#Artificial_Intelligence - A Guide for Thinking Humans - #Melanie_Mitchell - Review

As titled, the entire book is an optimal guide for all thinking humans.  The author begins the book with a sketch of the history of AI research from its birth in the 1950s, and outlines its key figures and significant ideological branches. These are broadly classified as symbolic (conscious reasoning) and sub-symbolic (sub-conscious learning) with the latter being biologically inspired structures which learn patterns and rules via lots of data.

     AI is a technological endeavour, and like other big sci-fi dreams - deep space travel, cheap clean energy, trans-humanism - there is an enormous gap between our current capability and our vividly imagined end point (with most registering it as ‘fear’). It's a gap that's easy to dismiss while breathlessly fretting over superintelligence and singularities, but that gap is filled with some extremely difficult challenges that we currently have little idea how to approach, let alone solve. That is why, probably, data sets and data analysis is to the fore in all branches of science and technology.

 The author comes out with various examples of the data sets and training basis for the machines. (and the book is full of illustrations as well). We have heard about ‘Big Data’ but never realized that this would end up with such an extended benefit. It is perhaps for this reason that any data in general and scientific one in particular is being accepted for publication by most publishers. Earlier, this used to be the same data that would be rejected citing no novelty or creativity in the matter. If you have a repeat data the caveat for its publication would be a geo-tag (as the project would be new for that place and the results vary, credibly).

Here is the brief summary of the various sections in the book, rather guide:

 # The first and the simple start is ‘definition’ - “Define your terms or we shall never understand one another”. IQ which is measured in single scale can thus be differentiated with various dimensions – emotional, verbal, spatial, logical social etc.

 That GPS actually was shorted for 'General Problem Solver' rather than what we have assumed today was a surprise for me.

    The author explains the initiation of logical coding with a simple old school puzzle of two men with a man-eater trying to cross a river with one boat so that only two species are allowed in the boat or trip once or at a time and the man should never be left alone at the mercy of man-eater. The coding would then be working out various combinations using symbols, ifs, buts….

     Mitchell wonders over why we trust a review from a person who is a friend and give weightage to his opinion over others’. She meant to convey that the machine might not be able to analyze the ‘trust’ factor despite it having all the data!  There is a lot more discussion on perception of figures by the machine so that data alone (imagine all sorts of) cannot help it.

Getting along new ideas create lot of optimism breakthrough!

 # Neural network takes the second (entire) chapter with a lot of discussion on the ‘layers’ and is discussed with a view on human brain from which most of the inspiration for analyses is procured.  There is always an input layer and an output layer which are not hidden and what happens inside the hidden layer is based on data and subsequent logics. AI spring and AI winter intervene in between.

 Anthropomorphize is a language that machines might think is based on Turing’s test.

In this connection Ray Kurzweils ‘Text to Speech’ analysis is suggested to give futuristic prognostications machine.  His books “The age of spiritual machines” and “The singularity is near” is suggested for further reading.

# The concept of exponential growth is mentioned with a story of a Sage visiting a neighboring rich king and challenging to answer any question asked by the ‘Darbar’. The sage extracts promise from the king that he be given grains that double over the chessboard, each time he answers correctly. The king at first laughs over it and agrees only to realize in dismay that by the time second row of the chess (16 squares) get covered, about 65,536 grains got accumulated (~ 2 kg). Here it should make a difference between the man and machine. While the king realizes the wisdom of the sage, the machine might not!

# Raw info from pictures in obtained from simple queries like: who, what, when, where and why. This section has many examples that are simple as well. But when it comes to transformation into ‘wisdom’, the marvelous nature of the brain could never be understood. How brain transforms visual info into what the scene can be hard for the machine to accomplish. For instance, a dog with human is hard for the machines to recognize if the picture is not clear. So, the data scientists built a network called ConvNet (Convolutional Neural network), where the Shades of pictures are pixelated and the values given and evaluation is done.

# PASCAL gets a tribute as one of the early machine level language and is implemented by Amazon as Mechanical Turk which still requires human intelligence to work with for its market place.

