Multi-Disciplinary Reading - Book Reviews

[phreakv6]

The Geometry of Wealth, Brian Portnoy, 2018 - The author’s intent was to help people manage personal finance better and he does it well but has no original ideas as he rehashes Kahneman, Tversky, Munger, Marks, Buffet for 200 odd pages. If you are already exposed to these ideas, there is hardly anything new to be found here. He tries to walk the middle-ground between being too technical and too philosophical which sort of works. It is a bit irritating though, the force-fitting of geometry of shapes into this paradigm.

The book talks about money and purpose of life, saving, time, philosophy, portfolio management, risk/reward, compounding, retirement etc. as you would expect a personal finance book to. It also deals a fair bit with behavior management when dealing with money. I went over my highlighted sections which weren’t much indicating that the amount of new documentable information/insights I wanted to revisit are minimal.

The last section (Shapeless) was actually interesting as it dealt with human experience with time or the non-linear lumpy nature and elasticity of it (have always believed this) - of having different selves and the conflicting inter-temporal choices of these various selves.

There is a phenomenal research on this subject of hyperbolic discounting (Pico in Nutshell) and inter-temporal bargaining (Pico - Breakdown of Will) which I happened to have stumbled upon earlier this year. This will turn your utility-theory worldview and most of economic-value calculations involving discounting like DCF upside down (No problem with the concept of discounting itself but with the discounting curve - we typically use exponential like a reverse of compound interest when we should be using hyperbolic - near-term rewards should be valued a whole-lot higher)

I haven’t found these being referred anywhere else so was pleasantly surprised seeing them referred to, in this otherwise mediocre book. For most people “The Psychology of Money” by Morgan Housel is probably a better alternative. 7/10

[Vijayalakshmi]

Hello Bharani,
Hope you are well and staying safe.
I have no book review but questions around how you go about reading/processing the information. Questions below:

1.How are you taking notes- in parallel or do you do it later? Do you type in some doc/ on paper. My train of though always gets cut when I stop to take notes and then ill have to restart to get the thinking going . How do you go about this?

2.Do you read a book in one sitting ? Do you prefer a time slot or is it more random?

3.Are you speed reading? What technique /s are you employing?

4.I see the subject matter is very varied. How do you go about building your knowledge when the whole topic is new. What is your process? How do you know what books are the right ones? I see an ocean for any new subject which puts me backwards than forward.

5.How frequently do you revisit the notes you take?
Anything else that you are doing differently?

Anticipating your response.

[phreakv6]

Hi Vijayalakshmi,
We are safe and hope you are too.

I am worried that you might be mistaking me as some sort of expert in reading. Will try to answer but not sure how useful they will be.

@Vijayalakshmi

1.How are you taking notes- in parallel or do you do it later? Do you type in some doc/ on paper. My train of though always gets cut when I stop to take notes and then ill have to restart to get the thinking going . How do you go about this?

I used to not even take notes as I explained here back in 2018. I still got a lot out of the books by just reading them slowly and mulling over the content between reading sessions. However, when I re-read some of these books from 2018, I noticed that big ideas were all in my head and I was putting them to use as well quite a bit but those were hardly 50% of those books. There were several more that I grasped in the re-reads. The re-reads however were tedious as a lot of the knowledge I already had absorbed. To minimize this sort of re-reads, I decided to summarize in my own words the parts I liked. I think I first did this for Poor Charlie’s Almanac in five parts. I have re-visited that summary many times since and it’s been as good as re-reading the book.

I typically highlight the parts that are interesting and after a week or two try to summarize all thoughts I have on the subject post thinking and re-thinking it during the time. So no live notes, and my process of reading is still the same except for the highlighting but I force myself to summarize at least the good books I would want to re-read as much as possible. This is why the length of my reviews/summaries varies. The ones I really love have exhaustive summaries (Poor Charlie’s Almanac being the benchmark with 5 long posts in this thread)

@Vijayalakshmi

2.Do you read a book in one sitting ? Do you prefer a time slot or is it more random?

Not at all. On average a 250 page book could take 5 sittings at least (Maybe 10 if I really love it and the information has a novelty or difficulty). Time slots are random but mostly at night (75% at least). No fixed schedules at all. Some days I read almost 16 hours a day… Sometimes go 2 weeks without touching a book.

@Vijayalakshmi

3.Are you speed reading? What technique /s are you employing?

Despise the concept. I believe if something can be speed-read, its not worth reading (Especially the Tim Ferris kind speed-reading). I read extremely slowly. When many of us (friends/family/colleagues) are reading the same document, I am almost always the last to finish and by a huge margin. I am always amazed by how quickly people are capable of reading (Even my 7 yr old reads faster than me at this point). I simply don’t have that skill

@Vijayalakshmi

4.I see the subject matter is very varied. How do you go about building your knowledge when the whole topic is new. What is your process? How do you know what books are the right ones? I see an ocean for any new subject which puts me backwards than forward.

I like being broad than narrow and so pick wider range subjects I can know the basic concepts about. Right now am reading a book on Geography and Cooking (In parallel) for eg. I don’t like reading things I already know and find no point in doing it (Unless its physics, which I am partial to). Picking the right books - Amazon AI is fantastic for this. At this point Amazon knows my brain better than me. Its easier to pick what people like me enjoy as well as find what books are great on a particular topic when you know the topic. A lot of books come from bibliography of books I read as well (This was the main driver in the beginning but nowadays Amazon is doing a better job). I will never pick a book/topic I have no intent on learning and have no interest in at that point and this has served me well.

@Vijayalakshmi

5.How frequently do you revisit the notes you take?
Anything else that you are doing differently?

Once every few months maybe. Nothing different. Just lot of patience and persistence and a family that allows me my space and time and a schedule that allows me to stay up till 3 am to read like today.

Just read whatever you like to read until you enjoy the process of reading - everything else will fall in place as long as we don’t force ourselves to read what others are reading or read for the sake of reading or read towards a statistic (hours/pages read, books read etc.)

Happy reading!

[saravananb1994]

The following thread has many investment readings compiled. You might find it insightful to gather new information.

Saravanan Balakrishnan ⏰ (3PEducator)

[phreakv6]

Bridgital Nation, N. Chandrasekaran & Roopa Rurushothaman, 2019 - We all know what’s ailing India - The problem is we think bullet trains and smart cities will solve our problems when our problems are more basic than that (Doesn’t mean we shouldn’t have bullet trains and smart cities) and require solving for basic problems at scale - from healthcare, education, judiciary so that our quality of life improves on average. This is actually low-hanging fruit which we refuse to pluck.

The book offers some solutions none of which are novel - increase women’s participation in the workforce, improve skills of the population as we have lot of jobs lying vacant but the people are unemployable for the same and take a digital + human approach so that there is a human connection in the last mile (bridgital workers) while leveraging the scale and intelligence of digital solutions - this way we don’t have to worry about automation and have it on our side and still produce more jobs.

The part I really loved about this book was that it offers these solutions with a lot of backing from data and there was a lot of human element to the solutions as it told stories of real Indians, their problems and how some of these solutions helped them.

The Tata group has been distinctly different from the other business houses in India and you can see why in this book - they actually care about Indians and genuinely seem to want to improve the country conscientiously. There are a lot of personal stories so the book appeals both emotionally and economically which I liked.

Some of the statistics I picked from the book - documenting here since this is information I found most useful

• Over 700 million Indians are over the age of 30 - more than twice the size of population of US. Every month of avg. 1 million more Indians become of working age. GDP per capita will increase from current $2000 to $4300 in the next decade
• India rarely lives in its averages - While avg income per capita is $2000 - For Delhi & Goa it is $5000 - close to global Median while Bihar it is $600 - in the bottom decile

• UP has more people than Brazil and Maharashtra’s population is more than Germany’s

• 10 states account for most of the value of country’s goods and services

• 2/3 of all manufacturing happen in 7 states

• 5 states are responsible for all postgraduates

• India’s urban and rural markets often pull in different directions at the same time, with one growing while other slows

• Even with GDP growth avg of 7% last decade, a large number of women have dropped out of the workforce

• Avg. firm in India employs just over 2 people

• Economic growth is driven by services sector - IT, telecom, finance - rather than manufacturing like in rest of Asia

• Households in India purchase TVs and mobile phones before they reach basic nutrition levels

• India is short of 600k doctors, 2.5 million nurses, a million teachers, 400k agri extension workers, and 1.7 million CV drivers. There aren’t enough judges (30 million cases pending), welders, researchers and plumbers either. Shortages are endemic

• India has a overwhelming demand for vital services and an overwhelming demand of human capital. It just doesn’t know how to build a bridge between them

• India struggles because the sector of the economy growing the fastest aren’t employment intensive

• Under a quarter of women in India engage in paid work. Nearly 120 million have at least secondary education but do not participate in the workforce

• Antarlaapika in sanskrit means a puzzle or riddle in which answer is hidden in the riddle itself

• Unexceptional tasks are made exceptional only by the difficulty in achieving them

• India has 190 million adults without a bank account - second largest unbanked population after China

• India has only half the number of docs and 2/3 fewer beds than global avg. (WHO). Even these are concentrated in big cities

• Median age of India’s population will be 28 by 2020 and 31 by 2030

• In India in 1990s, less than 10% farms of any size owned a tractor, mechanized plough or thresher

• India has much higher maternal and infant mortality rates than countries in the region like Sri Lanka, Vietnam and Mongolia

• India’s 26k UPHCs are designed to handle 25k patients but in reality they handle 3x that number

• 30 million cases are pending before Indian judiciary. Quarter of sanctioned judicial posts are vacant (consequently we are 163/190 in ‘enforcing contracts and efficiency of judicial system’)

• A 10% increase in India’s overall female labour participation would bump up economy by half a trillion dollars

• Among G20 states, India is second-to-last in women’s participation (only Saudi is lower than us)

• A large number of mechanized roles in agri since 2005, belonged to women.

• Unpaid work (shadow economy where most of women’s productivity goes unpaid), safety and mobility (women’s commute costs are higher when they seek safer modes), gender norms (80% women need permission from a family member to visit a health center and 58% needed permission to visit a kirana) prevent participation

• India’s 6% unemployment rate for working age population of 970 million hides from view the people who aren’t looking for work at all (A decrease in unemployment could mean people have given up search)

• 77% of India’s employed are in informal sector (unregistered, unorganized, without contracts, social security, regular salary or an assured vol. of work) like car repair shops, shop-owners, textile workers etc.

