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.

2020 Year in Review (By Dave Collum)

 Superb year end review by  David Collum full of keen perspective and plenty of wit. The downloadable pdf of the full article is available here.

Making sense of the craziest year we’ve yet lived through

Imagine, if you will, a man wakes up from a year-long induced coma—a long hauler of a higher order—to a world gone mad. During his slumber, the President of the United States was impeached for colluding with the Russians using a dossier prepared by his political opponents, themselves colluding with the FBI, intelligence agencies, and the Russians. A pandemic that may have emanated from a Chinese virology laboratory swept the globe killing millions and is still on the loose. A controlled demolition of the global economy forced hundreds of millions into unemployment in a matter of weeks. Metropolitan hotels plummeted to 10% occupancy. The 10% of the global economy corresponding to hospitality and tourism had been smashed on the shoals and was foundering. The Federal Reserve has been buying junk corporate bonds in total desperation. A social movement of monumental proportions swept the US and the world, triggering months of rioting and looting while mayors, frozen in the headlights, were unable to fathom an appropriate response. The rise of neo-Marxism on college campuses and beyond had become palpable. The most contentious election in US history pitted the undeniably polarizing and irascible Donald Trump against the DNC A-Team including a 76-year-old showing early signs of dementia paired with a sassy neo-Marxist grifter with an undetectable moral compass. Many have lost faith in the fairness of the election as challenges hit the courts. Peering through the virus-induced brain fog the man sees CNBC playing on the TV with the scrolling Chiron stating, “S&P up 12% year to date. Nasdaq soars 36%.” The man has entered The Twilight Zone.

Continue reading

Mindset Shift (Corporate management)

 

Mostly true, but I would go one step further: The two need to be integrated!

so:

Purpose without losing sight of profits.

Network with a minimum of hierarchy.

Empowerment with controls embedded in the system

Experimentation in a well structured (planned) environment

and finally Transparency with respect of privacy.

DATA QUALITY MANAGEMENT (YouTube Video - 5mn)

 

A very good introduction to Data Quality Management. 

More professional and less of general interest but still worth listening to if your work is remotely concerned with data.

We're Being Told South Africa's "Scary" Mutant COVID Is Even More Dangerous Than The UK's "Super COVID"

To summarize this article: Covid is a nasty bug but not the end of the world. 

As for the controls and especially control of information, they are far more dangerous and won't go away anytime soon, if ever.

Unfortunately, it goes one step further.  Over hundreds of millions of years, virus have learned one thing alone: to survive. Which is of course what they do exceedingly well. It is said that there are already multiple variants of Covid. The UK strain is one. The South African strain another one. The vaccine may or may not be effective against these two but eventually a newer more lethal Covid virus will emerge. Just a matter of time. Then we will have a real pandemic, just when people, society and the economy will be exhausted fighting the fictive one...

 

Authored by Michael Snyder via The Economic Collapse blog,

A new mutant strain of COVID-19 that has been dubbed “501.V2” has gotten completely out of control in South Africa, and authorities are telling us that it is an even bigger threat than the “Super COVID” that has been causing so much panic in the United Kingdom.  Of course viruses mutate all the time, and so it isn’t a surprise that COVID-19 has been mutating.  But mutations can become a major issue when they fundamentally alter the way that a virus affects humans, and we are being told that “501.V2” is much more transmissible than previous versions of COVID and that even young people are catching it a lot more easily.  That is potentially a huge concern, because up until now young people have not been hit very hard by the COVID pandemic.

The British press is using the word “scary” to describe this new variant, and at this point it has become the overwhelmingly dominant strain in South Africa…

The new mutant, called 501.V2, was announced in Cape Town last Friday and is believed to be a more extreme variant than Britain’s new Covid strain which has plunged millions into miserable Christmas lockdowns.

Cases in South Africa have soared from fewer than 3,000 a day at the start of December to more than 9,500 per day, with the mutant accounting for up to 90 percent of those new infections.

If this same pattern happens elsewhere as this new mutant strain travels around the globe, then “501.V2” could eventually almost entirely replace all of the older versions of COVID.

Authorities are optimistically telling us that the recent vaccines that have been developed will “likely” work against this new variant, but the truth is that they will not know until testing is done.

And if the vaccines don’t work against “501.V2”, we could be back to square one very rapidly.

For now, countries all over the globe are banning flights from South Africa in a desperate attempt to isolate this new version.  The UK, Germany, Switzerland, Turkey and Israel are among the nations that have banned those flights, but so far the United States is not on that list.

So people that are potentially carrying this new version of COVID continue to enter the U.S. on a daily basis.