 # That Facebook with which we were happier when it was started as a social platform had actually a hidden objective. It gathered all our data and registered a patent for classifying our photos with emotions behind expressions.  Using similar logic Twitter filtered pornographic images. And for the analytical part it required huge number of CPU’s and it was then that the Nvidia’s GPUS stock prices increased 1000% when ConvNet and ImageNet usage doubled.

#  Facebook says ‘thankyou’ because it was able to differentiate persons using imaging techniques and was able to offer ‘tag’ for persons. Likewise, Flickr used pictures for its training to recognize them via machines. There is description of Long tail graph with many good examples, simple though and that might give an idea on how anyone can consider simple failure possibilities into avoiding catastrophes. This is basically a figuring graph that talks about the likelihood of things that may appear while performing (i.e. if you want to train a driver-less car you have to mention what all it can come across, like more traffic lights and less often a lion on the street).

# Images with blurry background predicting animals and Camera face determination seeing blinking Asians or racially denigrating species have been some output by the machines, particularly with Deep Neural network.

# Ethical AI section deals a lot with the ethics a machine might not ‘know’. If you have asked a driver-less car to take you home it cannot determine if it really knows what it need to know. It might warrant a misuse of the car. Google’s DeepMind has thus postulated a lot for the beneficial ethics of face recognition (similar to FB asking to tag you).

 #  Fundamental rules of Robotics by Asimov get mentioned and it is always exciting to read.

“A robot must not injure a human” and further rules make a pleasant reading. It is here that the author talks about a simple Trolley problem. There is a picture of a woman with a trolley trying to cross a road and she is engrossed over mobile while doing the cross. If an unmanned vehicle suddenly came across such a woman and if it were to avoid hitting her it would have to make a swerve in a opposite direction that may kill more than this single woman. So then should the car go ahead and kill one instead of six?

# That Steve Jobs started his career when Atari, a breakout game was assigned to him might provide solace to all the gamers who are struggling with a slow IT future. But Steve was a hard worker and he probably knew the future of the machines that might have helped him achieve his goals. The concept of Supervised learning vs Reinforcement learning is then discussed to lessen the projected dangers expected of the machines. Similarly, another stalwart gets his name mentioned: Checkers and Chess code writer Arthur Samuel of IBM who coined the word ML.

     The probability and statistics are useful when you have prediction rather than performance. Deep Blue, another network-based ML firm has made a good foray into many areas, particularly chess where every position may have 35 moves on an average. Similarly, Monte Carlo, the simulation techniques on probability, including electron position evaluation, was first used to design the atom bomb (Manhattan project) with a family of computer algorithm and so these still do a great job (including Quantum Mechanics).

# The information derived from data with any result or conclusion often takes time for the machine. If not for the processor, it would never beat humans who have now started to lag behind machines leaving the latter do all the stuff. The various conclusions drawn from a single story is discussed in many pages with interesting results that prove that AI cannot beat human intelligence. Here is the story: In a restaurant a customer orders some food which gets unfortunately charred by the cook and the waitress presents the food to the customer with an excuse but the man leaves the restaurant murmuring some words (machines cannot get that while humans can guess his dismay). The waitress' last words were “why is he so bent”?

Now this story is fed to machine with a translator and different languages interpret different views which makes the reading interesting (Word2Vec initially). But it is only the human being who can understand well that the customer went without eating! (Gracias: Neural network layer). One language interpreting machine was able to conclude the angular geometry!!! (bent).

     A new rule of the thumb probably displaces 80-20 rule for the learners to 90-10 – the first 90% of a project takes about 10% of time and the last 10% taking 90% of time.

In the end, the author puts forward the speculations around AI as the expectations associated with it are very high. There is no exact conclusion about the future of the AI and only incremental or infinitesimal changes in the technological front over a period would be to the fore. People who know coding, algorithms and data science would have tough time to train machines to get closer to the natural intelligence. And those without any data or algorithm would be left pondering in uncertainty.

#Determined: Life_Without_Free_Will by #Robert_Sapolsky – Review

     The forty years that the author spent as a neuro-scientist of human beings has been funneled into a one-word fact ‘Determined’ in the form of a book with a big, splashy write-up, both in number of pages and ambitions. 

