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Is the Algorithm Plotting Against Us? A Layperson's Guide to the Concepts, Math, and Pitfalls of AI
Artificial intelligence is everywhere—it’s in our houses and phones and cars. AI makes decisions about what we should buy, watch, and read, and it won’t be long before AI’s in our hospitals, combing through our records. Maybe soon it will even be deciding who’s innocent, and who goes to jail . . .
But most of us don't understand how AI works. We hardly know what it is.
In Is the Algorithm Plotting Against Us?, AI expert Kenneth Wenger deftly explains the complexity at AI’s heart, demonstrating its potential and exposing its shortfalls. Wenger empowers readers to answer the question—What exactly is AI?—at a time when its hold on tech, society, and our imagination is only getting stronger.
Kenneth Wenger is senior director of research and innovation at CoreAVI and chief technology officer at Squint AI. His work focuses on the intersection of artificial intelligence and determinism, enabling neural networks to execute in safety critical systems. Beyond the research, his interests lie in people and how technology affects society. He lives with his family in Mississauga, Ontario.
But most of us don't understand how AI works. We hardly know what it is.
In Is the Algorithm Plotting Against Us?, AI expert Kenneth Wenger deftly explains the complexity at AI’s heart, demonstrating its potential and exposing its shortfalls. Wenger empowers readers to answer the question—What exactly is AI?—at a time when its hold on tech, society, and our imagination is only getting stronger.
Kenneth Wenger is senior director of research and innovation at CoreAVI and chief technology officer at Squint AI. His work focuses on the intersection of artificial intelligence and determinism, enabling neural networks to execute in safety critical systems. Beyond the research, his interests lie in people and how technology affects society. He lives with his family in Mississauga, Ontario.
264 pages, Hardcover
Published May 1, 2023
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June 24, 2023
Well... I've been mulling this review for over a month now - notes all disappeared and I had to go back and create some more. After the publisher contacted me, I downloaded a review copy through Edelweiss, read it, and then did some additional research (and, I admit, got a bit of both reader's and writer's block). The read is not difficult, though the math (yes, the math) and neural network details may be tedious for some. Mr. Wenger says, "The hope is that—with this book and others like it—the public will gain enough understanding of what artificial intelligence is and its basic capabilities to appreciate its formidability." There is much in the news on AI now so this is timely. And it seems that there is concern...
In 2022, the EU agreed on legislation aimed at social media and search engine algorithms regarding targeted ads and search recommendations ("Google, Meta, and others will have to explain their algorithms under new EU legislation".) This was (likely, but who knows?) before AI became part of the Microsoft's Bing search engine. That, it seems, invoked images of Collosus, Demon Seed, HAL, if not quite Skynet - "The New AI-Powered Bing Is Threatening Users. That’s No Laughing Matter".
In May 2023, the EU resolved to manage AI training (Mr. Wenger discusses training of neural networks at length) - "Europe takes aim at ChatGPT with what might soon be the West’s first A.I. law.".
And then this came out: "CEOs: Artificial Intelligence Poses an Existential Threat to Humanity". And who is pushing to use AI? Those CEOs. Now, do note that 58% think the thesis is nonsense. Some think AI will save the world. But, "Eventually, some people fall down the rabbit hole of conspiracy theories, and because the same algorithms have found other people with similar proclivities for conspiracies, they all start building on each other’s comments, and there is no one in the group with a different opinion to offer some perspective." A computer designer asked a roomful of colleagues how many wanted to live in the world they’re creating. No one did. (See Johann Hari's Stolen Focus.)
Mark Cuban thinks "There's two types of companies: those who are great at AI and everybody else." So like it or not, it's here to stay.
Mr. Wenger says in his wrap up of his third of his four chapters: "In chapter 1, we learned about the history of the neural network and its basic architecture. In chapter 2, we learned about computer vision and a popular architecture called the CNN [convolutional neural network], which is great for image processing. We learned that we can combine a CNN with a fully connected network—the architecture from chapter 1—and build good image-classification algorithms that power many of today’s computer-vision solutions. In this chapter, we learned what makes all that possible: probability distributions, calculus, and optimization through gradient descent. Now we know what neural networks are: mathematical approximation functions." He says, "This is not a technical book." And yet there is more than a layman’s bit of information here.
And Mr. Wenger says, "It took nearly three decades to get to any meaningful conversation about the internet—discussions that we have only recently begun concerning child safety, privacy, online identity, security, and so on. We have had to wait for internet (and computer) literacy to reach a point where we no longer must explain what the technology is so that we can discuss how it is affecting us. When it comes to artificial intelligence, the conversation has not yet matured to this level."
Bottom line, I don’t think that this book is scary enough - most of it is about building neural networks and training them, how difficult it is for them to be “everything.” Mr. Wenger acknowledges, "The more power we grant to these algorithms—connecting them to our power grid, essential services, internet, military—the more difficult it will be to control them." But… alarmists don’t sway, so perhaps this is the best approach… a start.
Curated highlights and notes:
"If the brain functioned exclusively based on mapping specific inputs to specific outputs—the collection of predefined algorithms necessary to perform all the functions we perform daily and throughout our lives—we would need a staggering collection of discrete algorithms in our brains. This would make the dream of creating artificial systems capable of emulating human-level intelligence almost certainly impossible. If, instead, the brain functioned as a statistical system mapping classes of inputs to classes of outputs, we would not need a specific algorithm for every function we perform; we’d just need an algorithm for each class of functions we perform. This at least offers a reduction in the number of systems we need to emulate if we want to artificially build human-level intelligence."
[on teaching a neural network "what makes a positive review" of a movie or show] "The first thing we must do is get hold of a training data set. In this case, a training data set is simply a large collection of movie reviews that we can present to the neural network to train it on what positive and negative reviews look like."
{The thing about AI is that it still needs us to discriminate between ambiguous inputs and the expected output. If we leave it to itself, will it ask an “expert” (a teacher, colleague, friend) if it made the right inferences?}
"When we are training the neural network, we want it to learn from the training data, but we don’t want it to “memorize” the training data. If the neural network learns the correct answer for each training sample by memorizing the training samples, that doesn’t really tell us how well the network will perform in a real-world situation. The testing samples help us gauge whether the neural network has truly learned information that can be applied to unseen data or if it’s simply memorizing our training data."
{I could have used this in a recent review of a book that attempts to refute materialism (in favor of dualism). The author kept harping on an old position of Steven Pinker on whether a computer could become conscious. Application of learning to an unfamiliar situation is one way we are distinguished from the brute force mountains-of-data approach of say, a chess program.}
"It should be noted that the people doing the estimating [of project completion times] are often experienced employees who are doing their best. But the reason estimating projects is so difficult is because of the number of variables that can affect the completion of the project. Sometimes requirements change halfway through the project. Sometimes, at the outset, the team does not have all the information needed to complete the project, and the information slowly trickles in as development progresses."
{This goes for any project. I had to come up with estimates for project costs and time for which I was held to much later, despite the indeterminacy of many factors before we even started.}
"We can think of classical artificial intelligence algorithms as decision trees following a hierarchy of rules: if this happens, then that happens, otherwise something else happens; and from these two branches, subsequent rules and decisions can follow."
{But…with ANN solving a problem, we don’t have an explicit algorithm. The algorithm tells the ANN how to adjust the weightings, but we don’t know what the end result is - that algorithm is self-generated.}
"When I started researching and learning about neural networks, I found that the stuff we don’t know about these systems (the stuff whose presence itself is especially remarkable because we build these systems) is far more interesting than the things we do know."
{I find this to be a common trait of much science.}
"That’s right—like the other algorithms we saw earlier, AlphaZero [currently the best chess and go program}is also learning a set of probabilities. It doesn’t even know it’s playing chess (or go). Just because it can beat a chess grand master does not mean that it can also drive a car or write a novel or even help a robot climb a set of stairs. This is very important to grasp because it demystifies these algorithms and helps us evaluate progress against reality."
