Bài giảng Innovation Mindse môn Design thinking in Big data & AI | Trường Đại học Văn Lang

8/1/26
AIRBNB ALLOWS TRAVELLERS TO STAY IN
PRIVATE HOMES BY ACCESSING A PLATFORM LINKING OWNERS WITH TRAVELLERS
TELEGRAM ALLOWS MOBILE PHONE OWNERS TO STAY IN
TOUCH FOR FREE BY USING INTERNET
AS AN ALTERNATIVE TO SMS MESSAGES
A [label] that al ows [users] to [benefit] by [method] 78 79
DISRUPTIVE INNOVATION IN BIG DATA/AI ROAD MAP • CREATIVITY VS. INNOVATION 1. Disruptive innovation • DEEPER NEED 2. Innovation Mindset • DISRUPT REAL-ABSTRACT-REAL
3. Introduction to Big Data and AI • QUESTIONS
4. Identifying Opportunities for Disruption
5. Implementing AI for competitive advantage
6. Overcoming Challenges in AI and Big Data
7. Disruptive innovation workshop 80 81 1 8/1/26 INNOVATION PADMAN MINDSET INVENTIONS ANCHORING BM SKILLS 82 84 A MATRIX for OPPORTUNITIES INVENTIONS 4 DIMENSIONS FOR EACH VARIABLE
TWIST create something that did not VARIABLES
COST AVAILABILITY HIGYENE MINDSET SOLUTION exist before EXPANSION Idea 13 Idea 13 IDEA 2 REDUCTION Idea 13 IDEA 5 OPPOSITE Idea 13 DENIAL Idea 13 ABSTRACTION SYNTHESIS
Prioritising what real y makes sense
Expanding, Reducing, Denying, Opposing 85 86 2 8/1/26
A MATRIX for DIGITAL OPPORTUNITIES
Creativity generates ideas (AGILE CREDIT CARD, HOME 4 DIMENSIONS FOR EACH VARIABLE
EATING, HYBRID SOCIAL PLATFORM) → VARIABLES
Innovation turns ideas into solutions (BBVA APP, McDonald DISTANCE SPEED THRUST COST SOLUTION conbo, Social media→ EXPANSION
Disruption occurs when an innovation changes the market IDEA 2 REDUCTION
dynamics (REVOLUT, EATWITH, MEETUP). IDEA 5 OPPOSITE NEED DEEPER SOLUTION NEED DENIAL ABSTRACTION SYNTHESIS DO IT
Prioritising what real y makes sense BETTER
Expanding, Reducing, Denying, Opposing 87 88 ARE YOU ANCHORED OR INNOVATOR? A BIT OF NEUROSCIENCE
HERDING Go with the crowd.
SELF-HERDING Go with your past-decision
CONFIRMATION BIASES Adapt or seek new
information, to your past decision
IS IT GOOD? DO YOU DO IT? 89 90 3 8/1/26 WHICH COMPANIES GOT ANCHORED?
Which companies are anchored, which are not? Was Kodak anchored? 91 92
READ THE COLOR, NOT THE WORD ANCHORING ORANGE, BLUE, RED, BLUE GREEN, BLACK, BLUE, ORANGE,
1. How would you estimate the cost per hour of jobs like: GREEN, ORANGE, BLUE, RED, - Smel ing used shoes BLUE, GREEN, RED, BLUE,
- Hugging people in the street - Cleaning shoes ORANGE, GREEN, ORANGE,
2. Write the last 4 digits of your mobile phone BLUE, RED, BLACK, GREEN, RED, number BLUE, ORANGE, GREEN 93 94 4 8/1/26 ANCHORING DATA INNOVATORS MINDSET
How would you react if these two digits represent the WHAT SKILLS? cost of:
- A pair of sunglasses with polarised lenses
- The H Couture Beauty Diamond lipstick
- The Macal an 64 in Lalique Cire Perdue 95 96 STEVE JOBS vs. STEVE BALMER WHO DID INVENT: Telephone Who discovered the waves? Who invented the line? Who created the device? Who sponsored it? 97 98 5 8/1/26 WHO DID INVENT: Computer WHO DID INVENT THE MONEY?
