervene directly in the classification process.
In the case of a deep learning model, the feature extraction step is completely
unnecessary. The model would recognize these unique characteristics of a car
and make correct predictions- completely without the help of a human.
LESSON 9

AND BIG DATA
64
Deep Learning models tend to increase their accuracy with the increasing
amount of training data, where’s traditional machine learning models stop
improving after a saturation point.
LESSON 9

PERCEPTION
MACHINE
PHOTO BY JAREDD CRAIG ON UNSPLASH

MACHINE PERCEPTION
Machine perception is the capability of a computer system to interpret data in a manner that
is similar to the way humans use their senses to relate to the world around them.
The basic method that the computers take in and respond to their environment is through the
attached hardware. Until recently input was limited to a keyboard, or a mouse, but advances
in technology, both in hardware and software, have allowed computers to take in sensory
input in a way similar to humans.
Machine perception allows the computer to use this sensory input, as well as conventional
computational means of gathering information, to gather information with greater accuracy
and to present it in a way that is more comfortable for the user.
These include computer vision, machine hearing, and machine touch.
66
LESSON 9

FACIAL RECOGNITION SYSTEM
A facial recognition system is a technology capable of identifying or verifying a
person from a digital image or a video frame from a video source.
There are multiple methods in which facial recognition systems work, but in general,
they work by comparing selected facial features from given image with faces within
a database.
Although the accuracy of facial recognition system as a biometric technology is
lower than iris recognition and ﬁngerprint recognition, it is widely adopted due to its
contactless and non-invasive process.
67
LESSON 9

THIS RECOGNITION PROBLEM IS MADE DIFFICULT BY THE GREAT VARIABILITY IN HEAD
ROTATION AND TILT, LIGHTING INTENSITY AND ANGLE, FACIAL EXPRESSION, AGING, ETC.
(…)
YET THE METHOD OF CORRELATION (OR PATTERN MATCHING) OF UNPROCESSED OPTICAL
DATA, WHICH IS OFTEN USED BY SOME RESEARCHERS, IS CERTAIN TO FAIL IN CASES
WHERE THE VARIABILITY IS GREAT. IN PARTICULAR, THE CORRELATION IS VERY LOW
BETWEEN TWO PICTURES OF THE SAME PERSON WITH TWO DIFFERENT HEAD ROTATIONS.
Woody Bledsoe, 1966
68
LESSON 9

DEEPFACE
DeepFace is a deep learning facial recognition system created by a research group
at Facebook.

It identiﬁes human faces in digital images. It employs a nine-layer neural net with over
120 million connection weights, and was trained on four million images uploaded by
Facebook users.

The system is said to be 97% accurate, compared to 85% for the FBI's Next
Generation Identiﬁcation system.
69
LESSON 9

FB FACE RECOGNITION
70
LESSON 9

APPLE FACE ID
Apple introduced Face ID on the flagship iPhone X as a biometric authentication system.

Face ID has a facial recognition sensor: "Romeo" the module that projects more than 30,000 infrared
dots onto the user's face, and "Juliet" the module that reads the pattern. The pattern is sent to a local
"Secure Enclave" in the device's CPU to confirm a match with the phone owner's face.

The system will not work with eyes closed, in an effort to prevent unauthorized access.

The technology learns from changes in a user's appearance, and therefore works with hats, scarves,
glasses, and many sunglasses, beard and makeup.

It also works in the dark. This is done by using a "Flood Illuminator", which is a dedicated infrared
flash that throws out invisible infrared light onto the user's face to properly read the 30,000 facial
points.[34]
71
https://en.wikipedia.org/wiki/Facial_recognition_system
LESSON 9

CHINESE AIRPORTS
72
As of late 2017, China has deployed
facial recognition and artiﬁcial
intelligence technology in Xinjiang.
Reporters visiting the region found

surveillance cameras installed every
hundred meters or so in several cities, as
well as facial recognition checkpoints at
areas like gas stations, shopping centers,
and mosque entrances.

https://www.youtube.com/watch?v=wcM5-E4Kze4
LESSON 9

ANTI-FACIAL RECOGNITION SYSTEMS
73
LESSON 9

ENOVIA SMART ROBOTS
74
Smart Robots has developed a
universal device that enables the
integration of cobots with human
activities.

The device can map the workspace in
real time, to recognize objects, to
command the robot to interact with
users and adapt to them, and to self-
learn new commands through
gestures.

LESSON 9

277 PEOPLE IN 177 CARS
75
https://imgur.com/gallery/sCvRIEd
LESSON 9

HOW COMPANY USE
AI?
THE BEST EXAMPLES

AI EXAMPLES
▸ Smart assistants (like Siri and Alexa)
▸ Disease mapping and prediction tools
▸ Manufacturing and drone robots
▸ Optimized, personalized healthcare treatment recommendations
▸ Conversational bots for marketing and customer service
▸ Robo-advisors for stock trading
▸ Spam filters on email
▸ Social media monitoring tools for dangerous content or false news
▸ Song or TV show recommendations from Spotify and Netflix
77
https://builtin.com/artificial-intelligence
LESSON 9