{One size doesn't fit all, most, or even many.}
"It’s important that we understand one simple fact: our artificial intelligence algorithms don’t wake up and decide to be biased toward a certain sample of the population. Instead, they pick up the bias from the data we use to train them. If there is bias in the data, the algorithm will be biased."
{There is always some bias in data.}
"Humans are not deterministic machines. But the AI algorithms we have discussed thus far are. A human faced with a given problem may respond differently each time. But an AI algorithm that is trained and deployed will always provide the same response to the same input."
{Introducing a random factor defeats the purpose of refining the algorithm.}
"Before we move on from the criminal justice and policing use case, it is important to restate that these algorithms do not have a personal agenda; they do not intend to discriminate; all they do is analyze data."
{So they aren’t “out to get you”, but that doesn’t mean that their outputs are without adverse consequences.}
"The point I am trying to make here is that accuracy is often overused when trying to indicate the effectiveness of an algorithm, but accuracy alone doesn’t always tell the whole story. In use cases or situations where the results of an algorithm carry a large weight of responsibility (e.g., in the judicial process or in health care), it is very important that, in addition to accuracy, we discuss the types of mistakes the algorithm makes."
{I do think that that discussion doesn't need a lot of prompting.}
"Another application of AI that should be generating a lot more discourse is advertising (or any area where AI systems can influence our behavior)"
{Cue ChatGPT, and others...}
"It’s easy to blame the public for their lack of interest in the sciences simply because “it’s hard.” But a fair bit of blame should also go to the scientists who obfuscate and convolute information with arcane phrases and difficult-to-understand language."
{Dawkins’ Law of the Conservation of Difficulty: obscurantism in an academic subject expands to fill the vacuum of its intrinsic simplicity. Physics is a genuinely difficult and profound subject, so physicists need to – and do – work hard to make their language as simple as possible [...]. Other academics – some would point the finger at continental schools of literary criticism and social science – suffer from what Peter Medawar (I think) called Physics Envy. They want to be thought profound, but their subject is actually rather easy and shallow, so they have to language it up to redress the balance.}
In 2022, the EU agreed on legislation aimed at social media and search engine algorithms regarding targeted ads and search recommendations ("Google, Meta, and others will have to explain their algorithms under new EU legislation".) This was (likely, but who knows?) before AI became part of the Microsoft's Bing search engine. That, it seems, invoked images of Collosus, Demon Seed, HAL, if not quite Skynet - "The New AI-Powered Bing Is Threatening Users. That’s No Laughing Matter".
In May 2023, the EU resolved to manage AI training (Mr. Wenger discusses training of neural networks at length) - "Europe takes aim at ChatGPT with what might soon be the West’s first A.I. law.".
And then this came out: "CEOs: Artificial Intelligence Poses an Existential Threat to Humanity". And who is pushing to use AI? Those CEOs. Now, do note that 58% think the thesis is nonsense. Some think AI will save the world. But, "Eventually, some people fall down the rabbit hole of conspiracy theories, and because the same algorithms have found other people with similar proclivities for conspiracies, they all start building on each other’s comments, and there is no one in the group with a different opinion to offer some perspective." A computer designer asked a roomful of colleagues how many wanted to live in the world they’re creating. No one did. (See Johann Hari's Stolen Focus.)
Mark Cuban thinks "There's two types of companies: those who are great at AI and everybody else." So like it or not, it's here to stay.
Mr. Wenger says in his wrap up of his third of his four chapters: "In chapter 1, we learned about the history of the neural network and its basic architecture. In chapter 2, we learned about computer vision and a popular architecture called the CNN [convolutional neural network], which is great for image processing. We learned that we can combine a CNN with a fully connected network—the architecture from chapter 1—and build good image-classification algorithms that power many of today’s computer-vision solutions. In this chapter, we learned what makes all that possible: probability distributions, calculus, and optimization through gradient descent. Now we know what neural networks are: mathematical approximation functions." He says, "This is not a technical book." And yet there is more than a layman’s bit of information here.
And Mr. Wenger says, "It took nearly three decades to get to any meaningful conversation about the internet—discussions that we have only recently begun concerning child safety, privacy, online identity, security, and so on. We have had to wait for internet (and computer) literacy to reach a point where we no longer must explain what the technology is so that we can discuss how it is affecting us. When it comes to artificial intelligence, the conversation has not yet matured to this level."
Bottom line, I don’t think that this book is scary enough - most of it is about building neural networks and training them, how difficult it is for them to be “everything.” Mr. Wenger acknowledges, "The more power we grant to these algorithms—connecting them to our power grid, essential services, internet, military—the more difficult it will be to control them." But… alarmists don’t sway, so perhaps this is the best approach… a start.
Curated highlights and notes:
"If the brain functioned exclusively based on mapping specific inputs to specific outputs—the collection of predefined algorithms necessary to perform all the functions we perform daily and throughout our lives—we would need a staggering collection of discrete algorithms in our brains. This would make the dream of creating artificial systems capable of emulating human-level intelligence almost certainly impossible. If, instead, the brain functioned as a statistical system mapping classes of inputs to classes of outputs, we would not need a specific algorithm for every function we perform; we’d just need an algorithm for each class of functions we perform. This at least offers a reduction in the number of systems we need to emulate if we want to artificially build human-level intelligence."
[on teaching a neural network "what makes a positive review" of a movie or show] "The first thing we must do is get hold of a training data set. In this case, a training data set is simply a large collection of movie reviews that we can present to the neural network to train it on what positive and negative reviews look like."
{The thing about AI is that it still needs us to discriminate between ambiguous inputs and the expected output. If we leave it to itself, will it ask an “expert” (a teacher, colleague, friend) if it made the right inferences?}
"When we are training the neural network, we want it to learn from the training data, but we don’t want it to “memorize” the training data. If the neural network learns the correct answer for each training sample by memorizing the training samples, that doesn’t really tell us how well the network will perform in a real-world situation. The testing samples help us gauge whether the neural network has truly learned information that can be applied to unseen data or if it’s simply memorizing our training data."
{I could have used this in a recent review of a book that attempts to refute materialism (in favor of dualism). The author kept harping on an old position of Steven Pinker on whether a computer could become conscious. Application of learning to an unfamiliar situation is one way we are distinguished from the brute force mountains-of-data approach of say, a chess program.}
"It should be noted that the people doing the estimating [of project completion times] are often experienced employees who are doing their best. But the reason estimating projects is so difficult is because of the number of variables that can affect the completion of the project. Sometimes requirements change halfway through the project. Sometimes, at the outset, the team does not have all the information needed to complete the project, and the information slowly trickles in as development progresses."
{This goes for any project. I had to come up with estimates for project costs and time for which I was held to much later, despite the indeterminacy of many factors before we even started.}
"We can think of classical artificial intelligence algorithms as decision trees following a hierarchy of rules: if this happens, then that happens, otherwise something else happens; and from these two branches, subsequent rules and decisions can follow."
{But…with ANN solving a problem, we don’t have an explicit algorithm. The algorithm tells the ANN how to adjust the weightings, but we don’t know what the end result is - that algorithm is self-generated.}
"When I started researching and learning about neural networks, I found that the stuff we don’t know about these systems (the stuff whose presence itself is especially remarkable because we build these systems) is far more interesting than the things we do know."
{I find this to be a common trait of much science.}
"That’s right—like the other algorithms we saw earlier, AlphaZero [currently the best chess and go program}is also learning a set of probabilities. It doesn’t even know it’s playing chess (or go). Just because it can beat a chess grand master does not mean that it can also drive a car or write a novel or even help a robot climb a set of stairs. This is very important to grasp because it demystifies these algorithms and helps us evaluate progress against reality."
{One size doesn't fit all, most, or even many.}
"It’s important that we understand one simple fact: our artificial intelligence algorithms don’t wake up and decide to be biased toward a certain sample of the population. Instead, they pick up the bias from the data we use to train them. If there is bias in the data, the algorithm will be biased."
{There is always some bias in data.}
"Humans are not deterministic machines. But the AI algorithms we have discussed thus far are. A human faced with a given problem may respond differently each time. But an AI algorithm that is trained and deployed will always provide the same response to the same input."
