Jack Bogle’s ghost warns about 401(k)s https://a.msn.com/r/2/BB1gUrmr?m=en-us&a=0
As the article says “It’s worth taking a moment to reflect on just how good things have been for investors for a decade.” But, “Enjoy making 12% a year, but don’t get used to it.” It’s not going to last forever.
The equation P(D) = R means the Processing of Data produces Results. Where P is Processing, D is the Data, and R is the Results. Processing is a function that acts upon the Data, producing the Results. This can be expressed as P of D yields R.
Algebraically we can solve for either of the variables (P, D or R). We can solve for any one of the variables (designating it as X) which then becomes the dependent variable, as long as we know the value of the other two. The other two are the independent variables. One independent variable is the experimental variable and the other is the control variable or constant.
Solving for the Results (R) we have equation 1: P(D) = X. This means that if we know the rules and procedures of the Processing (P) and we have the Data (D) we can calculate the Results (R). This is the classic Business Intelligence (BI) paradigm. In a classic star schema think of the fact and dimension tables as containing the Data and the various analyses and reporting as the Processing which produce the Results which are then used as a predictive model going forward. This can be called a “Results Driven Predictive Model” (RDPM) because the predictive power of the model is derived from the Results, the R factor of our equation. You use the Results (which you do not know ahead of time) which are derived from the interaction of Data and Processing, to inform your predictions.
Solving for the Processing (P) we have equation 2: X(D) = R. This means that if we have the Results (R) and have the Data (D) we can discover the rules and procedures of the Processing (P) that was applied to the Data (D) to produce those Results. This is the classic Machine Learning paradigm. Here through progressively measuring how close each iteration of processing allows you to get to the Results (which you already know), given the Data, you can produce a predictive model going forward. This is called a “Processing Driven Predictive Model” (PDPM). You use the rules and procedures of processing (which you do not know ahead of time) that produced the Results given the Data, to inform your predictions.
Solving for the Data (D), which is far less common than the previous two solutions, we have equation 3: P(X) = R. This means that if we have the Results and know the rules and procedures of the Processing we can deduce the Data (D) that had to be used. This equation has no classic application to what is typically thought of as business as far as I know. But has application to scientific and historical endeavors. It can be called the Historical paradigm. In other words, what Data had to be processed according to the rules and procedures of the Processing to yield the observed Results. This is called a “Data Driven Predictive Model” (DDPM). You use the Data (which you do not know ahead of time) upon which the Processing was used to produce the Results, to inform your predictions.
We manipulate the experimental independent variable while holding the control independent variable constant. This is done in order to observe and measure how changes in the experimental variable (the one being changed) effects the dependent variable. For example, in equation 1 we can change the Processing (P) while leaving the Data constant and observe how the dependent Results change. This of course is very common. A constant set of data will almost always produce different results if processed according to a different way set of rules and procedures.
We can also change the Data (the D factor) in equation 1 to observe how that changes the Results while the Processing stays constant. This opens up many predictive possibilities like comparing the different Results when different Data sets are processed the same way by constant Processing.
The same experimental design structure can be applied to equations 2 and 3 as well. This becomes interesting when the Results are held constant. That is, we know what we want to see in the Results. The Data may be out of our control (that is, it may be supplied by others) and we want to know how we can Process that Data to give us the Results we want. This scenario is, in fact, the basis of fraud,
This examination, of course, is an oversimplification but I believe it captures to essential interdependency between Processing, Data and Results. This interdependency follows the classic experimental model where we have two independent variables (one experimental and one control) and one dependent variable which is subject to the manipulation of either of the other two.
Are we really better off now than we used to be? In the old days we knew what we knew, and we knew there were things we didn’t know. Today with the growth of data, machine learning and artificial intelligence, there are many more things we know and even more that we know we don’t know. There are even things we could know but don’t bother to know, mainly because we don’t need to know them. Someone else, or increasingly something else knows them for us, thus saving us the bother of knowing them.
We even discover from time to time that there are things we know that we didn’t know that we knew. We are beginning to suspect that, more than all the things we know and know we don’t know, there are even more things we don’t know that we don’t know. But we don’t know for sure that we don’t know them. If we knew this for sure (if we knew them) then it would just add to the things that we know that we don’t know and would no longer be unknown unknowns.[i]
It is kind of like the names of things. Sometimes there are multiple names given to the same thing, and sometimes multiple things have the same name. Of the two suboptimal situations multiple things with the same name is always the more vexing. This is usually an intra-language problem and not inter-language problem, which makes it even more troubling. You would expect a person speaking a different language would have a different name for something, but you might expect (wrongly it seems) that people speaking the same language would have the same name or names for the same thing. Worse, people of the same language often have different polymorphic descriptors referring to the same object.
What is even worse, a monomorphic descriptor can refer to a set of objects that can either overlap (like a Venn diagram[ii]) or be totally discontinuous, often without people even knowing that they don’t know.
[i] Conceptualized by Donald Rumsfeld February 2002. Things we are neither aware of nor understand.
[ii] Conceived around 1880 by John Venn, according to Wikipedia.
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 predator 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 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 data to use and faster ways to use it. The success of prediction 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 constantly increasing. To make information work for you, often requires discovery of predictions made by others. Consequently you need to ask less questions to obtain a workable prediction. The less entropic a system, the less new 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 more entropic it is. So it follows that the more information there is in a system, the more units of surprise it potentially has and the less likely it is to work as a predicted.
A data model is a great way to und myerstand 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 symwbols 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.
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.
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.
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
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.
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.
Birkdale Computing 