think different, in finance
Post on 22-Jan-2018
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Think Different—in Finance FinxChance @ NCU
Albert Y. C. Chen, Ph.D.Chief Scientist
Viscovery
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.
The Death of a Good Business Model
• Foxconn 20 year revenue v.s. net profit (now at 5%)
What do 100 year old corporations do?
GE Schenectady, 1896
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
History of change at IBM• 1960s: mainframe computer • 1980s: personal computer • 2000s: integrated solutions • 2020s: AI, Watson
How about the leading Semiconductor companies?
NVidia reinventing itself —2 times in 20 years
“Bad money drives out good” in the desktop GPU market
The rise of mobile computing, and how NVidia missed the boat!
NVidia’s Tegra mobile processors never took off
then, the market saturated…
NVidia not just survived. NVidia is thriving!
Meet the new NVidia: Deep Learning, Deep Learning, and still, Deep Learning
The king is dead, long live the king!
Now, again, do we want to do OEM/ODM forever?
Optimizing an old business model is just delaying its eventual death.
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
StartupsA company, partnership, or temporary organization
designed to search for a new, repeatable and scalable business model.
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?
Visual Search, Simply Smarter
Once in a lifetime opportunity in China’s video streaming market
What do we need?
Face MotionImage scene Text Audio Object
Semantics
Viscovery VDS (Video Discovery Service)
Viscovery VDS (Video Discovery Service)
Viscovery VDS (Video Discovery Service)
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
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
Disruptive Innovation for Finance majors
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.
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.
p2p Currency Exchange
p2p Lending
Credit Scores, for those without credit history
Peer-to-business Lending
Data Analytics for Individual Investment Records
Wealth Management for the Masses
• Private banking starting with as little as £1000.
• Transactions free of charge.
The 21st century pawn shop
12% APR55.8% APR 18.8% APR
Where am I getting the money from?
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
More Demanding != More Likely to Fail
What role should a finance major play in a
startup?
A minimal startup team
• A hacker
• A hustler
• A hipster
Startup Timeline
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
Money!
Buildup your entry barrier!
• Market (users)
• Speed
• Team
• Technology
How much should a finance major learn about technology?
• Programming Skills?
• Machine Learning Skills?
• Data Science?
Programming Skills• Look ma! My first website!!
Programming Skills• Hard-core Computer Science
Programming Skills• Scripting language for actually doing stuff
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.
Machine Learning
Classification Clustering
Regression DimensionReduction
supervised unsupervised
cont
inuo
usdi
scre
te
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”?
Spurious Correlations
Spurious Correlations
Spurious Correlations
Deep Learning
Deep Learning
Deep Learning
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.
Thank You!albert@viscovery.com
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