Who created the keyboard? Coins Who invented the mouse? Banknotes (Schein) Who created the chip? Digital accounts 1960 Who assembled it? ATM machines 1965 Electronic trading 1970 Global payment networks 1977 Internet banking 1990 Mobile payments 2000
Now BAAS, Business as a Service, instead of CRM by Microsoft Dynamic, a platform. 99 100 INNOVATION MINDSET Mc Donald BM
Share with your neighbor examples of
innovation that faced skepticism and
worked immediately or worked after some time.
Then be honest and think something you are skeptical about. HOW IS YOUR MINDSET? 101 104 6 8/1/26
INVENTIONS, BUSINESS MODEL & STRATEGY BUSINESS MODEL 50+
Inventions create something that Franchise McDonald
Multi-sided platform model Linkedin did not exist before Razor & blade Nespresso
Business Model is the logic of the Freemium Zoom Crowdsourcing Wikipedia
firm to operate with stakeholders Pay-as-you-go Amazon AWS
Strategy is the choice of the Hidden Revenue Google Meta Subscription Netflix Navozyme business model to reach a goal Peer to Peer Airbnb Eatwith Brokerage Expedia Disintermediation Tesla Del Bundling Microsoft 105 106
BM INNOVATION THE GENESIS AND METAMORPHOSIS OF NOVELTY IMPRINTS: HOW BUSINESS MODEL INNOVATION EMERGES
IN YOUNG VENTURES SNIHUR ZOTT 2020 ANCHORING VS.“T” SHAPED Centralised decision making T-shaped talents: Horizontally fluent 108 T Vertical y world class Complex system Thinking Industry Spanning Research 107 7 8/1/26 GROWING DECLINING SKILL SKILLS SKILLS ANALYTICAL MANUAL DEXTERITY, THINKING ENDURANCE & PRECISION INNOVATION MEMORY, VERBAL & Knowledge SPATIAL HABILITIES ACTIVE LEARNING RESOURCES Experience MANAGEMENT CREATIVITY & TECHNOLOGY Human Judgment INITIATIVE INSTALLATION TECHNOLOGY DESIGN READING, WRITING & ACTIVE LISTENING CRITICAL THINKING PEOPLE MANAGEMENT COMPLEX PROBLEM- QUALITY CONTROL SOLVING 109 110 INNOVATION MINDSET INNOVATION MINDSET • INVENTIONS • ANCHORING WRITING WILL UNDERMINE OUR • BM ABILITY TO REMEMBER • SKILLS READING WILL MAKE OUR KNOWLEDGE MERE DATA PHAEDRUS- PLATO 111 112 8 8/1/26 ROAD MAP BIG DATA & AI 1. Disruptive innovation DATA NETWORK EFFECT 2. Innovation Mindset DATA BUSINESS MODEL (PAAS) THINK ECOSYSTEM
3. Introduction to Big Data and AI
ELECTRIC TOOTHBRUSH BUSINESS MODEL
4. Identifying Opportunities for Disruption
STAR SENSE-TRANSMIT-ANALYSE-RESPOND ROBOTICS
5. Implementing AI for competitive HUMAN&MACHINE advantage
6. Overcoming Challenges in AI and Big Data
7. Disruptive innovation workshop 113 114 A data network effect is when a product's value grows as a result of more usage via the accretion of data. WAZE 115 116 9 8/1/26 DATA DAILY EXAMPLE DATA CATEGORIES Time you woke up
Group the 30 observations into 3–5 Weather
categories (e.g., Energy, Work, Social, Number of emails received Health, Emotions). Coffee cups consumed
Decide the rule you use to assign each People interacted with data point to a category.
Mood at three different moments
Cross out any data that does not fit your structure.