    Robert Sapolsky sums up that we do not have ‘free will’ because our actions resulted from prior causes, which themselves resulted from prior causes, which….., corollary with the answer Einstein got over a question on how the Earth is balanced – Turtles all the way down (a seamless chain of causality stretching back in time), and uses a very sensible way of explaining ‘no free will’ on a large and small scale connecting both in the same instance. He leads us through this causal chain of events that goes back to seconds (caused by neurons firing), to minutes (caused by a thought, memory, emotion, or sensory stimulus), hours to days (hormones), to months (long-term experiences, e.g. depression), to years and decades (e.g. childhood, genetics), and centuries (the culture of ancestors).

     Sapolsky duly argues that the activity of brain precedes conscious awareness of actions and introduces us to some reliable episodes in the first few chapters. His central point is that “we are nothing more or less than the cumulative biological and environmental luck, over which we had no control”.

      This book is both about the science of why there is no free will (if you do not agree that there is no free will, at first instance) and the science of how we might best live once we accept that. Science, of course, is relevant; but that does not make free will a scientific question. It occurred to me that slavery, not on anyone's will and which has been rampant through ages is morally wrong beyond doubt.

The empirical facts about slavery are relevant, but this does not make the issue of the moral justifiability of slavery a logical question. How we should adjust our attitudes and behavior in light of a belief in determinism, if we were to acquire such a belief, is definitely not a scientific question.  Much of the book is devoted to establishing that our behavior (choices/formations of intentions and actions) is determined in this sense, and thus not free. Sapolsky holds that this implies we cannot legitimately be held morally responsible for it!

 Sapolsky concludes: “You cannot decide all the sensory stimuli in your environment, (Quantum effects) your hormone levels this morning (depending on the available chemicals inside you), whether something traumatic happened to you in the past (not in your control), the socioeconomic status of your parents (almost all can recount this fact), your fetal environment (no choice), your genes (again), whether your ancestors were farmers or herders - we are nothing more or less than the cumulative biological and environmental luck, over which we had no control, that has brought us to any moment”.

 Here is the unit-wise abstract covered in 15 chapters albeit with a lot of foot-notes and elaborate appendix, that is not included here.

 # The introductory chapter is well begun with the goal being “there is no free will” and those who do not believe in that being proved wrong. Due to the cumulative biological and environmental luck a society often holds people morally responsible for the actions every individual denies and this is attributed to the neuron which functions with a causeless cause.

# Slowly moving up to ‘consciousness’ that is often the unsolved mystery with the neuroscientists on where it is stored, this author calls it an ‘irrelevant hiccup’. This is where our matter meets the energy of the Universe.

 # The intention to shoot is crime and how missing a shot absolves the shooter of the crime is considered as something not in our control. Thus if the shooter can press the button for shooting, it is triggered by the molecule in the brain which connects the matter around us to the consciousness part of our existence. For such a task to be taken up the hormone glucocorticoids mobilises energy from the cell and the hippocampus raises the levels of these to execute it.

 # The actual work of the author is probably described in this unit where it was interesting to learn that the ‘frontal cortex’ is the last part of the brain to develop – and is least shaped by genes and most helped by environment!  That is why some mature slowly and hence childhood environment is held essential for the development of a person including the way he develops in the womb.

 # The process in Pre-Frontal Cortex is held responsible for the analyzing skills a person might develop. Doing the right thing when it is the hardest improves cognition and based on these, humans can evolve out what is right and what is not. An example that is quoted is the mention of ‘months’ in order. After September, October follows but with a mention of August the brain can try to correct the process  and might go on to account for the change in actual 'value'. It is here the value of dopamine comes to the fore and craves for something new or novel. This process is adequate during adulthood.

 # The influence of chaos on us is then narrated as predictability over time. An example is how the “Moon influences the Sun”, which actually means that “Earth influences how the Moon influences the Sun on Earth”! This part of the book has elaborate mazes and puzzles that would warrant a skip if one is not interested in probing them. I did skip.

 # After the rendition of chaos, the relationship between it and 'free will' is accounted next. Indeterminism of Chaos means that although it does not help you prove that there is free will, it lets you prove that you cannot prove that there is not! Even if Chaoticism is unpredictable it is still deterministic.

