20 events in 2020

 

Authored by Nick Routley via VisualCapitalist.com,

Can you remember a year more life-changing than 2020?

Over a million lives were lost in the pandemic, oil prices turned negative, and protests swept the streets. At the same time, 10 years of technology advancements seemed to happen in mere months—and now vaccinations are rolling out at a record speed.

Below, we round up some of the year’s biggest news events with charts and visualizations.

The Year in Review: 2020 in 20 Visualizations

Graphic #1   ⟩⟩   January 2020 - Australian Bushfires

For some in the Southern Hemisphere who ushered in the new year first, it started on fire.

Reuters assessed the scale of the damage caused by bushfires across Australia. In fact, total burned areas reached 18.6 million hectares (186,000km²) by March, bigger than the total land mass of entire countries like Cuba.

Here’s the damage done in the state of New South Wales alone, compared to previous years:

While bushfires are common in Australia, this year, dry conditions fueled the flames. The fires raged for nearly 80 days, displacing or killing nearly 3 billion animals—a devastating biodiversity loss for the country.

Graphic #2   ⟩⟩   January 2020 - Rising Iran–U.S. Tensions

In early January, a U.S. air strike incinerated the car of General Qassim Suleimani, a security mastermind and one of Iran’s most powerful military strategists. U.S. officials claimed that Iran was planning an “imminent” attack.

In retaliation, Iran fired two rockets at U.S. military bases located in Iraq. No one was killed. As tensions escalated, the U.S. House of Representatives passed a bill to try and restrict President Trump’s use of military power against Iran without approval.

Later, in mid-January, Iran’s Revolutionary Guard admitted that it mistakenly shot down a Ukrainian passenger jet, responsible for the death of 176 people.

Graphic #3   ⟩⟩   March 2020 - The Spread of the “Novel Coronavirus”

You’ve heard of Patient Zero, but what about Patient 31?

Before February, cases of the still unnamed virus were largely contained within China, with the rest of the world cautiously observing the country’s containment efforts. Slowly, but surely, the virus began to spread beyond China’s borders.

South Korea’s 31st confirmed COVID-19 case—which was behind the rapid spread of the virus to potentially up to 1,160 contacts in the country—served as a warning to the rest of the world of how fast the virus could spread.

» See the full graphic by Reuters

Reuters’ unique graphic explainer uncovers how just one typical day of multiple “normal” interactions had significant super-spreader effects.

Graphic #4   ⟩⟩   March 2020 - The Coronavirus Crash

The S&P 500 erased over a third of its value in under a month—the fastest 30% decline ever recorded on the benchmark index.

As a result, the global tourism industry suffered dramatic losses, with countless cruise ships docked and passenger flights traveling at half-capacity.

This graphic shows the BEACH stocks—booking, entertainment & live events, airlines, cruises & casinos, hotels & resorts—that were most impacted by worldwide travel bans.

While some of these stocks have since recovered, the ongoing impact of COVID-19 is still most widely being felt among companies in these types of industries.

Graphic #5 & 6   ⟩⟩   March 2020 - Lockdown Life Begins

From toilet paper hoarding to limits on gatherings, the pandemic’s immediate effects on our surrounding environment became clear as early as March. As daily life came to a standstill, commuter activity in major cities plummeted throughout the month.

One unintended positive consequence of these shutdowns? Air pollution, such as nitrogen dioxide (NO₂) emissions also steeply dropped alongside these restrictions on movement.

Possibly the most well-known diagram of the pandemic is the one that introduced the world to the phrase “flatten the curve”, showing why it was important to prevent and delay the spread of the virus so that large portions of the population aren’t sick at the same time.

Graphic #7   ⟩⟩   April 2020 - Historic U.S. Job Losses

After the World Health Organization declared COVID-19 a global pandemic on March 11, unemployment figures soon hit historic proportions.

Within a month, 22 million in the U.S. had filed jobless claims.

To put this in perspective, U.S. unemployment levels in 2020 were roughly 10 times higher than previous peak unemployment levels in absolute terms. Or, to look at it another way, this is equivalent to the entire population of Chile or Taiwan.

Graphic #8   ⟩⟩   April 2020 - Stimulus Announced in the U.S.

On March 27, the $2 trillion CARES Act came into law after facing minimal resistance from the House and Senate. We broke down the historic relief package in the Sankey diagram below.

The relief package included $1,200 direct deposits to individuals, over $350 billion in relief for small businesses, and an excess of $100 billion for the U.S. health system.

Graphic #9   ⟩⟩   April 2020 - Oil Prices Go Negative

In another historic event, oil prices went negative for the first time in history. Futures contracts for WTI oil fell to a stunning -$37.63 on April 20th, with producers actually paying traders to take oil off their hands.

Oil has since recovered from this shock, cruising back to more typical price levels.

Graphic #10   ⟩⟩   May 2020 - Black Lives Matter Protests

“I can’t breathe.” These few words sparked the ongoing flames of a significant movement this summer: Black Lives Matter (BLM).

After the killing of George Floyd on May 25, by police, the Armed Conflict Location & Event Data Project (ACLED) recorded over 7,750 BLM-linked demonstrations over a three month span.

The nationwide pattern of civil unrest is well-documented, but there’s been no time like the present to demand change. Though images of burning cars and police clashes dominated the headlines, in the end, 93% of the protests were peaceful.

There’s also been a ripple effect, with thousands of similar rallies reported in countries around the world.

Graphic #11 & 12  ⟩⟩   May 2020 - The World Works from Home

The dramatic shift to staying at home has resulted in a much higher reliance on technology for many people. Nowhere were these trends exemplified more than the rise of video conferencing software Zoom—the platform was used for work, education, and socializing alike.

As monthly users swelled, those who typically take to the skies also declined in a steep fashion. In this graphic from May, we noted that Zoom’s market capitalization had skyrocketed to eclipse the top seven airlines by revenue, combined.

As remote work became the new normal for significant shares of the workforce, unique benefits of this adjusted lifestyle arose, but it didn’t come without its challenges.

Perhaps the most significant lasting change from the COVID-19 pandemic might be the adoption of flexible work, even by firms that resisted the trend in the past.

If many employees continue to work remotely, even part of the time, then that will have a big impact on everything from the commercial office market to the bottom line of SaaS companies that help facilitate remote collaboration among teams.

Graphic #13   ⟩⟩   July 2020 - Tesla becomes World’s Most Valuable Automaker

2020 was a hallmark year for Tesla. In June, it became the most valuable automaker in the world—surpassing the likes of Toyota, Volkswagen, and Honda.

Tesla’s market valuation climbed over 375% since June 2019. While these soaring figures are one factor behind its rise, others include record Model 3 sales, which prompted market euphoria.

But Tesla’s story is far from over.

The company is now worth more than the largest nine automakers combined, and is set to enter the S&P 500 officially on December 21, 2020. Tesla will be the most valuable company to ever enter the index, ranking as the eighth-largest overall.

Graphic #14   ⟩⟩   July 2020 - Big Tech’s Dominance

In many ways, COVID-19 only accentuated differences in market share, earnings, and wealth.