Do not explain or connect them. Just list. Who decided the categories? What valuable
Is this information useful as it is? data might have Why or why not? been excluded? 117 118 ALGORITHM (RULE) AI role
Write a simple rule that transforms your structured data
If you had 10,000 days like this instead of into a decision. Example: one:
“If energy is low before 11am and coffee > 2, then
What would become impossible for a productivity wil be low.” human?
Apply your rule to your data and write the output decision.
What could a machine do better?
What would the machine stil not know without human input?
Does the rule oversimplify reality?
What assumptions did you encode Big Data scales pattern without noticing? detection. AI automates inference.
An algorithm is not intel igence; it is reasoning. Humans define meaning, goals, frozen and ethics. 119 120 10 8/1/26 WHAT IS AI? INTELLIGENCE: INPUT VS. OUTPUT an attempt of
§ AI is the reproduction of human reasoning and
intelligent behavior by computational methods Intelligent behavior Computer Humans 121 122 CAN MACHINES ACT/THINK INTELLIGENTLY? BINGO to UNDERSTAND MACHINE LEARNING
GET A FORECAST FROM A WEATHER APP DATA SET: what the weather was like in the past PREDICTION: what the weather wil be like in the future 123 124 11 8/1/26 18 MONTHS OLD CHILD
HCI, HUMAN COMPUTER INTERACTIONS FORGES A NEW
PARTNERSHIP BETWEEN HUMAN AND MACHINE. Hu man s excel at :
Co g n i t i ve Syst ems Common Sense excel at : Morals Locating Knowledge Imagination Pattern Identification Compassion Machine Learning Abstraction Eliminate Bias Dilemmas Endless Capacity Dreaming Generalization 125 126 AI 1. Reactive machines 2. Limited memory 3. Theory of mind 4. Self-awareness 127 128 12 8/1/26 1. REACTIVE MACHINES 2. LIMITED MEMORY
Basic types: purely reactive
Type I class contains machines can look into
No memories, no past experience the past. EXAMPLE:
Deep Blue, IBM’s vs. Garry Kasparov in 1997. EXAMPLE: Self-driving cars.
Deep Blue ignores everything before the present
moment. Al it does is look at the pieces on the
They observe other cars’ speed and direction.
chess board as it stands right now, and choose
from possible next moves.
That can’t be done in a just one moment, but
rather requires identifying specific objects and monitoring them over time. 129 131 AlphaFold 3. THEORY OF MIND
Future machines: creatures and objects
in the world can have thoughts and
emotions that affect their own behaviour. 132 133 13 8/1/26 LLM for Pizza Pizza Tarradel as? Producto Tarradel as? 134 135 4. CONSCIOUSNESS
“I want that item” is a very different
statement from “I know I want that item.”
Being aware wil give you an insight
into your beliefs and whether they are
positive or holding you back. I PROMPTING “KETCHUP…” 136 137 14 8/1/26 CONSCIOUSNESS & BACON COMPLICATION COMPLEXITY HIGH (NH3) PREDICTABILITY LOW
WHO IS SMARTER, A DOG OR A PIG? STABLE ELEMENTS UNSTABLE CONNECTION WE ASSIGN CONSCIOUSNESS BASED HIGH REPEATABILITY LOW ON RELATIONSHIP, NOT ON INDUSTRIAL REVOLUTION ORIGIN KNOWLEDGE ECONOMY INTELLIGENCE QUANTITATIVE SOLUTION QUALITATIVE PLAN & IMPLEMENT APPROACH TEST & LEARN Mitt Romney’s Orca Obama 2008 and 2012 138 138 139 MANAGE, NOT SOLVE NEW BUSINESS MODEL Mechanist failure A business model entails identifying "a new approach" TEST & LEARN (Continuous) for PLAN & IMPLEMENT (Batches) creating and P&G Questions Revenue capturing model or sales increase? VALUE to exploit business opportunities. 140 141 15 8/1/26
A MATRIX for DIGITAL OPPORTUNITIES 4 DIMENSIONS FOR EACH VARIABLE VARIABLES DISTANCE TIME SPEED # OF LINES COST SOLUTION EXPANSION IDEA 2 REDUCTION IDEA 5 OPPOSITE DENIAL ABSTRACTION SYNTHESIS
Prioritising what real y makes sense
Expanding, Reducing, Denying, Opposing 142 143 ZERO MOMENT OF TRUTH ZERO MOMENT OF TRUTH 144 145 16 8/1/26
DATA BUSINESS: ECONOMY OF SCALE THINK ECOSYSTEM Google Nest (paid 3.2
Think ecosystem, not supply chain. U$ Bil ion) wil not only Supply chain is binary: play in the $3 bil ion 1. Charge your users more global thermostat 2. Pay your suppliers less industry.
