Entropy and Consciousness, Part 2

September 25, 2020

This is a follow up to a previous article on consciousness and entropy:  https://birkdalecomputing.com/2020/05/03/entropy-and-consciousness/

We have entered into the age of uber-prediction.  No I don’t mean guessing when your hired ride will arrive, but an age when nearly everything is predicted.  Humans have, of course, always been predicting the outcomes of activities and events.  Predicting the future has been called an emergent behavior of intelligence. Our ancestors needed it to tell them the most likely places that the alpha preditor might be hiding, as well as telling them which route would most likely be taken by the prey they are trying to catch.

There is a natural feedback loop between predicted outcomes and expected outcomes. If a predicted outcome is perceived to be within a certain margin of error of an expected outcome the prediction is said to have “worked” and this positive assessment (i.e. that the prediction worked) when it occurs tends to reinforce the use of predictive behavior in the future.  In other words it increases the occurrence of predictions and simultaneously increases the the amount of data that can be used for future predictions.

In the past we did not have as much data or as much computing power to process the data as we have today. This had always acted as a constraint on, not only, the aspects of life that could be predicted (i.e. not enough data), but also on how quickly prediction worked in respect to the aspect of life being predicted (i.e. not enough processing power). Predictability now tends to “work” better than it ever did before because there is more dataa to use and faster ways to use it.  The success of prediction also creates a virtuous cycle that reinforces the desire for more prediction.

The state of the world around us seems to be increasing in its predictability.  This leads me to believe that we must be pushing back more and more against entropy which is defined by Claude Shannon in information theory as a state of zero predictability where all outcomes are equally likely. This means you need less new information to predict an outcome because the amount of ambient information is increasing.  Consequently you need to ask less questions to obtain a workable prediction. The less entropic a system the more information it contains.

Information is measured in the bit, or single unit of surprise. The more bits a system has the more possible surprises it can have and the less entropic it is. So it follows that the more information there is in a system, the more units of surprise it potentially has. 

What Is a Data Model?

August 10, 2020

A data model is a great way to understand the structure of a system. It requires the acceptance of the concept of an “entity”.  A data model depicts the relationships between the entities that make up a “real world” system.  A data model differs from a mathematical model in that it neither requires mathematical provability nor must it be expressed in numbers and symbols as a mathematical model does.  A data model also differs from a process model in that it does not represent the dynamic changes in a system over time, it depicts the structure of a system, the way the parts of a system fit together.

A data model can be either a representation of the physical reality of a system or a non-physical representation. The latter is usually called either a “conceptual model” or a “logical model”.  Though both of these phrases mean something non-physical they are not completely interchangeable with one another.  A conceptual model is a model of ideas, while a logical model is a model of the semantic relationships between entities and requires a shared and agreed upon vocabulary for it to be useful. 

In My Humble Opinion

June 14, 2020

I was just thinking about how so much of the present day protests remind me of the 1960’s, in spirit at least, if not strictly on issues.  Protests of the 1980’s and 2000’s era seemed, to me at least, to get watered down too easily with every well intended “people oriented” cause of the day.  IMHO, this is probably why, for the most part, they don’t appear to have been very successful (though they did keep the ball rolling, so to speak).  If the current BLM protesters can stay focused and disciplined they do have a decent chance of actually influencing the evolution of social change as much or more than their predecessors.  But they need to stay focused on a few issues and their leaders need to have a concrete, but flexible program for specific steps that can be taken to correct the social and economic inequity that has now surfaced for all to see.

Sooner or later in this process people who are not themselves protestors will turn to the protestors, and the leaders who support them and say “What changes do you want to see enacted?”  Not what meta-issues do you feel (or know) are unjust and need to be corrected, but what enforceable steps do you think need to, and can be, taken?  I hope there is a short list of answers to this question.  Because if there is not, then the initiative, and its spirit, will quickly dissipate and the motivation to take corrective action will simply compete with the other issues of the day.