{Introducing a random factor defeats the purpose of refining the algorithm.}
"Before we move on from the criminal justice and policing use case, it is important to restate that these algorithms do not have a personal agenda; they do not intend to discriminate; all they do is analyze data."
{So they aren’t “out to get you”, but that doesn’t mean that their outputs are without adverse consequences.}
"The point I am trying to make here is that accuracy is often overused when trying to indicate the effectiveness of an algorithm, but accuracy alone doesn’t always tell the whole story. In use cases or situations where the results of an algorithm carry a large weight of responsibility (e.g., in the judicial process or in health care), it is very important that, in addition to accuracy, we discuss the types of mistakes the algorithm makes."
{I do think that that discussion doesn't need a lot of prompting.}
"Another application of AI that should be generating a lot more discourse is advertising (or any area where AI systems can influence our behavior)"
{Cue ChatGPT, and others...}
"It’s easy to blame the public for their lack of interest in the sciences simply because “it’s hard.” But a fair bit of blame should also go to the scientists who obfuscate and convolute information with arcane phrases and difficult-to-understand language."
{Dawkins’ Law of the Conservation of Difficulty: obscurantism in an academic subject expands to fill the vacuum of its intrinsic simplicity. Physics is a genuinely difficult and profound subject, so physicists need to – and do – work hard to make their language as simple as possible [...]. Other academics – some would point the finger at continental schools of literary criticism and social science – suffer from what Peter Medawar (I think) called Physics Envy. They want to be thought profound, but their subject is actually rather easy and shallow, so they have to language it up to redress the balance.}
April 7, 2023
At a crucial moment, this book expands the conversation around AI to the broader public. A brief history of AI is provided; giving context to the current state-of-the-art in algorithms. It introduces critical concepts in an easy-to-read format with examples, diagrams, and plots. Through real-life tasks (e.g. image classification, language translation) and publicly available data, algorithms (e.g. convolutional neural networks) are explained to help the reader understand the “black box” that is AI. Mathematical theory and concepts that form the basis of AI algorithms are described to help readers connect concepts with, perhaps, their own mathematical training. This book includes numerous bibliographical references and resources that one may find useful for delving into more profound aspects of AI.
April 16, 2023
While this was such an easy read, I still walked away from this book with a much bigger picture of both AI's advantages and pitfalls. I feel much more equipped to make my own opinions and decisions surrounding AI now. I was especially engrossed in the book when Wenger talked about how neural networks work - went from just /imagining/ how AI works to actually /knowing/ how AI works - pretty incredible.
April 29, 2023
With the exception of a brief period during my dissertation (and it was 1996 so the last century) during which my path was hindered by neural networks, I had not read anything more about it, much less become informed/concerned about the current debate on AI.
This book not only traces the history of neural networks, the larger biases they incur because of how they work, but how from them we then came to talk about artificial intelligence. Not that I haven't seen Terminator, on the contrary, but let's just say that for now I see it a bit like the author, and I would say that it seems rather premature to pose the problem of artificial intelligence consciously deciding to destroy us, considering that we still don't understand what consciousness in humans consists of.
Clearly, however, the book is by no means easy to being summed up in these few words, but what is very appreciable in my opinion is the author's attempt to explain the functioning of the algorithms underlying convolutional neural networks (CNNs) and how they could be used in the medical field, for example, where they could greatly accelerate the discovery of tumors for example or facilitate a diagnosis based on a CT scan or PET scan.
Clearly, because the author is a serious person, there are also the various situations where the inferences, and thus the conclusions that the linear regression algorithms were arriving at, were absolutely wrong because they were poorly constructed from initial assumptions, and far from ready for a Skynet-like conspiracy.
Con l'eccezione di un breve periodo durante la mia tesi di laurea (e correva il 1996 quindi il secolo scorso) durante il quale il mio percorso é stato intralciato dalle reti neurali, non mi era piú capitato di leggere qualcosa a riguardo e tanto meno di informarmi/preoccuparmi dell'attuale dibattito sull'IA.
Questo libro non solo ripercorre la storia delle reti neurali, dei bias piú grandi nei quali incorrono per via del loro funzionamento, ma di come da esse siamo arrivati poi a parlare di intelligenza artificiale. Non che io non abbia visto Terminator, anzi, ma diciamo che per ora la vedo un po' come l'autore e direi che mi sembra piuttosto prematuro porsi il problema di un'intelligenza artificiale che coscientemente decida di distruggerci, considerato che ancora non abbiamo capito in cosa consista la coscienza negli esseri umani.
Chiaramente peró il libro non é affatto riassumibile in queste poche parole, ma quello che é molto apprezzabile secondo me, é il tentativo dell'autore di spiegare il funzionamento degli algoritmi alla base delle reti neurali convolute (CNN) e di come questi possano essere utilizzati per esempio in ambito medico, dove potrebbero accelerare di molto la scoperta dei tumori per esempio o agevolare una diagnosi basata su una tac o una pet.
Chiaramente, siccome l'autore é una persona seria, ci sono anche le varie situazioni in cui le inferenze e quindi le conclusioni a cui arrivavano gli algoritmi delle regressioni lineari, erano assolutamente sbagliate perché mal costruite a partire dalle ipotesi, e lungi dall'essere pronti per un complotto tipo Skynet.
I received from the Publisher a complimentary digital advanced review copy of the book in exchange for a honest review.
This book not only traces the history of neural networks, the larger biases they incur because of how they work, but how from them we then came to talk about artificial intelligence. Not that I haven't seen Terminator, on the contrary, but let's just say that for now I see it a bit like the author, and I would say that it seems rather premature to pose the problem of artificial intelligence consciously deciding to destroy us, considering that we still don't understand what consciousness in humans consists of.
Clearly, however, the book is by no means easy to being summed up in these few words, but what is very appreciable in my opinion is the author's attempt to explain the functioning of the algorithms underlying convolutional neural networks (CNNs) and how they could be used in the medical field, for example, where they could greatly accelerate the discovery of tumors for example or facilitate a diagnosis based on a CT scan or PET scan.
Clearly, because the author is a serious person, there are also the various situations where the inferences, and thus the conclusions that the linear regression algorithms were arriving at, were absolutely wrong because they were poorly constructed from initial assumptions, and far from ready for a Skynet-like conspiracy.
Con l'eccezione di un breve periodo durante la mia tesi di laurea (e correva il 1996 quindi il secolo scorso) durante il quale il mio percorso é stato intralciato dalle reti neurali, non mi era piú capitato di leggere qualcosa a riguardo e tanto meno di informarmi/preoccuparmi dell'attuale dibattito sull'IA.
Questo libro non solo ripercorre la storia delle reti neurali, dei bias piú grandi nei quali incorrono per via del loro funzionamento, ma di come da esse siamo arrivati poi a parlare di intelligenza artificiale. Non che io non abbia visto Terminator, anzi, ma diciamo che per ora la vedo un po' come l'autore e direi che mi sembra piuttosto prematuro porsi il problema di un'intelligenza artificiale che coscientemente decida di distruggerci, considerato che ancora non abbiamo capito in cosa consista la coscienza negli esseri umani.
Chiaramente peró il libro non é affatto riassumibile in queste poche parole, ma quello che é molto apprezzabile secondo me, é il tentativo dell'autore di spiegare il funzionamento degli algoritmi alla base delle reti neurali convolute (CNN) e di come questi possano essere utilizzati per esempio in ambito medico, dove potrebbero accelerare di molto la scoperta dei tumori per esempio o agevolare una diagnosi basata su una tac o una pet.
Chiaramente, siccome l'autore é una persona seria, ci sono anche le varie situazioni in cui le inferenze e quindi le conclusioni a cui arrivavano gli algoritmi delle regressioni lineari, erano assolutamente sbagliate perché mal costruite a partire dalle ipotesi, e lungi dall'essere pronti per un complotto tipo Skynet.
I received from the Publisher a complimentary digital advanced review copy of the book in exchange for a honest review.
