Catch Up Quick

• Officer Chauvin's charge for 3rd degree murder of George Floyd’s was dropped

• Biden proposes $10K of student debt forgiveness in exchange for public service

• U.S. reports > 71K new COVID cases as new infections & hospitalizations rise

• Climate change was a featured debate issue for the 1st time in U.S. history

• Tech companies cut pay of those who’ve moved away for long term remote work

• Investors worldwide prefer a Biden win, per a UBS survey of ~3K professionals

• Quibi, the new streaming service, is shutting down despite $1.8B in funding

• France has become the 2nd Western European country to pass 1M COVID cases

• Recent arrests suggest massive fraud in California’s unemployment system

Thought of the Day

• A new study in The Lancet's EClinicalMedicine revealed an A.I. model able to predict incoming Alzheimer's disease 7 years in advance of a diagnosis with ~71% accuracy

• This development by IBM Research and Pfizer analyzed speech samples provided by the Framingham Heart Study, a long-term study that has been tracking thousands of people since 1948

• This is one of many examples in which complex machine learning has objective value add potential in an important realm

• At a high level, this technology works very similarly to the human brain

• For example, if a 9 year old touches a hot bowl fresh out of the microwave, he / she, aware or not, will likely learn from this single experience and wait for it to cool down in the future

• Typically, machine & deep learning work similarly, except hosting huge amount of historical data points in a structure of intertwined algorithms often called a neural network

• However, many people oppose adoption of this technology for many reasons, especially the lack of transparency between the input (data) and output (prediction, insights, suggestions, etc)

• A validly interesting opposing viewpoint: this is ironic because more often than not, humans themselves, the alternative in this case, do not have full transparency when making forward looking decisions based on the past

The Bottom Lines

• There is a key difference between this parallel shortcoming in human / machine decision making— with systems, it is technologically possible to achieve full transparency, while the inner functions of the human brain are far more comparatively complex and will likely never be understood

• A.I. is bound to change the world— if regulated in an intelligent fashion, for the better!