 # The outcome of these is considered “Emergent complexity”. A waterfall maintains constant emergent features over time despite no water molecule participates in the waterfall-ness more than once. Here the author uses few more examples like Slime mould experimentation, Snow flake construction, branching capillary leaf, diffusion-based geometry etc. Somehow the Pareto distribution principle is discussed (the 80-20 rule). But an interesting part in this chapter was that most proteins in our bodies are specialists interacting only with the handful of other proteins forming small functional units. This is important as we do not supply interacting ‘chemicals’ for these interactions resulting in deficiency of essential chemicals in the body and subsequent malfunctioning.

 # The isomerism, a functional one that is often described in chemistry is best depicted here (keto-enol tautomerism). While the ‘enol’ form of the hormone may lead to estrogen production the ‘keto’ form leads to testosterone and thus a small flip between these isomers (of the order of 100000 flips per second) might result in the birth of a girl or a boy!

 # Quantum Indeterminacy is explained with ample examples including Brownian Motion of the molecules in solution. These particles bumping around randomly may interact and lead to quantum entanglement or non-locality or wave-function collapse or even QM tunneling. It was tough to account why the author says this here, but wave-particle duality could come in handy if one is aware of this – the outcome that is unpredictable.

 # If free will is random, then believing in religious person to be spiritual is left to one’s choice because a religion is organized over several years and gets refined over time so that spirituality has no connection whatsoever as it may be found in normal persons as well (how did he get that? No free will). Quantum healing by an Indian author and other similar books get mentions here. The author randomly choses several facts pertaining to the body functionality to prove this. Tubulin with 445 amino acids with a good number of atoms in this molecule plays a vital role in the hippocampus where one can find several synapses and the law of numbers cannot account for the quantum bubbling that happens there.

 # If free will is a myth, our actions are the mere amoral outcome of biological luck for which we are not responsible. Then why not run amok? There is random mention about DNA, ECG or EEG and the number of countries where people do not believe in God is related with spiritual thoughts.  Religious pro-sociality is mostly about religious people being nice to people like themselves! From micro to macro.

 # The biology underlying your change in behaviour is the same as when a sea slug learns to avoid a shock administered by a researcher. Some correlations with the negative feelings and human reflexes are correlated here with those of animals’ behaviour. This follows an elaborate discussion related to the experimental animal Aplysia whose functionality with a siphon system is sketched out with lots of figures, some including sensors. Suddenly there is Donald Trump’s quote on Mexicans!

 # The last chapters seem to be not very coherent with the aim with which the book was started. There is more about history and the society. How the medicinal system in earlier days was classified into four basic catergories like black and yellow bile, Phlegm, blood and how the seizures (epileptic) in humans were considered as an amusing by the evil spirit. This is then related to thoughts in our brain. Most people have an internal voice in their heads narrating events, reminding us with the tasks ahead and intruding with unrelated thoughts.

 # The Joy of punishment elaborately discusses the history related to punishing the cheaters over several ages. The shocking info about the Nobel Prize winner Breivik gunning down about 69 people just as casually as one could have a walk in the lawn and escaping punishment was something that the Norway government could not resolve amicably. A Nobel man running amok was an uncontrolled event.

 # “After you have pooped, do you wipe front to back or back to front” was a questionnaire over a few European countries.  Those choosing 'front to back' were considered right and those otherwise were supposed to lose friends!  Another one was on 'Leptin' which is there among obese people and has been remarked as a disease by many. There is a poem in the last chapter that I did not read because it seemed out of context. People in future will always marvel at what we did not know. Time!

#Brave_New Words - How_AI_ will_ Revolutionize_ Education (and why that's a good thing) - by Salman Khan #Review

     After coming across a praise heaped by Bill Gates over Salman Khan of Khan Academy, I decided to have a go with this book.  When everyone associated with education is swayed both in positive and negative directions under the influence of information flow, this book presents an optimistic and practical insight on how artificial intelligence will revolutionize education in the coming years. The author proves that the fear that AI will overtake jobs from humans is unfounded. Only persons who don’t accept AI will be left out and embracing AI is not a task no one can accomplish.