June 24, 2023
Post-pandemic, perhaps no STEM topic has gripped the news quite like Artificial Intelligence (AI). For almost a century (since Isaac Asimov), science-fiction writers have dreamed of computers gaining consciousness, but now, some propose those possibilities near fruition. Often, people who write about AI in the news focus solely on social aspects; those developing the technology, in contrast, focus solely on technical details. Few individuals can provide a balanced look that relates both levels. Kenneth Wenger’s book, fortunately, does just that by aiming to relate AI concepts to the general reading public. At 264 pages, this accessible guide can inform and elevate public dialogue to sort out fiction from fact and hype from essence.
One of the greatest impediments to reading books about AI is the math. The eyes of many folks – even including this math geek – glaze over or roll at seeing calculus or even algebra on the printed page. This book, for the most part, avoids that. Instead, he explains how AI works in plain, accessible language and unpacks how the math actually translates into action. Wenger understands English as much as he understands the technical algorithms, and it shows.
He organizes this book in an introduction, four long chapters, and a conclusion. The first three chapters are dedicated to explaining conceptually how AI “machine learning” algorithms work; only the fourth chapter discusses potential social implications. I suggest that readers not skip the first three chapters because they give readers a gut feel to what is actually possible – and importantly, what isn’t possible. After reading this book, you won’t be able to code AI, but you should be able to trace the defining features of AI from implementation in software to expression in society. The fourth chapter ties it all together with solid, reasonable analysis of social implications. Many computer folk don’t understand social impacts with levelness, and many socially prominent writers don’t understand computer science. Wenger transcends this divide and points out what we need to think about in coming months and years.
This book should receive a warm welcome, given what I see on popular news. It doesn’t fan flames of fear, nor does it promise too much. Instead, it merely informs as scientific books should. As the US Congress currently undertakes discussions on how to regulate AI, this book needs to be involved among the writers of policy. It is balanced, realistic, and expert. Journalists, too, must inform public sentiment with scientific facts, and this book can provide a informative deep dive for them as well. As a software developer myself with bookish interests, I am often put off by rampant, irresponsible speculation about the implications of AI. Wenger provides level-headed text to describe where we are. I hope the reading public is paying attention.
One of the greatest impediments to reading books about AI is the math. The eyes of many folks – even including this math geek – glaze over or roll at seeing calculus or even algebra on the printed page. This book, for the most part, avoids that. Instead, he explains how AI works in plain, accessible language and unpacks how the math actually translates into action. Wenger understands English as much as he understands the technical algorithms, and it shows.
He organizes this book in an introduction, four long chapters, and a conclusion. The first three chapters are dedicated to explaining conceptually how AI “machine learning” algorithms work; only the fourth chapter discusses potential social implications. I suggest that readers not skip the first three chapters because they give readers a gut feel to what is actually possible – and importantly, what isn’t possible. After reading this book, you won’t be able to code AI, but you should be able to trace the defining features of AI from implementation in software to expression in society. The fourth chapter ties it all together with solid, reasonable analysis of social implications. Many computer folk don’t understand social impacts with levelness, and many socially prominent writers don’t understand computer science. Wenger transcends this divide and points out what we need to think about in coming months and years.
This book should receive a warm welcome, given what I see on popular news. It doesn’t fan flames of fear, nor does it promise too much. Instead, it merely informs as scientific books should. As the US Congress currently undertakes discussions on how to regulate AI, this book needs to be involved among the writers of policy. It is balanced, realistic, and expert. Journalists, too, must inform public sentiment with scientific facts, and this book can provide a informative deep dive for them as well. As a software developer myself with bookish interests, I am often put off by rampant, irresponsible speculation about the implications of AI. Wenger provides level-headed text to describe where we are. I hope the reading public is paying attention.
May 22, 2023
I received a copy of this book from the publisher in exchange for an honest review.
Everyone's talking about AI and often it is with a sense of fear and paranoia. Many people hear the term and envision some dystopian landscape like I, Robot or The Matrix, rather than recognizing that AI is already present in our everyday lives, from language processing platforms (chatbots, ChatGPT) and targeted marketing (those hyperspecific ads as you scroll your social media) to self-driving cars and manufacturing robots.
This book's subtitle says the information inside is geared toward a layperson, and I have a bit of an issue with that. I suppose the problem there is with the term itself, as in who falls into that category. I have what I would consider an "intermediate beginner" knowledge regarding AI, in that I have taken several college courses on computer programming, am scientifically literate, and try to read any news articles regarding the topic. This book definitely tested my intelligence (lots of data sets, math, and statistical models) and to be honest, was probably about two levels over my head. I enjoyed the challenge of diving into this subject but my eyes definitely glazed over in spots. This is no fault of the author, but rather a note toward this book's intended audience. I would suggest this to readers with intermediate or advanced knowledge of AI.
Come chat with me about books here, too:
Blog | Instagram
Everyone's talking about AI and often it is with a sense of fear and paranoia. Many people hear the term and envision some dystopian landscape like I, Robot or The Matrix, rather than recognizing that AI is already present in our everyday lives, from language processing platforms (chatbots, ChatGPT) and targeted marketing (those hyperspecific ads as you scroll your social media) to self-driving cars and manufacturing robots.
This book's subtitle says the information inside is geared toward a layperson, and I have a bit of an issue with that. I suppose the problem there is with the term itself, as in who falls into that category. I have what I would consider an "intermediate beginner" knowledge regarding AI, in that I have taken several college courses on computer programming, am scientifically literate, and try to read any news articles regarding the topic. This book definitely tested my intelligence (lots of data sets, math, and statistical models) and to be honest, was probably about two levels over my head. I enjoyed the challenge of diving into this subject but my eyes definitely glazed over in spots. This is no fault of the author, but rather a note toward this book's intended audience. I would suggest this to readers with intermediate or advanced knowledge of AI.
Come chat with me about books here, too:
Blog | Instagram
April 26, 2023
A fantastic book written by an author who has an amazing knack for taking an incredibly complex subject matter and explaining it in a way that is very digestible for your average AI layperson. As someone just starting to wrap my head around this subject, I found Ken’s book to be extremely insightful and knowledgeable, while not going completely over my head. He writes with style and humour too! A great read that I would recommend to anyone who wants to better understand AI!
June 5, 2023
If you have any awareness of current science events, technological advances, social or public conversations, or media editorials, then you have read something about Artificial Intelligence, or AI. The discussion, highly public, held at a high level, and freighted with far-reaching statements by all involved, all too often—and very unfortunately—features sweeping gloom-and-doom pronouncements. These statements are like catnip to media outlets which crave them for the clicks they can get, but do very little to illuminate a very important emerging technology.
Cutting through this thicket is well worth it. This is where Kenneth Wenger comes in. He’s the director of research and innovation at CoreAVI and chief technology officer at Squint AI. His book, Is the Algorithm Plotting Against Us?, is a tonic. It’s a very useful and well-laid-out primer on the nuts and bolts of AI, and a convincing agenda for informing the discussion of many of the concerns being expressed.
He makes the logical assumption that his audience knows nothing about computer science, the structure of microchips, or the architecture of neural networks. And yes, he will lead you step by step to a good grounding in the science and technology of it all. Concise, highly readable, and logical, he takes his readers from ground zero to a good basic understanding of the pitfalls and the potential of this technology. That is the main reason he sat down to his word processor, and the chief virtue of the book. He is eminently successful at the task he set for himself.
Without digging too deeply into the normative social issues—you should read the book!—Wenger gives the reader a crystal-clear perspective on current problems, and thereby establishes where the current debate should be. While acknowledging the sometimes rash and far-fetched statements made by scientists and “thought leaders,” Wenger would have us focus on current problems besetting this technology, which is in its infancy. His finishing touch is polemical, in fact, since he has observed, and has grave doubts about, some of the applications to which AI has been put.
I could go on, because I enjoyed and value this book very much, but I would make a hash of it: I would never be able in a review of this length to present the flow and logic as elegantly as he does. There is a fair amount of math in it, but don’t let that put you off! Wenger always explains it, and always in terms that an 8th-Grade math student could follow.