For one, Big Tech’s market cap share of the S&P 500 soared. In the seven years preceding July, the market cap of the six stocks—Facebook, Apple, Amazon, Netflix, Alphabet, and Microsoft—grew over 500%. By contrast, the S&P 500 rose just 110%.

At the same time, Big Tech’s concentration reached record levels, with the five largest companies accounting for over 20% of the index’s total value.

Graphic #15   ⟩⟩   August 2020 - Beirut Explosion

While the world grappled with numerous biological and natural disasters, human-error led to a deadly explosion that rocked Beirut’s port. The blast was broadcast around the world in real time as people filmed the fire on their devices.

Using satellite data, NASA and NYT mapped the extent of the damage, which claimed 135 lives and affected 305,000 more.

» See the full interactive explainer by NYT

This explosion was the biggest accident of its kind in modern history, triggered by the exposure of combustible ammonium nitrate—a key ingredient in fertilizers—to an open flame due to poor storage. Beyond the human toll, the financial cost of this explosion is estimated at above $15 billion.

Graphic #16   ⟩⟩   August 2020 - Shortest Bear Market in History Ends

In a stunning reversal, the bear market of 2020 ended on August 18 when the S&P 500 exceeded previous February highs. As trillions of dollars in stimulus response got injected into global economies, markets recovered in record time.

Just two weeks before the shortest bear market in history ended, we published a graphic comparing previous stock crashes—from 1987’s Black Monday to the Nixon Shock of 1973—exposing the duration and intensity of market downturns since 1929.

Graphic #17   ⟩⟩   August 2020 - U.S. Wildfire Season

Reddish-orange skies might seem otherworldly, but this fall, they were a common sight across the West Coast of North America, where air quality reached the “hazardous” category for long stretches of time.

2020 was the most active year on record for wildfires yet, with California and Oregon being particularly hard-hit. While some wildfires are caused by natural occurrences like lightning strikes, an overwhelming majority (85-90%) happen because of human causes such as discarded cigarettes and campfire debris.

This is an unprecedented event. We now have the largest wildfire in [California’s] history, as well as the third largest and the fourth largest and five of the Top 10.

– Noah Diffenbaugh, professor and senior fellow at Stanford University

Graphic #18   ⟩⟩   November 2020 - The 2020 U.S. Presidential Election

In 2020, U.S. election spending hit over $13 billion, more than twice the amount spent on the entire 2016 election.

Of this total, congressional spending topped $7 billion, with Democrats spending 64% more than Republican candidates for the House and Senate.

President Biden was the first candidate ever to raise $1 billion, while Trump raised $596 million.

Graphic #19   ⟩⟩   December 2020

COVID-19’s Third Wave

Like history tells us, pandemics come in waves. The third wave of COVID-19 escalated in November, when cases began to surge.

On November 8, the seven-day average of new daily cases hit 100,000 in America. By the end of November, global cases soared to 60 million. Since then, cases have trended upward, leading local governments worldwide to enforce social distancing requirements for the winter holiday season.

The below graphic from Reddit helps show the latest surge in cases in the U.S.:

Graphic #20   ⟩⟩   December 2020 - Global Vaccination Effort Kicks Off

In more recent news, Pfizer made waves when it announced it was rolling out a 95% effective COVID-19 vaccine. Then followed Moderna, at 94.5% in mid-November. As the global vaccination race intensifies, Bloomberg tracks the progress of nine vaccines and 80 publicly disclosed distribution deals representing 7.95 billion vaccination doses.

However, even with viable vaccines, challenges still exist. All around the world, perceptions of vaccine safety have dropped significantly, which may complicate an economic recovery.

On to the Next One

After the wild ride that was 2020, many people are wondering what 2021 will have in store.

In the first half of the year, vaccine distribution will surely take center stage. As well, economic recovery will be in focus as physical businesses resume more typical activity and regions slowly open up travel and tourism again.

Much like the financial crisis of 2008 was an inflection point for the economy, the COVID-19 pandemic has changed the course of human history. Chaos can breed opportunity, and even though unemployment spiked to record highs in the U.S., new business applications did as well.

Will things return to “normal”? As the many twists and turns of the past year have demonstrated, our complex, interconnected world is far from static. The next black swan is always just around the corner.

2020: The Year Things Started Going Badly Wrong

 

Authored by Gail Tverberg via Our Finite World blog,

How today’s energy problem is different from peak oil

Many people believe that the economy will start going badly wrong when we “run out of oil.” The problem we have today is indeed an energy problem, but it is a different energy problem. Let me explain it with an escalator analogy.

Figure 1. Holborn Tube Station Escalator. Photo by renaissancechambara, CC BY 2.0 https://creativecommons.org/licenses/by/2.0, via Wikimedia Commons.

The economy is like a down escalator that citizens of the world are trying to walk upward on. At first the downward motion of the escalator is almost imperceptible, but gradually it gets to be greater and greater. Eventually the downward motion becomes almost unbearable. Many citizens long to sit down and take a rest.

In fact, a break, like the pandemic, almost comes as a relief. There is suddenly a chance to take it easy; not drive to work; not visit relatives; not keep up appearances before friends. Government officials may not be unhappy either. There may have been demonstrations by groups asking for higher wages. Telling people to stay at home provides a convenient way to end these demonstrations and restore order.

But then, restarting doesn’t work. There are too many broken pieces of the economy. Too many bankrupt companies; too many unemployed people; too much debt that cannot be repaid. And, a virus that really doesn’t quite go away, leaving people worried and unwilling to attempt to resume normal activities.

Some might describe the energy story as a “diminishing returns” story, but it’s really broader than this. It’s a story of services that we expect to continue, but which cannot continue without much more energy investment. It is also a story of the loss of “economies of scale” that at one time helped propel the economy forward.

In this post, I will explain some of the issues I see affecting the economy today. They tend to push the economy down, like a down escalator. They also make economic growth more difficult.

[1] Many resources take an increasing amount of effort to obtain or extract, because we use the easiest to obtain first. Many people would call this a diminishing returns problem.

Let’s look at a few examples:

(a) Water. When there were just a relatively few humans on the earth, drinking water from a nearby stream was a reasonable approach. This is the approach used by animals; humans could use it as well. As the number of humans rose, we found we needed additional approaches to gather enough potable water: First shallow wells were dug. Then we found that we needed to dig deeper wells. We found that lake water could be used, but we needed to filter it and treat it first. In some places, now, we find that desalination is needed. In fact, after desalination, we need to put the correct minerals back into it and pump it to the destination where it is required.

All of these approaches can indeed be employed. In theory, we would never run out of water. The problem is that as we move up the chain of treatments, an increasing amount of energy of some kind needs to be used. At first, humans could use some of their spare time (and energy) to dig wells. As more advanced approaches were chosen, the need for supplemental energy besides human energy became greater. Each of us individually cannot produce the water we need; instead, we must directly, or indirectly, pay for this water. The fact that we have to pay for this water with part of our wages reduces the portion of our wages available for other goods.

