Monday, May 25, 2020


An opportunity to remove these “Forrest Gumps” from the gene pool?
Cell-tower attacks by idiots who claim 5G spreads COVID-19 reportedly hit US
The Department of Homeland Security is reportedly issuing alerts to wireless telecom providers and law enforcement agencies about potential attacks on cell towers and telecommunications workers by 5G/coronavirus conspiracy theorists. The DHS warned that there have already been "arson and physical attacks against cell towers in several US states."
The preposterous claim that 5G can spread the coronavirus, either by suppressing the immune system or by directly transmitting the virus over radio waves, led to dozens of tower burnings in the UK and mainland Europe.


(Related) We can only hope.




Perspective and Infographic.
What’s Old Is New Again: Examining Privacy Enhancing Technologies
The term Privacy Enhancing Technologies (PETs) has been around for decades and is now experiencing a renaissance as the global awareness, demand, and regulation for privacy increases. While the label itself is intuitively powerful — who isn’t in favor of technologies that enhance privacy? — it is also ill-defined and often misunderstood.
To unpack what this means, it’s helpful to consider the three states of data — at rest, in transit, and in use — which can represent the three segments of the Data Security Triad.




Oh what an expensive web we weave when first we practice to deceive.
Volkswagen loses landmark German 'dieselgate' case
Germany's highest civil court has ruled that Volkswagen must pay compensation to a motorist who had bought one of its diesel minivans fitted with emissions-cheating software.
The ruling sets a benchmark for about 60,000 other cases in Germany.
The plaintiff, Herbert Gilbert, will be partially reimbursed for his vehicle, with depreciation taken into account.
VW said it will now offer affected motorists a one-off payment, and the amount will depend on individual cases.
The company has already settled a separate €830m (£743m) class action suit involving 235,000 German car owners.
It has paid out more than €30bn in fines, compensation and buyback schemes worldwide since the scandal first broke in 2015.




I think they have the main roles correct.
The Six Roles You Need on Your AI Team
Let’s say you were already sold on AI: You’ve gotten yourself and your executive team trained on the basics of AI; you’ve studied use cases; prioritised the business pain points that could align with AI solutions; and allocated funding to launch a few pilots.
So, if you’re ready to start assembling your cast of AI characters, who do you need? What skillsets should you look for to build your enterprise’s AI efforts?
Here are the six roles you need on your AI team:
The strategist
The data engineer
The data modeller
The data in production person
The infrastructure and scale builder
The data analyst/visualiser


(Related) A prelude to AI.
A Philosophy of Data
We argue that while this discourse on data ethics is of critical importance, it is missing one fundamental point: If more and more efforts in business, government, science, and our daily lives are data-driven, we should pay more attention to what exactly we are driven by. Therefore, we need more debate on what fundamental properties constitute data. In the first section of the paper, we work from the fundamental properties necessary for statistical computation to a definition of statistical data. We define a statistical datum as the coming together of substantive and numerical properties and differentiate between qualitative and quantitative data. Subsequently, we qualify our definition by arguing that for data to be practically useful, it needs to be commensurable in a manner that reveals meaningful differences that allow for the generation of relevant insights through statistical methodologies. In the second section, we focus on what our conception of data can contribute to the discourse on data ethics and beyond. First, we hold that the need for useful data to be commensurable rules out an understanding of properties as fundamentally unique or equal. Second, we argue that practical concerns lead us to increasingly standardize how we operationalize a substantive property; in other words, how we formalize the relationship between the substantive and numerical properties of data. Thereby, we also standardize the interpretation of a property. With our increasing reliance on data and data technologies, these two characteristics of data affect our collective conception of reality. Statistical data's exclusion of the fundamentally unique and equal influences our perspective on the world, and the standardization of substantive properties can be viewed as profound ontological practice, entrenching ever more pervasive interpretations of phenomena in our everyday lives.



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