#The_Singularity_is_Nearer: When we merge with AI - Ray_Kurzweil - #Review

    The book starts with “where we stand” in terms of epochs with the present one being a merger of bio-science with technology. The idea that one day it would be possible to upload our mind into the ‘cloud’ achieving cyber-immortality is the next. In general terms, the book describes the law of accelerating returns, which predicts an exponential increase in technologies like computers, genetics, nanotechnology, robotics and artificial intelligence.

    The Singularity Is Near, using the term “singularity” as a metaphor for the merger of human and artificial intelligence. By this the author means people augmenting themselves with computational power millions of times beyond what our innate biology provides. Kurzweil predicts this will happen around 2045!

    Artificial Narrow Intelligence (ANI) refers to technology designed to perform specific tasks or a limited range of functions, such as spam filtering, recommendation systems, and digital assistants like Siri and Alexa. Today, ANI is deeply integrated into countless industries, organizations, products, and services worldwide. The next stage involves advanced AI and machine learning systems that function as general-purpose technologies, capable of supporting a broad spectrum of applications. According to Ray Kurzweil, the accelerating progress of AI offers significant opportunities to expand, enhance, and amplify human intellectual capabilities. In his latest book, he describes how AI and machine learning are expected to drive ground-breaking innovations in biotechnology and nanotechnology, potentially leading to transformative advances that could reshape the future course of human civilization.

    The evolution of artificial intelligence spans several decades, beginning in the 1950s with the pioneering contributions of mathematicians Alan Turing and John McCarthy, and progressing to the modern connectionist approach to AI. Connectionism is based on networks of interconnected nodes that generate intelligence through their organizational structure rather than the specific information they contain. This model draws inspiration from the human neocortex, which is composed of simple, repeating modular units of roughly 100 neurons. These modules are capable of learning, identifying, and retaining patterns, while also organizing themselves into hierarchical layers where each successive level develops an understanding of increasingly complex concepts. As computing power became more affordable and large-scale training data became readily available, AI systems could be trained more efficiently, enabling the technology to achieve remarkable performance across a wide range of applications.

    To address the difficult problem of defining consciousness (and who possesses it), he borrows philosopher David Chalmers’s idea of “panprotopsychism.” Kurzweil explains that panprotopsychism treats consciousness as a fundamental force of the universe and not a force that can be reduced to a simple effect of other physical forces. In his interpretation, it is a “kind of information-processing complexity found in the brain that ‘awakens’ that force into the kind of subjective experience we recognize.”

    Pharmaceutical companies are today finding answers to biochemical problems and emerging viral threats by digitally searching through possible options and identifying solutions in hours rather than years.

    Nanotechnology—manipulating matter at the nanoscale to yield new technologies—offers considerable promise in medicine, electronics, energy production, and environmental mitigation. It also may deliver a wide range of inexpensive but extremely destructive offensive weapons. While responsible people can design safe nanobots, bad actors could design dangerous ones. Kurzweil argues for creating a nanotechnology “immune system” capable of contending with both obvious destruction (e.g., cleaning up a toxic spill) and potentially dangerous stealthy nanotechnology replication—that is, “good guy” nanobots (called “blue goo” in the literature) that combat bad nanobots (“gray goo”). Because gray goo is a potential extinction-level event for humanity, it is key that blue goo be deployed globally before gray goo self-replication chain reactions take off—a “catastrophe theory” scenario that should keep one awake at night.

     Lastly, according to Kurzweil, artificial super intelligence (ASI) - presents three distinct risks. First is misuse, where the AI functions as its human operators intend, but those operators deploy it to harm others. Second is outer misalignment, which means a mismatch between the programmers’ benign goals and unintentionally harmful prompts they give the AI in pursuit of those goals. Third is inner misalignment, which occurs when the methods the AI learns to achieve its goal result in undesirable behaviour.

    Kurzweil asks, “Will psychological and cultural forces make people more conservative about their choices” concerning merging AI with the human corpus—that is, the singularity? This was once a question for science fiction, but it is becoming a defining, present question for our age.

 Here are some highlights:

    If the strength of the gravity was slightly weaker (than the present) then there would be no supernova to create the chemical elements that life is made up from. If it were slightly stronger, stars would burn out and die before intelligent life could form. The six epochs are discussed in detail with the present humans in the 4th and the next being merging of biological human cognition with the speed and power of the digital technology

    It has been termed as the reinventing intelligence when AI is coupled with computer operations, drug development, medicines, neural network perceptron. But there is still a shortcoming - Two squares are shown where the machine could not detect the pattern whereas humans can detect the break in lines - all about neocortex.  Deep mind, Alpha go, Dall-E passed the test by extending the neocortex.

    Cockroaches have about one lakh neurons- about 0.001% of what humans have and thus the concept related to consciousness is our sense of ‘free-will’. A quote from Wolfram in his book, ‘a new kind of science’, tells about deterministic and non-deterministic properties. Some figures from his book, using fractal rules have been included.  The concept about Googol, which is 1 followed by 100 zeros, and further fall-out is discussed.

    An average man produces as many as 2 trillion sperms in his lifetime and an average woman about 1 million eggs – the odds of embryo formation is thus mentioned.

