think different, in finance
TRANSCRIPT
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Think Different—in Finance FinxChance @ NCU
Albert Y. C. Chen, Ph.D.Chief Scientist
Viscovery
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Think Different
• Good business model NEVER last forever.
• Average “shelf life” on S&P 500: 20 years.
• 100-year old companies constantly reinvent themselves every 10-20 years
• Startups contribute to 20% of USA’s GDP.
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The Death of a Good Business Model
• Foxconn 20 year revenue v.s. net profit (now at 5%)
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What do 100 year old corporations do?
GE Schenectady, 1896
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History of change at GE• 1886: one of the 12 original companies on the Dow
Jone Industrial Average (also the only one remaining). • 1889: lightbulbs • 1919: radios • 1927: TV • 1941: jet engine • 1960: nuclear power • 1971: room AC units • 1995: MRI
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History of change at IBM• 1960s: mainframe computer • 1980s: personal computer • 2000s: integrated solutions • 2020s: AI, Watson
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How about the leading Semiconductor companies?
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NVidia reinventing itself —2 times in 20 years
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“Bad money drives out good” in the desktop GPU market
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The rise of mobile computing, and how NVidia missed the boat!
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NVidia’s Tegra mobile processors never took off
then, the market saturated…
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NVidia not just survived. NVidia is thriving!
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Meet the new NVidia: Deep Learning, Deep Learning, and still, Deep Learning
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The king is dead, long live the king!
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Now, again, do we want to do OEM/ODM forever?
Optimizing an old business model is just delaying its eventual death.
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Why Startups?
Business Relevance
Academic Relevance
plentiful resources; bureaucratic organization
lack of resources; responsive organization
traditional corporations talking “innovation”
corporate research
startups struggling to survive
academic spinoffs
MSR
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StartupsA company, partnership, or temporary organization
designed to search for a new, repeatable and scalable business model.
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Your Idea• Are you passionate about it? • Is it disruptive enough? • What is your business plan?
• What is it? • Can it make money? • What is the future of the idea?
• What is your competitive advantage? • How do you build up your entry barrier?
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Visual Search, Simply Smarter
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Once in a lifetime opportunity in China’s video streaming market
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What do we need?
Face MotionImage scene Text Audio Object
Semantics
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Viscovery VDS (Video Discovery Service)
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Viscovery VDS (Video Discovery Service)
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Viscovery VDS (Video Discovery Service)
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Challenges Encountered Along the Way
• From Product Recognition in Images, to Face, Logo, Object, Scene recognition in Videos. • Number of Categories • Recognition Accuracy • Recognition Speed
• System Architecture
• Business Model
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Viscovery’s Edge• Market: first mover’s advantage in China’s video
streaming market. • Speed: we built the whole VDS thing in a few months! • Team: • Technology:
• Depth • Breadth • Cloud • Customizability • Self-Learning
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Disruptive Innovation for Finance majors
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Catering to banking customers after 3:30pm
• 362 days a year, open until 8pm on weekdays.
• Added nearly 1-million customers in just 6 years.
• Water bowls for dogs, free coin counting and sorting machines.
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Providing Cheaper International Wire Transfers
• Targeting Western Union and MoneyGrams. Bypassing the middlemen in remittances.
• Targeted at places where banks are not easily accessible.
• Remit money with bank deposit, cash pickup, e-wallets, mobile phone credit, home delivery.
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p2p Currency Exchange
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p2p Lending
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Credit Scores, for those without credit history
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Peer-to-business Lending
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Data Analytics for Individual Investment Records
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Wealth Management for the Masses
• Private banking starting with as little as £1000.
• Transactions free of charge.
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The 21st century pawn shop
12% APR55.8% APR 18.8% APR
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Where am I getting the money from?
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200M funding in 8 rounds, 500M valuation.
46M funding in 5 rounds
116.37M in 6 rounds, 1 billion valuation
68.63M funding in 7 rounds
273.24M in 6 rounds,
75M in 3 rounds
177.47M in 6 rounds
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More Demanding != More Likely to Fail
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What role should a finance major play in a
startup?
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A minimal startup team
• A hacker
• A hustler
• A hipster
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Startup Timeline
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Prototype• Hack out a prototype
• Spend 2-10 weeks max.
• Investors are much more likely to fund you if you have a minimal initial version of your idea.
• Hackathons are a good place to start.
• Iteratively improve the prototype
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Money!
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Buildup your entry barrier!
• Market (users)
• Speed
• Team
• Technology
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How much should a finance major learn about technology?
• Programming Skills?
• Machine Learning Skills?
• Data Science?
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Programming Skills• Look ma! My first website!!
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Programming Skills• Hard-core Computer Science
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Programming Skills• Scripting language for actually doing stuff
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Programming Skills
• Learn as you go.
• Learn enough to get the job done, then go!!
• Focus not on the language, but on the problem you have at hand.
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Machine Learning
Classification Clustering
Regression DimensionReduction
supervised unsupervised
cont
inuo
usdi
scre
te
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Data Science• The difference between:
• Data Engineer
• Data Analyst
• Data Scientist
• Does running some regressions and fitting some models justify the title of a “data scientist”?
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Spurious Correlations
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Spurious Correlations
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Spurious Correlations
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Deep Learning
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Deep Learning
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Deep Learning
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Life is not all rosy at startups
• High Risk, High Pressure, High Uncertainty!
• Resources are scarce, but you MUST DELIVER!
• Forming your all-star team is not that easy…
• Focus, and persistence.
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Thank [email protected]