• Construction labour absorbs farm workers and is the largest base for doing so

• In recent years 3 million people have been trained as tailors, welders, car mechanics, caretakers and mobile technicians under PMKVY (skill india)

• Country on two tracks - One end if organized - intensely productive, well-paying, highly skilled, with fewer workers. Opp. to it is unorganized, large numbers of less-paid workers who add less value per worker to the economy

• 5800 vacancies for judges, 32% vacancies for general physicians, elementary schools in UP and Bihar need 400k teachers. In private sector 56% of employers say they find it difficult to fill roles (lack of skill is our biggest problem)

• For a role required primary school ed. and experience riding a bicycle, paying 20k/mo, more than 50k graduates, 28k PGs and 3,700 doctorates applied

• India invests heavily in tertiary education while investing little in primary and secondary so citizens are either highly educated or minimally educated, with nothing in-between (South Korea, Japan, Taiwan got this right in terms of investments in primary and secondary education)

• Services sector contributes 52% to GDP but employs less than a third

• Avg. Indian firm employs just over two people (!). 70% work in micro firms, while just over 10% are with SMEs (US has 36% with SMEs and Germany 43%)

• Manufacturing firms with 50-100 employees are 4x as productive as ones between 5-50 employees (Formal sector is 8x as productive)

• In 2017, total SME credit was $1 trillion (84% of this came from informal sources including self-financing, loans from friends and family)

• It takes 116 days to register commercial prop. in MH, GJ and DL and 136 days in UP, CH - OECD countries its 20 days

• Resolving contract disputes takes 1445 days (vs 582 days in OECD)

• Labour laws in India have 17 definitions for “workers” and 22 definitions for “wages”. More than 42 laws regulate hiring & termination, wages, workplace safety and so on

This book is rich in information but I did not learn anything new from this book in terms of insights, but found it well-written in terms of bridging the gap between dry numbers articles from Economists and emotional pieces written by journalists. It has its heart in the right place and for that its worth a read. 8/10

[phreakv6]

Naked Statistics, Charles Wheelan, 2012 - Good book on basic statistics (which is mostly all you need) that teaches and entertains at the same time. Most of the concepts are taught with some real-life examples (mostly baseball statistics, American polls), though sometimes the author lands us with implausible conjured situations but they are charming in their own way. You can’t fault the author for not trying.

The book starts with descriptive statistics, with distributions, central tendencies (mean, median, mode), dispersion (std. dev.), understanding percentiles, deciles and quartiles and moves onto deceptive statistics - accuracy vs precision (no amount of precision can make up for accuracy) and onto correlation, relegating tables and calculations to appendices while trying to get to the intuition of the reader first.

There’s then probability which is an essential extension of statistics and the book delves into basic probability underlying an insurance business at a very high level in terms of expected loss and how premiums are computed and why it changes and again instead of burying readers in actuarial tables, it gets at the intuition of things.

We are also introduced to decision trees and how to compute expectation for a R&D driven biotech company trying to bring a new drug into the market. The perennial favorite of prob/stat books - the Monty Hall problem also makes an appearance with its own chapter considering how unintuitive the action of switching doors is for us, post reveal.

Law of large numbers, mean reversion, common pitfalls and misinterpretations, importance of clean relevant data (Garbage in, garbage out), sampling and things to consider in getting a representative sample, the advantages of longitudinal studies (same sample across time) and all the several biases that impact samples are also nicely covered.

Central-Limit Theorem (CLT) is arguably one of the greatest gifts of statistics to mankind, considering its applicability in such wide variety of contexts. The fact that sample-means themselves distribute into a normal distribution irrespective of underlying distribution type, and the deviation of sample means measured via std. error (std. dev of the dispersion of sample means) being a function of the dispersion of the underlying population (std error will be large when std. dev. of the underlying population is large) is behind a lot of statistical inferences we draw.

Inference and hypothesis testing (null hypothesis and alternative hypothesis are logical compliments - when one is true, other is false) and statistical significance (.05 that we are so used to), confidence intervals, p-value, type-i (false-positive) and type-ii errors (false-negative) and two-tailed hypothesis are covered in the chapter on Inference.

The chapter on Polling explains how things like exit polls work and why it is expensive. We also get an understanding of how sampling errors are a function of distribution of underlying views of the population (dependent on p(1-p)), so if a poll is closely contested with both contestant getting nearly 50%, the sampling errors are very high as compared to something that is 95/5 split.

The book moves onto Regression Analysis (linear regression) discussing dependent and independent variables, the complexities of choosing the right variables in the regression, our ability to control for the other factors, the lurking variables (confounding), and figuring out the sign, size and significance of the regression coefficient (t-distribution and t-statistic relegated to the appendix).

The author closes the book with program evaluation or how we measure the causal effect of some intervention (The book of why by Judea Pearl is a natural successor to this book).Randomized controlled experiments like clinical trials, double-blind studies, natural experiments (where nature accidentally controls variables for us) and the study of causality and counterfactuals (what would have happened if the treatment wasn’t given) requiring a control group in a clinical trial.

Some gotchas of using a statistical/probabilistic approach to be wary of - Better be sure that what you are measuring is what you want to manage. Understand relationships and incentives - the easiest way for a doctor to improve his mortality rate is by refusing to operate on the sickest of patients. Probability doesn’t make mistakes, people using them do. Our ability to analyze data has grown manifold and sophisticated than our thinking about what to do with the results. 9/10

P.S: Forgot to mention but coincidentally NNT started making these mini-lessons on statistics and probability recently. They are very good too.

N N Taleb's Probability Moocs

[phreakv6]

Prisoners of Geography, Tim Marshall, 2015 - This is a very quick intro to geography/geopolitics of the current world. The premise of the book is that a bulk of priorities of a country, govt, people, policies and politics are driven by their geography for good or for bad. Whenever geographies (boundaries) of people have evolved over time based on natural barriers like mountains and rivers, they have lasted for sometime and endured and kept large masses of people out of conflict and whenever these boundaries were made ad-hoc on a map (rather than the territory), these invariably straight-ish lines have led to serious conflicts and bloodshed. Even natural barriers with some weaknesses (say approach to Russia from Northern European plains) have led to lasting paranoia.

The book is divided into chapters concerning Russia, China, USA, Western Europe, Africa, Middle-East, India and Pak, Korea and Japan, Latin America and The Arctic.

Russia
• ‘Riddle wrapped in a mystery inside an enigma’ (Churchill)

• Russia has never been conquered from the northern European plains (via Poland), because of long supply chains required for an army before it reaches Moscow (Napoleon made this mistake in 1812 and Hitler in 1941)

• Russians were fighting in and around Northern European plains once in 33 years on average

• NATO was formed in 1949 to counter Soviet aggression. Warsaw pact in 1955 by Communist countries (under Russian leadership) for military defense and mutual aid. After fall on Berlin wall in 1989, in 15 years by 2004, every single former Warsaw Pact member bar Russia was in NATO or EU

• Russia as a concept dates back to the 9th century

• Russia can be seen from America across the Bering strait (Little Diomede Island)

• Russia is not an Asian power because though 75% of its territory is in Asia, only 22% of its population lives there

• The empty spaces in Russia’s Far East will more likely come under Chinese cultural control (maybe even political)

• The imposing plains of Kandahar and Hindu Kush mountains have made Afghanistan impenetrable to invading powers earning it the label "Graveyard of Empires’

• The lack of warm-water port with direct access to oceans has always been Russia’s weakness

• When the Soviet Union split apart into 15 countries, geography had the last laugh (language, culture and customs delineated by mountains, rivers, lakes and seas won over ideology)

• Russia annexed Crimea to keep its access of Sevastopol (only true major warm-water port it had access to)

• Russia most powerful weapons, aren’t its nuclear missles, army and air force. It is gas and oil (A bulk of Europe is dependent on Russia was warmth in winters). Shale gas production could technically wean Europe off (via LNG imports) but piped Russian gas is still cheaper

• Russia loses about $2 billion in revenue for each dollar drop in oil price

China
• China is a civilization pretending to be a nation

• It has taken 4000 years but China is now a maritime power. It was always a powerful land power and its merchants traveled far and wide but it never had territorial interests

• The Han Chinese make up 90% of the population and dominate politics and business despite speaking Mandarin, Cantonese and several different dialects but united by ethnicity and drive to protect the heartland

• The Great wall of China was built by Qin dynasty in 200 BCE

• The Mongols ruled China (Yuan dynasty) until 12th Century and the Han took over after that and established the Ming dynasty

• Coastal cities such as Shanghai are wealthy but the countryside is poor accentuating regional differences

• Xinjiang (twice the size of Texas), majority Muslim province was always a source of instability

• Mao centralized power in a way never seen before and extended influence into Mongolia and annexed Tibet

• Total control for Communist party in a Capitalist economy (Deng Xiaoping)

• Western sanctions have driven Russia into large deals with the Chinese (advantageous to the latter for the most part)

• Though India and China both have high population and close proximity, they have been kept out of trouble by the Himalayas

• Tibetan plateau would give India an advantage if it ever had control of it and so China pre-emptively annexed Tibet

• Tibet is also the water tower for China with 3 rivers the Yellow, Yangtze and Mekong originating there

• China finds it deeply irritating when the West talks about freeing Tibet

• China has built roads and railways into Tibet and Lhasa and brought in lot of tradable goods integrating the Tibetan population while also making Han settlers get into Tibet

• This also bring the Red Army all the way across the Tibet into Doklam plateau close to the Indian border (the roads are now capable of allowing an army to pass)

• It is technically possible for the Chinese to descend rapidly into India and cut off India’s access to the North-Eastern part of the country by cutting off Chicken’s neck (a narrow bit that connects rest of India to its North-Eastern states)

• Xinjiang’s Uighur people (Muslim population) speak a language close to Turkish and the province borders 8 countries. China perceives this as trouble and has massive re-education camps to wean religious beliefs and to keep Al Qaeda out of the reach of Uighur separatists

• Xinjiang is also strategically important as its rich in oil and gas and also connects Gwadar port (One Belt One Road), a deep water port in Pakistan that it has a lease for 40 years.