    For the universe to produce an Earth-like, atmosphere or a planet, the starting entropy should be as low as possible initially, and this is possible with one in 10^10^125 Universes (Roger Penrose estimation). This would be like a Boeing 747 being assembled out of a tornado striking a junkyard!

    Hans Moravec’s paradox is discussed which is about any mental task that seem hard to human beings, like square rooting or remembering large amount of info, are comparatively easy for computers whilst recognizing faces, keeping balance while walking, is much more difficult for AI.

     Life is getting exponential better while slow sense of progress does not make headlines, faster ones get noticed. Many graphs, linking literacy and education and other factors such as GDP, life expectancy, insurance in and around the USA have been discussed.

    Someone with an inexpensive smart phone today can use the internet to quickly and easily access almost all the world's educational information and translate and find directions which was not available even with millions of dollars decade ago!

    Broken Windows theory: where low-level vandalism would make people feel unsafe and believe they could get away with more serious and violent crimes - stopping minor offenses is thus a way of preventing more serious crimes.

    The growth of renewable energy like Solar, Photovoltaics and Wind power has been discussed along with machines’ performance with a wide range of CPU’s.  Most media is limited to sight and hearing, but for the next two decades brain-computer interface will become much more advanced and renewable energy sources will get a boost. For example, putting nanotubes and nanowires inside solar cells can steadily improve the ability to absorb photons. Nanobots can enter the body and carry out repair directly.

    Preventing re-contamination of water by adding certain chemicals, which are essentially Nano-particles and using 3-D printing technology to make shoes, homes and spinal disc are on the rise. IKEA and Lego find mention for simplifying lives by selling equipment easy to assemble.

    In the section “The feature of jobs, good or bad”, Waymo’s progress is discussed as an example and that post-Covid jobs in USA have increased by many times and the productivity day is measured as real output per hour. Facebook earnings over ads asking you to connect to more friends is discussed advance in detail.

    The next 30 years in health and well-being is the crux of next chapter. You can maintain your car through refining it parts, but this kind of thing may not be applicable to your body!

    AI can learn from more data than a human doctor ever could and can amass experience from billions of procedures instead of 1000’s a human doctor can!

    Promise and peril, a concluding chapter has details about the dangers of AI and the significant one includes the fact that there are approximately 12,700 nuclear war heads, 9440 of which are active. While USA and Russia maintain about 1000 war-heads ready for combat in an hour, these can be used to wipe the civilisation in half an hour time. Similarly, the use of biotech for generating pathogens like ‘black death’ can kill one third of EU population in a single spread.

    Advance AI tools to design and optimise mRNA can help speed up the manufacturing process. For example, Moderna’s vaccine for Covid was developed in 277 days- the fastest vaccine ever created. Nanotech can also be used to destroy biomass that can bring destruction to Earth.

#Are_you_smart_enough_to_work_at_Google - #William Poundstone - #Review

    This is a fun book of puzzles of all types; mathematical, logical, algorithmic with food for thought on estimation, mind games and creativity. There is also lots of interview advice, when applying for a job. The advice is good not just for Google, but for many other companies as well. 

I am straight away going to chapter six which has a gist: At Google, they believe in sharing ideas.

• It is okay to question interviewer if you don’t understand, ask for clarification.
• When asked to provide a solution, first define & framework the problem as you see it.
• If you don’t understand, ask for help for clarification.
• If you need to assume something verbally, check, it is a correct assumption.
• Describe how you want to tackle solving each part of the question.
• Always let your interviewer know what you are thinking as he will be interested in your process of thought as your solution. Also, if you are stuck, they may provide hints if they know what you are doing.
• Finally, listen, don’t miss a hint if your interview is trying to assist you!

    The book starts in a fairy-tale like narration. “If you are shrunk to a penny height and thrown into a blender, what would you do to survive”? There are then several solutions like, clinging to the central pivot, or hiding beneath the blades etc. The author finally reserves an answer mathematically. “If you are shrunk to 1/n of your usual size, muscle energy would be reduced to n³”, and thence comes the ‘idea’ that would be accepted by the Google.

What comes next, after 66? 10, 9, 60, 90, 70, 66,…

Ten, nine, sixty, Ninety, Seventy, Sixty-six, chose any number that has nine alphabets. At one point you would be required to go in terms of exponentials. It is then the birth of Google is explained. ‘Googol’ (1 followed by 100 zeros) was the predecessor to Google, and the word first came from Kasner. who wrote this in a book called “Mathematics and Imagination” that was later to be used up by the Google founders, after typing it mistakenly as Google, only to be sued by the family members of Kasner later.

    The next chapter talks about the history of Human Resource which happens to be the crux of many a industry. The first ever question by HR was during World War 2, “Did you ever build a model plane that flew”? The Russian Sputnik company was first to use IQ when it tested candidates for its launch programme.

Some more questions from HR:

• “It is difficult to remember what you read, after many years, how do you address this”?
• “How do you describe a person from Mars”?
• “Using a 4-minute and 7-minute hourglass how would you measure 9 minutes”?

    That IBM was the first to use ‘Software and Silicon Valley’ in their library of reserved words was a good information. Likewise, Stephen Wolfram was the person who introduced ‘Mathematica’ along with Feynman that solved for the first time the “Billboard problem” for first 10 digits of prime in consecutive digits of e.

The next chapter is on ‘Great Recession’ mainstreamed by bizarre interview questions.