(b) Metals. Whenever some group decides to mine a metal ore, the ore that is taken first tends to be easy to access ore of high quality, close to where it needs to be used. As the best mines get depleted, producers use lower-grade ores, transported over longer distances. The shift toward less optimal mines requires more energy. Some of this additional energy could be human energy, but some of the energy would be supplied by fossil fuels, operating machinery in order to supplement human labor. Supplemental energy needs become greater and greater as mines become increasingly depleted. As technology advances, energy needs become greater, because some of the high-tech devices require materials that can only be formed at very high temperatures.

(c) Wild Animals Including Fish. When pre-humans moved out of Africa, they killed off the largest game animals on every continent that they moved to. It was still possible to hunt wild game in these areas, but the animals were smaller. The return on the human labor invested was smaller. Now, most of the meat we eat is produced on farms. The same pattern exists in fishing. Most of the fish the world eats today is produced on fish farms. We now need entire industries to provide food that early humans could obtain themselves. These farms directly and indirectly consume fossil fuel energy. In fact, more energy is used as more animals/fish are produced.

(d) Fossil Fuels. We keep hearing about the possibility of “running out” of oil, but this is not really the issue with oil. In fact, it is not the issue with coal or natural gas, either. The issue is one of diminishing returns. There is (and always will be) what looks like plenty left. The problem is that the process of extraction consumes increasing amounts of resources as deeper, more complex oil or gas wells need to be drilled and as coal mines farther away from users of the coal are developed. Many people have jumped to the conclusion that this means that the price that buyers of fossil fuel will pay will rise. This isn’t really true. It means that the cost of production will rise, leading to lower profitability. The lower profitability is likely to be spread in many ways: lower taxes paid, cutbacks in wages and pension plans, and perhaps a sale to a new owner, at a lower price. Eventually, low energy prices will lead to production stopping. Without adequate fossil fuels, the whole economic system will be disrupted, and the result will be severe recession or depression. There are also likely to be many job losses.

In (a) through (d) above, we are seeing an increasing share of the output of the economy being used in inefficient ways: in creating deeper water wells and desalination plants; in drilling oil wells in more difficult locations; in extracting metal ores that are mostly waste products. The extent of this inefficiency tends to increase over time. This is what leads to the effect of an escalator descending faster and faster, just as we humans are trying to walk up it.

Humans work for wages, but they find that when they buy a box of corn flakes, very little of the price actually goes to the farmer growing the corn. Instead, all of the intermediate parts of the system are becoming overly large. The buyer cannot afford the end products, and the producer feels cheated by the low wholesale prices he is being paid. The system as a whole is pushed toward collapse.

[2] Increasing complexity can help maintain economic growth, but it too reaches diminishing returns.

Complexity takes many forms, including more hierarchical organization, more specialization, longer supply chains, and development of new technology. Complexity can indeed help maintain economic growth. For example, if water supply is intermittent, a country may choose to build a dam to control the flow of water and produce electricity. Complexity tends to reach diminishing returns, as noted by Joseph Tainter in The Collapse of Complex Societies. For example, economies build dams in the best locations first, and only later build them at less advantageous sites. These are a few other examples:

(a) Education. Teaching everyone to read and write has significant benefits because it allows the use of books and other written materials to disseminate information and knowledge. Teaching a few people advanced subjects has significant benefits as well. But after a certain point, the need for additional people to study a subject such as art history is low. A few people can teach the subject but doing more research on the subject probably won’t increase world GDP very much.

When we look at data from about 1970, we find that people with advanced education earned much higher incomes than those without advanced degrees. But as we add an increasing large share of people with these advanced degrees, jobs that really need these degrees are not as plentiful as the new graduates. Quite a few people with advanced degrees end up with low-paying jobs. The “return on investment” for higher education drops increasingly lower. Some students are not able to repay the debt that they took out in order to pay for their education.

(b) Medicines and vaccines. Over the years, medicines and vaccines have been developed to treat many common illnesses and diseases. After a while, the easy-to-find medicines for the common unwanted conditions (such as diabetes, high blood pressure and inflammation) have already been found. There are medicines for rare diseases that haven’t been found, but these will never have very large total sales, discouraging investment. There are also conditions that are common in very poor countries. While expensive drugs could be developed for these conditions, it is likely that few people could afford these drugs, so this, too, becomes less attractive.

If research is to continue, it is important to keep expanding work on expensive new drugs, even if it means completely ignoring old inexpensive drugs that might work equally well. A cynical person might think that this is the reason why vitamin D and ivermectin are generally being ignored in the prevention and treatment of COVID-19. Without an expanding group of high-priced new drugs, it is hard to attract capital and young workers to the field.

(c) Automobile efficiency. In the US, the big fuel efficiency change that took place was that which took place between 1975 and 1983, when a changeover was made to smaller, lighter vehicles, similar to ones that were already in use in Japan and Europe.

Figure 2. Estimated Real-World Fuel Economy, Horsepower, and Weight Since Model Year 1975, in a chart produced by the US Environmental Protection Agency. Source.

The increase in fuel efficiency between 2008 and 2019 (an 11 year period) was only 22%, compared to the 60% increase in fuel efficiency between 1975 and 1983 (an 8 year period). This is another example of diminishing returns to investment in complexity.

[3] Today’s citizens have never been told that many of the services we take for granted today, such as suppression of forest fires, are really services provided by fossil fuels.

In fact, the amount of energy required to provide these services rises each year. We expect these services to continue indefinitely, but we should be aware that they cannot continue very long, unless the energy available to the economy as a whole is rising very rapidly.

(a) Suppression of Forest Fires. Forest fires are part of nature. Many trees require fire for their seeds to germinate. Human neighbors of forests don’t like forest fires; they often encourage local authorities to put out any forest fire that starts. Such suppression allows an increasing amount of dry bush to build up. As a result, future fires spread more easily and grow larger.

At the same time, humans increasingly build homes in forested areas because of the pleasant scenery. As population expands and as fires spread more easily, forest fire suppression takes an increasing amount of resources, including fossil fuels to power helicopters used in the battles. If fossil fuels are not available, this type of service would need to stop. Trying to keep forest fires suppressed, assuming fossil fuels are available for this purpose, will take higher taxes, year after year. This is part of what makes it seem like we are trying to move our economy upward on a down escalator.

(b) Suppression of Illnesses. Illnesses are part of the cycle of nature; they disproportionately take out the old and the weak. Of course, we humans don’t really like this; the old and weak are our relatives and close friends. In fact, some of us may be old and weak.

In the last 100 years, researchers (using fossil fuels) have developed a large number of antibiotics, antivirals and vaccines to try to suppress illnesses. We find that microbes quickly mutate in new ways, defeating our attempts at suppression of illnesses. Thus, we have ever-more antibiotic resistant bacteria. The cost of today’s US healthcare system is very high, exceeding what many poor people can afford to pay. Introducing new vaccines results in an additional cost.

Closing down the system to try to stop a virus adds a huge new cost, which is disproportionately borne by the poor people of the world. If we throw more money/fossil fuels at the medical system, perhaps it can be made to work a little longer. No one tells us that disease suppression is a service of fossil fuels; if we have an increasing quantity of fossil fuels per capita, perhaps we can increase disease suppression services.