• Chinese thought prizes the collective above the individual unlike Western thought

• “Why do you think your values would work in a culture you don’t understand?” (China’s answer to Western modes of thought)

• China as a maritime power is asserting its influence in the South China sea (disputes with Vietnam, Malaysia, Taiwan, Philippines, Brunei) and also in East China Sea (Japan)

• Diplomatically China will attempt to pull South-East Asian nations away from the USA with both carrot and stick

• China too intends to become a two-ocean power (Pacific and Indian) like the USA (Atlantic and Pacific)

• Gwadar port is crucial as an alternative trade route and so is stability in Baluchistan

• Biggest risk for China is a trade war with the world. If rest of the world doesn’t buy its products, it will paint itself into a corner it can’t get out of

USA
• The 50 American states add up to one nation in a way the 28 sovereign states of the EU never can (Most EU member states have a very strong national identity coming in the way)

• Louisiana Purchase doubled the size of the USA and gave it access to inland water transportation (Mississippi). Interestingly this could have led to a war with France but a deal was negotiated and conflict avoided

• Monroe doctrine effectively put an end to further colonization of the western

• California gold rush helped settlers settle in the west. The Homestead act of 1862 allowed anyone who cultivated land for 5 years own it. Poor men from Germany, Scandinavia and Italy chose the USA over Latin America for this sole reason

• While the civilian head of the NATO could be a Belgian or a Brit, it was always US that called the shots

• Russia is now for the Americans, mostly an European problem

• The Cuban missile crisis is considered an American victory but the US also removed Jupyter missiles from Turkey (which could target Moscow) so its effectively a compromise

• Indonesia, Malaysia and Singapore sit astride the straits of Malacca, which at its narrowest is only 1.7 miles

• The Americans have a treaty with Taiwan which states that if Chinese invade them, USA will go to war. What could spark a war is declaration of independence by Taiwan or a formal recognition of Taiwan by the USA

• If Middle-east oil is no longer needed to power American homes, the USA has no need to have a presence there. Its relationship with Israel as well might cool

• America’s experiments with nation-building overseas appears to be over

• The US spends more money on R&D for its military than all the NATO countries combined

Western Europe
• Europe has grown organically over millennia with its mountains, rivers and valleys as borders separating people linguistically and culturally

• Europe’s major rivers don’t meet and there are developments which became capital cities and the rivers themselves as borders

• Greece’s 1400 islands need a decent navy to patrol but it can’t afford one

• The EU was full of economic and cultural conflict - Germans work till 65 to pay for Greece’s retirement by 55 (German view of course). Austerity measures imposed are seen as an assault on sovereignty

• EU was setup so that Germany and France could hug each other tight and not get a hand free to punch each other

• EU turned a blind eye on members like Greece cooking the books as the Euro was more than a currency, it was also an ideology

• Relative security of the UK is what probably lead to less despotism compared to countries across the English channel (in the past few hundred years)

• What British have now is a collective memory of greatness

• Middle-eastern refugee crisis threatens to weaken the union as some countries started to check documents of travelers after decades hampering trust

• Europe has an inverted pyramid of people with its ageing population at the top and less young, tax-paying population to take care of them

• While both Germany and France are working to keep the union together. Germany also has a plan-B in Russia

• The last 65 years have been unprecedented stretch of peace in Europe that some may have taken for granted

Africa
• Lovely beaches but terrible natural harbors, lot of rivers but none useful for transport as most of them have waterfalls have kept Africa poor

• Africa as seen in a Mercator map doesn’t do justice to its size (Due to flattening of what’s curved and large) - Africa is 14x Greenland and yet appears same size on map. You could fit USA, Greenland, India, China, Spain, France, Germany and the UK into Africa and still have space of Eastern Europe (wow!)

• Suez canal cuts down trade routes by 6000 miles (Western Europe to India for eg)

• The Sahara is almost as big as the USA

• The continent’s great rivers Niger, Zambezi, Congo, the Nile and others don’t connect to each other and this has kept population away from each other and consequently there are 1000s of languages spoken

• Most Africans are prisoners of political georgraphy made by the Europeans

• The nation state is an European concept of artificial structure, alien to rest of the world. It mostly leads to weak and divided states perennially at conflict

• When leaders say “I’m the only one who can hold this nation together”, they have truly failed to build their nation

• There are hardly any trees in Egypt. Without trees a navy is impossible

• 2.5% of the world’s oil passes through the Suez canal. Closing the canal would add 15 days transit time to Europe (around cape of good hope)

• By size, resources and population Nigeria is west Africa’s most powerful

• China is mining iron ore in Liberia, Copper in Zambia and DRC, Cobalt in DRC and so on

• China doesn’t have a view on human rights or corruption in Africa - only Economics

The Middle East
• The very name is based on the European view of the world

• After WW-I there were fewer borders in the Middle East than exist today. The notion that man couldn’t visit a relative from the same tribe without a document issued by another man in a far away town made little sense (nation-state boundaries of Syria/Iraq)

• There were no state of Syria or Jordon or Iraq, Saudi, Kuwait, Israel or Palestine before Sykes-Picot

• The split between Sunni-Shia dates back to 632 CE when prophet Muhammed died, leading to dispute over his succession. The Sunni Muslims form the majority among Arabs (85%). The name comes from Al Sunna (people of tradition)

• The ultra-puritanical Salafist thought dominates jihadists. Shia has several offshoots with some like Alawites and Druze not even considered to be part of Islam by the Sunni. These divides run across centuries and the nation state of “Iraq” cannot magically unite these diverse people together in the same borders

• When the Ottoman Turks ruled this region, they divided them into administrative regions of Mosul, Baghdad and Basra (These regions in antiquity were referred as Assyria, Babylonia and Sumer)

• British promised the tribes that helped them overthrow the Ottoman empire during WW-I the Arabian peninsula as reward. Unfortunately both the Saud and Hashemites were promised the same thing (mischief very typical of the British) and so lines were drawn and Saudi Arabia and Jordan were born

• Assad is an Alawite and they comprise 12% of the population - the French put them in power though they were minority as part of the divide and conquer strategy

• Toxic mix of piety, unemployment and repression (most of middle-east)

• Egypt, Syria and Jordan are suspicious of Palestinian independence. If it ever became independent, all 3 might stake claims to parts of territory

• Israel regards Jerusalem as its indivisible eternal right as the rock upon which Abraham prepared to sacrifice Isaac is there. Its the third most holy place to the Muslim world as the Prophet is said to have ascended to heaven from that same rock. The place otherwise has no real industry, river, airport or resources.

• Iran is non-Arabic, majority Farsi speaking giant. Bigger than France, Germany and UK combined but with a thin population

• Iran’s trump card is the strait of Hormuz through which passes 20% of world’s oil

• The Turks have never truly been recognized as part of Europe by their neighbors

• Mustafa Kemal (Ataturk) modernized Turkey, making it a part of Europe

• Turkey wants its influence in the *stans (Kyrgyzstan, Tajikistan, Turkmenistan etc.) which Russia is wary of. Turkey wants to overthrow Assad regime while Russia supports it.

• Turkey doesn’t get along with Israel either, though they are both NATO partners

• Turkey is a key country as it controls the Bosporus strait through which traffic moves from Black Sea to the Mediterranean and ultimately to the Atlantic (key for Russia)

• Arab spring is a misnomer coined by the Western media

Not covering the India-Pakistan, Korea and Japan as well as the Latin America chapters due to familiarity and lack of relevance (to me).

The Arctic

• The polar route is 40% shorter (Canada to China) than going through the Panama canal and uses deeper waters which allows for much higher freight to be carried (cheaper in terms of fuel too). Could cost Egypt and Panama a lot of revenue in the long run
• Due to melting of Arctic ice, a lot of natural resources (oil and gas) are now available
• It takes $1 billion and 10 years to build an icebreaker. Russia has 44 icebreakers. USA appears uninterested in the Arctic compared to Russia

This is a fascinating book and is definitely worth a read to understand Geography and Geopolitics. I was able to better place a lot of countries (For eg. Lebanon, Syria, Iraq and some of the African countries) and also have a knowledge of all the straits (Hormuz, Bosporus, Bering, Malacca) and canals (Suez, Panama) that connect world trade, the exact way in which the Black Sea, Mediterranean Sea and Atlantic connect to each other and also the irredeemable mess a whole lot of Africa and Middle-East are in. Its a breezy read as well. 11/10

[phreakv6]

The Joy of X, Steven Strogatz, 2012 - This is a book that celebrates mathematics from the earliest days of counting, algebra, geometry and trigonometry, calculus, probability & statistics, networks, data mining to modern concepts like topology, group theory and the like.

My notes of the book, mostly paraphrased or written in own words (may contain errors in interpretation)

Numbers
• Math involves both invention and discovery. We invent the concepts but discover their consequences

• Multiplication tables can be re-imagined as rectangular rock groups with rows and columns (sort of basis for matrices)

• Sum of all consecutive odd numbers lead to perfect squares Eg. (1+3=4, 1+3+5=9, 1+3+5+7=16 and so on). The intuition behind this is to arrange the next numbered rocks in a L-shape around the previous existing square to make a bigger square

• Double negatives don’t always make a positive, they sometimes make the negative more intense

• Every decade or so a new approach to teaching math comes along and creates fresh opportunities for parents to feel inadequate

• Commutative law feels counterintuitive in finance. For eg. if you pay 10% tax upfront and your capital doubles (Say 100 - 10 → 90x2 = 180) - its same as capital doubling pre-tax and then you paying 10% tax (100x2 = 200 → 200-20 = 180).

• Heisenberg and Paul Dirac discovered that p * q != q * p when p and q represent the momentum and position of a quantum particle

• Rational numbers are so called because they are ratios of numbers (Eg 4/5)

• Rational numbers (fractions) usually terminate in decimal form or repeat periodically forever - non-repeating decimal forms are hence irrational

• Babylonian numerals were based on 60 and not 10 (base 10) like Roman numerals. 10 has significance due to our 10 fingers but 60 is more detached from us (Still used in 60 minutes to the hour and 360 degrees to a full circle etc)

• Arabic numerals (also the Hindu numeral system on which it is based) uses the place-value system so digits acquire value not based on just the symbol but the positions of the symbol (eg 100 vs 1000). With place-value systems its easier to do arithmetic for both humans (base 10) and computers (base 2).

• Morse code is the technological forerunner for today’s binary

Relationships
• (1-x)(1+x) = 1-x^2 - If your portfolio is down x% in one year and up x% in the next - you are still down because x^2 is always positive.

• Subtraction lead to negative numbers, division to fractions and decimals and squaring-root expanded numbers again to imaginary numbers (i^2 = -1)

• imaginary numbers don’t live on the number line but at right angles to it The 2-d plane encompassing these imaginary numbers and the number line is the complex-plane - complex not because its complicated but because it contains both the real and the imaginary (Eg. 5+3i)

• Multiplying by i produces a quarter-turn counter-clockwise (Useful for electrical engineers to represent electricity and magnetism, alternating currents)

• i^2 = -1 means two quarter counter-clockwise turns leading a vector at (0,1) to go to (0,-1)

• Hubbard’s work on finding roots of equations in the complex plane, lead to complex dynamics which is a mix of chaos theory, complex analysis and fractal geometry

• Sanskrit manual from 600 BC provides ways to calculate square-roots used for constructing temple altars

• Cause-and-effect, supply-and-demand, input-and-output, dose-and-response all involve relationships between pairs of numbers (can be plotted as curves)

• In an equation like y=5-x^2, x^2 acts like a tool for bending and pulling while 5 there performs the function of a nail (Curve hangs from 5 on the y-axis)

• Power functions of the form x^n - parabolas with n=2, constants with n=0, lines with n=1, with n=-2, we get 1/(x^2) or the inverse-square function which describes how waves and forces attenuate as they spread in 3-dimensions (gravity, sound). Power functions are used in describing growth and decay

• Exponential functions of the form n^x (2^x, 10^x) where power is the variable and the base is constant - these grow way faster than power functions

• We can’t fold a sheet of paper further when its thickness is greater than its length

• Compound interest - money grows at (1+r) every year - After 2 years its (1+r)^2 and after x years (1+r)^x which is an exponential function

• logarithms are the mathematical complements to exponentials. 10^2 = 100, 10^3=1000 and 10^4=10000. Inverting, log(100) = 2, log(1000)=3 and log(10000)=4 (Base-10 log used here)

