Ethan Zuckerman:

Consider a thought experiment on funding: The digital advertising industry is currently a $333 billion global market, and the share of advertising that’s digital continues to grow.

 Three companies that use intensely surveillant advertising practices – Google, Facebook, and Alibaba – represent $200.3 billion of that market. If we posit a 1% levy on highly surveillant advertising – advertising that incorporates user tracking, combines demographic and psychographic data to create user profiles, or targets using factors other than a user’s stated intentions and geography – we can easily posit a $1–2 billion annual fund to support public service digital media.43

43. The $1-2b figure assumes that platforms like Google will change behavior to make services like keyword targeted ads on search less surveillant, a positive side effect of this experiment. It’s also worth acknowledging that many countries prevent tax revenue from being earmarked for specific purposes – it may be structurally challenging to construct taxes on digital advertising that go towards public service digital media. Thanks to Anya Schiffrin for that helpful critique.

 What might we do if we thought on the scale of a $2-billion-a-year project, twenty-two times the scale of Wikimedia44

This is not a new idea, to be clear. Free Press has proposed a targeted ad tax that they anticipate could generate support for “diverse, local, independent and noncommercial journalism that’s gone missing, and to support new news-distribution models, especially those that don’t rely on data harvesting for revenue.”

 They propose a “Public Interest Media Endowment” to manage the funds generated. In May 2019, Nobel Prize–winning economist Paul Romer proposed a progressive digital ad revenue tax designed to encourage platform companies to explore models other than surveillant advertising, like subscription-based models.47

 The Romer model is designed to discourage companies from using surveillant advertising, so the tax is likely to be significantly higher than 1-2 percent and increases with the size of surveillant ad revenues earned by a company. In February 2017, Emily Bell of Columbia’s Tow Center earlier proposed an endowment to support independent journalism, funded with billion-dollar donations from tech giants like Google and Facebook.

Colin Woodard:

“My parents, they were coming out of that generation of the ’60s and the civil rights movement and you voted,” she says. “Now people just don’t get it. They look at it like they have other things to do, like grocery shopping or sending the kids off to school. But elections don’t happen every day!”

Riggins-Furlow’s sense of a fickle, distracted citizenry touches on one of the biggest mysteries of United States electoral politics: Why nearly half of the nation’s eligible voters almost never exercise that fundamental right. The sheer size of the group—approximately 92 million eligible voters—makes it a potential wild card in the 2020 presidential election. That is if the political world understood what keeps them away from the polls, and, more importantly, what might lure them in the first place.

On Wednesday, the Knight Foundation released the results of “The 100 Million Project,” the largest survey of chronic nonvoters in history, and the most robust attempt ever to answer some of the questions that have long bedeviled political scientists. More than 13,000 people were polled across the country, with special emphasis on 10 battleground states, followed by in-depth focus-group conversations with thousands of them. They were asked about their political preferences, media diets, social networks, income levels, general life satisfaction, and about their demographic characteristics and social connectivity, their reasons for not voting, and their assessments of electoral and political institutions. The result is the most comprehensive survey of the politically disengaged to date, with lessons political consultants, candidates and civic educators won’t want to miss.

Notes and links on the 2020 Madison School Board election.

MG Siegler:

I cannot believe I missed this past weekend’s Bitchmeme,¹ especially since it’s right in my wheelhouse: the ever-chilling war between the press and insiders. It seems to me this is likely a normal state of being in every industry. But it becomes far more pronounced as said industry grows in power and import. And so here we are in the tech industry in 2020.

This is my wheelhouse because I have been both a part of the press and, more recently, an insider. Well, as “insider” as a VC can be. This varies pretty wildly, in my experience. But I digress. Point is: I may have an interesting vantage point here having been on both sides. You decide.

So when I read Hunter’s post seemingly walking straight down the middle of those two sides, my instinct was to find this approach too vanilla. Surely some side is worse here, right?²

Hideyoshi Kume:

This kind of electronic platform, with a powerful computer at its core, holds the key to handling heavy data loads in tomorrow’s smarter, more autonomous cars. Industry insiders expect such technology to take hold around 2025 at the earliest.

That means Tesla beat its rivals by six years.

The implications for the broader auto industry are huge and — for some — frightening.

Tesla built this digital nerve center through a series of upgrades to the original Autopilot system it introduced in 2014. What was also called Hardware 1 was a driver-assistance system that allowed the car to follow others, mostly on highways, and automatically steer in a lane. Every two or three years, the company pushed the envelope further, culminating in the full self-driving computer.

There should be nothing stopping Toyota or VW from doing the same much earlier than 2025, considering their immense financial resources and vast talent pools. But technological hurdles are not the reason for the delay, according to the Japanese engineer who said “we cannot do it.”

David Fickling:

That’s not a comforting thought. We live in an era where everything seems quantifiable, from our daily movements to our internet search habits and even our heartbeats. At a time when people are scared and seeking certainty, it’s alarming that the knowledge we have on this most important issue is at best an approximate guide to what’s happening.