It is also important to understand that whatever solutions percolate out of the protests they have to produce two results, both measurable in a defined time frame.  Number one is a reduction in the occurrence of undesirable events, for example, unjustified murders by those who are expected to maintain order.  And second, a skewing, or slight flattening, of the wealth distribution curve.  I say “slight” because I would hate to see a disincentive to wealth creation in general.

I wish the peaceful protesters well.

Entropy and Consciousness

May 3, 2020

https://futurism.com/new-study-links-human-consciousness-law-governs-universe

A curious finding according to the study referred to in the article above is that the human brain displayed higher entropy when fully conscious than when not. “Normal wakeful states are characterized by the greatest number of possible configurations of interactions between brain networks, representing highest entropy values,” the team wrote in the study.  This means that, at least to me, that the higher number of connections between brain cells at any point in time, the higher the level of entropy at that point.  One could extrapolate that the more wakeful one is the more entropy there is in the brain and by inference, as one goes to sleep the less entropic the brain becomes.

This seems to go against the idea that learning, reasoning and awareness about the world is a type of “push back” against entropy.  Instances of life are generally seen as organized, metabolizing and replicating pieces of matter that eventually are overcome by entropy and die.  Almost like little islands of anti-entropy in a sea of entropic chaos.  How life can maintain this uphill struggle has always been a fascinating subject of study for biological scientists. Individual instances cannot keep up the struggle forever.  We eventually fall below a minimal level of energy production and consumption and die. This is probably the motivator for the evolution of replication and reproduction.[i]

We think of consciousness as a characteristic of life and thus of order, but this study seems to say the opposite.  Consciousness is a characteristic of chaos and disorder, and that pieces of matter at various locations and periods of time, when they display local order, tend to have less entropy and less consciousness.  This seems to me to infer a type of “cosmic consciousness” associated with entropy.  A concept that, at least from my experience dates back to, at least the hippie era of the 1960’s and 70’s, when expressions such as this were quite often externally stimulated.

Can life’s tendency to continue to live, that is to be less entropic, be a natural reaction against the cosmic consciousness which tends to disorganize our local order?  Can states like sleep, for example, be temporary reversals in the flow toward entropy while consciousness pushes forward our flow toward it?  Is it possible that cosmic consciousness is just the sum total of all local consciousnesses, and after one dies consciousness in the form of entropy, in a sense, lives on?

Another article along the same vein is this:  https://futurism.com/the-byte/mathematicians-think-universe-conscious

An earlier post by me about entropy and information loss can be found at:  https://birkdalecomputing.com/2019/02/08/information-entropy/

[i] A subject for another day.

The Scientist: DNA Could Hold Clues to Varying Severity of COVID-19

April 19, 2020

I can’t really add much to this article except to say I think it is right-on and exactly the appropriate approach needed to be taken by a genetics focused research group if we hope to find the contingencies that most influence the course of COVID-19. I wish Dr. Chung and her team all the luck in the world in her efforts and plan to follow it closely. 

Genetics is almost certainly not the only factor influencing the course of this disease, but with all we’ve learned about the role of DNA, and RNA, in the course of human development I will not be surprised if individual base pair sequencing differences plays a role in what reactive pathways the genes of a body take at a molecular level to protect themselves, and hence the body as a whole, from this particular invader.

The age old interplay between “nature” and “nurture” is never ending.

https://www.the-scientist.com/news-opinion/dna-could-hold-clues-to-varying-severity-of-covid-19-67435 

From the front lines of the COVID-19 War

March 25, 2020

25 March 2019

Let me start by saying I (Wayne Kurtz) am NOT a front line Medical Worker. I received this as an email from someone I know who is, and decided to share it here. It is a first hand observation and assessment of the situation in one emergency room in one hospital in one city (New Orleans, LA) within the first two weeks of the COV-19 Pandemic in the USA.

This is from the front lines. Read carefully and look up the words you do not understand. There are a few things I take away from this, number one if you think rubber gloves and a mask will save you from getting it you are deadly wrong.  I am an ER MD in New Orleans. Class of 98. Every one of my colleagues have now seen several hundred Covid 19 patients and this is what I think I know.