(c) Suppression of Weeds and Unwanted Insects. Researchers keep developing new chemical treatments (based on fossil fuels) to suppress weeds and unwanted insects. Unfortunately, the weeds and unwanted insects keep mutating in a way that makes the chemicals less effective. The easy solutions were found first; finding solutions that really work and don’t harm humans seems to be elusive. The early solutions were relatively cheap, but later ones have become increasingly expensive. This problem acts, in many ways, like diminishing returns.

(d) Recycling (and Indirectly, Return Transport of Empty Shipping Containers from Around the World). When oil prices are high, recycling of used items for their content makes sense, economically. When oil prices are low, recycling often requires a subsidy. This subsidy indirectly goes to pay for fossil fuels used to facilitate the recycling. Often this goes to pay for shipment to a country that will do the recycling.

When oil prices were high (prior to 2014), part of the revenue from recycling could be used to transport mixed waste products to China and India for recycling. With low oil prices, China and India have stopped accepting most recycling. Instead, it is necessary to find actual “goods” for the return voyage of a shipping container or, alternatively, pay to have the container sent back empty. Europe now seems to have a difficult time filling shipping containers for the return voyage to Asia. Because of this, the cost of obtaining shipping containers to ship goods to Europe seems to be escalating. This higher cost acts much like diminishing returns with respect to the transport of goods to Europe from Asia. This is yet another part of what is acting like a down escalator for the world economy.

[4] Another, ever higher cost is pollution control. This higher cost also exerts a downward effect on the world economy, because it acts like another intermediate cost.

As we burn increasing amounts of fossil fuels, increasing amounts of particulate matter need to be captured and disposed of. Capturing this material is only part of the problem; some of the waste material may be radioactive or may include mercury. Once the material is captured, it needs to be “locked up” in some way, so it doesn’t pollute the water and air. Whatever approach is used requires energy products of various kinds. In fact, the more fossil fuels that are burned, the bigger the waste disposal problem tends to be.

Burning more fossil fuels also leads to more CO2. Unfortunately, we don’t have suitable alternatives. Nuclear is probably as good as any, and it has serious safety issues. In my opinion, the view that intermittent wind and solar are a suitable replacement for fossil fuels represents wishful thinking. Wind and solar, because of their intermittency, can only partially replace the coal or natural gas burned to generate electricity. They cannot be relied upon for 24/7/365 generation. The unsubsidized cost of producing intermittent wind and solar energy needs to be compared to the price of coal and natural gas, not to wholesale electricity prices. There are a lot of apples to oranges comparisons being made.

[5] Among other things, the growth of the economy depends on “economies of scale” as the number of participants in the economy gradually grows. The response to COVID-19 has been extremely detrimental to economies of scale.

The economies of many countries changed dramatically, with the initial spread of COVID-19. Unfortunately, we cannot expect these changes to be completely reversed anytime soon. Part of the reason is the new virus mutation from the UK that is now of concern. Another reason is that, even with the vaccine, no one really knows how long immunity will last. Until the virus is clearly gone, vestiges of the cutbacks are likely to remain in place.

In general, businesses do well financially, as the number of buyers of the goods and services they provide rises. This happens because overhead costs, such as mortgage payments, can be spread over more buyers. The expertise of the business owners can also be used more widely.

One huge problem is the recent cutback in tourism, affecting almost every country in the world. This cutback affects both businesses directly related to tourism and businesses indirectly related to tourism, such as restaurants and hotels.

Another huge problem is social distancing rules that lead to office buildings and restaurants being used less intensively. Businesses find that they tend to have fewer customers, rather than more. Related businesses, such as taxis and dry cleaners, find that they also have fewer customers. Nursing homes and other care homes for the aged are seeing lower occupancy rates because no one wants to be locked up for months on end without being able to see other members of their family.

[6] With all of the difficulties listed in Items [1] though [5], debt based financing tends to work less and less well. Huge debt defaults can be expected to adversely affect banks, insurance companies and pension plans.

Many businesses are already near default on debt. These businesses cannot make a profit with a much reduced number of customers. If no change is possible, somehow this will need to flow through the system. Defaulting debt is likely to lead to failing banks and pension plans. In fact, governments that depend on taxes may also fail.

The shutdowns taken by economies earlier this year were very detrimental, both to businesses and to workers. A major solution to date has been to add more governmental debt to try to bail out citizens and businesses. This additional debt makes it even more difficult to maintain promised debt payments. This is yet another force making it difficult for economies to move up the growth escalator.

[7] The situation we are headed for looks much like the collapses of early civilizations.

With diminishing returns everywhere, and inadequate sources of very inexpensive energy to keep the system going, major parts of the world economic system appear headed for collapse. There doesn’t seem to be any way to keep the world economy growing rapidly enough to offset the down escalator effect.

Citizens have not been aware of how “close to the edge” we have been. Low energy prices have been deceptive, but this is what we should expect with collapse. (See, for example, Revelation 18: 11-13, telling about the lack of demand for goods of all kinds when ancient Babylon collapsed.) Low prices tend to keep fossil fuels in the ground. They also tend to discourage high-priced alternatives. Unfortunately, all the wishful thinking of the World Economic Forum and others advocating a Green New Deal does not change the reality of the situation.

COVID "Mutation" Stories Show That The Lockdowns Are Designed To Last Forever

 

This article is for the record. This idea proposed is that what is happening right now was planned for a long time and the Covid crisis just offered the opportunity. Maybe so, I don't know. Let's wait and see, it will get clearer soon.

Wednesday, Dec 31, 2020

Authored by Brandon Smith via Alt-Market.us,

For many months now I have been warning that the design behind the pandemic lockdowns is a perpetual one; meaning, the lockdowns are MEANT to last forever. We can see this in the very commentary of the establishment elites that are pushing for the mandates; their most frequent argument being that the pandemic restrictions are the “new normal”. This assertion is outlined by globalists like Gideon Lichfield of MIT in his article ‘We’re Not Going Back To Normal’. In it he states:

“Ultimately, however, I predict that we’ll restore the ability to socialize safely by developing more sophisticated ways to identify who is a disease risk and who isn’t, and discriminating - legally - against those who are.

…one can imagine a world in which, to get on a flight, perhaps you’ll have to be signed up to a service that tracks your movements via your phone. The airline wouldn’t be able to see where you’d gone, but it would get an alert if you’d been close to known infected people or disease hot spots. There’d be similar requirements at the entrance to large venues, government buildings, or public transport hubs. There would be temperature scanners everywhere, and your workplace might demand you wear a monitor that tracks your temperature or other vital signs. Where nightclubs ask for proof of age, in future they might ask for proof of immunity—an identity card or some kind of digital verification via your phone, showing you’ve already recovered from or been vaccinated against the latest virus strains.”

In my article ‘Waves Of Mutilation: Medical Tyranny And The Cashless Society’, I dismantled Lichfield’s arguments and outlined why the controls the establishment is attempting to put in place have been planned far in advance. The so-called “great reset” and “Fourth Industrial Revolution” has been in development since at least 2014 when the terms were first being injected into the mainstream economic media. The ideas of a cashless society, the “sharing economy”, biometric mass surveillance, social credit scores, etc, have all been part of the globalist agenda for decades. The coronavirus is merely a useful crisis for them to exploit as a rationale for the draconian measures they have always wanted.