• If exponentials are staplers - logarithms are stapler pin removers

• Exponentials grow multiplicatively (2,4,8,16,32) while logarithms grow additively (1,2,3,4,5)

• Notes in a musical scale do-re-mi-fa-so-la-ti (or sa-re-ga-ma-pa-da-ni) rise in frequencies multiplicatively (equal multiples) but are perceived by us logarithmically (Additively), hence we perceive them to be separated by equal steps

• From richter scale for measuring earthquakes to measuring acidity in pH, logarithms work as wonderful compressors
• We use logarithms as a short-hand for referring to salaries as well when we say 6 or 7-figure salary

Shapes
• Geometry is fun because it uses both halves of the brain (logic and intuition)

• parabolic mics can be used to pick up hushed conversations (used in surveillance and espionage) because the waves always land at the focus (An elliptical pool table with pockets at the focuses will may any ball heading in any direction land in it)

• A parabola is defined as a set of points equidistant from a point and a line not containing that point

• Ellipse is a set of points whose sum of distance from two points (the focuses) stays constant

• Parabolas and Ellipses are conic sections (Cut a cone and you will end up with either a parabola or an ellipse (or a circle where both focuses of the ellipse coincide). Ellipse turns into a parabola at the critical angle where the slope of the cut is equal to the slope of the cone (Must look at pics in the book to understand)

• If a cone is sliced too steeply at an angle greater than the slope, we get a hyperbola in two pieces at the top and bottom half of the cone

• Conic sections play a great part in describing gravity (the conic sections are trajectories of the tug of gravity)

• Sine wave - when one tracks the horizontal and vertical movements of something moving in a circle

• Ripples on a pond, sand dunes, stripes on a zebra are all nature’s sinusoidal manifestations to bland uniformity

• Whenever a system loses stability around an equilibrium, sine pattern results (some form of it) - be it in biological, physical or chemical processes

• Sine waves are nature’s building blocks (Hence sine, qua non) - after the big bang, it was sine waves in the density of matter and energy that spawned life

• The key to thinking about curves is to think of them as made of infinitesimally small pieces (circle cut into tiny pie slices and arranges into a rectangle whose length is pi*r and width is r)

Change
• Integrals we born in 250 BC in Greece and Derivatives in 1600 AD in Germany

• Snell’s law describes how light bends when it travels from air to water. It bends so as to minimize travel time - just like a hiker in the snow would hike towards a point in the grass - the hiker’s brain considers all possible options and chooses the path and minimizes time (zero-derivative at lowest point) - but light doesn’t have brains and yet it appears to optimize its path the same way!

• If your money got you 100% in a year (100 would be 200), it would double. Instead if it got 50% interest semi-annually, you would get 225. If it compounded every quarter at 25%, you get 244 and so on - Will it get to infinity if it compounded every second at a tiny fractional rate? - No - it will converge to 271.83 - Because… “e”

• Three-body problem is intractable and leads to chaos and unpredictable long-term behavior - Not unlike a love-triangle though it first arose in astronomy

• Vector calculus - used to describe invisible fields - be it magnetic, or gravitational or even microwave. Maxwell chanced upon the true nature of light using vector calculus by shuffling a few symbols

• Vector calculus involves vectors that change - these can change with time (as in movement of bodies, be it a ball or a planet) or over space (like in a magnetic field)

• Divergence and curl of a vector field - Divergence shows how quickly the strength of the vector field moves outward from its source (like leaves carried away by ripples). Curl shows how quickly the field is swirling about a given point (regions in weather maps having hurricanes have a large curl). Curl is very useful for scientists working on fluid dynamics and aerodynamics

• Dragonflies, hummingbirds and bumblebees flap their wings to create counter-rotating vortices like little hurricanes under their wings that keep them afloat (unlike a fixed-wing aircraft being propelled forward with conventional aerodynamics)

• Maxwell’s equations express 4 fundamental laws - two for curl and divergence of electric field and 2 for the magnetic field. The divergence equations relate the electric and magnetic fields to their sources, the charges particles that produce them. The curl equations describe how they interact and change over time. They express the beautiful symmetry of how the rate of change of one field over time links the rate of change of the other in space (quantified by its curl)

• Maxwell understood light to be an electromagnetic wave, thereby uniting electricity, magnetism and light.

Data
• Power Laws - of the form y=C/x^a (say x can be a city’s population and y the number of cities of that size - a is the exponent). When plotted in a log-log plot, it yields a straight-line with slope “a”

• Income distribution too follow power laws where median is way less than the mean

• Power laws result in fat-tails or long-tails with extremely large outliers

• Probability of event A happening conditional upon event B - conditional probability is often conflated with probability of B given A (probability that a plant would die if you didn’t water it vs probability that you didn’t water it given that the plant is dead) - Its easier to work with these problems with a frequency table and work with ratios and sums (intuitive) than bayes formula (unintuitive)

Frontiers
• Twin primes - primes that are so close to each other than only an even number sits between them (13-17, 41-43 etc)

• Number theory - study of whole numbers and their properties

• Every composite number can be factorized into a unique combination of prime numbers. If 1 is allowed to be prime, this will fail as 2x3=6 and also 1x2x3=6. So 1 is the truly loneliest number. 2 is a freak upon even numbers being the only even to be prime

• Percentage of prime dwindles with the population - Among first 30 numbers, there are 10 primes (33%), among first 100, there are 25 (25%) but in the first billion, there are only 5% primes.

• Natural logarithm is logarithm to the base “e” - represented as ln x

• If N is a large number, the average gap between prime numbers around N is approx. equal to ln N (Doesn’t work too well for small numbers but for large numbers error tends to zero - first noticed by Gauss when he was 15 and is called prime number theorem)

• The largest known twin prime have 100,355 decimal digits each (separated by just an even number)

• Group Theory - Underlines everything from choreography of square dancing, fundamental laws of particle physics to the mosaics of Alhambra

• group theory straddles science and art with its fascination for symmetry

• Topology - two shapes are considered topologically same if you can bend, twist or stretch one into the other without ripping or puncturing (a rubber band shaped like a square or a circle are topologically same)

• Mobius strip - A cut strip of paper attached on its ends with a single twist (Vi Hart has a fantastic video on mobius strip told through the tale of Wind and Mr.Ug)

• Escher used mobius strips in his drawings of eternal loops

• Geodesics - Locally shortest paths on the surface of the object (like great-circle arc on a sphere)

• Einstein figured that light beams followed geodesics and were bent by gravity with his now famous experiment of the bending of starlight around the sun in 1919

• Alternating harmonic series appear to defy the commutative laws of addition (By adding the infinite series in different order, the result may appear to be different)

• Fourier series - has sine-functions in an infinite alternating harmonic series (produces saw-tooth plots)

• Hilbert Hotel - George Cantor’s set theory of an infinite set applied Hilbert’s parable to illustrate a paradox of having a hotel with infinite rooms, still having room for an infinite number of buses each with infinite number of guests (the elegant zigzag fanning out from the corner solution - check the book for illustration)

You must read this even if you hated math in school because this is not like those books. This brings back the joy in mathematics relating it to everyday things the way it should be. To say I loved it would be a tremendous understatement. 11/10

[Apurva_Dubey]

Coming out of a bad bout of Covid I took a special interest in reading about history of medicine. The first book I picked up was Thomas Hager’s “Ten Drugs”. It’s a spellbinding commentary on the discovery of the “greatest hits” of medical science. While most of the book talks about modern medicine but the first two chapters are about pre-Penicillin era.

My key takeaways from this book were:

• Modern medicine is surprisingly “modern” (in terms of timescale) – so much so that we have a colored photograph of the guy who “accidentally” ushered the era (Alexander Fleming). Modern medicine has achieved a lot in a very very short span and fundamentally changed the way we live. There is a sea difference in life and life-style choices between pre and post Penicillin era.
• We are very lucky to be living in the current era – an era where there is no small pox and no fear of deadly staph bacteria. The book disabused the halcyon romanticism of the past centuries for me.

Some really interesting facts I learnt:

• Discovery of “monoclonal antibodies” or MABs (by Milstein and Kohler, later awarded Nobel) is perhaps the greatest gift of modern medicine after Penicillin. MAB-based drugs are like magic bullets that can cure deadliest of diseases within days. Almost 50% of revenue of Pharma companies (globally) comes from MABs. Humira (adalimumab) – the top selling drug globally – is an MAB. Tocilizumab: used extensively for critical COVID cases is also an MAB. In fact, any drug that ends with “–mab” is monoclonal antibody based.
• Penicillin wasn’t the first antibiotic! Sulfa drugs were forerunners of antibiotics. Prontosil was the first drug to successfully treat bacterial infections. These were discovered by a German scientist named Gerhard Domagk who was forced by Hitler to turn down the Nobel prize (he picked-up the prize after WW II). FDR’s son – about to die from a bacterial infection – became the first person to be administered Prontosil.
• Edward Jenner is touted as the guy who pioneered vaccines. This is not true! A lady named Mary Montagu had spent time in Istanbul with her husband (who was the British ambassador to the Ottoman Empire). Mary observed that Turkish people had a “cure” for small pox – they inoculated young kids with small pox pus and then they didn’t contract the virus afterwards. She was the one who pushed the case for small pox vaccination after going back to Britain. With princess Caroline’s help (King George II’s daughter-in-law) she was instrumental in carrying out the world’s first clinical trial (administered on 13 prisoners and 6 orphans) which convinced the physician community that small pox vaccination worked.

There are many more interesting stories in the book – I highly recommend it!

[Apurva_Dubey]

I had read this one a few months ago. It’s a light read – recommended if you are interested in the history of markets.

The author (James Weatherall) is a mathematical physicist and an academic. During GFC (2007) – while pursuing his PhD – he got interested in understanding how top-notch physicists ended up working at the Wall Street. The book traces the history of attempts made by various individuals (from Bachelier to Mandelbrot to Black-Scholes) to model the stock price and its derivatives using advanced physics and mathematical models.

The story starts with a French Mathematician named Louis Bachelier. He was the first person to seriously model stock price in a mathematical sense. Bachelier came up with the novel idea of modeling the stock price as a random variable sampled from a normal distribution. While the random walk idea was Bachelier’s original but Einstein (wrongly) gets credit for its discovery (Brownian motion). Bachelier’s work was lost to history until he was accidentally discovered (though posthumously) by the father of modern economics – Paul Samuelson. One key problem with Bachelier’s approach was that the stock price could theoretically take negative values.

M. F. M. Osborne enters the picture next. Osborne’s key contribution was modeling returns (and not stock price) and sampling it from a log-normal distribution – this solved the issue of negative stock prices in Bachelier’s approach. Osborne was a fiercely original physicist and made many contributions to the field.

Benoît Mandelbrot comes in next: his key insight was that stock price follow a fat-tailed distribution (generally called Lévy-stable distribution) i.e., the extreme events happen more often than accounted for in a normal distribution. The challenge with using fat-tailed distribution was the volatility (standard deviation) is infinite so one could not apply standard statistical techniques to it. Mandelbrot’s ideas were not accepted warmly by the academic community. After a point Mandelbrot lost interest in markets and focused his energy on fractals instead.