Ecosystem: other parties benefit when your innovation succeeds. WHO ELSE IS IN YOUR ECOSYSTEM? It will help shape the $6 tril ion energy sector 146 147 STANDARD REVENUE STREAM DIGITAL REVENUE STREAM The Power Brush Revenue model:
WHAT CAN BE MEASURED CAN BE CHARGED
Smart Digital TT Power Brush model: 1.
44€ = 33 for toothbrush+ 11 for 2 Heads (5,5 € 1. each) 0,1€ per minute
2. Brushing Service PaaS 20€ per month 2.
10€ for Toothbrush + 20 for 2 Heads (10 € each)
3. Free to insurance companies’clients and then paid from savings 3.
Subscription model for replacement heads 18€ per months
4. Become as trusted partner- money made on toothpaste and dental floss 4.
Toothbrush is free: al money on replacement heads
5. Collect user data and sell it 148 149 17 8/1/26 DATA & BUSINESS MODEL PRODUCT AS A SERVICE (PaaS)
People Buy Holes, Not Dril s &
WHAT CAN BE MEASURED CAN BE CHARGED
What if we deliver lighting hours instead of bulbs?
Power per hour instead of jet engine? Certificates vs. Safety? Miles instead of tyres? 150 151 TRADITIONAL DIGITAL
THE CHALLENGE FOR DIGITAL SUCCESS HIGH LATENCY LOW TO REAL TIME
The chal enge: prioritizing strategy over technology. PROBLEM FIRST COSTS FOCUS OUTCOME
Do not be dragged by disruptive COMPANY CONTROL CUSTOMER trends. SINGLE AND SILOED FUNCTION INTEGRATED AND KPI SIMPLE & COMPLEX & INDEPENDENT SYSTEM INTERDEPENDENT (ECOSYSTEM) 152 153 18 8/1/26
STANDARD/DIGITAL REVENUE STREAM
EXECUTING TASKS: SHOP&SHIP TO SHIP&SHOP
Share with your neighbor examples of
different business models for the same product/service. Extract from your personal
experience/work or use a popular case. 154 155 WHAT DATA DOES AMAZON HAVE?
THE SELLER IS ALSO THE ADVERTISER BROWN BOXES TRAFFIC Amazon is tel ing advertisers that the best predictor of what you, the consumer, are going to buy is what you’ve already bought. 156 157 19 8/1/26 FROM DATA TO PROFIT (STAR)
AIRBNB ALLOWS TRAVELLERS TO STAY IN
PRIVATE HOMES BY ACCESSING A PLATFORM LINKING OWNERS WITH TRAVELLERS
TELEGRAM ALLOWS MOBILE PHONE OWNERS TO STAY IN
TOUCH FOR FREE BY USING INTERNET
AS AN ALTERNATIVE TO SMS MESSAGES Profits Respond
A [label] that al ows [users] to [benefit] by
Data Science analytics Analyse [method]
Data engineering Transmit Infrastructure Sensing 158 159 HOW MANY DATA CCTV CAMERAS CAN WE HANDLE?
Evidence for crime or prevention Body, clothes What problems?
More mistakes for black women vs. White men.
SenseTime has 176 Mil ion Cameras in China
COVID TEMPERATURE CONTROL in BEIJING
People consume far less information UNDERGROUND
than expected before deeming things good or bad 160 161 20 BY - NC - nicolamattina.it