Khan's book serves as an invaluable guide for anyone seeking to understand the implications of AI for learning - from parents and teachers to administrators and policymakers. ‘Brave New Words….’ presents a breath of air in that sense, clearly explaining how the Khan Academy has partnered with OpenAI, to provide its own platform for AI named "Khanmigo" promising to offer the best possible integration of AI and education.

As a respected pioneer in educational technology He accessibly explains the core technologies underpinning this AI revolution and lays out specific ways they can be leveraged to provide each learner with adaptive instruction and feedback tailored to their unique needs, interests and pace. While acknowledging that AI is still in a beta stage technologically, Khan compellingly makes the case that its thoughtful adoption in classrooms will enhance, rather than replace, human interaction and creativity.

            Beyond the classroom, Khan explores the broader societal implications of educational AI - from its potential to make hiring more meritocratic to the ethical considerations in its development and deployment. His insights provide a valuable roadmap on how we can proactively shape these technologies as an overwhelming force for good.

            At its core, Brave New Words is a profoundly hopeful book, reflecting Khan's lifelong passion to harness technology to improve lives through learning. While educational AI undoubtedly brings new risks and challenges that require ongoing vigilance, Khan convincingly argues the opportunities for expanding educational access and efficacy are simply too great to ignore. For anyone who cares about the future of education, this book is essential reading.

            Khan sees this technology being used not just in the classroom, but as a way to help parents monitor their children's progress, offer therapy, and even facilitate conversation among families, which he shows he has already carried out.  These are helpful, because of the anxiety of speaking in class, or the difficulty of getting our kids to tell us about their day.

The beginning is a bang, but as you go on you may realise that there is more about his own AI platform and this entire book is basically a really long ad for Khanmigo with ample quotes and examples from it.

#The_Laws_of_Human_Nature by Robert Greene - Review

 

    This is another book that I would classify under the 'fast-read' category because of its narration about human tendencies rather than laws. For an efficient slow reading, one must be in his early 20s as later on he might learn by himself these facts of life. It just appeared to me that with each chapter I was assigning people around me with all the laws and characters the author was mentioning. As most of his 'reviews' were around human nature one might find some or other author of these types of thoughts or reviews around the literature of his time. I found some of his despairs in the voices of some Urdu writers like Akbar Allahabadi, Meer Taqi Meer, and Khaleel Gibran (Persian). But this book is not void of worth as the author has classified the tendencies into laws that can serve to forewarn a beginner who experiences rational emotions around him as he starts working.

 Here is the list of 'laws' from this book

 1. The Law of Irrationality

Often people are dominated by emotions and behave irrationally without realizing it. This is the source of bad decisions and negative patterns in life. Example: Athenes prospered when it was led by Pericles in 400 BC, who is believed to have been a very rational man. After he left the political arena Athenes started to regress.

 2. The Law of Narcissism

Many people are narcissists and thus focus and admire on themselves. This hinders success when interacting with others is essential.   Example: Joseph Stalin — the premier of the Soviet Union — was a very charming and influential person. He was also a narcissist who killed many people during his reign. Leo Tolstoy — a Russian novelist — and his wife Sonya were both narcissists. Their relationship was complicated.

 3. The Law of Role-playing

People tend to wear the mask that shows them in the best possible light hiding their true personality. Example: Milton Erickson — an American psychiatrist and psychologist of the 20th century — was paralysed when he was young and became a master reader of people body language.

 4. The Law of Compulsive Behaviour

People never do something just once. They will inevitably repeat bad behaviour.    Example: Howard Hughes Jr. — an American business magnate, investor, record-setting pilot, engineer, film director, and philanthropist — had a weak character since his childhood. He managed to disguise it in his early career which brought him success. However, it manifested later in his life and resulted in many failures including Hughes Aircraft Company.

5. The Law of Covetousness

People continually desire to possess what they don’t have.  Example: Coco Chanel — a French fashion designer and businesswoman — became so successful not only because she created great products but because she understood that people desire what they don’t have and created an air of mystery around her work.

6. The Law of Short-sightedness

People tend to overreact to present circumstances and ignore what will happen in the future. Example: The South Sea Company — a British joint-stock company founded in 1711 — became known as the South Sea Bubble. It was obvious that the company could not succeed long-term but it didn’t stop many people from investing in its shares.

