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.
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.
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
Protagonist is in his 70s
Freedom fighter who fought the communist in Vietnam and Russia
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.
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,
Autonomous AI – Tesla self driving, Google self driving
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
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
Abundant data – quality and quantity aided by their online to offline initiatives
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
Google – impeccable safety
Tesla / China – trial by fire
key to winning the Autonomous AI race
is the bottleneck technology (Silicon Valley) or policy (China)?
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
environment – unstructured versus structure
tasks nature – level of dexterity versus high dexterity
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
Humans freed up from repetitive tasks can now focus on becoming more human oriented
www.Arvix.org – an online repository of scientific papers