1. Clinical course is predictable. 2-11 days after exposure (day 5 on average) flu like symptoms start. Common are fever, headache, dry cough, myalgias(back pain), nausea without vomiting, abdominal discomfort with some diarrhea, loss of smell, anorexia, fatigue.

2. Day 5 of symptoms- increased SOB, and bilateral viral pneumonia from direct viral damage to lung parenchyma.

3. Day 10 – Cytokine storm leading to acute ARDS and multiorgan failure. You can literally watch it happen in a matter of hours.

4. 81% mild symptoms, 14% severe symptoms requiring hospitalization, 5% critical.

5. Patient presentation is varied. Patients are coming in hypoxic (even 75%) without dyspnea. I have seen Covid patients present with encephalopathy, renal failure from dehydration, DKA. I have seen the bilateral interstitial pneumonia on the xray of the asymptomatic shoulder dislocation or on the CT’s of the (respiratory) asymptomatic polytrauma patient. Essentially if they are in my ER, they have it. Seen three positive flu swabs in 2 weeks and all three had Covid 19 as well. Somehow this ***** has told all other disease processes to get out of town.

6. China reported 15% cardiac involvement. I have seen covid 19 patients present with myocarditis, pericarditis, new onset CHF and new onset atrial fibrillation. I still order a troponin, but no cardiologist will treat no matter what the number in a suspected Covid 19 patient. Even our non covid 19 STEMIs at all of our facilities are getting TPA in the ED and rescue PCI at 60 minutes only if TPA fails.

7. DiagnosticCXR- bilateral interstitial pneumonia (anecdotally starts most often in the RLL so bilateral on CXR is not required). The hypoxia does not correlate with the CXR findings. Their lungs do not sound bad. Keep your stethoscope in your pocket and evaluate with your eyes and pulse ox.

8. Labs- WBC low, Lymphocytes low, platelets lower then their normal, Procalcitonin normal in 95%
CRP and Ferritin elevated most often. CPK, D-Dimer, LDH, Alk Phos/AST/ALT commonly elevated.

9. Notice D-Dimer- I would be very careful about CT PE these patients for their hypoxia. The patients receiving IV contrast are going into renal failure and on the vent sooner.

10. Basically, if you have a bilateral pneumonia with normal to low WBC, lymphopenia, normal procalcitonin, elevated CRP and ferritin- you have covid-19 and do not need a nasal swab to tell you that.

11. A ratio of absolute neutrophil count to absolute lymphocyte count greater than 3.5 may be the highest predictor of poor outcome. the UK is automatically intubating these patients for expected outcomes regardless of their clinical presentation.

12. An elevated Interleukin-6 (IL6) is an indicator of their cytokine storm. If this is elevated watch these patients closely with both eyes. Other factors that appear to be predictive of poor outcomes are thrombocytopenia and LFTs 5x upper limit of normal.

13. Disposition – I had never discharged multifocal pneumonia before. Now I personally do it 12-15 times a shift. 2 weeks ago we were admitting anyone who needed supplemental oxygen. Now we are discharging with oxygen if the patient is comfortable and oxygenating above 92% on nasal cannula. We have contracted with a company that sends a paramedic to their home twice daily to check on them and record a pulse ox. We know many of these patients will bounce back but if it saves a bed for a day we have accomplished something. Obviously we are fearful some won’t make it back.
We are a small community hospital. Our 22 bed ICU and now a 4 bed Endoscopy suite are all Covid 19. All of these patients are intubated except one. 75% of our floor beds have been cohorted into covid 19 wards and are full. We are averaging 4 rescue intubations a day on the floor. We now have 9 vented patients in our ER transferred down from the floor after intubation.