7. The Law of Defensiveness

People don’t like when someone is trying to change their opinion. Example: Lyndon Johnson — the 36th president of the United States — gained his influence and power by focusing on others, letting them do the talking, letting them be the stars of the show.

8. The Law of Self-sabotage

Our attitude determines much of what happens in our life.  Example: Anton Chekhov — a Russian playwright and short-story writer — had a tough childhood but despite that was able to change his life by changing his view of the world from negative to positive.

9. The Law of Repression

People are rarely who they seem to be. Lurking beneath their polite, affable exterior is inevitably a dark, shadow side consisting of the insecurities and the aggressive, selfish impulses they repress and carefully conceal from public view. Example: Richard Nixon — the 37th president of the United States — always had a positive image in the public. Everything changed after the Watergate scandal which revealed his hidden personality.

10. The Law of Envy

People are envious.  Example: Mary Shelley — author of the novel Frankenstein — was betrayed by her close friend who envied her.

11. The Law of Grandiosity

Even a small measure of success can elevate our natural grandiosity — an unrealistic sense of superiority, a sustained view of oneself as better than others. This can make us lose contact with reality and make irrational decisions.  Example: Michael Eisner had to resign from the CEO position of The Walt Disney Company. In the author’s opinion, the cause is Eisner’s grandiosity elevated by previous successes.

12. The Law of Gender Rigidity

All of us have masculine and feminine qualities. But in the need to present a consistent identity in society, we tend to repress these qualities, overidentifying with the masculine or feminine role expected of us. Thereby we lose valuable dimensions to our character.  Example: Caterina Sforza became an Italian noblewoman and Countess of Forlì and Lady of Imola. Such titles were unusual for women in her time. In the author’s opinion, her masculine qualities helped her to achieve this.

13. The Law of Aimlessness

People become most successful when they have a sense of purpose in their life. Example: Martin Luther King Jr. is best known for advancing civil rights through nonviolence and civil disobedience. His calling directed his actions and helped him go through many failures in his life.

14. The Law of Conformity

We have a side to our character that we are generally unaware of and is related to social life. We tend to become different people when we operate in groups of people such that we unconsciously imitate others. Thus we act and believe differently. Example: Gao Yuan’s story in his book Born Red showed that people in groups behave emotionally and excitedly.

15. The Law of Fickleness

People are always ambivalent about those in power. They want to be led but also to feel free; they want to be protected and enjoy prosperity without making sacrifices; they both worship the king (leader) and want to kill him.    Example: Elizabeth I — Queen of England and Ireland in the 16th century — had to constantly prove herself as the leader of the country.

16. The Law of Aggression

On the surface, the people around you appear so polite and civilized. But beneath the mask, they are all inevitably dealing with frustrations. They have a need to influence people and gain power over circumstances.    Example: John D. Rockefeller — an American oil industry business magnate — used aggressive strategies to gain power and control.

17. The Law of Generational Myopia

You are born into a generation that defines who you are more than you can imagine and this generation wants to separate itself from the previous one to set a new tone for the world. In the process, it forms certain tastes, values, and ways of thinking that you as an individual internalize. As you get older, these generational values and ideas tend to close you off from other points of view, constraining your mind. Example: King Louis XVI of France is shown as an example of someone out of tune with the times. He fell victim to the French Revolution when France was declared to be a Republic and abolished the monarchy.  Keep yourselves updated?

18. The Law of Death Denial

Most people spend their lives avoiding the thought of death.  Example: Mary Flannery O’Connor — an American novelist and short story writer — was diagnosed with systemic lupus erythematosus when she was 27. Her proximity to death was a call to stir herself to action and she used this aspect to teach her selves what really mattered and to help her steer clear of the petty squabbles and concerns that plagued others.

    Some of the chapters can drag you as the author repeats the same point, and you get the feeling that the book could have been made shorter without loss of content. It is also difficult to tell which ideas are supported by solid science/research and which are not, as this is not referred to anywhere. Some ideas are backed by solid historical and scientific evidence, such as a chapter on narcissism, but in other sections, the ideas are more ambiguous, as, for example, when the author seems to believe that Milton Erickson recovered quicker from polio through the mental stimulation of his nerves.