Ed Thorpe comes in next: the quintessential genius. Before stock markets he first got interested in gambling. He modeled the game of Black Jack on an IBM computer and then came up with a strategy with which he beat the casinos over and over again. He even partnered with Claude Shannon to make a contraption to predict the outcome of a Roulette spin. Once he got interested in stock markets he devised his own models/techniques to come up with a delta-neutral trading strategy – which made money irrespective of the direction of the market. In fact he invented his own version of option/warrant pricing model before Black Scholes – he did not publish it so as not to lose his competitive edge. Ed Thorpe once met Warren Buffet (at a common friend’s place) – Thorpe was so impressed with Buffet’s intellect that he told his wife that Warren is going to be the richest man in America someday.

Next comes the famous Black-Scholes model. Fisher Black and Myron Scholes got interested in solving one of the toughest problems (of their day) in finance: how to price options. They took inspiration from heat transfer equation in physics to come up with a model to price options. Robert Merton came up with another unique approach independently. They were awarded Nobel for this work; sadly Fischer Black had died some years before the Nobel event.

Last couple of chapters talk about few more researchers but the one that stood out for me is Didier Sornette. He is a polymath of the highest order with significant contributions in physics, geophysics, economics, and finance (his h-index stands at 100+). Sornette had worked on earthquake predictions for many years. Once he got interested in markets he tweaked his earthquake prediction models to instead predict market crashes! He has successfully predicted and shorted markets many times over and made a lot of money.

Takeaways:

• To repeat the cliché “all models are wrong but some are useful”
• Fat-tailed nature of markets can catch you unawares so be wary of over-reliance on models

[kailasthube]

Thanks @phreakv6 Bharani for creating the thread and adding your reviews on the books. Highly appreciate your efforts in sharing your learnings.

[Adhiraj]

Einstein - His Life and Universe (2007)
By Walter Isaacson

In 1905, an unknown patent clerk at the Swiss Patent Office published a paper that would revolutionize physics. A few years ago, he couldn’t find a job under any professor in the whole of Europe despite trying extremely hard. His father was worried over seeing his unemployed depressed son. He didn’t have a good rapport with his old professors and had made some good enemies due to his rebellious nature. He was stubborn, brave, courageous and paradoxical. That was often not an attribute for someone normal those days.
Einstein is a face who is synonymous with the word ‘genius’. Time magazine voted him as the ‘Person of the century’ just above Gandhi. He was a superstar in his day. Someone not celebrated in just Europe but loved in the USA back in the day. And this is a scintillating read of one of the greatest minds of all time.
Walter Isaacson is one of my favorite writers. He has captured the personal and professional life of Einstein in a brilliant fashion. This is a wonderfully written biography which would easily rank in one of the best biographies I have ever read.
Some insights from this book which are worth mentioning:

1. Einstein had a penchant for thought experiments. Most of what he accomplished was due to his unconventional thought experiments to simplify things. This includes the famous Railway platform experiment to understand relativity.
2. He focused a lot on imagination. He placed imagination on a higher pedestal than knowledge. He was a vividly imaginative person who was often lost in his own thoughts. Imagination can be a pretty effective tool and it is a pity that it is not often not stressed enough in modern culture.
3. The things that often make you are often the ones that hurt you the most. Einstein had a contempt of authority. This attitude was pivotal for him to come up with theory of relativity. Almost no one was brave enough to challenge the supremacy of Newtonian Physics back in the day. Henri Poincare among others came close. But Einstein took the leap and came out victorious in his youth. But his stubborn nature started to hurt him when he waged a losing battle against Quantum Mechanics where probabilities took the front seat and strict laws were no longer applicable (Hence his famous quote rebutting it “God does not lay dice”). “To punish me for my contempt of authority, fate has made me an authority myself’ Einstein wrote this later in his life expressing his disappointment regarding this.
4. He was someone who believed in strict causal determinism. He didn’t even believe in free will. And quantum mechanics bothered him. Not scientifically I presume, but philosophically too I suspect. Believing that the universe is random to a degree made him uncomfortable. He never accepted Quantum Mechanics and tried to disprove it all his life. He had intellectual battles with great scientists such as Schrodinger, Bohr, etc. on this issue. It might not be irrational to assume that his search for a Unified field Theory was an attempt to disprove Quantum Mechanics. He failed terribly in his quest to disprove it.
5. The rise of anti-Jewish sentiments has been portrayed wonderfully in the book. It was self-evident on how the culture was degrading in the Nazi Germany. Intellectuals and philosophers kept quiet when Nazi’s ascended to power. Jews were slowly and steadily outcasted. Many of Einstein’s theories were rejected by Ultra-nationalists and were labelled ‘Jewish Science’ by most including his rival Nobel Laureate Philip Lenard. He had his holiday home destroyed and relatives/friends killed in the coming years.
6. It is also amazing to see how anti-communist sentiments had taken over the US in late 1940’s and early 1950’s. There was a paranoia is the US against communists. And being linked to them could have landed anyone in big trouble. Einstein often battled such allegations due to his pro-socialism stance.
7. He often faced personal tragedies. Sometimes in the form of his troubled first marriage or mental problems with his second son who suffered schizophrenia or a strained relationship with Hans, his first son. And his response was almost usual, he drowned himself in work. His productivity often ballooned in such times. Sometimes one needs work to escape rather than the usual escapes that individuals take from work.
8. While reading the book, one can not help but appreciate the amazing sense of humor and wit that Einstein incorporates to address scientific, political and even sometimes personal matters. Life is too small to not have some sense of humor. It is amazing to see how a good sense of humor can help you carry through tough times.
9. Einstein was a ‘pacifist’ for a lot of time. He advocated the youth to resist joining the army in order to prevent future wars. He attended conferences and gave speeches advocating his idea. But as Hitler and Nazi’s took power, he understood flaws in his own thought approach and toned down his ‘militant pacifist’ stance by multitudes. Einstein would have been a good investor. He was a pure Bayesian. One of his best qualities was his ability to change views on new evidence. This is a rare skill that I admire. It is hard for most people to do it. And Einstein did it multiple times in his career despite being a global celebrity. Putting aside your ego and accepting flaws in your prior thinking hits to me as a wonderful trait that very few admire.
10. Every human is flawed. And in their own way. Einstein wasn’t someone without flaws. For example, he often had extra-marital affairs when married and when drowned in work, could turn extremely indifferent and unempathetic to his loved ones. Human beings are complex, even the extremely smart ones. Even though he seemed indifferent many times, he often cared and many times wept at the loss of someone close. There was a paradoxical element to his nature that made it hard for many to understand him.
11. As the years went by, he became more actively involved in politics and took a deep interest in the welfare of the Jewish community. Young Einstein often stayed away from such matters. But his interest in these issues seems to be a consequence of the degradation of culture of tolerance he witnessed in Germany. His political comments often landed him in trouble even though he was revered globally back in the day.
12. Around early 1950’s, Einstein had almost devoted 30 years of his life to a proving a unified field theory that connected relativity and electromagnetism. After 30 years of hard work and rigor, he wasn’t even any closer to it. Yet, when asked about his failed endeavor, he refused to have any regrets. Why? Because it was worth the risk. He had made his name, secured his position, so he could take the risk of proving something that was probably didn’t exist in the first place. The last 30 years of Einstein’s work don’t ever receive a mention because he couldn’t produce anything tangible. On his last day at the hospital, he asked for a paper and wrote a bunch of equations that would have hoped him to get near that elusive theory. He didn’t get an inch closer. Not many would do what he did. This was the mark of someone who loved to do what he did.

My admiration for Einstein has grown by multitudes after reading this book. It helps that the author wrote his story in a brilliant way as he usually does. I don’t have any complaints from the book other than the fact that sometimes it seemed to focus too much on his battles with Quantum Mechanics or Political views towards the latter part. But even though they are sometimes lengthy, they make for a compelling read. It would easily rank as one of the most brilliant biographies I have ever read.
10/10

[Deven]

Book Review: Alchemy – The surprising power of ideas that don’t make sense
By Rory Sutherland

Books on Human behaviour. You must read if you are too logical (like me) like Engineer or Finance people. It’s an eye-opener for me.
Tips: Check the Ted talk by Rory Sutherland.

Also if you have liked books by Thinking, Fast and Slow by Daniel Kahneman or books by Nassim Taleb then you will surely like this.

Some takeaway:

It starts with a story of red bull – high price, low quantity and disgusting taste make it 3rd largest brand.

Why Do people clean their teeth? – Think the answer and compare it with toothpaste advertisements?!? After eating ice cream or when you are going on a date?

The biggest takeaway: Irrational people are much more powerful than rational people. (Youtuber who gives tip vs pure analysis)

If something works – go for it. Don’t wait for the reason first, discovery later? E.g. Aspirin was known to work as an analgesic for decades before anyone knew how it worked. (Technical analysis?)

Be careful before calling something nonsense.

You can never be fired for being logical (Fund manager holding HDFC Bank)

Amazon can be very big by selling one thing to 50 people but it can’t sell 50 things to one person.

It doesn’t always pay to be logical if everyone is also being logical (ITC?!?)

Steve jobs had koumpounophobia or a fear of buttons (success of apple – his dressing, never wear anything with visible button)

Logic is everywhere is saying you are solving a problem even when no such process is possible (Predicting why the market fallen by 0.2%)

End…

[phreakv6]

The Drunkard’s Walk, Leonard Mlodinow, 2008 - You have got to read this if you are systematically underestimating the role of luck in your life. This is almost like a cousin to “Fooled by Randomness”. Having read a fair bit of Kahneman, Taleb and with good knowledge of probability and statistics at this point, I did not expect to learn much from this but as with Euclid’s Window, this book was engaging because the author injects a lot of history and trivia into it.

