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