Walmart wanted to hire anyone with ‘pulse’ – ‘One size fits all’ was the expected ‘response’, for their question “if anyone came without appropriate clothing, what would you say”?

Here are some sample questions:

• “What is your favourite internet product and how would you improve it”?
• “Estimate the amount of cash in your wallet”.
• “If you were a cartoon character, what would you be”?
• “How weird are you in a scale of 1 to 5”.
• “At 3:15 what is the angle between the minute and hour hands on analogue clock”?
• “How many integers between 1 and 1000 contain 3”?

    The next chapter has details specific to Google. Human resource at google was known as People’s Operation where they would ask “When did you get your first computer”? Google founders had no privacy, and their cubicles are small enough to find out who was doing what. So, in one of their interviews, they had asked the candidates in a scale of 1 to 5, what they would choose - 1 is working alone, and 5 is working in team.

    There would be at least five on-site interviews for any engineer who would be asked to develop an app they liked and for any question in between, the candidates’ opinion should likely be close to truth.  No discussion among the candidates was allowed until report was submitted by all.  Many have a rule of the thumb that they can’t go wrong, hiring a Stanford Ph.D. It would be a bias if the employer insisted that the degree our university do not matter and thus Google assigned optimum weight to everything. At Google, hiring is more bureaucracy than algorithm.

     A false positive is the outcome when candidate passing the vetting process is hired only to be a poor employee. The opposite is false negative. Thus, it has become more and more difficult for companies to hire and fire. This puts the employers in a tight situation. Interviews are thus a big task for them, and they are considered as ‘noisy signal’ and Google was aware of this.

Trivia also forms part of the people at the helm of the interviews.  

    One question that evoke awe was to explain the significance of ‘dead beef’. See if you can get through the below paragraph.

In computer memory, they use display using the letters (like) B290023F.  
Certain decimal numbers look like English words in strident, for example:

FEEDFACE
0993FF10
7229B236
223774290
DEADBEEF  

 Other trivial question is ‘Use a programming language to describe a Chicken’.

‘Why is a manhole round and not a square’?

    Richard Feynman got an interview at Microsoft, (apocryphal story) that why manhole covers were round to which he replied, “covers are round by definition”, and that tautology was most trivial answer considered.

    Imagine a country where parents want to have a boy and they stop when a boy is born (else the births continue when girls are born). “What is the proportion of boys to girls in this country”?  Google doesn’t want people who instinctively do things the hard way because they can – instead it expects simple solutions that is human element. The capacity to ignore what you have learnt is appreciated when it is not helpful.

    A motive behind one of the interviews has been to find out what separates an entrepreneur from an engineer through the candidate. It should be the ability not to think like an engineer, when the entrepreneur has to ignore ideas and judge whether the end users will want or be able to use product.

How the people (should) think was humorously narrated with a story:

    A helicopter flying over Seattle malfunctioned and all its navigation panels failed. Due to fog and smog the pilot was not able to locate any identity of any location. The pilot then held out a sign in large letters asking -“where am I”, over a tall building that was partially visible. The people from the building replied in a similar manner- “you are in a helicopter”. The pilot, then safely landed in an airfield, and when the copilot asked how he was able to judge the airfield, the pilot replied that only people from Microsoft can give technically correct answer, (but a completely useless one), that helped him guess the building location.

 Some more questions to think:

    If you had a stack of pennies as tall as the Empire State building, could you fit them all in one room now. The answer is 'no' because a room of the size of 102 stories room doesn’t exist. But you can go on discussing it. 

You’ve got a clock with a second hand, how many times in a day, all the three-hands overlap?

There are 11 alignments, like:
 12:00:00
 01:05:27
 02:10:54
 03:16:21  etc.
If you answer this rightly you might be asked to determine if any of these times is a three-hands overlap? (digitally)

Some logic puzzles like X +Y+ Z = N can be asked 

Lateral thinking, puzzles like there are three women in swimsuits: Two are sad, and One is happy. The Sad One is smiling and happier and the Happier One is crying. Explain.

Test of divergent thinking, like how would you compare the 'search engines' by taking out an example?

    Fermi question like 'how many tennis balls can you fit in this room', warrants a casual look around the room that should be around 1000 to 2000 cubic feet, and one must remember the size of the ball to be between 2.5 and 2.7 inches, along.

    Algorithmic questions like “you have a wardrobe full of shirts, and it is hard to find what you want to wear. How would you organise them for easy retrieval”?  (You can organize it based on colour style, stripes, checks, sleeves, less, etc. without suggesting extra cupboards!)

Explaining the sequence like what comes after

 1
11
21
1211
111221

Well, you can literally read to find out the answer (like two 1 for the second line).

Art of visual solution is also on the anvil once you get through all answers.

“Break a stick into three random pieces. What is the probability that pieces can be put together to form a triangle”?

Picture-based questions like “a pizza must be cut into equal slices based on your consumption in 3 second and your friend's in 5 seconds".

*Add any standard arithmetic sign to this equation to make it true: 3 1 3 6 = 8
*How many different ways can you paint a cube with three colours of Paint?
*How to estimate just about anything in 60 seconds or less like how much money Google makes daily on Gmail ads (roughly $70,000).
*Name a piece of technology you read about recently and tell your own creative execution for an ad for that product.