If you want to follow the public debate, or if you want to participate in discussions with friends and family, this book is a superb place to start. It’s a straightforward, basic guide not only to the brand-new technology, but to the social issues surrounding it. Wonderful! Take it up!
https://bassoprofundo1.blogspot.com/2...
Cutting through this thicket is well worth it. This is where Kenneth Wenger comes in. He’s the director of research and innovation at CoreAVI and chief technology officer at Squint AI. His book, Is the Algorithm Plotting Against Us?, is a tonic. It’s a very useful and well-laid-out primer on the nuts and bolts of AI, and a convincing agenda for informing the discussion of many of the concerns being expressed.
He makes the logical assumption that his audience knows nothing about computer science, the structure of microchips, or the architecture of neural networks. And yes, he will lead you step by step to a good grounding in the science and technology of it all. Concise, highly readable, and logical, he takes his readers from ground zero to a good basic understanding of the pitfalls and the potential of this technology. That is the main reason he sat down to his word processor, and the chief virtue of the book. He is eminently successful at the task he set for himself.
Without digging too deeply into the normative social issues—you should read the book!—Wenger gives the reader a crystal-clear perspective on current problems, and thereby establishes where the current debate should be. While acknowledging the sometimes rash and far-fetched statements made by scientists and “thought leaders,” Wenger would have us focus on current problems besetting this technology, which is in its infancy. His finishing touch is polemical, in fact, since he has observed, and has grave doubts about, some of the applications to which AI has been put.
I could go on, because I enjoyed and value this book very much, but I would make a hash of it: I would never be able in a review of this length to present the flow and logic as elegantly as he does. There is a fair amount of math in it, but don’t let that put you off! Wenger always explains it, and always in terms that an 8th-Grade math student could follow.
If you want to follow the public debate, or if you want to participate in discussions with friends and family, this book is a superb place to start. It’s a straightforward, basic guide not only to the brand-new technology, but to the social issues surrounding it. Wonderful! Take it up!
https://bassoprofundo1.blogspot.com/2...
June 21, 2023
VERDICT: A must read study on AI, its origin, its inner workings, its advantages and current risks, and the important discussion we need to have right now about it.
Kenneth Wenger’s book is being published at the perfect time, when the discussions on AI are getting more widespread, as well as all kinds of crazy theories about it.
Personally, I was just in the process of (finally) listening to two popular science-fiction novels for YA. Only when I was deeper into them did I realize Marie Lu’s books, Warcross and Wildcard, actually contained an important conversation on AI.
So this was wonderful reading this nonfiction at the same time.
I like to remain informed about new technology, and I wanted a good tool to evaluate AI.
I found it in Is the Algorithm Plotting Against Us? A Layperson’s Guide to the Concepts, Math, and Pitfalls of AI.
My (much longer than usual) review will contain many quotations and paraphrases of the book, while avoiding too many technical aspects.
My goal is to show you how important this book is, and to invite you to read it as well, as I can only offer you a glimpse of it.
The main propose of the book is to explain how AI works, in order to allow us to evaluate and gauge our response from an informed place.
Please check my detailed review here:
https://wordsandpeace.com/2023/06/21/...
Kenneth Wenger’s book is being published at the perfect time, when the discussions on AI are getting more widespread, as well as all kinds of crazy theories about it.
Personally, I was just in the process of (finally) listening to two popular science-fiction novels for YA. Only when I was deeper into them did I realize Marie Lu’s books, Warcross and Wildcard, actually contained an important conversation on AI.
So this was wonderful reading this nonfiction at the same time.
I like to remain informed about new technology, and I wanted a good tool to evaluate AI.
I found it in Is the Algorithm Plotting Against Us? A Layperson’s Guide to the Concepts, Math, and Pitfalls of AI.
My (much longer than usual) review will contain many quotations and paraphrases of the book, while avoiding too many technical aspects.
My goal is to show you how important this book is, and to invite you to read it as well, as I can only offer you a glimpse of it.
The main propose of the book is to explain how AI works, in order to allow us to evaluate and gauge our response from an informed place.
Please check my detailed review here:
https://wordsandpeace.com/2023/06/21/...
May 31, 2023
Book Review
Title: Is the Algorithm Plotting Against Us?: A Layperson's Guide to the Concepts, Math, and Pitfalls of AI by Kenneth Wenger
Genre: Non-Fiction, Technology
Rating: 3.75 Stars
The opening to this book was interesting as it has the aim of educating the reader about AI and the mathematics involved behind the scenes. The author rightly states that the media overexaggerates how far AI has come in recent years leading to a lot of fear and misunderstanding about what AI actually is. Now I don’t normally delve into technology because I don’t understand a lot of the technical aspects but since this is supposed to be a layman’s guide I thought I would give it a try.
Chapter 1 introduces us to the idea of polarization and its consequences, it begins by introducing us to the history of the human mind. We look at how the neuron was discovered and how we learnt of its function and while this alone was an amazing leap in science for humankind, it links even further with how these neuron pathways were used as the basis for almost all modern technology that we have today. McCulloch-Pitts developed the artificial neuron using logic gates and almost all modern technology is based on logic gates. The simplest logic gates are NOT AND and OR, AND gates are used in heavy machinery and are essentially used as a double switch safety function, so the machine will not operate unless both of these switches are “on”. NOT gates are used as signal inverters commonly found in car fuel sensors. The sensor detects the fuel level and when it falls below the sensor’s range then it indicates using a switch that fuel needs to be added. OR gates are used in simple payment systems where the person can pay by either cash or card like in train stations. If no payment is made the gate remained closed but once either payment is made the gate opens.
These logic gates can also be combined to make complex circuit systems, if enough are combined you can build modern computers. Logic gates are also the backbone of most programming languages and it was here that scientists realised that the logic gates could be paired with the artificial neuron and could theoretically produce a functioning model of the human brain. However, it was soon realised that binary inputs were a drawback and this led to Donald Hebb introducing Hebbian learning. Hebbian learning proposed that neurons firing together, strengthened connections between them and was vital to the learning process. For the artificial neuron they realised they had to give weight to the connections and this meant you could fine tune the neuron by adding or decreasing the weight of a connection. Frank Rosenblatt went one step further and developed the perceptron.
The perceptron changed the binary input to a value between 0 and 1 which was more closely aligned with biological neurons. Through this two approaches to creating models were developed: monotypic and genotypic. Monotypic is non adaptable while genotypic is, this meant monotypic was used for studying the brain with a specific set of inputs and desired outputs while genotypic uses well defined functions and compares these to the artificial system. The genotypic approach allows more flexibility in artificial networks and this showed the human mind relied on a statistical system rather than decision based one. For those working on artificial systems this offered a reduction in the number of systems needed for artificial intelligence. In reality, the human brain actually used both systems as it uses specific algorithms for special functions and generical algorithms for most other functions. This introduces the idea of a bias and we are focusing heavily on the input/output process.
The perceptron actually had many practical applications as it was first used in IBM 704 software for punch cards. This software distinguished cards punched on the left versus the right and was later designed for image recognition for images up to 20x20 pixels which developed further into a system that could eliminate noise from phone lines. These systems were ADALINE and MADALINE which are still used today, although it was later pointed out the single-layer perceptron couldn’t implement the XOR logic gate. The single-layer perceptron only solves linear problems and most problems aren’t linearly separable so system needed to be adapted again and this was the beginning of the AI winter.
The book goes into much greater depth about how AI actually work, however, I did find that have a good understanding of either mathematics or computers would be extremely helpful when reading. While it claims to be a layman’s guide, it does get very technical and while the author does their best to explain it I found myself struggling at times. Overall, I found the book to be informative and interesting but slightly too technical to be a true layman’s guide to AI. If you have a background in maths or computer science then this might be the perfect read for you.
Title: Is the Algorithm Plotting Against Us?: A Layperson's Guide to the Concepts, Math, and Pitfalls of AI by Kenneth Wenger
Genre: Non-Fiction, Technology
Rating: 3.75 Stars
The opening to this book was interesting as it has the aim of educating the reader about AI and the mathematics involved behind the scenes. The author rightly states that the media overexaggerates how far AI has come in recent years leading to a lot of fear and misunderstanding about what AI actually is. Now I don’t normally delve into technology because I don’t understand a lot of the technical aspects but since this is supposed to be a layman’s guide I thought I would give it a try.