     Also, there could have been more evolutionary psychology as there is nothing more fundamental to our nature and this could have included the cognitive biases. But overall the book can be enjoyed as there is a lot of advice related to the ‘laws’ mentioned.

 

 

#Superintelligence: Paths, Dangers, Strategies - Review

    The initial chapters would enter the cranium smoothly but midway the reader’s intuition might suggest that the author is determined to make us believe more about the risks of AI than its advantages, in a repetitive style, albeit with a variety of words on clairvoyance. The concept of superintelligence means that the machine can perform better than humans at all tasks including such things as using human language to be persuasive, enhancing the economy, developing strategies, designing and making robots, advanced weapons, and other similar applications! A super-intelligent machine will solve problems that humans don't know exist.

The first and the last two chapters were rather like a parenthesis within which we may be deliberating on the debate around the concept of the boon or bane of AI, as the book is more about epistemology than science. Thus, the bulk of the book would probably be best for undergraduate philosophy students or AI students, foraying more like a textbook than anything else, particularly in its details with a wide variety of synonymic words. 

The real subject, however, is how we, the intelligent beings, would deal with a ‘cleverer than us’ AI. What would we ask it to do? How would we motivate it and control it? And, bearing in mind it is more intelligent than us, how would we prevent it from taking over the world or subverting the tasks we give it to its own ends? A truly fascinating concept, explored in great depth, there are enough reasons and logic for every issue raised. Supplementary to this is the frequent text in shaded boxes and synopsis at the end of each chapter. At times we might think that reading these would suffice.

The theorem set up is as follows: As artificial intelligence (AI) becomes more proficient in the future it will have the ability to learn (Machine Learning) and improve itself as it examines and solves problems. It will have the ability to change (i.e. reprogram) itself in order to develop new methods as needed to execute solutions for the tasks at hand. The observation is the chronology of certain events related to ‘memory’ and neural networks.  These will be using techniques and strategies of which the originating human programmer will be unaware. The result is that once machines are creatively strategizing better (i.e. smarter) than humans, the gap between machine and human performance (i.e. intelligence) will grow exponentially. Here are some chapter-wise excerpts.

# “Problems that look hopelessly complicated turn out to have surprisingly simple solutions” – looks something like Occam’s razor principle. That Japan was the first to go after GOFAI (Good old-fashioned AI) after the AI winter – 1980, was a piece of news to me and several countries followed suit. More than 1,50,000 academic papers have since been published on artificial neural networks which is nothing less than an approach to ML.

# Bayesian inference and Monte Carlo methods find mention while ML gets the nod.

 “As soon as it works, no one calls it AI anymore - John McCarthy”

     The next chapter discusses every concept about evolutionary algorithms and neural networks, including the great genome project. There is a quote about Genetic Algorithms,

“One planet out of 10³⁰ on which replication arises would try Genetic Algorithm as a path to Machine Learning via Brain science”. This seems out of comprehension as there is not a single habitable planet to consider at least now. 

That GA or ML might not directly influence the 'intelligence' is summed up with a quote – “The existence of birds demonstrated that heavier than air flights is possible, yet first functional airplanes did not flap their wings”. Emphasis is that AI need not resemble a human mind but whole brain emulation would be of great assistance to ML. This is done via a simple procedure: Scanning – translation –emulation and there are tables that have classification over the technological front as Imaging –scanning- CPU storage (respectively). The author wishes to imagine emulating a brain at the level of elementary particle ‘hits’ using the QM Schrodinger Equation.

     There is a reference to the lifelong depression of intelligence due to Iodine deficiency which has been found to be widespread. But there is also the idea of developing specific genotypes of embryos so that elite schools can be filled with generically selected children (prettier, healthier, and more conscientious). Brain-computer–interface collective intelligence is limited by the abilities of its member minds.

# Three forms of Super-intelligence is suggested:

 Speed SI – that can do all that a human intellect can do, but much faster - To fast minds events in the external world appear to unfold in slow motions- thus a fast mind might commute mainly with other fast minds rather than with bradytelic, molasses-like humans.

Collective SI – A system composed of a larger number of smaller intellects such that problems can be readily broken into parts and parallel solutions are ensued.