Fermi questions like these were more: Advantage: Fermi questions can help invent new ones easily.

*How many piano tuners are there in Chicago?
(Because Fermi believed that anyone with PhD in physics should be able to estimate it)
*Estimate the number of taxis in New York.
*How many golf balls would fit in a stadium.
*How many seniors are there in four-year course in a University?
*Once Intel asked how many lines of C or C++ code the candidate has written for an app.
*How many toilet papers would it take to cover Greater London?
*How many ridges are there in the rim of US quarter?
*How many bottles of shampoo are produced in the world over a year?
*What is two power 64? The final chapter has a story to all the questions that follow.

    You work in 100 story building and are given two identical eggs. You have to determine the highest floor from which an egg can be dropped without breaking. You are allowed to break both eggs in the process. How many drops would it take you to do it? Carl Mill of a School created this idea because he found that raw eggs usually do not break when dropped on grass, regardless of the height of the drop.  A man then climbed a top 70-foot ladder and dropped 10 eggs into the grass out of which 7 survived and an RAF officer dropped 18 eggs from helicopter 150 feet above the ground and 15 eggs survived, which is 83%. So, for 100 floors, how would you estimate the floor from where egg would not break?

    For this, you have to use the formula and try dropping from 10, 20, 30… floors gradually.  If your second egg breaks at 58, that means the eggs-safe floor is 57.  Google algorithm takes maximum number of drops needed to determine the correct floor. It is called as D and assuming from a single series D + (D -1) + (D -2) + … +3+2+1 equation can be written and from there algebra can be summoned for the solution.

This section has another insightful question.

“How would you estimate the weight of your head”?

    Invoke Archimedes principle. The amount of water displaced is equal the volume of the part immersed. So, dip yourselves first and find out the water displaced. Then dip your head only to find out the water displaced and subtract the latter from former. But wait….. you have to define where the head ends and the body starts!

Many of the questions have no “correct” answers, but more accurately, the company has a “preferred” answer. And for inquiring minds you may be smart enough to work at Google.

    A very interesting aspect of this book is that the last half of the book supplies not just the answers to all the puzzles, but detailed explanations as well. The style of the writing has a light touch and is often subtly humorous. The book is recommended for all those who like a diverse range of challenging puzzles.

The goal is to find out where the candidate run out of ideas.

#Weapons_of_Math_Destruction - Cathy_O'Neil - #Review

There is no mathematics, though, rather it is all Statistics and the apprehension associated with Big Data when it comes to application either through AI or ML. Most of the time we may agree with almost all of the author’s views but there is no algorithm anywhere as the title suggests, and instead O'Neil takes us on a tour of a collection of business and public policy malpractices, stating that the solution is to "encode values into our algorithms.". This might really be useful for some. With a few real stuff inside I think it would be an excellent book for those with skills and interest in social justice to take to an interview with Big Data firms.

    Still, I am inclined to think this book is best targeted to thoughtful high schoolers and college-aged students who are thinking about planning their careers, who have a penchant for mathematical and computer modeling. You may be able to conclude the effect of:

 # Targeted advertising, especially the way it allows predatory advertisers

# Predictive policing, with equality before the law replaced by an algorithm that sends different degrees of law enforcement into different communities.

# Automated algorithms sorting and rejecting job applications, with indirect consequences of discrimination against classes of people.

# Poorly thought-out algorithms for evaluating teachers, sometimes driving excellent teachers from their jobs.

# Algorithms that score credit, determine access to mortgages and to insurance, often with the effect of making sure that those deemed losers stay that way.

 Some of these are broken down chapter wise:

        After teaching math at Barnard College for several years, O’Neil left Academia for a new “laboratory”—the global economy. As a “quant” (quantitative analyst) for D.E. Shaw, a major hedge fund, she was amazed by how the operations she and her team performed each day translated into “trillions of dollars sloshing” between accounts. But in the fall of 2008, everything changed and the financial crisis brought the economy to a halt.

     The Big Data economy emerged as mathematicians began calculating human potential (as students, workers, lovers, criminals), but its math-powered algorithms were encoded with their creators' human prejudices and biases. Despite deepening the global wealth divide, Big Data seemed unimpeachable. Author O'Neil calls these harmful models "Weapons of Math Destruction." While baseball statisticians also use complex, game-defining models, those models are transparent, offering everyone access to the statistics that rule the sport.

    Human beings carry models in their heads all day—as an example, O’Neil uses the “informal model” of how she decides what to cook for her large family each night. She has data (each person’s likes and dislikes), and she has new information concerning that data all the time: fluctuating grocery prices, changing tastes, and anomalies like special meals for special occasions.

     Prosecutors in Harris County are three times more likely to seek the death penalty for Black people. And sentences imposed on Black men (who comprise only 13% of the U.S. population yet make up 40% of the U.S.’s prison population) are 20% longer than those for white people.  The three elements of a WMD, according to O’Neil, are opacity, scale, and damage.

     To illustrate the scale of a Weapon of Math Destruction (WMD), O'Neil asks readers to imagine the "caveman diet" becoming a mandatory national standard for all 330 million Americans, creating a distorted economic climate. This large-scale distortion is what happened to higher education when the U.S. News & World Report, in 1983, began ranking approximately 1,800 colleges based initially on opinion surveys. The rankings, which attempted to statistically measure "educational excellence," created a "rat race" in U.S. academia.