14. Luckily we are part of a larger hospital group. Our main teaching hospital repurposed space to open 50 new Covid 19 ICU beds this past Sunday so these numbers are with significant decompression. Today those 50 beds are full. They are opening 30 more by Friday. But even with the “lockdown”, our AI models are expecting a 200-400% increase in covid 19 patients by 4/4/2020.

15. Treatment – Supportive.
Worldwide 86% of covid 19 patients that go on a vent die. Seattle reporting 70%. Our hospital has had 5 deaths and one patient who was extubated. Extubation happens on day 10 per the Chinese and day 11 per Seattle.

16. Plaquenil which has weak ACE2 blockade doesn’t appear to be a savior of any kind in our patient population. Theoretically, it may have some prophylactic properties but so far it is difficult to see the benefit to our hospitalized patients, but we are using it and the studies will tell. With Plaquenil’s potential QT prolongation and liver toxic effects (both particularly problematic in covid 19 patients), I am not longer selectively prescribing this medication as I stated on a previous post.

17. We are also using Azithromycin, but are intermittently running out of IV.
Do not give these patient’s standard sepsis fluid resuscitation. Be very judicious with the fluids as it hastens their respiratory decompensation. Outside the DKA and renal failure dehydration, leave them dry.

18. Proning vented patients significantly helps oxygenation. Even self proning the ones on nasal cannula helps.
Vent settings- Usual ARDS stuff, low volume, permissive hypercapnia, etc. Except for Peep of 5 will not do. Start at 14 and you may go up to 25 if needed.
Do not use Bipap- it does not work well and is a significant exposure risk with high levels of aerosolized virus to you and your staff. Even after a cough or sneeze this virus can aerosolize up to 3 hours.
The same goes for nebulizer treatments. Use MDI. you can give 8-10 puffs at one time of an albuterol MDI. Use only if wheezing which isn’t often with covid 19. If you have to give a nebulizer must be in a negative pressure room; and if you can, instruct the patient on how to start it after you leave the room.

19. Do not use steroids, it makes this worse. Push out to your urgent cares to stop their usual practice of steroid shots for their URI/bronchitis. We are currently out of Versed, Fentanyl, and intermittently Propofol. Get the dosing of Precedex and Nimbex back in your heads.

20. One of my colleagues who is a 31 yo old female who graduated residency last may with no health problems and normal BMI is out with the symptoms and an SaO2 of 92%. She will be the first of many.

21. I PPE best I have. I do wear a MaxAir PAPR the entire shift. I do not take it off to eat or drink during the shift. I undress in the garage and go straight to the shower. My wife and kids fled to her parents outside Hattiesburg. The stress and exposure at work coupled with the isolation at home is trying. But everyone is going through something right now.

Everyone is scared; patients and employees. But we are the leaders of that emergency room. Be nice to your nurses and staff. Show by example how to tackle this crisis head on.

Good luck to us all.

Chromebook 101: how to use Android apps on your Chromebook – The Verge

March 10, 2020

With Google discontinuing support for the Google Chrome Store apps (see:  https://chromeunboxed.com/google-announces-timeline-for-the-end-of-chrome-apps-on-chromebooks/ ) over the next year, it is probably a good idea to get familiar with Google Play Store (i.e. Android) apps.

https://www.theverge.com/2019/10/9/20903281/chromebook-os-android-apps-how-to-use

If you use your Chromebook as primarily a web browser, and run Android apps on your Android Phone then this change probably has little impact on you.  Especially considerinng that support for Chrome browser extensions will continue to be supported.

Full Stack Data Science. Next wave in incorporating AI into the corporation.

October 22, 2019

https://www.forbes.com/sites/cognitiveworld/2019/09/11/the-full-stack-data-scientist-myth-unicorn-or-new-normal/#1eb0d4f32c60

I like the concept of “Full Stack Data Science”, especially the way the author depicts it in the included graphic.

One thing I would like to point out is the recognition that the process is really a circle (as depicted) and not a spiral, or line.  What I mean by that, is the path does not close between what can be perceived as the beginning “Business goal” and the end “Use, monitor and optimize”.