ALIBABA
Chinese company Alibaba is the world's largest e-commerce platform that sells more than
Amazon and eBay combined.
AI is integral in Alibaba’s daily operations and is used to predict what customers might want to
buy. With natural language processing, the company automatically generates product
descriptions for the site.
Another way Alibaba uses artificial intelligence is in its City Brain project to create smart cities.
The project uses AI algorithms to help reduce traffic jams by monitoring every vehicle in the
city.
Additionally, Alibaba, through its cloud computing division called Alibaba Cloud, is
helping farmers monitor crops to improve yield and cuts costs with artificial intelligence.
78
LESSON 9

ALPHABET – GOOGLE
Waymo, the company’s self-driving technology division, wants to bring self-driving
technology to the world to not only to move people around, but to reduce the number of
crashes. Its autonomous vehicles are currently shuttling riders around California in self-
driving taxis. Right now, the company can’t charge a fare and a human driver still sits
behind the wheel during the pilot programme.
Google acquired DeepMind. Not only did the system learn how to play 49 different Atari
games, the AlphaGo programme was the ﬁrst to beat a professional player at the game of
Go.
Google Duplex - Using natural language processing, an AI voice interface can make
phone calls and schedule appointments on your behalf.
79
LESSON 9

AMAZON
Not only is Amazon in the artificial intelligence game with its digital voice assistant, Alexa, but
artificial intelligence is also part of many aspects of its business.
Another innovative way Amazon uses artificial intelligence is to ship things to you before you even
think about buying it. They collect a lot of data about each person’s buying habits and have such
confidence in how the data they collect helps them recommend items to its customers and now
predict what they need even before they need it by using predictive analytics.
Amazon Go. Unlike other stores, there is no checkout required. The stores have artificial
intelligence technology that tracks what items you pick up and then automatically charges you for
those items through the Amazon Go app on your phone. Since there is no checkout, you bring your
own bags to fill up with items, and there are cameras watching your every move to identify every
item you put in your bag to ultimately charge you for it.
80
LESSON 9

WHAT IS NETFLIX?
82
Netflix's initial business model included DVD sales and rental by mail, but
Hastings abandoned the sales about a year after the company's founding to
focus on the initial DVD rental business. Netflix expanded its business in 2010
with the introduction of streaming media while retaining the DVD and Blu-ray
rental business. The company expanded internationally in 2010 with streaming
available in Canada, followed by Latin America and the Caribbean. Netflix
entered the content-production industry in 2012, debuting its first series
Lilyhammer.
▸ https://en.wikipedia.org/wiki/Netflix
LESSON 9

A NEW COURSE
83
In 2006, Netflix launched an unusual, and highly successful, competition
designed to improve its recommendation system. It released a database of 100
million movie and TV show ratings from nearly 500,000 users and, in 2009,
awarded the $1 million jackpot the first team to increase the accuracy of its own
movie recommendation algorithm by more than 10 percent.
The rapid rise of streaming content has exploded the amount and types of data
available to the company’s data science team.
▸ https://www.technologyreview.com/s/428867/why-there-wont-be-a-netflix-prize-sequel/
LESSON 9

HOUSE OF CARDS
84
Netﬂix decided to make its original programming bet on House of Cards,
speciﬁcally, based on what it knows about the viewing habits of its users—it knew
which and how many users watch movies starring Kevin Spacey and the director
David Fincher, and, through its tagging and recommendation system, how many
sat through other similar political dramas. It has shown different trailers to people
depending on their particular viewing habits, too.
▸ https://www.technologyreview.com/s/511771/house-of-cards-and-our-future-of-algorithmic-programming/
LESSON 9

EVENTS TRACKED
85
▸ When you pause, rewind, or fast forward
▸ What day you watch content (Netﬂix has found people watch TV shows
during the week and movies during the weekend.)
▸ The date you watch
▸ What time you watch content
▸ Where you watch (zip code)
LESSON 9

EVENTS TRACKED
86
▸ What device you use to watch (Do you like to use your tablet for TV shows and
your Roku for movies? Do people access the Just for Kids feature more on their
iPads, etc.?)
▸ When you pause and leave content (and if you ever come back)
▸ The ratings given (about 4 million per day)
▸ Searches (about 3 million per day)
▸ Browsing and scrolling behavior
▸ https://neilpatel.com/blog/how-netﬂix-uses-analytics/
LESSON 9

5 USE CASES OF AI/DATA/MACHINE LEARNING AT NETFLIX
87
▸ Personalization of Movie Recommendations
▸ Auto-Generation and Personalization of Thumbnails / Artwork
▸ Location Scouting for Movie Production (Pre-Production)
▸ Movie Editing (Post-Production)
▸ Streaming Quality
▸ https://becominghuman.ai/how-netﬂix-uses-ai-and-machine-learning-a087614630fe
LESSON 9

PERSONALIZED IMAGE THUMBNAIL / ARTWORK
88
▸ Problem: How (and when) do we best present that movie recommendation to the user in a way that
maximizes viewership and monthly subscriber loyalty?
▸ Users spent an average of 1.8 seconds considering each title they were presented with while on Netﬂix
▸
LESSON 9

LESSON 9
90https://www.bbc.com/news/technology-50779761

LESSON 9
91

LESSON 9
92

LESSON 9
..AND MORE
93
https://www.nytimes.com/2019/02/05/business/media/artiﬁcial-intelligence-journalism-robots.html

Digital communication (v. 2021 ITA)

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