     Big tech platforms like Google and Facebook allow for-profit universities to segment and target vulnerable populations with ads based on A/B testing, similar to massive credit card mailings. These ads often use fake job postings or misleading promises (like routes to food stamps/Medical aid) to gather user information and send it to recruiters. Separately, after police force cuts in 2013, Pennsylvania's police chief invested in PredPol, a Big Data crime prediction software. This system used historical data to forecast crime hotspots, and within a year, burglaries in vulnerable areas were down by over 20 percent.

     While human programs like stop and frisk (a program in which NYPD offers were given the go-ahead to stop, search, and frisk anyone who seemed suspicious anywhere at any time) have created friction and danger in vulnerable communities, mathematical models now dominate law enforcement. The author suggests justice system data scientists must understand the realities inside prisons (like solitary confinement, rape, and malnutrition) and instead study how improvements—such as better food, sunlight, and educational programs—could impact recidivism rates. Similarly, Human Resources departments rely on automated processes to screen resumes, forcing jobseekers to adapt their writing to the algorithms' prioritized buzzwords. The overall issue is that WMDs (Weapons of Math Destruction) often serve unfair objectives instead of helping people.

     American workers have coined a new idea: “clopening” -closing and opening, and refers to when an employee works late closing one night and comes in early, sometimes just a few hours later, to open up shop the next morning. U.S. government data shows that over two-thirds of food service workers and over half of retail workers find out about scheduling changes with less than a week’s notice.

     Modern-day scheduling technology is rooted in the discipline of applied mathematics called “operations research” (OR). Mathematicians used OR to help farmers plan crop plantings. During World War II, OR was used to help the U.S. and British militaries optimize their resources. After the war, OR was used in manufacturing and supply chain logistics, and now it underpins huge companies like Amazon, FedEx, and UPS. But these models exploit workers, bending their lives to unfair schedules. Optimization programs are everywhere now, and they’ve contributed to the creation of what O’Neil calls a “captive workforce.”

    Local bankers, who knew their neighbors and their backgrounds, previously controlled lending with a human, intelligent judgment. Now, companies like Neustar use metrics like location and internet history to assign "e-scores," creating destructive and biased feedback loops that move society away from fairness. Instead of slowing down to allow for greater human oversight, the tech world is doubling down on predictive models. For example, Facebook has patented a credit rating system based on social networks that can unfairly privilege a person (e.g., a white, connected college graduate with no credit) while penalizing a hardworking person (e.g., a Black or Latino housecleaner) whose social network may include unemployed or incarcerated friends.

     In 1896, a German statistician named Frederick Hoffman, who worked for the Prudential Life Insurance Company created a WMD. He published a 330-page report claiming that the lives of Black Americans were so precarious that “the entire race was uninsurable.” It was he who first correlated smoking with cancer! However, like many other WMDs, Hoffman’s analysis was statistically flawed, racist, and unfortunately widespread.

     In 2015, Swift Transportation, the largest U.S. trucking company, installed dual-facing cameras in long-haul trucks to reduce accidents and insurance costs. This also allowed them to gather data to optimize profits and compare drivers. Now, insurance companies offer regular drivers discounts if they share data via a small, in-car telemetric unit. Similarly, companies like Michelin and CVS have implemented wellness programs that charge employees extra for failing to meet goals (like glucose or cholesterol targets) or for refusing to report their health data. Finally, the author notes that posting a petition for tougher WMD regulations on Facebook means the site's algorithm controls who sees it, based on its existing data about one's friends.

     During the 2010 and 2012 U.S. elections, Facebook created experiments to hone a tool called the “voter megaphone” that would allow people to spread the word about voting. Facebook was encouraging over 61 million American users to get out and vote by leveraging peer pressure against them. Because the profits of companies like Facebook, Google, Apple, Microsoft, Amazon, and Verizon are heavily regulated by government policies, these companies often spend a lot of money lobbying and donating to the political system. Now, they can influence Americans’ political behavior and, as a result, the shape of American government.

     Rayid Ghani, one of the campaign’s data scientists, when Obama was presidential candidate had previously worked on projects for a consulting that analyzed grocery stores’ consumer data. This information was used to create customized shopping plans for many kinds of shoppers: coupon-clippers, brand loyalists, foodies, and so on. Now, Ghani was trying to see if similar calculations would work on swing voters. The shoppers who switched brands to save a few cents often behaved like swing voters.

     The main difference between the WMDs of the present and the prejudiced human error of the past is simple: humans can evolve, learn, and adapt. But automated systems are stuck in time—engineers have to change them as society progresses. So essentially, “Big Data processes codify the past” rather than inventing the future. Only humans have the “moral imagination” needed to create a better world. 

 

#Quantum_Physics_Made_me_do_it - Jeremie_Harris - Review

There have been so far relevant guides to the most paradoxical notions proposed around quantum world throughout the last century. Some cut out the unnecessarily complex way of explaining concepts, and this one really hits its mark in terms of being the perfect introduction to quantum mechanics and the questions that arise from it for complete novices or for those looking for a light-hearted science reader. The book happens to be packed with science and excellent discussions on the repercussions for humanity if certain theories are in fact correct but fails midway when the author switches over to criticizing other writers. Here is the brief break-up as we move through the books.