The results of applying Data Science to business problems not only helps solve these problems, but actually changes the motivators that drive the seeking of solutions in the first place.  Business goals are usually held up as the ends with the lowest dependency gradient of any component of any complex enterprise architecture.  While this may be true at any point in time, the dependency is not zero.  Business goals themselves change over time and not just in response to changing economic, societal or environmental factors.  The technology used to meet these goals does itself drive changes to the business goals.

A party, whether person or organization, tends to do what it is capable of doing.  Technology gives it more activities to undertake and things to produce and consume, which then feedback to the goals that motivate it.

I think this article is one of the best I’ve seen in making that point.

 

Machine Learning and Database Reverse Engineering

October 13, 2019

Artificial intelligence (AI) is based on the assumption that programming a computer using a feedback loop can improve the accuracy of its results.  Changing the values of the variables, called “parameters”, used in the execution of the code, in the right way, can influence future executions of the code.  These future executions are then expected to produce results that are closer to a desired result than previous executions.  If this happens the AI is said to have “learned”.

Machine learning (ML) is a subset of AI.  An ML execution is called an “activation”.  Activations are what “train” the code to get more accurate.  An ML activation is distinctly a two-step process.  In the first step, input data is conceptualized into what are called “features”.  These features are labeled and assigned weights based on assumptions about their relative influence on the output.  The data is then processed by selected algorithms to produce the output.  The output of this first step is then compared to an expected output and a difference is calculated.  This closes out the first step which is often called “forward propagation”.

The second step, called “back propagation” takes the differences between the output of the first step, called “y_hat” and the expected output, called “y” and, using a different but related set of algorithms, determines how the weights of the features should be modified to reduce the difference between y and y_hat.  The activations are repeated until either the user is satisfied with the output, or changing the weights makes no more difference.  The trained and tested model can then be used to do predictions on similar data sets, and hopefully create value for the owning party (either person or organization).

In a sense, ML is a bit like database reverse engineering (DRE).  In DRE we have the data, which is the result of some set of processing rules, which we don’t know[i], that have been applied to that data.  We also have our assumptions of what we think a data model would have to look like to produce such data, and what it would need to look like to increase the value of the data.  We iteratively apply various techniques to try to decipher the data modeling rules, mostly based on data profiling. With each iteration we try to get closer to what we believe the original data model looked like.  As with ML activation we eventually stop, either because we are satisfied or because of resource limitations.

At that point we accept that we have produced a “good enough model” of the existing data.  We then move on to what we are going to do with the data, feeling confident that we have an adequate abstraction of the data model as it exists, how it was arrived at, and what we need to do to improve it.  This is true even if there was never any “formal” modeling process originally.

Let’s look at third normal form (3NF) as an example of a possible rule that might have been applied to the data.  3NF is a rule that all columns of a table must be dependent on the key, or identifier of the table, and nothing else.  If the data shows patterns of single key dependencies we can assume that 3NF was applied in its construction.  The application of the 3NF rule will create certain dependencies between the metadata and the data that represent business rules.

These dependencies are critical to what we need to do to change the data model to more closely fit, and thus be more valuable for, changing organizational expectations.  It is also these dependencies that are discovered through both ML and DRE that enable, respectively, both artificial intelligence and business intelligence (BI).

It has been observed that the difference between AI and BI is that in BI we have the data and the rules, and we try to find the answers.  In AI we have the data and the answers, and we try to find the rules.  Whether results derived from either technology are answers to questions, or rules governing patterns, both AI and BI are tools for increasing the value of data.

These are important goals because attaining them, or at least approaching them, will allow a more efficient use of valuable resources, which in turn will allow a system to be more sustainable, support more consumers of those resources, and produce more value for the owners of the resources.

[i] If we knew what the original data model looked like we would have no need for reverse engineering.

Google Sheets or Microsoft Excel? The differences are disappearing — Quartz

September 19, 2019

https://qz.com/1283203/google-sheets-or-microsoft-excel-the-differences-are-disappearing/