Evening out watching Rambo, last blood

From Sujit on trading

  • not necessary to get numbers further back than six months
  • stock market subjected to fractal distribution
  • it is possible to generate returns of up to 140% per year by trading on stocks that are moving within a range
  • going all in on each position each time leads to a very low Sharpe ratio
  • Sharpe ratio should be calculated separately for method and for SnP benchmarked against US treasury interest rates. The difference is the actual returns

On Rambo Last blood

A movie is a reflection of the culture and attitude of an age. Rambo was a very popular cultural icon during the eighties and the early nineties when memories of the Second World War and the Cold War against the communist were still very fresh in the minds of the people in America.

If you looked at the world today through the eyes of someone like Rambo, you would have been able to easily draw facts to back the narrative painted by Trump prior to being elected president.

When operating in an environment of uncertainty, a decision maker formulates multiple often competing narratives in the head that best explains majority of the facts presented. He calibrates the weightage assigned to the probability of each narrative as new pieces of data become available. He simultaneously utilizes multiple ones that are assigned high plausibility in his decision making to strive for the best possible expected outcome . It is a cognitively demanding iterative activity that goes on indefinitely.

  • common themes between movie and Trump’s narrative
    • Mexico drug cartels
    • Mexico prostitution rings
    • The world is a dark place
    • illegal border crossing
    • poor border fence
    • white male
    • Rust belt
    • Protagonist is in his 70s
    • Freedom fighter who fought the communist in Vietnam and Russia
    • guns and lots of blood
    • man of steel
    • manifest destiny
  • Cognitive biases
    • Narrative fallacy
    • Framing bias
    • selective bias

Related readings

  • Expert political judgement, Philip Tetlock

Afternoon with Tomasso on the limitations of Artificial intelligence’s application

For us to be able to successfully apply artificial intelligence on any domain, the following needs to be true

  • The behavior the system to be modeled must not be stochastic
  • The state of the system must be decipherable by the data scientist
    • it should be possible to understand the state in which the system is at through interpretation of data gathered
  • The domain can be modeled
    • the parameters for modeling the domain must be well defined

Only when all three premise are true can we determine where the adjustment should be made when a model fails to predict an outcome

The financial markets is stochastic  in the short run.

The underlying parameters are constantly changing and thus hard to model due to the emergent nature of impacts caused by human activities. The data is qualitative and thus hard to convert into clean quantitative datasets.

While the price movements are obvious it is hard, it is hard to attribute impact to the various parameters.

As such, it requires human neural networks that consumed all these qualitative data to perform the prediction/decision making.

The case for hastening the replacement of workers with AI

If the issue of aging population is an inevitable affliction of all industrialized countries and majority of countries will become industrialized within the next 30 years, then we should be expecting our population to collapse by 2050. Based on this premise rather than being worried that majority of workers will get replaced by Robots and made irrelevant, we should instead be worried that robots are not replacing tasks handled by forthcoming retirees fast enough,

Related References

https://www.bloomberg.com/amp/news/articles/2019-07-24/u-s-truck-driver-shortage-is-on-course-to-double-in-a-decade

https://amp.businessinsider.com/elon-musk-reiterates-global-population-is-headed-for-collapse-2019-6

 

Book summary AI super-powers, China, silicon valley and the new world order by Kai Fu Lee

The difference waves of AI

  • Internet AI – Facebook, Netflix, Google search
  • Business AI – Palantir
  • Perception AI – Tesla cars
  • Autonomous AI – Tesla self driving, Google self driving

Key locations

  • Silicon Valley
  • Zhong Guan Cun – Beijing

State of the Union

  • We are in the stage of implementation/application as opposed to RnD
    • having access to more data is more important than have expertise to do more RnD
    • having solid AI engineers is more important than AI researchers
  • We are still far from general AI
  • Key ingredients
    • data
    • computing
    • maybe work of strong AI algorithms engineers

Key differences between eco-systems

  • Silicon Valley businesses are mission and core values driven while Chinese businesses are pragmatically focused on profitability.
  • Silicon Valley businesses stay in bits and binaries offloading the brick and mortar to external vendors vendors while Chinese businesses extend their business model into the brick and mortar (online to offline)
  • Silicon valley prefers one size fit all strategy, Chinese businesses utilized localized solutions often investing/acquiring in local startups
  • Americans treat search engines like Yellow Pages (come and leave fast) while Chinese treat search engines like shopping mall (come to linger around long)
  • Silicon Valley is adversed to copying preferring to be unique Chinese business copy the heck out of each other