Chapter 1 introduces us to the idea of polarization and its consequences, it begins by introducing us to the history of the human mind. We look at how the neuron was discovered and how we learnt of its function and while this alone was an amazing leap in science for humankind, it links even further with how these neuron pathways were used as the basis for almost all modern technology that we have today. McCulloch-Pitts developed the artificial neuron using logic gates and almost all modern technology is based on logic gates. The simplest logic gates are NOT AND and OR, AND gates are used in heavy machinery and are essentially used as a double switch safety function, so the machine will not operate unless both of these switches are “on”. NOT gates are used as signal inverters commonly found in car fuel sensors. The sensor detects the fuel level and when it falls below the sensor’s range then it indicates using a switch that fuel needs to be added. OR gates are used in simple payment systems where the person can pay by either cash or card like in train stations. If no payment is made the gate remained closed but once either payment is made the gate opens.
These logic gates can also be combined to make complex circuit systems, if enough are combined you can build modern computers. Logic gates are also the backbone of most programming languages and it was here that scientists realised that the logic gates could be paired with the artificial neuron and could theoretically produce a functioning model of the human brain. However, it was soon realised that binary inputs were a drawback and this led to Donald Hebb introducing Hebbian learning. Hebbian learning proposed that neurons firing together, strengthened connections between them and was vital to the learning process. For the artificial neuron they realised they had to give weight to the connections and this meant you could fine tune the neuron by adding or decreasing the weight of a connection. Frank Rosenblatt went one step further and developed the perceptron.
The perceptron changed the binary input to a value between 0 and 1 which was more closely aligned with biological neurons. Through this two approaches to creating models were developed: monotypic and genotypic. Monotypic is non adaptable while genotypic is, this meant monotypic was used for studying the brain with a specific set of inputs and desired outputs while genotypic uses well defined functions and compares these to the artificial system. The genotypic approach allows more flexibility in artificial networks and this showed the human mind relied on a statistical system rather than decision based one. For those working on artificial systems this offered a reduction in the number of systems needed for artificial intelligence. In reality, the human brain actually used both systems as it uses specific algorithms for special functions and generical algorithms for most other functions. This introduces the idea of a bias and we are focusing heavily on the input/output process.
The perceptron actually had many practical applications as it was first used in IBM 704 software for punch cards. This software distinguished cards punched on the left versus the right and was later designed for image recognition for images up to 20x20 pixels which developed further into a system that could eliminate noise from phone lines. These systems were ADALINE and MADALINE which are still used today, although it was later pointed out the single-layer perceptron couldn’t implement the XOR logic gate. The single-layer perceptron only solves linear problems and most problems aren’t linearly separable so system needed to be adapted again and this was the beginning of the AI winter.
The book goes into much greater depth about how AI actually work, however, I did find that have a good understanding of either mathematics or computers would be extremely helpful when reading. While it claims to be a layman’s guide, it does get very technical and while the author does their best to explain it I found myself struggling at times. Overall, I found the book to be informative and interesting but slightly too technical to be a true layman’s guide to AI. If you have a background in maths or computer science then this might be the perfect read for you.
This entire review has been hidden because of spoilers.
June 11, 2023
This is a difficult book to review because while it is professionally written on an interesting topic, it must also be judged by whether it achieved its objectives, where it was not so successful.
This is a scientific book, and as such a certain amount of the success will be in targeting a readership at the right level of knowledge and communicating its ideas to them. And since I am the one giving an opinion, you need to know my level of knowledge. I chose the book because I thought it might be aimed at me: a moderately intelligent guy with several university degrees and a basic knowledge of calculus and statistics (from 50 years ago). I am also writing a Science Fiction novel on Artificial Intelligence, and I have found it very useful as research.
Unfortunately, I suspect this book is trying to do two things at once for two separate sets of readers. The target readership as stated is “everyone interested in recent developments in AI.”
This includes two groups — educated scientists and the non-scientific socially conscious — that are too far apart in training and interest for the author to bridge the gap, no matter how well he writes.
This work is in standard textbook format. “Tell them what you’re gonna tell them. Then tell them. Then tell them what you told them.” This keeps both the author and the reader focused on what’s being taught. Like a good lecture, there is also a smattering of gentle humour that keeps readers entertained. There are clear diagrams and useful practical examples to maximize our understanding.
The first two chapters lay out the history and the basic mathematical logic behind the programming of Artificial Intelligence. It is clear and understandable, and does exactly what the author has led us to expect. Because the logic is so well explained, the reader can sort of follow the mathematics, although it becomes increasingly difficult. By the time I had finished that section, I felt I had learned a great deal about AI, how it functions, and how we create increasingly complex versions of it. I had a hazier view of how and why the programming part works.
Chapter 3 is about statistics. Once again, we start with the basics, carefully laid out. If you understand simple statistics, you will find this material useful and fascinating. However, when the topic turns to loss function, the complicated formulas start appearing, and I find myself losing track. Then it moves into calculus. I last studied that art in 1967. Enough said.
Chapter 4 goes back to logic and discussing the practical uses of AI, and this is the real meat of the book. Its problems when used in the judicial system are shown clearly and have a great deal of social import. Its use in advertising involves concepts that have been discussed for many years, so this section of the book really brings us back to solid ground.
The conclusion wraps it up neatly, but the author says he wants readers “…to understand how neural networks function so that you can have an intelligent conversation and formulate your own opinion about the ethics of AI.” By including a great deal of mathematics I don’t understand, he leaves me with the impression I can’t have that conversation or formulate an opinion. The chapter of equations has effectively created a wall preventing this from happening. For many readers, it will be the place they stop reading the book.
One opinion that did come through loud and clear; despite what Science Fiction writers and social media hystericals maybe telling tell us, “…there is still a large gap between human-level intelligence and our best efforts at artificial intelligence.” I’m willing to accept the fact that I can’t understand the math as a pretty good indication that this is so.
So, despite the fascinating information I learned from my reading, I think the author tried to reach too broad an audience, so is bound to disappoint some. This book is highly recommended for those with a scientific education. Many other readers might want to browse through and skip the tough math part, knowing that the ending returns to important material that is easier to grasp.
This is a scientific book, and as such a certain amount of the success will be in targeting a readership at the right level of knowledge and communicating its ideas to them. And since I am the one giving an opinion, you need to know my level of knowledge. I chose the book because I thought it might be aimed at me: a moderately intelligent guy with several university degrees and a basic knowledge of calculus and statistics (from 50 years ago). I am also writing a Science Fiction novel on Artificial Intelligence, and I have found it very useful as research.
Unfortunately, I suspect this book is trying to do two things at once for two separate sets of readers. The target readership as stated is “everyone interested in recent developments in AI.”
This includes two groups — educated scientists and the non-scientific socially conscious — that are too far apart in training and interest for the author to bridge the gap, no matter how well he writes.
This work is in standard textbook format. “Tell them what you’re gonna tell them. Then tell them. Then tell them what you told them.” This keeps both the author and the reader focused on what’s being taught. Like a good lecture, there is also a smattering of gentle humour that keeps readers entertained. There are clear diagrams and useful practical examples to maximize our understanding.
The first two chapters lay out the history and the basic mathematical logic behind the programming of Artificial Intelligence. It is clear and understandable, and does exactly what the author has led us to expect. Because the logic is so well explained, the reader can sort of follow the mathematics, although it becomes increasingly difficult. By the time I had finished that section, I felt I had learned a great deal about AI, how it functions, and how we create increasingly complex versions of it. I had a hazier view of how and why the programming part works.
Chapter 3 is about statistics. Once again, we start with the basics, carefully laid out. If you understand simple statistics, you will find this material useful and fascinating. However, when the topic turns to loss function, the complicated formulas start appearing, and I find myself losing track. Then it moves into calculus. I last studied that art in 1967. Enough said.
