Why I built GetData.IO

When training neural networks, until enough training data is collected there will be a period when the output of the neural network is full of false positives and false negatives (aka junk)

The same could be said of the human brain (a biological neural network), unless you have access to another human brain whose output you can totally trust and rely on, there will be a prolonged period when you fumble around while struggling to gather enough data to build a mental model of the new domain.

Based on my experience, the main challenge when breaking into new domains is that no pre-trained neural networks exists. During such situations, expect a prolonged period of confusion and fumbling around. Persistence (aka brute force iteration) is probably the only thing you can fall back on.

Thankfully, totally new domains seldom exists. Whatever “new domain” you think you are trying to break into, someone else is probably either doing it right now or has already done it.

That is why I built GetData.IO. It is to help people who need data to make good decision quickly find the data they need as well as people who might already have a trained model.

Weapons of Math destruction by Cathy O’Neil

Weapons of Math Destruction

Key take aways

  • Human values like justice and mercy is hard if not impossible to encode as rules
  • Data scientist use proxies as an approximate gauge for the existence of values. These are inherently inaccurate if not downright wrong
  • While using of race as a feature to determine if loan should be approve is obviously racist, the use zip codes though not obvious is equally racist since race tends to segregate around geographical territories
  • Models are increasingly used to across various domains to help increase the speed of decision making. This increases the negative impact of badly designed models will have on humans
  • A feedback is necessary to ensure continuous correction of badly design models – transparency of how your credit score is calculated
  • Regulations are necessary on the use of models as companies driven by quarterly reporting requirements of shareholders are primarily be focused on the bottom line

 

Thoughts on intuition and logic

The limbic and reptilian parts of the human brain have had more time to evolve. Compared to these two parts, the frontal lobe of the human brain where logic resides is a more recent phenomena. Ironically, the logical functions carried out by this part of the brain, which makes man distinct from other animals, are the ones most easily replicated by machines.

The entire human body, not just the deliberate thinking portion of it, should be considered to be a neural network. Using only the thinking portion of the human body for decision making purposes is sub-optimal. This is especially true for a human that has actively engaged in calibrating his body for a specific purpose. Prime examples are deliberate cultivation and heavy reliance on muscle memory by professional athletics, chefs, actors, music composers and detectives.

Intuitive gut feel can be considered muscle memory cultivated over time for specific functions yet expressed as formalized equations. To free up time, individuals can actively convert what they “intuitively know” into formalized equations and have the corresponding functions delegated to machines. Thereafter they could either further compound the effects of this process by building up muscle memories in other domains or sit by the beach and do nothing.

Humans will always have a role available to play in the future regardless of society’s degree of automation.

Related readings

 

Chat with Quynh on trading

News sources utilized

  • Zacks
  • Motley Fools

Buy rumors and sell on news

  • rumors are not official news but signals that a news might be coming soon
  • continuous upwards movement of share price for few days means news might be announced soon
  • once news is out share price will adjust based on actual numbers

Buy on over reaction to bad news and sell on recovery

  • There is usually overreaction

The dichotomy between privacy and health

1984: Big Brother is Watching

Across multiple literature, its been stated privacy versus health will be one of the primary dichotomy societies around the world will need to juggle with as technological advances are made in the fields of artificial intelligence, communications (surveillance) and medical science (genetic research).
 
What is surprising was the rate at which the Corona pandemic catalyzed this change. In light of this, it is fascinating to observe how different societies position along the spectrum. Some societies has opted for surveillance to the maximum extend possible with current technology while others opted for its polar opposite going to the extend of staging mass protests against it use. 
 

Related readings:

  • The AI Economy, Roger Bootler
  • To Be a Machine, Mark O’Connell
  • Irrational Exuberance, Shiller, Robert J.
  • Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark
  • Mind Children The Future of Robot, Hans Moravec
  • The Singularity Is Near, Ray Kurzweil
  • 1984, George Orwell

Observations on our news reporting system as well as investment bank forecasting.

On the quality news reporting

Good news reporting should seeks to inform rather than sensationalize with attention grabbing headlines. Its easy to appear data driven but still be misleading if you do not use the proper frame for understanding the numbers

An example of bad news reporting
An example of bad news reporting

An example of quality news reporting

On investment bank predictions

When on the receiving end of predictions made by external parties it is important to understand the underlying agenda they are trying to achieve. When examined thoroughly, predictions made by investment banks are so bad and contradictory, they should just stop making public declarations.

However if taking into account their objective is not to inform but to incite a trading decision by their clients so as to make a commission or offload losing positions on their trading books, it makes perfect sense.

Related references

The AI economy, Roger Bootle

Paradoxes

  • Polanyi Paradox
  • Moravec’s paradox

Key skill sets for the AI era

  • complex communication
  • Creativity
  • Strategic thinking / critical thinking
  • Empathy / humanity

Key themes

  • AI as labor cost versus AI as capital expenditure
  • Taxes on AI development versus edge in global competition
  • Labor versus leisure
  • Global positioning
  • Population size as advantage for big data

Federal reserve rate cuts

3rd March 2020

  • reduce interest rates from 1.5-1.75% to 1-1.25%
  • purchase of government bonds
  • purchase of agency back mortgage securities

15th March 2020

  • reduce interest rates from 1-1.25% to 0-0.25%
  • effects are in very early stage within the US
  • First signs affected industries
    • Tourism
    • Hotel
    • Travel industry
    • otherwise not showing up in data but sentiment forecasts

Key take aways

  • mandate
    • maximum employment
    • price stability
  • Context
    • Economy propped up by US consumers
    • US unemployment is low
  • Dealing with corona issue
    • Actual impact of US economy is uncertain
    • Ultimate solution will come from health professionals
    • Broader spread of the virus is what changed hence potential risk to the economy
    • Uncertain how long the economy will take to recover
    • Health care, Fiscal and Monetary policies

Thoughts on Fake News

A stroll through the peaceful streets of Rome. In contrast, it felt like the end of the world is here if you read news about Italy recently.

The human brain is a remarkable pattern recognition engine. When given incomplete information it will conjure up the “missing” pieces to generate a coherent whole that could be comprehended. More often than not what gets generated is the worst case imaginable scenario. In a normal time and age this is a wonderful function to have running automatically in the background to keep this primate alive.

However, this automatic function becomes problematic as three trends converge.

Trend 1 – big tech like Google and Facebook consolidated advertising revenue putting news entity under increasingly pressure to sustain themselves as their advertising revenue dwindled

Trend 2 – the proliferation of publication medium means anyone can now claim to be a news entity.

Trend 3 – Proliferation of Growth hacking techniques perfected by tech companies like Facebook that hijacks the human brain’s automatic fight or flight to generate user action that gets converted to revenue.

News entity generate revenue with hyper inflated news that drives readership and vitality by tapping into fear and anger. This drives wide spread panic. Corona  is perfect catalyst.