Chinese Advantage

  • Abundant data – quality and quantity aided by their online to offline initiatives
  • hungry entrepreneurs
  • AI scientist
  • AI friendly policy environment – strong emphasis by Chinese government
  • Hardware manufacturing know how – Shen Zhen
    • unparalleled supply chain flexibility – XiaoMi

Silicon Valley Advantage

  • Microchip manufacturing know-how

Trends within the Chinese eco-system

  • Darwinian eco-system has lead to extreme levels of competition
  • Chinese companies have already moved past the stage of clone Silicon Valley business models
  • Businesses innovate to build a defensive moat around themselves. Local businesses have advantage, with no timezone differences to deal with, decision making is relatively faster.
  • Online to offline
    • an essential ingredient to building strategic moats
    • caused the decline of cash use
  • Chinese government information systems will be able to leap frog US government information systems

Policy approaches

  • Google – impeccable safety
  • Tesla / China – trial by fire
  • key to winning the Autonomous AI race
    • is the bottleneck technology (Silicon Valley) or policy (China)?

Key concerns

  • having cheap labor is no longer going to be a source of advantage in a world heavily powered by automaton.  Developing countries hoping to employ this well tested strategy to progress will not be able to do so anymore
  • Estimated 60% potential job loss worldwide barring policy interventions
  • Job loss probability assessment
    • physical labor
      • environment – unstructured versus structure
      • tasks nature – level of dexterity versus high dexterity
    • cognitive labor
      • social – high versus low
      • cognitive – optimization based versus creativity/strategy based
  • AI replacement approach
    • single tasks approach
    • ground up rethink re-imagination
  • A population of irrelevant (no longer employable) as opposed to unemployed

Tackling Key concerns

  • Silicon valley – reduce, retrain and redistribute
  • Kai Fu Lee – stipends for care, service, education

New promise

  • Humans freed up from repetitive tasks can now focus on becoming more human oriented

Related readings

  • Disruptor, Zhou
  • www.Arvix.org – an online repository of scientific papers
  • Folding Beijing – Hao JingFang

Mark Zuckerberg chats with Yuval Noah Harrai on the Future of AI

Key take aways

  • Spread of inequality where some countries have the ability to harness AI while others don’t
  • AI based recommendation systems moving from being just an oracle to becoming a sovereign
  • AI as a tool is an amplifier
    • concerns that it will benefit totalitarianism more than democracy leading to totalitarianism becoming a more favorable governance model worldwide
    • surveillance
    • psychological manipulation – the inability to know your true self through your thoughts
    • what happens if morality and expediency diverge when it comes to governance
  • Effectiveness of curbing the negative effects of AI by encoding values within policy frameworks governing these AI based systems
    • Companies based in Democratic countries will encode democratic values within their systems vice versa for Totalitarian countries
  • Personalization versus Fragmentation
    • when everyone in a country chooses his own community that is mainly online there is no longer a glue holding the local community together
  • Long term versus short term
    • The long term benefits might come sooner than expected when taking a short term trade off

 

Book summary: Everybody lies by Seth Stephens-Davidowitz

Signals from Search

  • What people search for is in itself a signal
  • The order of keywords in which they search is also a signal
  • Quality of google search data is better than in Facebook because
    • You are alone with no fear of being judged
    • You have an incentive to be honest

On Big data

  • the needle is still the same size but the haystack has been getting bigger
  • Be judicious by cutting down the sample size of the data to be used

Data science

  • Trust your intuition as the initial signal but verify quantitatively to avoid narrative bias
  • Correlation is most often sufficient for utilization purposes – often the explanation of why the model works comes after the fact
  • critically assess the actual data underlying the narrative. At times it might tell a very different story than narrative presented
  • Clustering of groups of people helps predicts behavior – Netflix and baseball
  • AB testing to discover causations

Social Impact

  • great business are found on:
    • secrets about nature
    • secrets about people
  • Modeling
    • Physics – utilize neat equation
    • Human behavior – probabilistically via Naive Bayes classification

Managing angry people

  • Lecturing them will provoke their anger
  • Provoking their curiousity will cause their attention to be diverted causing anger to subside

Related readings

  • Zero to one, Peter Thiel

Book summary – The Signal and the noise

Risk versus uncertainty

  • Risk can be mathematically modeled to yield a probability
  • uncertainty cannot be mathematically modeled

Conditions for quality data

Why google’s Search data is better than Facebook profile data

  • subject feels she has privacy privacy
  • subject feels she is not judged
  • subject sees tangible benefit from being honest