Chapter 4 goes back to logic and discussing the practical uses of AI, and this is the real meat of the book. Its problems when used in the judicial system are shown clearly and have a great deal of social import. Its use in advertising involves concepts that have been discussed for many years, so this section of the book really brings us back to solid ground.
The conclusion wraps it up neatly, but the author says he wants readers “…to understand how neural networks function so that you can have an intelligent conversation and formulate your own opinion about the ethics of AI.” By including a great deal of mathematics I don’t understand, he leaves me with the impression I can’t have that conversation or formulate an opinion. The chapter of equations has effectively created a wall preventing this from happening. For many readers, it will be the place they stop reading the book.
One opinion that did come through loud and clear; despite what Science Fiction writers and social media hystericals maybe telling tell us, “…there is still a large gap between human-level intelligence and our best efforts at artificial intelligence.” I’m willing to accept the fact that I can’t understand the math as a pretty good indication that this is so.
So, despite the fascinating information I learned from my reading, I think the author tried to reach too broad an audience, so is bound to disappoint some. This book is highly recommended for those with a scientific education. Many other readers might want to browse through and skip the tough math part, knowing that the ending returns to important material that is easier to grasp.
September 17, 2023
I thought I would share with you, Is the Algorithm Plotting Against Us? by Kenneth Wenger, an AI expert and computer scientist.
Wenger explains this book is a layperson's guide to understanding artificial intelligence—its potential, limitations, and societal impact. Artificial Intelligence (AI) is machine-displayed intelligence that simulates human behavior or thinking and can be trained to solve specific problems.
In analyzing the information I want readers to know the book may be geared for the layperson but still is a lot to take in due to the complexity. Throughout much of the book Wenger talks about the use of Logic gates which control flow and decision systems that form the backbone of programming language
Keep in mind that many of us already use AI for a number of things as algorithms can analyze extensive amounts of data to personalize products, services, and experiences.
The main portion of the book is broken down into chapters.
Chapter 1. Discusses the first artificial neuron created by humans.
Chapter 2 Examines the problems that computer vision presents.
Chapter 3 Analyzes artificial neural networks. It looks more closely at information processing and describes the training processes to assist in understanding the limitations.
Chapter 4 Discusses the risks and rewards of AI.
To many, it is no surprise companies promise futuristic chips - chips that can be inserted into our brains and interface with our neural connections.
In considering a neural connection one will need to explore the fact that our actions have the potential to have unintended harmful consequences.
The section that I really keyed in on was where Wenger discusses the process that depends on the aggregate of many subprocesses, the aggregate process can be modeled using the normal distribution.
In closing out this read, I believe overfitting instead of learning underlying patterns could remain an issue. One thing is assured - we'll need to be prepared to think ahead of those looking to exploit vulnerabilities.
I received a copy of this book from Andrés C. with Working Fires Foundation
Wenger explains this book is a layperson's guide to understanding artificial intelligence—its potential, limitations, and societal impact. Artificial Intelligence (AI) is machine-displayed intelligence that simulates human behavior or thinking and can be trained to solve specific problems.
In analyzing the information I want readers to know the book may be geared for the layperson but still is a lot to take in due to the complexity. Throughout much of the book Wenger talks about the use of Logic gates which control flow and decision systems that form the backbone of programming language
Keep in mind that many of us already use AI for a number of things as algorithms can analyze extensive amounts of data to personalize products, services, and experiences.
The main portion of the book is broken down into chapters.
Chapter 1. Discusses the first artificial neuron created by humans.
Chapter 2 Examines the problems that computer vision presents.
Chapter 3 Analyzes artificial neural networks. It looks more closely at information processing and describes the training processes to assist in understanding the limitations.
Chapter 4 Discusses the risks and rewards of AI.
To many, it is no surprise companies promise futuristic chips - chips that can be inserted into our brains and interface with our neural connections.
In considering a neural connection one will need to explore the fact that our actions have the potential to have unintended harmful consequences.
The section that I really keyed in on was where Wenger discusses the process that depends on the aggregate of many subprocesses, the aggregate process can be modeled using the normal distribution.
In closing out this read, I believe overfitting instead of learning underlying patterns could remain an issue. One thing is assured - we'll need to be prepared to think ahead of those looking to exploit vulnerabilities.
I received a copy of this book from Andrés C. with Working Fires Foundation
May 19, 2023
Is the Algorithm Plotting Against Us? by Kenneth Wenger is a wonderful overview, or introduction depending on your previous knowledge, of the history and current state of AI functionality as well as a glimpse both at the ethics of current and future uses.
While my first degrees were in EE which, at the time, included the burgeoning field of computer science (I had to start more Fortran programs over because someone knocked all the punch cards off my desk), once I went back to school in the 90s in the humanities my math skills have slowly deteriorated. This book does an excellent job of making the math as well as the logic of AI understandable for any reader who is interested. Depending on what you're bringing to the book, it is still going to take being an active reader to get the most from it, but it is quite accessible and actually a very engaging read.
We all need to have some understanding of what is and is not, at this point, possible with AI, as well as where it could go and whether we want it to go there. There are plenty of over-the-top "news" stories that overblow either the advantages or the pitfalls, designed to make their readers either oppose or welcome the technology with blinders on. It is our responsibility to have at least a basic knowledge so we can voice our opinions, and have those opinions make sense based on what actually is. Wenger has given us just such a resource, one that arms us with a solid foundation whether we want to dive deeper or simply maintain a layperson's level of understanding. We have to be informed in order to make informed decisions.
While this is certainly an excellent introduction for those with no real understanding of what AI actually is, it is also a valuable text for those with some decent understanding but perhaps has focused so much on one aspect that the big picture has become hazy. In other words, this is for any reader with an interest in AI and our future, no matter what your current level of understanding may be.
Reviewed from a copy made available by the author via Edelweiss.
While my first degrees were in EE which, at the time, included the burgeoning field of computer science (I had to start more Fortran programs over because someone knocked all the punch cards off my desk), once I went back to school in the 90s in the humanities my math skills have slowly deteriorated. This book does an excellent job of making the math as well as the logic of AI understandable for any reader who is interested. Depending on what you're bringing to the book, it is still going to take being an active reader to get the most from it, but it is quite accessible and actually a very engaging read.
We all need to have some understanding of what is and is not, at this point, possible with AI, as well as where it could go and whether we want it to go there. There are plenty of over-the-top "news" stories that overblow either the advantages or the pitfalls, designed to make their readers either oppose or welcome the technology with blinders on. It is our responsibility to have at least a basic knowledge so we can voice our opinions, and have those opinions make sense based on what actually is. Wenger has given us just such a resource, one that arms us with a solid foundation whether we want to dive deeper or simply maintain a layperson's level of understanding. We have to be informed in order to make informed decisions.
While this is certainly an excellent introduction for those with no real understanding of what AI actually is, it is also a valuable text for those with some decent understanding but perhaps has focused so much on one aspect that the big picture has become hazy. In other words, this is for any reader with an interest in AI and our future, no matter what your current level of understanding may be.
Reviewed from a copy made available by the author via Edelweiss.
June 29, 2023
AI has no real knowledge, only probabilities based on correlations found in training data. This book gives you a feel for how it all works under the covers. It's written for the layperson, but any experience you have writing software will help greatly in absorbing some of the technicalities. The math AI uses to classify objects is fairly simple, not going much beyond linear regression. A dash of calculus is used to deal efficiently with curved boundaries, as needed. If you want to become an AI programmer, much of your work will be designing filters for specialized information types and topologies and finding ways to improve those designs via customized training. Loss algorithms and back propagation are terms you'll encounter in discussions of how AI uses new input data to refine weights and biases so its overall performance keeps improving.
An excellent way of reading this book is by concurrently watching some of the many excellent YouTube videos discussing the AI concepts and techniques you will encounter.
Should we be afraid of AI? The author seeks to dispel irrational fears, but rational ones are left over. It's a good idea not to give any AI system too much responsibility or to act too much on its own. It should probably always be monitored for accuracy, reliability, and to guard against unintended side effects. Unfortunately, bad actors will get their hands on AI and use it irresponsibly, so law enforcement and security agencies will have their hands full trying to anticipate and react to rogue events. That part is scary.