The plan was so predictable that I even pointed out at the beginning of the coronavirus outbreak that lockdowns would not end even if a working vaccination was developed because all they have to do is declare that a “new mutation” of the virus has been found which is resistant to existing treatments. Or, they could engineer a whole new virus and release it into the population in order to keep the Reset machine rolling forward.

Not surprisingly, just as news hit the wires that the barely tested and highly suspect Pfizer and Moderna vaccines were being released to the public, reports have begun to trickle in of “more infectious” Covid mutations found in places like the UK, India and South Africa.

I’m not sure how much more transparent the elites can get.

Take the Pfizer vaccine now and you might receive an immunity passport for a few months, and then it will become void with every new mutation of the virus. So, you must then submit to ENDLESS vaccinations, many of then untested and potentially hazardous. As the former VP of Pfizer and other medical professionals have warned, these vaccines are like Russian Roulette and could cause an autoimmune response that leads to sterility or other harmful reactions.

The vaccines themselves are a conveniently short lived solution even if they do work. They require multiple doses over the course of a month, and renewed vaccinations are to take place possibly every few months. Basically, it never ends. With the mutations and limited antibodies from the vaccines, the elites could keep the lockdowns and mandates in place for many years to come.

The World Health Organization is making it clear that vaccination will not necessarily be considered a solution to viral spread. Meaning, even if you are vaccinated you will still be considered a potential carrier and transmitter of Covid, therefore the lockdowns and mask mandates will not stop. This begs the question – What’s the point of the vaccine?

The WHO chief scientist cites the fact that there is not enough evidence to prove that the vaccines prevent transmission. By that logic, we could also argue that there is no evidence that the vaccines are 95% effective, or that they are safe in the slightest.

In the meantime, the WHO and our friendly neighborhood fascist Dr. Anthony Fauci are consistently spreading the narrative that the “worst outbreak” is yet to come. Gotta keep that fear train chugging forward on the track to the “Great Reset”, right?

For the people that actually believe that the covid crisis will end after mass vaccinations, I’m sorry to say, but you have been duped. Every single element of the establishment response and every public statement they make indicates that they plan to violate your civil liberties for a long time to come. Those promises of relief right around the corner? All lies. The claim that if you go along to get along everything will go back to normal? It’s a con. It is hollow rhetoric designed to make you shut up and submit to medical tyranny for just long enough that it becomes irreversible.

I suspect they are hoping they can condition the public over the next few years to simply adapt to the controls until we forget what life was like before the pandemic and the reset. It seems, however, that the globalist reset plan is not going very well.

The vaccines and the mutation news feel rushed, to say the least. Initially, the establishment said that it would take at least 18 months just to develop a vaccine for trials and testing, and that the lockdowns would continue well beyond that time frame until a majority of the population was shown to have immunity. Instead, they tossed out multiple vaccines within 6 months and the mutation narrative is already in the news.

I believe this is because resistance to the pandemic lockdowns is growing and the number of people refusing to take the vaccines appears to be high. As they say, the revolution will not be televised, but it is still impossible to hide completely.

In Europe, a huge percentage of the population (around 50% or more depending on the country) are hesitant to take the vaccine. In the US, polls show that at least 30% of the population will refuse outright, while 60% of people are hesitant about effectiveness.

Even large numbers of health care workers are refusing the vaccine, and these are the people with the most pressure to submit or face consequences.

Hilariously, the media is arguing that though there have been “some allergic reactions” to the shot, there is “no evidence of serious long term side effects”. Perhaps that is because there are NO STUDIES of the long term effects and there were minimal trials before the vaccines were released? I mean, is this not basic logic? Do they really think we are that dumb?

So far it seems hundreds of millions of people are not that dumb. Surprisingly, even sheriffs and police across the country are openly refusing to enforce mandates and carry out color-of-law punishments against citizens that do not submit. This is really a huge obstacle for the globalists and their reset.

The virus has produced a 0.26% IFR (Infection Fatality Ratio) among anyone not in a nursing home with preexisting conditions. Over 40% of Covid deaths are attributed to elderly people that were already suffering from numerous ailments. Only around 10% of people that end up hospitalized for covid suffer from long term health concerns (more than three months). And, only around 15% of ICU beds are in use across the US, meaning that the claims of over-capacity and full hospitals were nothing more than fear mongering all along.

Consider the fact that hundreds of thousands of people already die each year from infectious diseases like the flu and pneumonia and Covid starts to seem far less threatening. It is certainly not an excuse for medical lockdowns and Orwellian contact tracing measures.

On top of that, numerous studies are revealing that the lockdowns and the masks are completely ineffective in stopping the spread of the virus. The states and countries with some of the most strictly enforced mandates also tend to be the places with the highest infection spikes.

Because of this, it makes sense that many people are refusing to comply with the mandates. The media claims we are conspiracy theorists that believe the virus “doesn’t exist”; this is not the case. In fact, I have long suspected that the narrative that the virus “doesn’t exist” was a psyop or strawman that would be used against the liberty movement later to discredit our resistance to medical lockdowns.

Most of us are well aware the virus exists. Some of us have already dealt with it and recovered from it. What we are saying is that the CDC, the WHO and the medical community’s OWN STATISTICS show that Covid is not a threat to more than 99% of the population. If we are to accept their stats as even remotely accurate, then Covid becomes a non-issue for most people.

Again, I will ask the question that the mainstream refuses to ask:

Why is 99% of the population being told they must sacrifice their jobs, their businesses and their liberties in the name of making less than 1% of the population feel safer? Why not ask the 0.26% of the people under threat from the virus to volunteer to stay home so that the rest of us can get on with normal life? Why are we doing the opposite of what makes the most sense?

The answer is that the pandemic response is about dominance, not public health. People are starting to recognize this, and they are about to revolt.

So, the next logical step for the establishment if they really want to institute their reset agenda is to introduce a new threat. Meaning, they need a “mutation” of the virus or a completely new virus in order to create the kind of fear that is required to manipulate the public into going along with further control.

Will a new and deadlier virus be found? Maybe. In most cases viruses tend to evolve into less deadly strains of the original. They also tend to balance out their rate of spread versus their rate of mortality. In other words, like any other creature, viruses evolve to survive, and a virus cannot survive if it kills off a majority of its potential hosts. So, they mutate to become more infectious, but invariably less deadly.

If a “mutation” does show up on the scene that is more deadly than the current form of Covid-19, then I would be highly suspicious of its origins. What is most likely is that that the elites are in a panic and they are using the mutation narrative as a propaganda tool to illicit terror and conformity in the public. There may be no mutation at all, or the mutations will have no significant bearing on the death rate.

Ironically, by rushing out the vaccines as well as the mutation stories, the elites have sabotaged themselves. They wanted to blitzkrieg the public with the lockdowns and they met heavier resistance than they expected. So, they put the vaccination program on a bullet train and now the public is wary of being injected with a vaccine model that is barely tested. Now, they are promoting the mutation bogeyman and this only makes people question why they should take any vaccine at all? If the virus is going to continually mutate then why take a questionable vaccine that could be useless in a matter of a months?