    The author explains that the most scientists have misunderstood how nature works. To begin with the author, Harris, starts with Youngs double slit experiment giving due credit to Max Planck on Quanta and then goes over to Schrodinger and Heisenberg’s theories.

    The use of Kats has always been confusing when describing quantum states. Using illustrations and simple points like, | ¶ > | ↑ > + | ↓ >, Harris goes on to explain the probabilities that are confusing the scientific minds and ends up with Schrodinger’s cat experiment on collapsing that forms the ultimate basis of quantum mechanics.

    That Einstein did not like randomness formed part of debate with Bohr and if we really looked hard enough we would actually discover that random experimental outcomes are controlled by variables we aren’t aware of! Thus, consciousness-based QM was born and the author, therefore, illustrates that Multiverse exist and vanish (with Kats, of course).

Here are some extracts from the chapters:

    When consciousness collapses the physics around should also. Ancient Egyptians used to treat tooth aches by killing rats, grinding them and applying them over the affected place.  What mouse paste is to dentistry is to invention was before brush. Using microscope of observation in kats is discussed - shouldn’t microscope also exist at various states

Neumann showed that there is no cut off between big and small.

    Science treats world as it if it is made of up of inanimate objects but consciousness is s subjective thing. Deepak Chopra with almost 80 books and over 80 million copies sold is fully criticized because his receipt includes gimmicks using Quantum age physics that works with new age meditation stuff for gullible audience. Quite similarly another author, Amit Goswami, who said electron does not exist before it was observed, is also denounced. Kets again with observer and light!

There is nothing wrong with being wrong.

Schrodinger and big bang would look alike!

Free will and do we have it?

    Goswami again - consciousness becomes the source of all your decisions and make those decision by selecting possible timelines to collapse into existence

    Collapse without consciousness - Kat diagram on spontaneous collapse. Free will and the law of quantum world. Brain cells – signals are made of tiny quantum particles so they can exist at many places at once and collapse into one location. If deontology won’t do, then what are your other options?

    Quantum Multiverse and Dunning Kruger effects – people tend to overestimate themselves. About 65% of Americans think that they are more intelligent than average.  If you can calculate the time for a 30 g ball to fall to the ground from a height of 1.5 m, you can develop superiority complex!

    Kat again! On climbing Mount Everest, but ever thinking about zombie cat? Dead and live cat - enter academic politics - Bose idea. A world of small things which can exist at many places at the same time and big things which can’t. This was Einstein versus Bose dialogue.

     Why do I have to empty water bottle on this stupid flight? 10 octillion cells come together to make your body. Its presence in the universe. Universe is probability from physics to biology and vice versa. It talks about Femi paradox, which says aliens do exist of Multiverse in a particular religion story. You probably believe in it to the exclusion of others and all others or false.

Kats on quantum suicide! We can’t experience being dead is a state. We experience life as a series of digital moments, and in each moment, we feel complete existence in a particular moment. The Mandela effect is unrelated to his death, but it is about winning a lottery after surviving three lightning strikes!

    About chemistry on atoms, if they existed to figure out electrons in the orbital refinement, clarity and confidence go together. General discussion on nuclear weapon program leaves, MAGA theory and Saddam Hussain in relativity.

Bohemian mechanics again, double slit experiment in a duet discussion.

 A little philosophy is a dangerous thing, fatalism

    Physics has any kind of explanation for emergence of consciousness. Plants consciousness, feature of consciousness.  AI needs data processing power and model and use this to learn calculus in the era of AIGPTx. We are offloading more and more of our thinking to machines. Who is to blame when something goes wrong? The free will has to be informed, at least by quantum physics doing physics.

 If you can make out the connections between the lines given above, then the book is a good read!

 

#Humble_Pi: A comedy of Maths Errors - #Matt_Parker - Review

    This is certainly the first book I have ever read in which page numbers start from higher to lowest. And it has been done to justify the title (a comedy of maths errors). The book is filled with informative and relentlessly entertaining matter right from the beginning and has illustrations that are related to real-time incidents. The author explores the series of glitches, near-misses, mathematical mishaps leading to severe bugs over the PCs or internet, chunks in big data, logic in street signs, the chances over winning lotteries and bizarre ways the maths can equip or trip us. The book is well organized so that similar themes are framed together into chapters, but each incident is only a few pages long so that it never gives a feeling of bogged down or boring.

     

    There are surprises with some punchlines that are explained in a way that they would actually be only one of the things that would be needed to go wrong for the whole thing to come crashing down. The author explains that sometimes stuff ups are necessary, rather than just tragic accidents. Progress in science and engineering often involves someone needing to go beyond the cutting edge, on to the bleeding edge. The mistakes were thus costlier that would have been avoided if there was no complacency in making decisions.

 Here is the brief break-up of the book, chapter-wise:

    The number 4,294,967,295 was the most attractive one the author mentions in the first chapter, more so because for a few pages it was kept suspense before revealing to us that this happens to be the 32-digit '1's full capacity (in binary form), attaining which, any system would restart. Quite similar to BSOD, the dumping of memory and the micro-second timed restart of the PC went unnoticed in California’s airport, leading to loss of radio connections with almost 800 aeroplanes. Now that this memory has been replaced with 64-bit ones, it might take 584.9 million years for the system to restart upon reaching the maximum limit of that capacity by a memory device! 