An excellent way of reading this book is by concurrently watching some of the many excellent YouTube videos discussing the AI concepts and techniques you will encounter.
Should we be afraid of AI? The author seeks to dispel irrational fears, but rational ones are left over. It's a good idea not to give any AI system too much responsibility or to act too much on its own. It should probably always be monitored for accuracy, reliability, and to guard against unintended side effects. Unfortunately, bad actors will get their hands on AI and use it irresponsibly, so law enforcement and security agencies will have their hands full trying to anticipate and react to rogue events. That part is scary.
May 18, 2023
I am a former humanities major working in education that enjoys reading introductory STEM books written by professionals in the field. My knowledge of AI and the math behind it is minimal, and while it does seem intimidating I was curious about the concepts behind it and how it works.
Is the Algorithm Plotting Against Us shows us the basic history of innovation that led to artificial intelligence as we know it today and the neural networks and mathematical concepts behind them. It helps us understand the essentials behind discerning the uses of artificial intelligence, current concerns, and limitations. Wenger sought to inform the "layperson" on how to understand the concepts behind artificial intelligence and where actual concerns could and should be placed, and I think that was accomplished as my "one-mandatory-college-statistics-course self" managed to follow along.
Overall, it is a good introductory book into the language and world of artificial intelligence for those of us who are not educated in this field. I think the basic understanding I gained from some of the concepts presented has made me feel more confident in understanding some of the limitations and current concerns present in the use of artificial intelligence. I appreciate a beginner-friendly book that can illustrate how artificial intelligence affects us now and how it can affect us in the future.
I was sent a free book and am voluntarily leaving my honest review.
Is the Algorithm Plotting Against Us shows us the basic history of innovation that led to artificial intelligence as we know it today and the neural networks and mathematical concepts behind them. It helps us understand the essentials behind discerning the uses of artificial intelligence, current concerns, and limitations. Wenger sought to inform the "layperson" on how to understand the concepts behind artificial intelligence and where actual concerns could and should be placed, and I think that was accomplished as my "one-mandatory-college-statistics-course self" managed to follow along.
Overall, it is a good introductory book into the language and world of artificial intelligence for those of us who are not educated in this field. I think the basic understanding I gained from some of the concepts presented has made me feel more confident in understanding some of the limitations and current concerns present in the use of artificial intelligence. I appreciate a beginner-friendly book that can illustrate how artificial intelligence affects us now and how it can affect us in the future.
I was sent a free book and am voluntarily leaving my honest review.
July 19, 2023
Is the Algorithm Plotting Against Us? is a beginners guide to understanding Artificial Intelligence. Written in plain language and with gentle walk -throughs of the math with plenty of graphics, you do not have to be a professional to understand. With the benefits, biases, and potential dangers of Artificial Intelligence explained, Is the Algorithm Plotting Against Us? strives to build knowledge and responsible practice around this amazing technology in our lives.
Beginning with early studies in understanding the human brain, the history and purpose of artificial intelligence unfolds, focusing on neural networks as a tool to help us solve problems, classify data and forecast problems. The math section is next, which I admittedly skimmed, but was also fairly easy to understand with many charts, graphics and broken down equations if this is not your strong suit. I found the section on training AI and bias in AI particularly interesting and will remember the point that models only learn patterns from the data that it is given and if we produce and input biased data, we will get biased results. Overall, a good overview covering the history, purpose, usefulness and potentials of Artificial Intelligence.
This book was received for free in return for an honest review.
Beginning with early studies in understanding the human brain, the history and purpose of artificial intelligence unfolds, focusing on neural networks as a tool to help us solve problems, classify data and forecast problems. The math section is next, which I admittedly skimmed, but was also fairly easy to understand with many charts, graphics and broken down equations if this is not your strong suit. I found the section on training AI and bias in AI particularly interesting and will remember the point that models only learn patterns from the data that it is given and if we produce and input biased data, we will get biased results. Overall, a good overview covering the history, purpose, usefulness and potentials of Artificial Intelligence.
This book was received for free in return for an honest review.
September 29, 2023
“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”
– Pedro Domingos
Wenger's book does a deep dive into the history and mathematics behind the algorithms that are now part of our everyday lives. He does some comparisons of neural networks, which are arguably the most significant advanced algorithms on the planet to their biological neural roots. His work shines in his detailed discussion and development of the mathematics that make neural nets and deep learning possible. Starting from the lowly perceptron of Rosenblatt back in 1958, AND and OR gates, he moves into convolutional neural nets, RELUs, hidden layers, and one hot encoding. "Is the Algorithm" provides the reader with a sense of how training and bias affects the outcome but in the end its a case of garbage in - garbage out. Great overall presentation of difficult material. A must read for anyone stumbling to separate their classification from regression problems.
– Pedro Domingos
Wenger's book does a deep dive into the history and mathematics behind the algorithms that are now part of our everyday lives. He does some comparisons of neural networks, which are arguably the most significant advanced algorithms on the planet to their biological neural roots. His work shines in his detailed discussion and development of the mathematics that make neural nets and deep learning possible. Starting from the lowly perceptron of Rosenblatt back in 1958, AND and OR gates, he moves into convolutional neural nets, RELUs, hidden layers, and one hot encoding. "Is the Algorithm" provides the reader with a sense of how training and bias affects the outcome but in the end its a case of garbage in - garbage out. Great overall presentation of difficult material. A must read for anyone stumbling to separate their classification from regression problems.
April 19, 2023
Ken's book, Is the Algorithm Plotting Against us? is all about providing everyone access to the how, the why, and the rest of all the questions you may be asking yourself when you hear the terms neural networks and AI. There are many interesting facts about AI I learned and lots of the background to it I think will allow everyone to become more familiar and speak intelligently on these subjects. Great for all the IT professionals out there in distribution and more.
May 22, 2023
I enjoyed this book. Unlike other books I’ve read on AI, this book actually explains how AI works. It’s based on statistics and probability, and the book goes into quite a bit of detail on this. I didn’t quite understand everything but I easily gleaned enough that I was able to understand and enjoy the great discussion on the implications of AI. This makes the book a worthwhile read. Thank you to Edelweiss and Working Fires Foundation for the digital review copy.
April 5, 2023
An important read at a time when AI is dominating the news cycle—and much of our lives. It seems urgent to understand what AI can and cannot do, and Ken offers wonderful explanations of the basics of AI. He then delves into the philosophical implications of this technology, philosophical points and questions that become less and less hypothetical as AI's popular use spreads. Thank you, Ken!
April 21, 2023
A friend gave me an advance copy of this book. I was amazed because, I'm not a math person, but I came away really feeling like I understand the basics of how AI works. And now with AI being in the news all the time, I'm much more confident in my ability to tell what's hype, what's actually worrisome, what's exciting.
May 12, 2023
Even if it talks about complex concepts and algorithms it's easy to follow and a very informative book.
It's relevant as AI is going to affect us in more than one way.
Highly recommended.
Many thanks to the publisher for this arc, all opinions are mine
It's relevant as AI is going to affect us in more than one way.
Highly recommended.
Many thanks to the publisher for this arc, all opinions are mine
April 19, 2023
A clear explanation of the nuts and bolts of AI algorithms. We're hearing so much about AI these days, and this book is perfect for anyone who's curious about how these things work.
After giving the reader a basic understanding of how AI algorithms function, the author then discusses the ethical implications of where we apply them. He makes clear how the two go hand in hand: If we don't know how they operate, how can we make engage in reasonable discussions about where they should or shouldn't be deployed?
And these discussions are essential in a world where AI's influence isn't going away but will continue to get stronger.
After giving the reader a basic understanding of how AI algorithms function, the author then discusses the ethical implications of where we apply them. He makes clear how the two go hand in hand: If we don't know how they operate, how can we make engage in reasonable discussions about where they should or shouldn't be deployed?
And these discussions are essential in a world where AI's influence isn't going away but will continue to get stronger.
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