All the mutation narrative does is further expose what the true agenda is – What the elites want is never-ending lockdowns. There is no program to save lives or flatten the curve. The entire health argument is utter nonsense. Nothing that has been done so far supports the notion that public health is the priority. Instead, what we are seeing is a mad dash towards totalitarianism using Covid as the excuse, and the effort is failing.

Asymptomatic Spread Does not Exist

As the WHO told us recently, there is no evidence vaccine prevents transmission., Not that it matters very much since also from the WHO, a worse pandemic than COVID could be around the corner and that what we’ve seen so far in 2020 is “not necessarily the big one”!

 And now this bellow: "Asymptomatic Spread Does not Exist!" In other words, every single assumption about Covid has been proven wrong!

Covid is but a slightly worse than usual flu virus which spreads quite normally, kills mostly old and weak people, as the flu does every year. Does not under scrutiny show much tendency to spread asymptomatically and finally, will, as any other flu virus mutate sooner than later (which already seems to be the case) rendering any vaccine inefficient.  

So the question for this year end is "Why all this?"

Asymptomatic Spread Doesn’t Exist

Post by Frank Salvato

In complete contradiction to the popular narrative used by Democrat politicians and governors across the United States, a new study of 10 million people in Wuhan, China – ground zero for the COVID virus, showed that asymptomatic spread of COVID does not occur, nullifying all reasoning for business closures and lockdowns.

The study, published in the November issue of the peer-reviewed scientific journal Nature Communications, studied 9,899,828 residents of Wuhan, screening them between May 14, 2020 and June 1, 2020. The results provided clear evidence as to the possibility of any asymptomatic transmission of the virus.

The study was compiled by 19 scientists from the Huazhong University of Science & Technology in Wuhan, and highly respected scientific institutions in the UK and Australia.

Titled Post-lockdown SARS-CoV-2 nucleic acid screening in nearly 10 million residents of Wuhan, China, the study thoroughly debunked the concept of asymptomatic transmission.

Out of the nearly 10 million people in the study, results revealed “300 asymptomatic cases” were found. Utilizing contact tracing, of those 300, not a single case of COVID-19 were detected in any of them.

“A total of 1,174 close contacts of the asymptomatic positive cases were traced, and they all tested negative for the COVID-19,” the study concluded.

Both the asymptomatic patients and their contacts were placed in isolation for a period of no less than two weeks and the results remained the same. “None of detected positive cases or their close contacts became symptomatic or newly confirmed with COVID-19 during the isolation period,” the study found.

Asymptomatic spread of COVID not a thing. https://t.co/TwF4A43JHi. Have some well considered hugs. Post-lockdown SARS-CoV-2 nucleic acid screening in nearly ten million residents of Wuhan, China | Nature Communications https://t.co/gFgSsKxjPo

— Dr Naomi Wolf (@naomirwolf) November 22, 2020

Further examination of the study subjects revealed that “virus cultures” in the positive and re-positive asymptomatic cases were all negative, “indicating no ‘viable virus’ in positive cases detected in this study.”

The age range of those found to be asymptomatic was between 10 and 89 years of age. The asymptomatic positive rate was “lowest in children or adolescents aged 17 and below” and the highest rate was found among people older than 60.

The study also concluded with high confidence that due to a weakening of the virus itself, “newly infected persons were more likely to be asymptomatic and with a lower viral load than earlier infected cases.”

In June of 2020, Dr. Maria Van Kerkhove, head of the World Health Organization’s emerging diseases and zoonosis unit, stated publicly that she doubted the narrative advanced by the political class on asymptomatic transmission.

Van Kerkhove explained during a press conference that, “from the data we have, it still seems to be rare that an asymptomatic person actually transmits onward to a secondary individual.”

In fact, Van Kerkhove couldn’t point to a single case of asymptomatic transmission, noting that numerous reports “were not finding secondary transmission onward.”

The false narrative of asymptomatic transmission has been the justification used by the political and activist classes for lockdowns enacted all across the world.

Even the US Centers for Disease Control has been politicized in its advancing of the asymptomatic transmission false narrative. They falsely advance claims that asymptomatic people “are estimated to account for more than 50 percent of transmissions.”

There is no scientific data to corroborate this position.

The "other" side of AI: Consciousness. (Video)

This is the other side of AI: Consciousness. It is still to my opinion far into the future as we first need to understand and define what consciousness is. 

But as this video explains, I may well be wrong on this count. Consciousness would still be independent of AI but related in the sense that consciousness is generated by integration and network complexity which is created to improve AI.

If this is the case, then Ray Kurzweil too could be wrong: We may just be a few short years away from the singularity!    

GPT-3, getting closer to true AI (Video)

Although Machine Learning is making great progress right now, we are still a long way from true AI. Automatic translation and recognizing faces were the low hanging fruits of ML. My educated guess is that writing computer code, the next step, will arrive sooner than people expect. Would that prove to be true, we may have a early experience of run-away exponential progress as computers will very quickly write code too complicated for humans to make sense of. In a nutshell, we would end up with true black boxes with millions of lines that nobody could understand. A plane on auto-pilot but with the caveat that the destination is unknown! 

More than the elusive risk of consciousness which for the time being is still far in the future and therefore not a real risk, this is an actual and far more immediate danger. Conversely, the competitive advantage to be gained from this kind of software is such that necessarily both China and the US will compete ferociously and therefore accelerate its appearance. The result at this stage is unpredictable...

 

 

 

 Of course a Vaccine whatever the results will solve nothing! This is what doctors have been saying from the beginning. The specificity of the Covid flu virus is its very high speed of mutation. Consequently, we were going to have significant mutant sooner than later... 

Mask and social distancing are forever because they are not medical but political decisions. The WHO instead of taking its time to listen to medical experts has bent twice over, first to deny the artificial origin of the virus under pressure from China, then to push aggressively for the unreasonably fast vaccination of population while knowing that it would probably be useless against new strains. Now what?

 Here's the article from Zero Hedge:

WHO Chief Scientist Warns "No Evidence COVID Vaccine Prevents Viral Transmission"

Once again, the WHO has stepped in to offer some confusing comments about the coronavirus vaccine, warning that there is "no evidence to be confident shots prevent transmission" and that people who receive the vaccine should continue wearing masks and following all social distancing and travel guidelines.

The comments were made by WHO chief scientist Soumya Swaminathan during what appears to have been a virtual press conference held Monday.

A clip of the offending line has begun circulating on social media.

"At the moment, I don't believe we have the evidence on any of the vaccines, to be confident that it's going to prevent people from getting the infection and passing it on,"

Of course, a close look at the research released by Pfizer and Moderna shows the studies haven't actually tested whether the vaccines actually prevent transmission of the virus; the goal of the trials was to see whether vaccinated patients presented with COVID symptoms at a rate that was substantially less frequent than individuals who hadn't been vaccinated. That's pretty much it. Though the data might hint at lowering transmission rates, that's still tbd, apparently.