For the United Kingdom, this flight ban may have come too late because two cases of “501.V2” have already been identified on British soil…

Two cases of a new, “more transmissible” COVID-19 variant linked to South Africa have been identified in the UK, the health secretary has said.

Both cases are contacts of people who travelled from South Africa over the last few weeks, Matt Hancock said at a Downing Street news conference.

If the new vaccines are effective against “501.V2”, authorities believe that they already have the long-term answer to this new variant.

But if those vaccines don’t work, this pandemic could be entering a far more deadly new phase.

And of course we are hearing about more problems with these new vaccines on a daily basis.  Thousands of adverse reactions have already been reported to the CDC, and more reports continue to pour in as more people get the shots.  Here is one example from New York City…

A health care worker in New York City had a serious adverse reaction to a coronavirus vaccine, officials said on Wednesday.

New York City Health Commissioner David Chokshi said during a news conference that the unidentified worker experienced a “significant allergic reaction” to the vaccine. He added that the worker was treated for the reaction, and is in stable condition and recovering.

We should not be surprised that there are major issues with experimental mRNA vaccines that are based on entirely new technology that were rushed into production without proper testing.

And of course there are tens of millions of Americans that will never take any mRNA vaccine that literally “hijacks your cells” under any circumstances.

On the other hand, most of the U.S. population seems to think that these new vaccines will bring this pandemic to an end, but if they don’t work against new mutant versions of the virus that won’t be true at all.

It is so important to take a balanced view of these things.

Unfortunately, when it comes to COVID most people fall into two camps.

The first camp is totally freaked out because they think that COVID is about the worst thing that could ever happen to the United States and they tend to favor extremely draconian measures to prevent the spread of the virus.

But the truth is that the COVID pandemic pales in comparison to other great pandemics throughout human history.  The Black Plague and the Spanish Flu Pandemic each killed at least 50 million people.  As for the COVID pandemic, the global death toll has not reached the 2 million mark even if the official numbers are accurate.  If a pandemic of this nature is freaking people out so much, what is going to happen when a truly killer plague is unleashed in our society?

The second camp either thinks that the pandemic is greatly exaggerated or that the virus doesn’t even exist at all.  Even though hordes of people are catching the virus all around us, many out there continue to deny the reality of this crisis.

I simply do not understand that.  So many people that I know around the country have gotten the virus, and that includes quite a few big names.  For example, the following is an excerpt from an article in which Daisy Luther shares what her experience with COVID was like…

Days 3-5: Over the next three days, chills and fever were almost constant. My joints and muscles hurt. Getting up to go to the bathroom felt like an expedition up a mountain.  I was tired and winded. I had very little appetite and even less of an inclination to cook food so I existed mostly on peanut butter and crackers and leftover soup. I was absolutely exhausted and so cold that I shivered violently when I got out from under my bed piled high with blankets. I had super-weird dreams. My cough worsened, my head hurt, and my throat was still mildly sore.

I drank lots of water and electrolyte beverages. My thirst remained unquenchable regardless of how much I drank. I took vitamins (C, D3) and took Zinc supplements. These are my regular supplements but I doubled that.

Days 6-9: The line to get a test at the local clinic was long and filled with people who were coughing up a lung. There was no way I’d be able to stand in that line for an hour, as sick as I felt. Besides, I figured if I didn’t have Covid, I’d get it standing in the line so I opted not to be tested.

This part made me think of the worst case of the flu I ever had, except intensified by about four times. It was terrible.

I usually let a fever run its course but by Saturday I felt so awful that I gave in and began treating symptoms. My normal temp is in the 96s and my temperature throughout these days stayed between 101-103. I staggered ibuprofen and acetaminophen, and I also used a mild muscle relaxant and my Ventilyn inhaler. The meds didn’t get rid of my fever but reduced the chills to a tolerable level. I slept almost around the clock, waking up for a couple of hours here and there to check on website stuff. Fortunately, I have a wonderful team who kept things running for us. One day blurred into the next and I considered going to the doctor again, but couldn’t muster the energy. I felt like if I just got a little more sleep I’d be okay.

My cough was getting far worse and now my ribs and abdominal muscles hurt. It was a deep painful cough that caused me to clutch my chest every single time inhaled deeply.

So to summarize, yes the COVID pandemic is real, but it is not the end of the world.

More people are going to get sick, and some will suffer intensely, but the vast majority of those that get the virus will survive.

If you want to wear a mask, then wear a mask.

If you don’t want to wear a mask, then don’t wear a mask.

We should be free to make our own choices, and we should also be free to experience the consequences for those choices.

Unfortunately, there are way too many people out there that think that they have the right to censor and control what we say and what we do, and that trend is likely to only get worse as our society continues to spin out of control in the years ahead.