My notes

• MRIs show that the amygdala which controls our emotions is activated when we make decisions under uncertainty

• The right brain guesses the frequency and the left, the pattern (when shown a sequence of colors in a split-brain patient)

• Naive realism - things are what they seem (reference point we start from)

• Our intuition on probabilities help us reduce complexity of decision-making

• Our past is not easy to understand and our future, hard to predict

• Chance events are often misinterpreted as accomplishments or failures

• Skill vs Luck is not easy to discern - random events can be in misleading streaks and clusters

• Fortune is fair in potentialities, not fair in outcomes

• Extraordinary events can happen without extraordinary causes

• What’s less probable may sound more likely with specifics (sales will increase vs sales will increase because - former is a superset of latter but latter may sound more likely)

• The Greeks loved logic, axioms and absolute truth - in Plato’s Phaedo - Simmilas to Socrates - “arguments from probabilities are imposters”

• Cicero understood probabilities when he claimed any man could throw a Venus cast twice or even thrice in succession, without personal intervention of Venus, out of pure luck if he threw them long enough

• Rome had degrees of proof - a bishop cannot be condemned without 72 witnesses, a cardinal priest without 42, a cardinal deacon without 36, a subdeacon, acolyte, exorcist etc. without 7 witnesses (appears driven by probabilities and priors)

• Compound probability - throwing 6 after throwing 4 - works by multiplying individual probabilities, if (and only if) events are independent. If you want either a 6 or a 4, you add individual probabilities

• Cardano came up with the law of sample space (set of all possible outcomes, favorable/unfavorable - chances of an event depend on the number of ways in which it can occur)

• Marilyn vos Savant - person with world’s highest IQ at 228 - ran “Ask Marilyn” column distributed widely in several newspapers (that legendary goat/car Monty Hall problem of conditional probability originated here)

• Black death is three distinct diseases - bubonic, pneumonic and septicemic plagues

• Knowledge of fractions from India made its way to the west through the Arabs and paved the way for probabilities (since these are always fractions less than or equal to 1)

• Cardano’s book of games and chance - chapter 26 title - “Do those who teach well also play well?” - he concludes its a different thing to know and to execute

• Isomorphism - where one problem is another in disguise - eg. sample space having (boy,girl) is akin to (heads,tails)

• Marilyn vos Savant faced serious backlash from misunderstood mathematicians and public on Monty Hall problem and still continued publishing her problems on probability and probably contributed most single-handedly to understanding of probability by public

• Crux of monty hall problem - the host uses his prior knowledge to bias the result

• when science parts ways with theology, the scientists could focus on the “how” and theologians on the “why” (My take - And yet someone Einstein would wonder “God does not play dice” which led to his difficulty embracing quantum theory)

• Cardano was a gambler turned mathematician - Blaise Pascal, the reverse

• Pierre de Fermat is considered the greatest amateur mathematician of all time

• Pascal’s wager - computation of expectation of being pious/non-pious when god exists vs doesn’t and concluding that gain is infinite if God exists and you are pious vs small loss if he doesn’t - and so everyone must be pious (probably the foundation for game theory)

• Do truly random numbers really exist?

• Benford’s Law - Distribution of numbers has a bias towards lower digits (more frequent than higher) - financial data obeys benford’s law

• Frequency interpretation studies the sample as it turned out, subjective interpretation studies the process that produced the sample

• Sequence, series and limit - sequence is a sequence of numbers, series is the sum of the sequence and limit is where the sequence is heading towards (fundamentals of integral calculus)

• Benoulli’s theorem is also called as the law of large numbers - Results reflect underlying probabilities from a large number of observations

• Bernoulli trials - making observations from a sample (say picking balls from an urn). Number of trials necessary to discern the distribution is based on the golden theorem that gives a way to compute this - varies based on confidence and accuracy

• Law of small numbers - is a parody on law of large numbers when conclusions are drawn from small/unrepresentative samples

• Galbler’s fallacy - The idea that odds of an event with a fixed probability increase or decrease depending on recent occurrences of the event

• While Bernoulli’s theorem concerned itself with how many heads to expect from a series of tosses, Bayes theorem concerned itself with certainty that the coin was fair given a certain series of outcomes

• P(A/B) is often conflated with P(B/A) - probability that I would be cheating if I sneaked out with probability that I would sneak out if I was cheating - or probability I would test positive to a disease if I did not have the disease vs I would not have the disease if I tested positive.

• Use intuition - if a disease has very low prevalence, don’t trust positive test - re-test (Rare events rarely occur. Extraordinary claims needs extraordinary evidence)

• Prosecutor’s fallacy - probability of innocence given evidence conflated with small possibility of observing evidence given innocence

• probability vs statistics - predictions based on fixed probabilities vs inference of probability from data

• Any measurement is susceptible to random variance and error

• Laplace, Lavoisier and Coulomb transformed experiential physics

• The perceived taste of wine arises from 600-800 volatile organic compounds on both the tongue and nose

• When we measure, our measurements are polluted by our expectation unconsciously

• Random errors in astronomical observations are like deviations in flight of archer’s arrows (Daniel Bernoulli)

• De Moivre discovered the bell curve, though its associated with Gauss

• Normal dist - 68% of observations lie within 1 std dev. of mean, 95% within 2 std devs and 99.7% within 3 std dev.

• A wine rating of 91 is meaningless without knowing random error - the variation that would occur if same was rated again by someone else

• The patterns of randomness are so reliable that in social data, their violation can be interpreted as wrongdoing

• Processes that don’t exhibit reversion towards the mean would eventually go out of control (sons of tall fathers would keep getting taller sons and so on) - (This of course applies only to normal dist. Things that follow power laws don’t)

• chi-square test by Pearson - to determine if a certain data came from certain distrbution

• Much of order we perceive belies an invisible underlying disorder

• Drunkard’s walk is present in foraging of mosquitoes through a jungle, in chemistry of nylon, in formation of plastics, in free quantum particles, in stock prices, and even in intelligence

• Table moving - a form of seance where people sit around a table holding it and the table starts to move (rotate) when contact is made - debunked by Faraday that a form of consensus gets formed among believers in the first few minutes of random fidgeting on the direction of movement and then the self-fulfilling belief sets in - others aren’t aware if its the spirit or themselves that are moving the table - but they believe and participate - much like technical breakouts in charts

• Many of the assumption of modern society are based on shared illusions, as with table moving above

• A process being random vs product of a process appearing to be random - shuffle in earlier iPod repeating songs while being random was made “less-random” to not repeat but was perceived by people as more random

• If you look long enough, you are bound to find someone who has made startlingly successful predictions - through sheer luck (as in Cicero’s Venus cast eg.)

• Sharpshooter effect - a person that shoots a blank piece of paper and then draws a target around it

• Random patterns can seem like compelling evidence if it conforms with our beliefs

• Determinism - belief that current state of world determines its future state (chance is a more fundamental conception than causality)

• We unreasonably believe mistakes of the past must be consequences of ignorance or incompetence and so can be remedied by further study and improved insight (Applies to all of us that claim we made mistakes in complex systems like stock markets)

• We must focus on the ability to react to events than attempt to predict them (especially in complex systems)

• moviegoers will believe a movie is good if they are influenced ahead that its good. (snowball effect) - chance events may dictate success of similar movies/songs

• We cannot see a person’s potential, only his outcomes - so we mistakenly judge based on that

• Known random differences in pay lead to inferences of (possibly non-existent) differences in skill among co-workers

• Its easy to fall victim to expectations and also easy to exploit them. Eg. Vodka brands which set expectations based on indistinct differences

• Ability does not guarantee achievement, nor is achievement proportional to ability

• We must learn to judge decisions by spectrum of possible outcomes than by outcome

• If you want to succeed, double your failure rate (Watson)

This is much more easier to read for someone getting a first introduction into the subject of randomness and human behavior than fooled by randomness or kahneman’s works. Even otherwise its worth a read as its short, fun and packed and doesn’t meander. 10/10

[phreakv6]

Deep Medicine, Eric Topol, 2019 - This book is a heady blend of medical science and AI. The author is a doctor and scientist appears to closely follow latest developments in AI research in his field. What I liked is how recent the information is - I have done scattered research on a lot of what’s in the book but if I had known this book was so good, I would have just read this first.

My notes -

• EHRs - Electronic Healthcare Records - were designed for maximizing billing and not for ease of use by physicians and nurses

• Each man is ill in his own way

• It is important to know what sort of person has disease than to know what sort of disease a person has - Hippocrates (Bayes and conditional probability)

• Opioid epidemic with over-prescription of drugs caused by inability of doctors to listen to patients

• Big datasets in medicine - genome sequences, high-resolution images, output from sensors

• ML in medicine traditionally used logistic regression, random forests, bayesian networks and SVMs but field is now completely on DNNs/CNNs to recognize patterns

• ML in medicine is very narrow - depression prediction network cannot do dermatology

• ML will disrupt doctors with patterns - i.e radiology, dermatology

• AI in medicine still in 3rd industrial revolution (still no standardization in EHRs)

• Deep Phenotyping - Taking into account ones medical, social, behavioral and family histories as well as anatomy, physiology and environment, along with ones DNA genome, RNA, microbiome, immunome, epigenome and more

• Healthcare is the largest service industry in the US - bigger than Retail (4x growth in jobs since 1975)

• Doctors spend a lot of time facing the computer fiddling with EHRs than looking at and listening to patients. AI will simplify this process and doctors with better EQ will be preferred in the profession when AI takes over

• AI will also transform biomedical science in discovery of new drugs

• Detecting Atrial Fibrillation - a small band-aid like patch can detect and record every heartbeat over 14 days (Zio by iRhythm)

• Systemic problem of mistaken and excess diagnosis/prescriptions

• 12 million misdiagnoses made in a year in US - due to not ordering right test, misinterpreting test, not doing proper diff diagnosis, missing abnormal finding

• Most over-ordered tests - CT Scans, MRIs and PET scans for normal ailments like lower backaches and headaches

• Individual physicians overestimate the benefits of what they themselves do

• Surgeries (placing stents) are performed on a huge number of people who are unlikely to benefit

• Avg. time spent by patient with doc is 7 mins (returning) and 12 mins (new)

• ATM card can work in Mongolia but EHR cannot be used in a hospital across the street

• Shallow medicine - insufficient data, insufficient time, insufficient context and insufficient presence

• Although we know 12% of women can develop breast cancer, it doesn’t mean every woman has 12% chance for developing it

• $3.5T spent on healthcare ($1T in hospitals and $350b in drugs)

• Doctors have cognitive bias that medicine they prescribe will work and patience want to believe it will - End result - bulk of top prescribed meds don’t work - Responsiveness of patients for eg. #1 Ability 20%, #2 Nexium 4%

• Physicians have not honed the ability to predict which medicine will work for whom and so prescribe it for everyone

• Life expectancy in US is on the decline while healthcare spending is increasing

• Medical diagnosis depends on priors (Bayesian reasoning) - experienced docs do better as they have better priors

• Most people will experience at least one medical misdiagnosis in their lifetime

• If you don’t get feedback, your confidence grows faster than your accuracy

• Availability bias skews diagnosis (doctors can’t remember all 10,000 human diseases while diagnosing)

• 80% of docs don’t think probabilities apply to their patients

• Jeff Williams, responsible for Apple Watch has vision for it to become essential medical device of the future

• Bloodless bloodtests - determining potassium (K+) in blood using correlation - these tests have an unacceptable error as the detection is based on T-wave correlation with K+ in blood

• ROC curve - Receiver Operating Curve that plots false-positive rate against true positive rate is used to detect usability of algorithm (prescription medicine cannot afford high false-positive rates)

• self-driving cars are at Level 3 autonomy (human backup needed) and may achieve Level 4 (no human backup, but usable only in limited circumstances). AI in Medicine will not move beyond Level 3. It is currently at Level 2 (Like some cars that have cruise control - minimal automation)

• DeepMind is a press-hungry organization that hypes things (AlphaGo Zero is hyped to have learned on its own when it wasn’t true)

• AI takeoff in medicine will find it very difficult as the blackbox nature of NNs doesn’t have many takers (99% accuracy blackbox may find it difficult to beat a 80% accurate explanatory system)

• Bias in medical research is inherent as patients are self-selected and don’t reflect population

• Technology for automatic elevators was existent from 1900 but it wasn’t accepted until 1950 - people were too uncomfortable to ride without operator

• Most physicians only look at report written by radiologist as they are too busy to look at the X-rays they order

• when high profile ML people oversell their results to public, it leaves everyone else worse off

• Inattentional blindness is very common with radiologists - when they are looking one thing, they miss the others - false-positive rate 2% and false-negative rate 25%

• There are CNN based ML models with >99% accuracy, comparable to performance of radiologists

• Radiology is one of the top-paid medical specialties with annual compensation of about $400k. ML models can do 260 million scans in a day for $1000. It is inevitable that radiologists will be replaced by computers. They can develop empathy and deliver results and explain the false-positive rates (may perhaps be paid much less)

• Pathologists face a similar fate with WSI - whole-slide imaging allowing CNNs to classify lung and brain cancer with an accuracy of 70-85% (similar to level of pathologists)

• Dermatology as well is another one profession of doctors with patterns and again prone to disruption by AI

• AI can classify pictures of lesions as benign or malignant, and if malignant, whether it is melanoma (kills 10k Americans a year) - it outperformed dermatologists on decision to biopsy (Algo trained on people with EU ancestry - may need re-training for rest of population - Is this an opportunity?)