Some on twitter scoffed at the comment.

The doctor went on to explain that there's no evidence to suggest that those who have been vaccinated wouldn't be a risk if they traveled to a foreign country, say Australia, with relatively low COVID rates.

At this point, it might be helpful for the WHO to produce some kind of clarification that either offers substantially more context to explain this remark.

But we suspect they won't.

Why? Well, perhaps because that context might undermine certain government officials' insistence that there's absolutely no reason to question the efficacy, and potential side effects (both long-term, and short) tied to the new COVID-19 vaccines.

The New COVID-19 Strain Is A Political Disaster Of Our Own Making

 Covid-19, from bad to worse!

Will incompetent politicians succeed in crashing the economy?

Endless cycles of lock-downs and resurgences may well do the job!

Here's what's wrong with the "new wave":

 

Authored by Rob Sutton via TheCritic.co.uk,

By seeking answers to scientific questions no-one had asked, we find ourselves assigning importance to discoveries which may have none...

In justifying the move to a new national lockdown, the leaders of the UK briefly enjoyed the political fortune of a headline-grabbing finding: a new strain of Covid-19, possibly more virulent than the old.

This strain, despite the paucity of scientific data, has been described as “up to 70 per cent more transmissible than the old variant,” and it is this figure which has gripped the media and policymakers. The tendency towards catastrophism is palpable.

Yet this new strain, VUI-202012/01, quickly transcended its role within national politics as the justification for introducing Tier 4 lockdowns. The fear of a new, super-transmissible mutant strain has spread to other nations, who are similarly eager to display the sort of knee-jerk reactionary interventions being generously described as “decisive leadership.” Over 30 countries have banned entry by UK citizens over fears of the new strain, with chaotic scenes at Dover exacerbating already tetchy Brexit negotiations.

Never mind that the Department of Health committee whose recommendations regarding the new strain expressed considerable uncertainty about the transmissibility and dangers posed. At present, the precautionary principle completely dominates decision making in Westminster and the devolved assemblies. “Better safe than sorry,” we hear, as further lockdowns are announced without the slightest hint of legislative oversight.

How has this happened so quickly? It seems that hardly had news of a mutant strain of Covid-19 broken that we were promptly shepherded into Tier 4 and became a global pariah. To understand how this panic has developed, we need to understand the nature of diagnostic medicine, its relationship to the scientific method, and how both might be abused for political ends.

In Britain, we have one of the most advanced scientific, medical and technological infrastructures in the world. This infrastructure was greatly expanded during the early months of the pandemic, with Covid-19 diagnostic testing capacity rapidly increased. The reasons for this increase were largely political. By pushing to achieve 100,000 tests per day, the government hoped it might reassure an anxious public.

Under normal circumstances, medical tests are generally not used with such political goals in mind. They form part of a process of hypothesis testing and Bayesian reasoning to guide the rational medical management of patients with diagnostic uncertainty. We begin by forming a question, choosing a test to answer that question, and applying that test, bearing in mind the limits of diagnostic certainty for a given investigation.

The key here is that a diagnostic test is used to answer a specific question. We do not, as a matter of both economic feasibility and ethical restraint, apply scattergun testing to vast swathes of the population without a good reason. In populations at risk of a disease but otherwise asymptomatic, we might use screening to identify disease in an early stage and to improve treatment outcomes. But never before have we attempted to apply such intensive “screening” for such a poorly understood disease to guide such far reaching policies as the infringements of civil liberties we are currently seeing.

At present, national testing programmes are being used as political vehicles to justify pre-determined policy prescriptions, instead of as scientific instruments to answer well-formulated diagnostic questions. Those policy makers who saw testing infrastructure as a way to tally-up some quick political points have instead scored something of an own goal, subjecting us to a torrent of data which, instead of reassuring us, only serves to give us more questions. The perversion of the scientific method doesn’t get much worse than this.

As an anxious patient who is subject to a battery of tests will only become more anxious as incidental findings lead to further follow-up questions, so too do our policymakers find themselves with more problems than answers through the indiscriminate application of the full arsenal of testing methods at the disposal of the British state. And these problems have a habit of producing even more problems through a cycle of positive feedback.

Since the early days of the pandemic, the UK’s testing capacity has been aggressively expanded. The original target of 100,000 tests per day was no sooner reached than it was replaced by a new target of 200,000 tests per day. The political thinking here is obvious: a big number ought to reassure the public. But this is extraordinarily myopic.

More intensive testing leads to new justifications for even more intensive testing. The cycle is as follows: we start with a moderate testing capacity which is primarily used to detect cases among the sickest and most vulnerable patients, in order to guide further treatment. Concerns are raised by those not able to access testing for themselves. The government pledges to expand testing beyond its initial scope, and broadens the eligibility criteria to include doctors, nurses, care home workers and others.

We start to include more and more asymptomatic carriers for whom a positive case has an essentially negligible risk of serious harm. Yet the number which captures the public’s attention is the absolute number of positive cases. With a vastly increased number of tests, we get a vastly increased number of positive cases. And the government, seeing a situation running away from it and desperate to regain control by those limited means available to it, promises to further increase testing capacity. The cycle continues.

More tests will naturally lead to more cases, particularly if those tests are used indiscriminately and with no real strategy in mind. The problem compounds when we consider the increase in the absolute number of false positives. The growth in false positives is linear with increase in number of tests, but the negative consequences for society spread out as a highly non-linear network, with isolation of contacts of (falsely) positive cases having expansive and synergistic negative consequences for broader society. But even without this, and assuming that all our positive results are true positives, by using testing as a form of mass-surveillance we have set ourselves up for a never-ending cycle of lockdowns.

The same logic applies to the genetic testing which has unearthed this “new” strain, although we may yet find that it has been in circulation globally for a long time. By testing more, without knowing what we are testing for, we will find things which, from a political perspective, necessitate further intervention.

The corpus of data which can be poured over to find new justifications for ongoing restrictions continues to grow. With the added dimension of genomic studies, the potential for the noise to smother the signal grows, particularly at a time when there is strong public and political demand for a coherent narrative. There will always exist some metric sufficiently intimidating that it might be used to justify a new lockdown. Yet we keep searching without really knowing what we are searching for or why we are doing it.

There is essentially no logic upper limit to how intensively we can test and how many different techniques we can apply to elucidate Covid-19 and its various strains. Some strains will inevitably be more virulent, and will, by definition, have a greater tendency to spread. This is not, in itself, a cause for alarm; it is simply Darwinism on a microscopic scale. And whether these findings matter from a policymaking perspective is an altogether different question.

The scientific method begins with a question and sets out to find an answer. If we decide to seek answers without questions, then we end up with data which must be interpreted and given significance post hoc, regardless of whether that significance really exists. Positive feedback cycles are difficult to escape from. The various governments of the UK and its devolved legislatures urgently need to rationalise the use of testing and clearly justify the introduction of any new investigative methods. Otherwise, we will be trapped by a political crisis of our own making.

 

Common Errors in Machine Learning due to Poor Statistics Knowledge (By V Granville)

This article explains a lot of what we see currently with many fake news based on misinterpreted data, badly used statistics and erroneous conclusions. Sometimes innocent, often with an agenda.