Quality SI -A system that is at least as fast as the human mind and vastly qualitatively smarter. ‘A zebrafish has a quality of intelligence that is excellently adapted to its ecological needs’.

Bio intelligence is compared with computational elements: neuron speed is about 200 HZ – a full 7 orders of magnitude slower than a modern microprocessor (2GHZ), but the human brain relies on parallelization. Thus, the latter cannot do rapid calculation which is sequential. Human minds have fewer than 100 billion neurons but in terms of storage and reliability, the computing power is still comparable with digital.

 The Kinetics of intelligence explosion describes the “Rate of intelligence” = [optimization power /recalcitrance]. This seems to prove well as optimization is a factor of computing power while recalcitrance is in the human hands. Thus, there is a focus on Non-machine intelligence.

About the emulation of the AI path, there is little point in reading the entire library if you have forgotten all about Aardvark by the time you get to Abalone.

 # Hubble volume – cosmic endowment finds a mention and could not be related

 # Our intuitions are calibrated on our experience, so we should view persons differently  - AI doesn’t care about that.

# Intelligence and final goals are orthogonal

# Human beings tend to seek and acquire resources sufficient to meet their basic biological needs and that makes them selfish which the AI cannot judge.

 # AI may operate human instruments including military vehicles and if such an AI project runs out of funding this might result in failure to extend cognitive capacities.

# Benign Failures are bound to occur – one feature of a malignant failure is that it eliminates the opportunity to try again. IOT also presupposed a great deal of success.  

 There is a separate chapter dedicated to Goals which talks about every other thing and this section spurts out the need for ethical values.

 # Mind crime is another failure mode of a project whose interests incorporate moral considerations

 # Control problem: Two agency problems like ‘human vs human’ (sponsor vs developer) are supposed to occur mainly in the developmental phase, while the other problem, namely, ‘human vs superintelligence’ (project vs system) is bound to occur in operational phase. The author comes up with a plethora of suggestions on methods to control them. The table at the end of the chapter summarizes all.

# Three laws of robotics by Asimov get a meager mention and the rules are so esoteric in nature that one might wonder what their significance might be.

# Everything is vague to a degree you do not realise till you have tried to make it precise – Bertrand Russell.

# Oracles / Genies/ Sovereignty Tools is a section where you would feel that you should finish the book as fast as you could because of the summarization of the entire written ideas in a new Avtar of words.

 # Wages + unemployment: The only place where humans would remain competitive may be where customers have a basic preference for work done by humans.

 # Subjective experience may have ideological and religious roots – and an example of such ideology is both Muslims and Jews having haram and Treif vying for it when food matters.

 # The world evolution is not necessarily a synonym for progress

 # When AI reaches a certain state of cognitive development it may start to regard the continued operation of the accretion mechanism as a corrupting influence!

About digital hierarchy: how police hierarchy is made – each layer that oversees another has at least half the other number of the layers it oversees. The supervisor has a high advantage over subordinates (obvious?)

    Choosing the criteria for choosing is a title that re-kindles what you need to remember again when you are about to finish the book! How can we get superintelligence to do what we want and what we want superintelligence to want!

 #Theory of Hedonism: states that all and only pleasure has value and only pain as dis value.

Extrapolation: An individual might have a second-order desire that some of his first-order desire not be given weightage when his volition is extrapolated.  An alcoholic who has a first-order desire to booze might also have a second-order desire not to have that first-order desire! 

    Coherent Extrapolated volition is a concept where one would be unlikely to get a delicious meal by mixing together all the best flavours from everyone’s favorite dish. What is utopia is dystopia for others!

    It is not necessary for us to create a highly optimized design rather focus should be on creating a highly reliable design! A strategic picture talks about Afghan Taliban and Swedish human rights supposing themselves morally right while suppressing terrorists and renegades at home.

 # Whole Brain Emulation would be safer than AI but might lead to the worst outcome (neuromorphic AI) and second-best outcome (synthetic AI)

 # The highest honour in Maths (Fields medal) is given to one who is capable of accomplishing something important and that he did not – means a life spent solving the wrong problem!

 The author ends the book expecting another AI winter.