• Dermatology isn’t just about binary calls of cancer/not - sometimes individual risk factors, history of lesion may have to be analyzed and observed over time - so again ML may only reach Level 3 automation. Only diagnostics may get disrupted - excision and treatment will remain in their domain

• Watson beat humans in Jeopardy! by ingesting Wikipedia (Show’s questions were anyway sourced from here) - Another oversell on AI intelligence here, just as in DeepMind’s AlphaGo Zero

• Doctors spend a lot of time at the keyboard trying to access patient records from the EHR and taking notes - Digital Scribes combined with CDSS (clinical decision support systems) can help reviewing patient data, suggesting diagnosis, lab tests or scans, recommending vaccinations, flagging drug allergies/interactions etc

• HbA1C or serum creatinine used for monitoring diabetes and kidney function vary based on ancestry (normal ranges hence vary)

• Diabetic retinopathy is #1 cause of vision loss (100m people worldwide) - ML models by Google and IBM outperform ophthalmologists in both sensitivity (TPR) and specificity (FPR) - similar models exist of Macular degeneration and glaucoma

• Retinal images can predict a person’s age, gender, BP, smoking status, diabetes control and cardio risk (could be mobile phone driven)

• Nurses might be the group deep medicine may find hard to replace

• Role of hospitals will come down drastically due to sensors and remote monitoring (only ICU, operating and emergency rooms may remain)

• In an chat experiments, participants were willing to disclose much more when they thought they were communicating with a virtual human than a real one

• Digital phenotyping of mental state can be done using speech (prosody, volume), voice (valence, tone, pitch), use of keyboard (Reaction time, attention), smartphone (activity, movement, communication), face (emotion, tics) and sensors (heart rate, breathing patterns, sleep)

• Machine accuracy of detection of depression is 70% (better than GPs with FPR of >50%) - depression is by far most common mental health disorder

• There are 25 million suicide attempts in a year and 140 million contemplate it

• CBT (cognitive behavioral therapy) is now available on phones (Lantern, Joyable)

• Number of psychiatrists per 100k - 8 in US, less than 1 in most countries

• 74% of mental health disorders arrive before age 24

• Maximizing Gross-domestic happiness could be our future goal than GDP (YNH from Hono Deus)

• Palliative care - New tools can predict time of death with unprecedented accuracy (DNNs based on just EHRs - 90% specificity)

• Surprise Question - “Would I be surprised if this patient died in the next 12 months?” is used by doctors and nurses as screening tool to judge lifespan currently

• More than 20% of healthcare costs go towards administration in the US

• MIT’s CSAIL can predict when a patient needs ventilator or vasopressors. viz.ai analyzes brain CT images to identify and classify stroke while scanning

• Indian AI - Tricog health cloud-based heart condition diagnosis. Aindra - cervical cancer detection from path samples, Niramai - early breast cancer detection. Aravind Eye Hospital with Google for detecting diabetic retinopathy

• Medical AI in China will do way better than rest of world as citizens can’t opt-out and China can enforce uniformity and inter-operability of EHRs

• GATK - Genome analysis toolkit (Deep Variant by Google complements it)

• Genomics, along with gene expression, transcription factors, RNA binding proteins, protemics, metagenomics, gut microbiome and single-cell data are all ripe for deep learning. Most CRISPR design algos use ML

• More than 60 startups and 16 pharma cos using AI for drug discovery (2018). AI tools used in searching through biomedical lit., mining million of molecular structures (can disrupt CRO/CDMO) - 10^60 molecules

• InSilico Medicine uses GANs to screen 72 million compounds for cancer drug discovery (Recursion Pharma, Deep Genomics, Atomwise - others in the space)

• ATOM - goal is to reduce 4 year to 1 year the time to identify drug target and developing drug candidate for target

• Computer scientists may come to mean scientists who are computers, than scientists who study computers

• Grid cells in brain - place cells for position, head-direction cells for orientation and grid cells arranged in hexagonal shape (in the hippocampus, the GPS of our brain) function as a map

• Our brain is very energy-efficient and uses only 10W of power, less than a lightbulb and less than 2L volume - compared to supercomputers that uses 10MW power and 1.3 million litres of space. It is slow (200ms) but very fault tolerant

• Hebbian Learning - neurons that wire together fire together - if we use knowledge frequently it doesn’t get erased

• Neuromorphic computing - modeling artificial neurons after the brain

• Let food be thy medicine and medicine be thy food (Hippocrates)

• Harvard scientists indicting dietary fats for heart disease were paid by the Sugar Association (1967)

• The link of excess sodium with risk for adverse heart and vascular events has been debunked (salt doesn’t increase risk of heart attacks)

• nutrigenomics - how our food reacts with our unique genome

• Glucose response depended more on the gut microbiome than food constituents (hence personalized nutrition is very important) - DayTwo and Viome already do this

• Virta - remote guidance app for reversing type-2 diabetes

• iCarbonX (China) - probably the most ambitious virtual medical assistant project - collecting lifestyle, dna sequencing, proteomics, metabolomics, immune system, transcriptomics, gut microbiome, glucose monitoring, smart toilets data

• Human empathy is not something machines can truly simulate - this is what will differentiate doctors in a world of deep medicine

Some books are insightful, some full of wisdom - this one was full of information I wasn’t aware of and opens up so many avenues to benefit from, considering how recent most of this research is. Can be a great primer for someone interested in using deep learning in medicine, to sort of get a hang of what’s going on before picking on a problem to tackle. 10/10

[rajput_delhi]

Hats off to you mate…for the amount of reading you do…and for sharing your thoughts…am sure many are benefiting in many ways…and getting inspired. More power to you.

Cheers
Rajesh

[Apurva_Dubey]

The Rise and Fall of Modern Medicine by James Le Fanu (2012)

James Le Fanu is a medical doctor and a journalist. His scholarly work has been published in The Lancet and the British Medical Journal. I establish the credentials of the writer upfront given the provocative title of the book.

In this excellent book Fanu provides a contrarian view to the widely held belief that modern medicine has been on a continual upward trajectory starting with discovery of Penicillin in 1940s. He argues that therapeutic revolution of modern medicine had its “Golden Age” during 1940-1975, and has been in continual decline 1980 onward.

The Golden Age of drug discovery got a further boost by advances in technology which can be put in three broad categories (i) life-sustaining (ii) diagnostic, and (iii) surgical. Fanu says of all tech inventions “operating microscope” had the highest impact – it not only enabled complex surgeries and increased success rates but also elevated the performance of relatively inexperienced surgeons.

• Life-sustaining
  • Intensive care
  • Dialysis
  • Ventilator
  • Pacemakers
• Diagnostic
  • CT scanner
  • PET scanner
  • MRI scanner
  • Angiography
  • Ultrasound
  • Cardiac catheterisation
• Surgical
  • Joint replacement
  • The pump
  • Intraocular lens implant
  • Operating microscope
  • Cochlear implant
  • Endoscopy

The “Golden Age” came to an end because most of the drug discovery in this age was accidental, serendipitous, or based on brute-force screening of thousands of molecules – there was little attempt made to understand the origin of diseases…and it was not for a lack of will or intent but because of the inherent complexity of human physiology.

According to the author the “fall of modern medicine” encompasses many aspects like:

• Tremendous slowdown in the discovery of new molecules starting 1980s. Many of the ‘new’ drugs introduced since the early 1970s were just more expensive treatments for diseases already taken care of by older and cheaper medicines.
• Healthcare cost spiraling out of control. Expenditure on Britain’s famously ‘cheap and cheerful’ National Health Service doubled from £23.5 billion in 1988 to £45 billion in 1998.
• Misuse of technology: primarily using life-sustaining technologies to prolong the process of dying. In 1995 the expenditure on intensive care in the United States had escalated to $62 billion (equivalent to 1 per cent of the nation’s GNP), one-third of which – $20 billion – was being spent on what had euphemistically come to be known as PIC or potentially ineffective care. 25% of healthcare cost in the US is attributable to last 6 months of patient’s life.
• Big Pharma’s focus is top-line and not really to improve the quality of lives. They have consciously shifted focus towards lifestyle diseases (and away from understanding the cause of life-threatening diseases) which are big money spinners.
• Medicalizing normal problems such as balding or redefining common psychological traits as quasi-psychiatric illnesses – ultimately leading to mass medicalization which is what he calls an iatrogenic catastrophe
• Soaring Popularity of Alternative Medicine. In the United States there are more visits to providers of ‘unconventional therapy’ (425 million) than to ‘primary care physicians’ (388 million annually).
• Almost 50% decrease in the in the percentage of doctors opting to go into research (pre-1980 vs post-1980), primarily because they could make much more money through practicing medicine than pursuing research.
• Despite billions of dollars poured into research we still don’t know the cause of most of the diseases (various forms of cancer, multiple sclerosis, schizophrenia, rheumatoid arthritis to name a few). And without knowing the cause the treatment will continue to be expensive and half-baked.
• The New Genetics (biotechnology) started with a lot of promise but it has yielded too little for the time, effort and money that has been poured into it. The supreme aspiration of The New Genetics was/is gene therapy: the correction of genetic defects by physically changing the genes themselves. This aspiration was based on the thesis that each diseases/defect can be tied back to one specific gene. However, it turned out (as scientists learned more through Human Genome Project) that for most diseases there are multiple genes at play. For example: there could be as many as 800 different genes (potentially many more) contributing to a common disorder like diabetes – each with a tiny predictive value. So a targeted gene therapy goes out of the window. There are very few diseases found so far which are caused by a single gene: most notably cystic fibrosis and Duchenne’s muscular dystrophy (DMD).

The book provides a rich history and a deep insight into the marvel of modern medicine. I highly recommend this book to anyone trying to understand how the medical science got to its current state and what are the challenges thwarting its progress.