We keep hearing about "science" but science is messy, data is dirty and statistics are complex to understand. Here's some examples. 

Common Errors in Machine Learning due to Poor Statistical Knowledge

• Posted by Vincent Granville on September 2, 2019 at 3:00pm
• View Blog

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. Compute all the (99,999 * 100,000) / 2 cross-correlations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle and fix it.)

This is being done on such a large scale, I think it is probably the main cause of fake news, and the impact is disastrous on people who take for granted what they read in the news or what they hear from the government. Some people are sent to jail based on evidence tainted with major statistical flaws. Government money is spent, propaganda is generated, wars are started, and laws are created based on false evidence. Sometimes the data scientist has no choice but to knowingly cook the numbers to keep her job. Usually, these “bad stats” end up being featured in beautiful but faulty visualizations: axes are truncated, charts are distorted, observations and variables are carefully chosen just to make a (wrong) point.

Trusting data is another big source of errors. What’s the point of making a 99% accurate model if your data is 20% faulty, or worse, you failed to gather the right kind of data to start with, or the right predictors? Also, models with no sound cross-validations are bound to fail. In Fintech, you can do back-testing to check a model. But it is useless: what you need to do is called walk-forward, a process of testing your model trained on past data split into two sets: most recent data (the control case) and older data (the test case). Walk forward is akin to testing your data on future data that is already in your possession, it is called cross-validation in machine learning lingo. And then, you need to do it right: if the control and test data are too similar, you may end up with overfitting issues.

Trusting the R-squared is another source of potential problems. It depends on your sample size, so you can’t compare results for two sets of different sizes, and it is sensitive to outliers. Google alternatives to R-squared to find a solution. Also using the normal distribution as a panacea leads to many problems when dealing with data that has a different tail or that is not uni-modal or not symmetric. Sometimes a simple transformation, using a logistic map or logarithmic transform will fix the issue.

Even the choice of metrics can have huge consequences and lead to different conclusions based on a same data set. If your conclusions should be the same regardless of whether you use miles or yards, then choose scale-invariant modeling techniques.

Missing data can be handled inappropriately, being replaced by averages computed on available observations, even though better imputation techniques exist.. But what if that data is missing precisely because it behaves differently than your average? Think about surveys or Amazon reviews. Who write reviews and who do not? Of course the two categories of people are very different, and what’s more, the vast majority of people never write reviews: so reviews are based on a tiny, skewed sample of the users. The fix here is to have a few professional reviews blended with those from regular users, and score the users correctly to give the reader a better picture. If you fail to do it, soon enough all readers will know that your reviews are not trustworthy, and you might as well remove all reviews from your website, get rid of the data scientists working on the project, and save a lot of money and improve your business brand.

Much of this is discussed (with fixes) in my recent book Statistics: new foundations, toolbox, and machine learning recipes, available (for free) here.

 

General Myths to avoid in Data Science and Machine Learning

 Simple but clear definitions!

General Myths to avoid in Data Science and Machine Learning

What is Machine Learning, Data Science or Artificial Intelligence? is one of the most common questions which I have faced from people. Be it newcomers, recruiters or even people in leadership positions, this is a question which is puzzling everyone in its own way.

For beginners it takes the form of how do I become a data scientist? For leaders it becomes a question of whether it has an imperative business impact? and for people in the field it takes the form of what I should call myself, a data scientist a data engineer or a data analyst.

This post is an attempt to clear some of the myths and develop a basic understanding around what Data Science is, and its different interpretations in corporate world.

Myth 1: Data Scientist/Engineer/Analyst are one and same.

This is a warped myth which I have faced many times in my career and which basically does harm to both employee and the company. It’s like calling a software engineer and QA the same thing.

To put things in perspective, a Data Scientist is someone who has experience and knowledge in at least 2 of these 3 fields, Statistics, Programming and Machine Learning. Primary expectation of such an employee is to be able to work on a challenging business problem where he/she can use their knowledge to find solutions. Such a person would love to spend a major portion of their work in building predictive models and performing statistical experiments to obtain a working solution. It’s a mixture of a research and a programming job, and the nature and workload differs depending on the size of the company/team.

Data Engineering is a job where a person focuses on building the infrastructure for deploying applications performing jobs like predictive modeling, updating dashboards with streaming data, running daily jobs to generate reports and maintaining continuous flow of data. A really good knowledge of SQL is fast becoming a necessity for a good data engineer followed by knowledge of spark.

Data Analyst is a person with more of a bend towards interpreting and analyzing business results rather than being in the process of their creation. Such a person will prefer to use tools to generate those results and will spend a major portion of their time in interpreting and deriving business value out of them. Data Analysts have been in the industry a long time before data scientists came into picture and the primary tool of there choice has been Excel. In fact , even today for small amount of data excel is most efficient. At present, there are tool like PowerBI, Azure which provide the ability to perform analytics on Big Data. Primary focus however for this position is accurately communicating day to day results as well as results of new hypothesis which they test. These inputs are critical and form a base for important decision making for a business.

Myth 2: Deep Learning is Machine Learning or AI

Deep learning has no doubt become a big name nowadays, and with all the hype and marketing around it, it has also led to people believing that deep learning is an ultimate solution to every data science/machine learning problem. Truth cannot be farther away than this.

Deep learning, no doubt is one of the most complex concepts to understand in today’s scope of machine learning but that is it. Deep learning gets its name since the “neural network” implied in this framework contains multiple layers and is hence called a “deep” network. What is offered via tensorflow, pytorch or keras is just a framework to apply this concept easily.

No doubt, learning the framework is hard and framework is efficient as well but it is not equivalent to gaining expertise in machine learning. Machine learning is a vast field which takes in concepts and algorithms from a number of fields such as statistics, information theory, optimization, information retrieval, neural networks etc. and has an abundance of algorithms each of which are more useful than others in particular use cases.

Deep learning for instance has been extremely efficient in computer vision and speech recognition but it is an absolute overkill to use it in sentiment analysis or a simple prediction problem which can be solved with linear regression.

It is always a wise decision to invest time in exploratory analysis and understanding the scope of a problem before fixing on the algorithm to use for the problem.

This pic explains it the best.

Myth 3: Data Science can be picked up in 3 months.

As much as I wish this to be true, this is not the case. To be an efficient data scientist one needs to know a lot more than just importing the libraries through scikit-learn and tensorflow and calling their train and predict functions.

It is one of those illusive fields where the results are not deterministic, meaning same sequence of steps will not always end in same result. It highly depends on the quality and the quantity of the data provided and there is a lot of stuff which needs to happen before calling the “train” function.

Sure, you can learn how to call libraries and write the sequence of steps to generate a model, but that model will not always be efficient. To understand things properly one needs to have a considerable understanding of working and dependencies of the algorithm which is being applied. It is imperative to have this knowledge, or else tweaking models or explaining the results to leadership becomes a real pain.

I always remember this answer to , how to learn coding in a single night

This is a small attempt to underline and clear the prevalent myths in the field of machine learning and data science. Hope it helps.