The subtle account of Earth’s rotation around the sun has been considered for accuracy calculations of periods. The difference between sidereal and tropical year has been discussed thus. There is this mention about the Y2K38 bug that would make microprocessors stop working on 19^th January 2038 for the same reasons mentioned above. The info that February 1847 has 31 days did not prove to be true when the author asks the readers to check their iPhones for this ‘feature’. Probably it has been corrected.

    The next chapter is an elaborate account on Engineering mistakes with ample examples that I never thought would be the result of miscalculations. A building in Fenchurch London designed with a sweeping curve behaved like a massive concave mirror in a season such that it set fire to everything that came in its way of convergence.  A few suspension bridges in UK had matched severely with a frequency of 1 Hz leading to wobbling and collapsing. It was Boston tower that collapsed when 167^th person entered it, a threshold for the frequency mentioned as the bolts and nuts used had an individual capacity bearing (approx. 9280 kg, integrated). We are considered as approximation machines and maths can get us to correct values.

            That some English names have been making us go crazy is well known and Steve Null was a name whose entry was often non-existent wherever he tried to enter. So it was important that the names are well defined (reserved words). Similarly, the flaws in open spread sheets in excel is mentioned with some examples. A similar incident with data entry led to vanishing of war in Afghanistan!

    A wrong design of a road signal in UK was brought to notice legally by a person as he found that the football image over the signal light was not in tune with a regular one where one pentagon is surrounded by hexagons. The author goes on to describe the efficacy of bee-hive type structures.  The Apollo program’s space craft had a door that opened inside, finds a vital mention to prove the structural efficiency.  Similarly, the ‘Challenger’ disaster had a design flaw. Richard Feynman who was in the inquiry team demonstrated that the O-rings (rubber gasket), that separated two fuel tanks, failed to spring back when the temperature was cooled to ice’s. Though a simple one, the place it was used has a significant impact that was not rightly addressed. A brief discussion on gears and mechanism ensues till the end of this chapter.

    Someone going to gym was very particular about the number of days in a week. Though the concept is silly, but in a month as weeks get repeated working every alternate day would actually let you do so four times a week! A few pages are devoted to McDonald’s menu in which some combination of the menu legends did not mean breakfast or meal. The customer once chose such a combination on flavours and refused to pay on par with meal in the menu as prescribed by the outlet. The number of ways to arrange these eight menus are given as 8x7x6x5x4x3x2x1=81. 

The number of digits on zip codes is discussed with regard to USA. A game on M K Gandhi, who is revered in India, was responsible for losing the reputation merely because the game ‘Civilization’ treated a number 255 as aggressive. Downloaded and played by more than 33 million, a 1-2 subtraction was resulting in '255', which was a flaw in binary system. Remember 255 contacts as the limit in WhatsApp? (256 being the order of 8-bits). This 255-type problem was there with Swiss rail system as well. The author writes that this rail network was probably the only one which issued bureaucracy patch rather than software – 256 axles should not be engaged in the system. 

The 256- error was a life taking issue with Therac-25 a medical radiation device which was used to treat cancer patients with electron beam bursts. A setup required a routine check-up as class 3=0. A subroutine would slightly activate whenever the class was zero and the beam would fire the electrons. Unfortunately, it was 8-digit binary number which should roll back to zero after it had maxed out. Every 256^th time it would roll back 0.4% of time to skip running. This error led to continued bursts resulting in the death of a patient in 1987.

An interesting truncation error that was encountered during the first Gulf war. Every time a 'Scud' was fired 'Patriot' would destroy it before it reached the target. Due to a mathematical oversight in ‘Excel’ the fraction of 1/3^rd of a second was omitted leading to a lapse of about 500 meters. This accumulated calculation error misled the Patriot, and it could not fire on time. The software error was later located, patched up and imported from US for later use.

A large number of examples on probability can be found in this part of the book which is titled ‘probably wrong’. A woman was wrongly sentenced twice in UK due to statistical error. The work out was there but it did not evince any interest in me. Factors like these were also documented when using dice and lotteries. But the probability of error with regard to the Columbia shuttle drew my attention. When Feynman was inquiring into its disaster, he was told by over-confident engineers that the probability of errors was like 1 in 100000. Feynman put it as ‘it would mean a daily launch of the shuttle for 300 years with only one disaster', and which was probably improbable. That was just some data like 46087 road deaths were in USA in spite of them driving about 1838240000000 miles that year!

    Putting money where your mistakes is all about stock exchange and commerce related.  On 8 December 2005 the Japanese investment firm Mizuho Securities sent an order to the Tokyo Stock Exchange to sell a single share in the company J-COM Co. Ltd for ¥610,000 (around £3,000 at the time). Well, they thought they were selling one share for ¥610,000 but the person typing in the order accidentally swapped the numbers and put in an order to sell 610,000 shares for ¥1 each. They frantically tried to cancel it, but the Tokyo Stock Exchange was proving resistant. Other firms were snapping up the discount shares and, by the time trading was suspended the following day, Mizuho Securities were looking at a minimum of ¥27 billion in losses. It was described as a ‘fat fingers’ error. 