+++

Some interesting notes from the book –

A) Fanu lays out twelve definitive moments of modern medicine:

• 1941: Penicillin: serendipitous discovery of Penicillin is the “big bang” moment of modern medicine. Alexander Fleming was running a biology experiment on staphylococcal bacteria and accidentally forgot to put a petri dish back in the incubator before leaving for vacation. When he returned after nine days, he saw fungus in the petri dish had inhibited the growth of bacteria and thus Penicillin was discovered. BTW, Fleming - being lazy - didn’t work on his discovery further. Antibiotics were rediscovered (and subsequently commercialized) by Ernst Chain (a Jewish refugee from Germany) and Howard Florey almost 12 years later. Penicillin laid the foundational framework for discovery of other antibiotics.

• 1949: Cortisone (Steroids): together with Penicillin, Cortisone forms the other pillar of modern medicine. It was accidentally discovered by Philip Hench while talking to a colleague who was suffering from jaundice – his colleague told him that for the duration he had jaundice his arthritic pain had magically diminished. Hench worked with his colleague Edward Kendall to isolate Cortisone. Cortisone and its derivatives completely transformed the treatment of six medical specialties – rheumatology, ophthalmology, gastroenterology, respiratory medicine, dermatology and nephrology, as well as enabled the two most remarkable therapeutic developments – organ transplantation and the cure of childhood cancer.

• 1950: Streptomycin, Smoking and Sir Austin Bradford Hill: Austin Bradford Hill is credited with bringing in statistics (confidence intervals, correlation etc.) to clinical trials – this was a paradigm shift in assessing the efficacy of new medicines. Streptomycin for treatment of TB was the first drug where statistics was used in clinical trials. Later Bradford Hill also established that smoking is a major cause of lung cancer. In the postwar (WW2) years there was a significant rise in lung cancer cases – it was widely believed that vehicular pollution caused lung cancer – Bradford Hill used statistics to show that smoking was the real culprit.

• 1952: Chlorpromazine and the Revolution in Psychiatry: Henri Laborit – a French surgeon – was experimenting with a variety of molecules with the intent to find something that would calm down his patients before a surgery. This is how he accidentally discovered Chlorpromazine (CPZ). Before 1952 the mental institutions across the world were bursting at the seams. CPZ ensured that patients with a variety of mental disorders could be cared for at home and lead a better quality of life. In the 1950s (post discovery of CPZ) – six entirely new types of drug were introduced into psychiatric practice and remain its mainstay today. But their discovery was not based on a scientific knowledge of brain chemicals rather the drugs came first, being discovered for the most part by chance.

• 1952: The Copenhagen Polio Epidemic and the Birth of Intensive Care: The ventilator is the most important piece of equipment in the ICU as it ensures the heart carries on beating and buys time for tissues to heal. The role of oxygen in human physiology has been known for more than 200 years, but the appreciation of its central role in the survival of the critically ill starts abruptly with the Copenhagen polio epidemic of 1952. Dr. Bjorn Ibsen – an anesthesiologist – came up with a clever hand-operated contraption which was the first ventilator. His insight on the role of oxygen in treating polio patients was the real deal. This hand-operated ventilator reduced the mortality rate from 90 percent to 25 percent!

• 1955: Open-Heart Surgery – The Last Frontier: Conquest of this “Mount Everest” of surgeries became possible because of the invention of the heart-lung machine aka ‘pump’ by John Gibbon. In the five years from 1955 to 1960 the pump transformed cardiac surgery into much the largest and most sophisticated of all surgical specialties. Establishment of ICUs also played an important role in making this surgery possible.

• 1961: New Hips for Old: John Charnley’s hip replacement surgery was a major breakthrough in orthopedics which was otherwise an endangered branch of medicine by late 50s. It also paved the way for other orthopedic surgeries. Initial prosthetic implants were made from Teflon but Teflon particles used to cause inflammation plus the implant deformed overtime. John Charnley luckily met a German engineer recommended using HMWP (High Molecular Weight Polyethylene) based implants which turned out to be much superior to Teflon implants.

• 1963: Transplanting Kidneys: Kidney transplant became possible because of convergence of multiple factors: (i) Peter Medawar showed that the immune system was responsible for the ‘rejection’ of a transplanted organ plus he demonstrated that the immune system could be tricked into tolerating transplanted tissues (ii) Wilhelm Kolff’s invention of renal dialysis machine (iii) George Hitchings and Gertrude Elion’s discovery of the immunosuppressant drug azathioprine – this was a perfect drug that would allow tolerance of the transplanted organs and at the same time not impair the immune system as to leave the patient vulnerable to overwhelming infections.

• 1964: The Triumph of Prevention – The Case of Strokes: Strokes are the third most common cause of death, and thus the ability to prevent them is of enormous significance. Chlorothiazide (diuretic) and Propranolol (beta blocker) made hypertension a very manageable condition. Also, these are among the safest medicines known with very little side effects. Propranolol is one of the rate drugs which wasn’t discovered by accident but very methodically and purposefully designed by British chemist James Black.

• 1971: Curing Childhood Cancer (aka Acute Lymphoblastic Leukaemia aka ALL): This is one of the biggest triumphs of modern medicine. It took multiple researches and doctors more than 25 years to find the perfect cure for this heartbreaking disease. Some key figures in this story are Dr. Y Subba Rao who discovered methotrexate and is popularly called the father of targeted chemotherapy; Dr Sidney Farber – the ‘grandfather of cancer treatments’ – and his protégé Dr Donald Pinkel who experimented multiple drugs over many years at St. Jude’s Hospital; Richard Nixon’s administration which gave a big grant of $1 billion to National Cancer Institute (NCI) – NCI screened 82,700 synthetic chemicals, 115,000 fermentation products and 17,200 plant products (214,900 in total) for their anti-cancer potential over a 10 year period.

• 1978: The First ‘Test-Tube’ Baby: making in-vitrio fertilization (IVF) possible was perseverance of one man over many years – Bob Edwards. Invention of laparoscope was one of the key success factors for IVF – permitting eggs to be removed from the ovary without the necessity for a major operation thus making IVF a practicable proposition.

• 1984: Helicobacter – The Cause of Peptic Ulcer: Peptic Ulcer is one of the rare cases where the cause of the disease was determined. Barry Marshall ran experiments on himself and found out that peptic ulcer is caused by a new type of crescent-shaped bacterium. Once the cause the determined the cure could also be easily discovered in antibiotics. Before Barry Marshall it was widely believed that peptic ulcer was caused by psychological troubles.

B) Other significant moments in modern medicine:

• 1935 Sulphonamides
• 1941 ‘Pap’ smear for cervical cancer
• 1944 Kidney dialysis
• 1946 General anaesthesia with curare
• 1947 Radiotherapy (the linear accelerator)
• 1948 Intraocular lens implant for cataracts
• 1950 Tuberculosis cured with streptomycin and PAS
• 1954 The Zeiss operating microscope
• 1955 Polio vaccination
• 1956 Cardiopulmonary resuscitation
• 1957 Factor VIII for haemophilia
• 1959 The Hopkins endoscope
• 1960 Oral contraceptive pill
• 1961 Levodopa for Parkinson’s
• 1964 Coronary bypass graft
• 1967 First heart transplant
• 1969 Prenatal diagnosis of Down’s syndrome
• 1970 Neonatal intensive care
• 1970 Cognitive therapy
• 1973 CAT scanner
• 1979 Coronary angioplasty
• 1987 Thrombolysis (clot-busting) for heart attacks
• 1996 Triple therapy for AIDS
• 1998 Viagra for the treatment of impotence

C) Biotech products in use in 1995 (product: use)

• Human insulin: Diabetes
• Interferon alpha: Hairy cell leukaemia; hepatitis B & C; keeps lymphoma, leukaemia in remission
• Human growth hormone: Dwarfism
• Interferon beta & gamma: Chronic granulomatous disease (decreases infections); multiple sclerosis; hepatitis B & C
• Tissue plasminogen activator: Clot-buster drug
• Erythropoietin: Treatment of anaemia in kidney failure
• G-SCF, GM-CSF: Stimulates white blood cells after cancer chemotherapy
• Ceredase: Gaucher’s disease
• Hepatitis B vaccine: Immunisation against hepatitis B
• DNAse: Cystic fibrosis; chronic bronchitis
• Interleukin-2: Kidney cancer; melanoma; leukaemia; ovarian cancer
• Factor VIII: Haemophilia
• Anti IIb IIIa Antibody: Prevents narrowing of coronary arteries after angioplasty

+++

[nitinwason]

Simply awesome mate , youve beautifully captured the essence and laid the book out threadbare with key takeaways and glimpse into future of medicine .

hats off

[Divyanshu_Bagga]

The Great Wave: Price. Revolutions and Rhythm of History, by David H. Fischer:

The author studies the price data of essential commodities over the last millennium to identify common repeating patterns which he terms as price revolutions. There were four such price revolutions identified in last 1000 years, with us currently being in the late stages of one such revolution.

These revolutions are punctuated by era of equilibrium, where there is no long term inflation, and prices only oscillate about an equilibrium due to short term events. In these periods, the cost of capital and the rent from assets is low relative to wages, leading to lower inequalities between rich and poor, and there is much optimism in the population about the future. This peaceful and prosperous era leads to a rise in population as well as in the living standards of people, resulting in greater aggregated demand, and so begins the price revolution.

In the early stage, prices rise slowly over a period of prolonged prosperity, with the magnitude of increase being within the range of fluctuations. The greatest upward movement is in food and fuel, with manufactured goods and services lagging behind. Eventually the prices break through the boundaries of equilibrium, making the new secular trend obvious. As people recognize the new trend of inflation, they respond by making individual choices which cause even more inflation, such as hoarding assets. The money supply is deliberately enlarged to meet growing demand. The rate of capital and land increases, the price surges increases, and so does political disorders, social disruption and cultural anxiety. Eventually people resign to inflation as an inexorable condition Governments and individuals expand the supply of money and increase the velocity of its circulation. As inflation becomes institutionalised, prices become unstable, and began to surge and decline with increased volatility. Financial markets become unstable and public debt increases at a rapid rate. Not only is the return of capital becomes much better than return of labor, the rich usually have better options to protect themselves from wealth erosion due to inflation, which eventually leads to growing inequality. Eventually these imbalances create instabilities, which crash with shattering force in a crisis that includes demographic contraction, economic collapse, and political revolutions.

With the progress of technology, the demographic impact of price revolutions is decreasing while their social impact is rising. The current price revolution is not due to inability to feed world’s population, but due to unrestrained fiat printing by governments.
Which is why I believe Bitcoin will be key to the crisis of current price revolution, which can only end when money creates its own identity independent of any government control.

[imnishantg]

Hi @phreakv6
I’m curious. How do you read so quickly and capture your notes. I’m more interested in the process and tools that you use. I would’ve assumed you are speed-reader if you didn’t had the distilled notes of what you read. Would love to learn about how you do it, but it’s no compulsion on you to share.