“It’s so easy these days to capture data on anything, but to make meaning of it is not easy at all,” said John Carlin, a professor at the University of Melbourne specializing in medical statistics and epidemiology. “There’s genuinely a lot of uncertainty, but that’s not what people want to know. They want to know it’s under control.”

That’s most visible in the contradictory information we’re seeing around how many people have been infected, and what share of them have died. While those figures are essential for getting a handle on the situation, as we’ve argued, they’re subject to errors in sampling and measurement that are compounded in high-pressure, strained circumstances. The physical capacity to do timely testing and diagnosis can’t be taken for granted either, as my colleague Max Nisen has written.

Early case fatality rates for Severe Acute Respiratory Syndrome were often 40% or higher before settling down to figures in the region of 15% or less. The age of patients, whether they get sick in the community or in a hospital, and doctors’ capacity and experience in offering treatment can all affect those numbers dramatically.

Even the way that coronavirus cases are defined and counted has changed several times, said Professor Raina MacIntyre, head of the University of New South Wales’s Biosecurity Research Program: From “pneumonia of unknown cause” in the early days, through laboratory-confirmed cases once a virus was identified, to the current standard that includes lung scans. That’s a common phenomenon during outbreaks, she said. 

Related: the hype cycle.

Tom Formski:

Accenture’s CEO Julie Sweet and Paul Daugherty, chief technology and innovation officer, begin the report — called We, the post-digital people: Can your enterprise survive the tech-clash? — with a warning:

“Enterprises that ignore this message will face an existential tech-clash, in which today’s models are incongruous with people’s needs and expectations. To avoid this impending crisis, companies must reexamine their fundamental business and technology models in a responsible way — to create a new basis for competition and growth. Trust and accountability are the new litmus tests for businesses in a world where digital is everywhere.”

This is the 20th report in the Vision series. Each year, Accenture identifies five tech trends that will impact its client companies over the next three to five years.

Here are the five Visions for 2020 as described by Accenture:

Samuel Githae:

Cutting the cost:

If you are looking at reducing the cost, hybrid, and cross-platform app development will not only decrease the price, they will also lower the time needed to complete the app development. The other side of the coin is that the performance can be slow, and the UX may be suboptimal. Also, there can be some functionality issues so it’s important to know if the features you need in your app, can be achieved in hybrid and cross-platform app development.

Logan Wroge:

Three candidates for an open Madison School Board seat aligned on several issues facing the school district while offering their own solutions to other topics during a forum Tuesday.

The trio seeking the board’s Seat 6 — Karen Ball, Christina Gomez Schmidt and Maia Pearson — spoke of rebuilding trust between the community and the Madison School District, identified areas they would cut in a funding shortage, and made their pitches before the Feb. 18 primary.

Ball, director of academic success at Edgewood College, said she wants to ensure an effective transition for the new superintendent and prepare the community for two potential referendums this fall.

Madison’s taxpayer supported K-12 school district, despite spending far more than most, has long tolerated disastrous reading results.

In addition, Madison recently expanded its least diverse schools.

Meanwhile, Madison’s taxpayer supported K-12 district continues to plan for a substantial tax & spending increase referendum this fall.

retool:

Every year companies spend $41B on enterprise resource planning software, commonly known as ERP. Today, almost every large business has some sort of ERP system implemented. But most smaller businesses generally don’t purchase any ERP system off the shelf, and most engineers probably haven’t seen them in the wild. So for those of us who haven’t used an ERP system ourselves… what’s the big deal? How does a company like SAP sell $25B worth of ERP software every year? And how does 77% of the world’s transaction revenue, and 78% of the world’s food all flow through SAP?

ERP is where companies store their core operational data. We’re talking about sales projections, purchase orders, and inventory, as well as the processes that act upon that data (e.g. paying out vendors when a purchase order is issued). In a sense, ERP is the “brain” of a company — it stores all important pieces of data and all of the actions possible in data-driven workflows.

But before taking over the modern business world, how did ERP software get started? The story of ERP begins with major automation efforts of the 1960s: while the 1940s and 50s were focused on mechanical automation of blue collar work — think General Motors establishing their automation department in 1947 — the automation of white collar work (often via the computer!) began in the 1960s.

Midcentury automation: Computers enter industry

Payroll and billing were among the first business processes that computers automated. Employers needed armies of white-collar labor to manually tally employee hours in ledgers, manually multiple hours with hourly rates, and manually subtract taxes and benefit deductions… all just to run a single month of payroll! This time-consuming, repetitive process was error-prone for humans, and was a natural fit for computerized automation.

By the 1960s, many companies used IBM computers to automate payroll and billing tasks. Data processing – an outdated term whose lasting legacy is

Automatic Data Processing, Inc’s name – is what we’d call IT today. Software engineering wasn’t yet a discipline, so these departments often took employees from analytical backgrounds taught them to program on the job. Purdue had just formed the first Computer Science department in the U.S. in 1962, and the first CS grads started appearing a few years later.