    The world’s most expensive book ‘The making of a fly’ was purchased by the author. What made it so expensive? Well, the seller used algorithm that raised the price of the book each day by 26.8% when the buyer visited the site.  It was a whopping 23,698,000 USD! After Stocks and Commerce, political issue finds a way into the book. The election in Schleswig Holstein where an MP can get seat only if he scores 5, got a breakthrough when his actual number 4.97 was rounded off to 5. Trump also gets a mention.

 Salami slicing is the term used when something is gradually removed one tiny un-noticeable piece at a time.

Because we had no accurate clock in the 1917 someone might have actually run faster than the present-day athlete whose clocks now show accurate times.  When the US switches all coal power to solar it would save 51999 lives - roughly 52000 lives, and each life matters. 13.8 million years do not mean 13,800,003 years when mentioned 3 years later. In view of all the above a round-about is always essential but with a caveat. R Sikdar of Kolkata actually calculated the tallest mountain, but GTS which was run by someone not aware of minute details about calculations, ignored the value and put forth the name of Everest so that this became the tallest mountain in the world!

Hubble Space Telescope was put into orbit in 1990, and its first images were blurred - out of focus due to mirrors’ wrong shape. The wrong paraboloid 1G to 0G lead to edges of 2.4 m mirror - about 2.2 µm longer. The correction was carried out in the repair mission.

Mecca of mistakes carries the photo of Mecca over a mobile phone having a compass app on play. The direction is not straight towards it, but rather tilted. Due to small uncorrected errors proper west is not thus shown. Similarly, a small error in a flight windshield bolts A211 had two almost similar notations - 7D and 8C. A lot of difference was there in size at the micro level. A BAC 1-11 jet liner of British Airways fixed wrong bolt and during the flight the wind screen exploded outward. The chronology of this event, right from checking to take off is mentioned in great detail showing the ignorance of the minute mathematical error leading to major accident when the pilot was flown out!

The conventional unit mistake, often quoted in many books, when in 1998 NASA’s Mars Climate Orbited flopped due to discrepancy in units, finds a mention.

The funny thing about British prime minister was a question “how long has Theresa been pm?” and the engine threw an answer 1.72 trillion pm! PM was for minister and the pm it mentions is ‘Pico-meter’!

Aircraft fuel is calculated in mass and not in volume. Flight T143 of Air Canada was wrongly fuelled but accident was avoided during a cross check. The two-way connection from the aircraft’s two wings over a common sensor circuit is discussed clearly and was a pleasure to read.

Defining average has always been a serious task, particularly in Australia. An average Australian meant a 37-year-old living with a spouse and two children and to fit this data a man at a place emerged from nowhere during a survey. There are often incidents of people appearing from nowhere, just like people disappearing. The concept of average gets a go all along this chapter. Fitting of clothes with average in mind is another example when dress makers carry out the region wise average assessment. Some statistical measurements have more than one formula to produce desired result and many examples are quoted in this chapter.

Sampling bias like conducting a survey about what people might think of modem tech but only accepting submission by fax, makes a lighter read of the bias. A negative result from a drug trial is twice as likely to remain unpublished as positive result (should be published).  In 1980 anti-arrythmias drug trial was conducted whereby out of 48 patients, 9 died but only 1 of the 47 given placebos died.  And it took 13 years for this fact to get published. It is in Maths that you can find any pattern you want as long as you are prepared to ignore enough data. 

Direct correlation between the number of mobile phone towers and number of births in UK was fun to read - with every tower 17.6 birth was recorded. Woolworth a chain shop went bankrupt, the GPS coordinates of 800 ex Woolworth was downloaded, and they revealed a kind of equilateral triangle between nearest locations, and it was illustrated for us to understand. The author mentions that everywhere the triangular pattern was found the Woolworth went bankrupt. Did Aliens help?

Totally Random: Netscape navigators id generation was random so was IBM’s randomness calculation about any id.  Chrome had to fix a random number generator because math.random, a java script they used was returning used numbers after few million tasks. It was then that the chrome download was shown as zero even though thousands downloaded it.

Three things are certain in life: death, taxes and people trying to cheat on taxes.

If you can write a short computer program to generate s sequence of numbers, then the sequence cannot be random – Kolmogorov complexity.

    ESA’s Ariane 5 in June 1996 went on self-destruction mode 40 seconds after launch. Inquiry revealed that the on-board computer tried to copy a 64-bit number into a 16-bit space. The space craft was using an old sensor meant for Ariane 4. This tracked the rocket based on inertia and as the space exceeded the available space, the SRI (Inertial Reference System) device which gets raw data to convert into meaningful one calculated that the trajectory was different, and it meant that rocket was going haywire. To avoid debris damage in the residential area the self-destruction mode, programmed inside, was invoked and it blasted the entire system over the sea. 

There was this case of University of North Carolina not able to send mail to far off places. it was a problem with sendmail.cf and is a classic case of a program trying to digest data that was not intended for it. More than 500 miles, an update set the things right.

So, the advice to the programmer is to recheck and reverify the code that is written. The metaphor the author uses at the end of the book is hot Swiss cheese, that is, there are always holes, you need to make sure that the holes in your defences don't all line up or rather line up when you want a get-through. If you are smart, the challenge is to create something that will be as close to fool proof as you can, rather than blaming people afterwards for not using it properly.

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