The hedgehog versus the fox

  • The hedgehog approaches reality through a narrative/ideology while the fox thinks in terms of probabilities
  • The hedgehog goes very deep in an area while the fox employs multiple different models
  • The fox is a better forecaster than the hedgehog
  • The fox is more tolerant of uncertainty

Big data

  • More data does not yield better results and predictions
  • Deciding the right kind of data from the abundance available
  • To do prediction it is important to start from intuition and to keep model simple
  • qualitative data should be weighted and considered
  • Be self aware of your own biases

Prediction

  • Similarity scores – clustering in Netflix and baseball
  • Be wary of confirmation biases
  • Be wary of overfitting using small sample size – Tokyo earthquakes and global warming
  • Correlation does not equal causation
  • short hand heuristics to reduce the computational space – for example chess

Related references

  • Irrational exuberance, Robert Shiller
  • Expert political judgement, Philip E. Tetlock
  • Future shock, Alvin and Heidi Toffler
  • Principles of forecasting, J Scott Armstrong
  • Predicting the unpredictable, Hough

Insights from Klaren’s birthday

Conversations with Yi (EverString)

The forthcoming trend for engineering

Machine learning is increasingly becoming commoditized. DevOps becomes more important. Demand for specialized service where DevOps is encapsulated will further increase as demand for engineering tasks further outstrips engineering supplies.

On lead generation market

Companies in the lead generation space have need for scalable web crawlers. This helps offset the cost of retaining three in-house engineers.

Lead generation space has consolidated. There were priorly 120k such companies. There is 7k companies in operation. Majority of players are generating leads by scraping LinkedIn.

Consumer space require constant development of new features. Enterprise space requires service heavy. Enterprise space requires not just lead generation but entire channel marketing service suit (physical mail, online advertising, email marketing)

Lead gen hard to retain. The list becomes less valuable once it’s been used. 80% yearly churn is normal. One company reduces yearly churn to just 10% this by reducing second year subscription from USD800/yr to USD200/yr. further discount to USD100/yr if they don’t like. Recurring service is for grabbing fresh leads from same data source.

On Tele conference

Zoom’s product team compared with UberConference has developed a better understanding of the true conference needs of their users in various context. They have worked harder to ensure their product work seamlessly in identified scenarios. A typical example is the ability to join s conference bybthe press of a button on their mobile phone while driving instead of having to type the typical 4 pin digits.

Insights from the week

From Connie (Edmodo)

  • the key to consulting is to organize data into high level mutually exclusive buckets to allow easy defeating by decision makers

From Tim (Edmodo)

  • Kano model

From Val (Totango)

  • Company is concerned with increasing revenue and profitability. This will drive higher valuation during further exit

From Yip (ATT)

  • Analytics from Facebook page comments and twitter hashtag
  • need to balance customer support demand and cost of running department:
    • customer support hotline
    • Direct comments from influencers  which trigger negative sentiment to support staff
  • Business analyst reads comments manually to get qualitative needs and understands business needs
  • Data scientist explores data might not know the business needs
  • business analyst have problems working with data scientist
  • tools to help business analyst get directly at the insight instead of via data scientist
  • build model to predict call support volume by category
  • build model to quantify feature demand level needs
  • correlation of weather and commodity prices

Highlights from The Future of Humanity by Michio Kaku

  • Organisms on earth eventually will meet one of three fates, leave, adapt or die. Earth has already sustained 5 extinction cycles.
  • The threats we face are largely self inflicted
  • Scientific revolution comes in waves often stimulated by advances in physics
    • 19th century
      • mechanics and thermodynamics: locomotive and industrial revolution
    • 20th century
      • electricity and magnetism bring forth the electric age
    • The forthcoming wave
      • nano-technology
      • AI, neural networks
      • Quantum computing
      • CRISPR revolution
      • Transhumanism – the need to deal with ethical questions
  • Technological regression occurs when the population becomes complacent,
    • Admiral Zheng He and his fleet under subsequent rulers
    • US space program after the cold war is over
  • Interesting phenomena worth exploring
    • wormholes
    • rogue planets – planets that do not orbit any particular stars
    • caloric restriction and increased life expectancy
    • falling birthrates and education of women
    • uploading and downloading of consciousness (Transcendence and Mnemonic Johnny)
    • achievement of super strength on new planets
    • artificial enhancement of body, seamless interfacing with machines (telekinesis)
    • big bang happening over and over again and the universe does not grow only in one direction
  • Civilization categorization method 1:
    • energy based
      • Type 1: utilizes all the energy of the sunlight falling on the planet
      • Type 2: utilizes all energy its sun produces
      • Type 3: utilizes energy of an entire galaxy
      • Type 4: utilizing energy beyond the galaxy
    • information consumption based