What do you think?
Rate this book
Artificial Intelligence For Dummies (For Dummies
Step into the future with AIThe term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it’s being used today.Inside, you’ll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating—and this hands-on guide makes it more accessible than ever!
368 pages, Paperback
First published March 16, 2018
364 people are currently reading
457 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 30 of 48 reviews
September 12, 2018
I am interested in artificial intelligence so I thought I would read this book. I enjoyed reading about AI safe jobs. I also liked reading about AI and space. I also liked reading the chapter on 20 things that AI has failed at. I thought I learned a lot about AI by reading this book. I enjoyed reading it. I wish I could write better book reviews. Maybe there is a book on writing book reviews.
February 6, 2019
I read this for scifi novel research. A good intro into the world of AI... a bit repetitive at the end and I'd quibble with some of his assertions and characterizations as being too rigid, but on the whole, useful.
July 24, 2025
Starting from the broadest possible definition of AI (any attempt to by a mechanical or computerized process to imitate any of the seven sorts of human intelligence), this book then delves into the practical and impractical implications of an assortment of these technologies.
There is some technical knowledge interspersed throughout and the book is kind enough to consolidate a large quantity of literature into, essentially, a survey course. And, hey, survey courses are good sometimes. But if you're looking for anything more than a starter menu of areas to dig into or the occasional pithy bon mot about AI, you're unlikely to strike gold here.
There is some technical knowledge interspersed throughout and the book is kind enough to consolidate a large quantity of literature into, essentially, a survey course. And, hey, survey courses are good sometimes. But if you're looking for anything more than a starter menu of areas to dig into or the occasional pithy bon mot about AI, you're unlikely to strike gold here.
March 15, 2022
I am intrigued by the term Artificial Intelligence but I don't even what does it means. There are a lot of movies using the idea of AI but it's all nonsense. How would an AI fall in love with a human if the only function of AI is to think like a human based on the input given to it? Without further ado, I wanted to discuss what I learned from this book. An old friend of mine told me to try Data Analysis since I enjoy numbers; hence this is the perfect book to start what I am supposed to be working on later on.
The difference between AI and humans is that self-awareness only happens to the latter. An AI can only process what input is given to it. There are a lot of limitations to what AI can do because to function as a human, there is the hardware needed to replicate it. The function of AI is to simplify redundant work.
Machine learning is a technology that helps computers learn from data. A perfect analogy for this is Jane teaching Tarzan how to be human by showing him what things/person looks like. You keep inserting data to teach him. And that's where DATA ANALYSIS enters. The data entered should be transformed, cleaned, and accurate data. It is similar to the student and teachers relationship. The teacher must reference his/her lessons correctly in order not to confuse the students. That's how important Data Analysis is in AI.
To elucidate this topic: Data Analysis is the first step to combine and extract accurate information. Followed by machine learning to feed the computer of the extracted information. You can successfully apply machine learning only after data analysis provides correctly prepared input. Deep learning is a technology that strives to imitate the human brain. It is just a small part of machine learning to master the idea of something. And finally, the AI where the computer finds the patterns and provides a requested output. It doesn't understand anything, it can't create or discover anything new, and it has no intrapersonal knowledge, so it can't empathize with anyone about anything. It behaves as designed by a human programmer, and what you often take for intelligence is only a mix of clever programming and vast amounts of data analyzed in a specific manner.
---> In a nutshell, Data Analysis is the provider of information i.e. books, teachers, Internet, etc; while Machine Learning is the user of that information to become something i.e. students becoming professional in the future.
There are a lot of applications of AI in this book. There's even a device powered by AI that allows a quadriplegic person to walk and that's forking awesome. And others...
I like this idea of Data Analysis where data is compared to oil.
After reading the book, the field of Data Analysis, Machine Learning, Deep Learning, and Artificial Intelligence is interesting that it makes me excited and fear how to learn all this stuff. I get the gist of these topics but the question now is HOW TO LEARN THIS STUFF? I am already learning Python and hopefully, that's the start of changing my behavior towards programming. I needed to get this skill and get out of my own version of RAT CYCLE.
As my computer's wallpaper, "Talk is cheap. Show me the code." -Linus Torvalds.
I am showing you now.
I want to clarify when I wanted this field. 1)This is what I learned for 5 years. 2)This is an interesting field that will not bore me and find other stuff to fill my stupid mind i.e. dating, marriage, and society's so-called norms. 3)This is the only solution that I see to end my suffering for my current job.
The difference between AI and humans is that self-awareness only happens to the latter. An AI can only process what input is given to it. There are a lot of limitations to what AI can do because to function as a human, there is the hardware needed to replicate it. The function of AI is to simplify redundant work.
Machine learning is a technology that helps computers learn from data. A perfect analogy for this is Jane teaching Tarzan how to be human by showing him what things/person looks like. You keep inserting data to teach him. And that's where DATA ANALYSIS enters. The data entered should be transformed, cleaned, and accurate data. It is similar to the student and teachers relationship. The teacher must reference his/her lessons correctly in order not to confuse the students. That's how important Data Analysis is in AI.
To elucidate this topic: Data Analysis is the first step to combine and extract accurate information. Followed by machine learning to feed the computer of the extracted information. You can successfully apply machine learning only after data analysis provides correctly prepared input. Deep learning is a technology that strives to imitate the human brain. It is just a small part of machine learning to master the idea of something. And finally, the AI where the computer finds the patterns and provides a requested output. It doesn't understand anything, it can't create or discover anything new, and it has no intrapersonal knowledge, so it can't empathize with anyone about anything. It behaves as designed by a human programmer, and what you often take for intelligence is only a mix of clever programming and vast amounts of data analyzed in a specific manner.
"Data analysis concentrates on understanding and manipulating the data so that it can become more useful and provide insights on the world, whereas machine learning strictly focuses on taking inputs from data and elaborating a working, internal representation of the world that you can use for practical purposes."
---> In a nutshell, Data Analysis is the provider of information i.e. books, teachers, Internet, etc; while Machine Learning is the user of that information to become something i.e. students becoming professional in the future.
There are a lot of applications of AI in this book. There's even a device powered by AI that allows a quadriplegic person to walk and that's forking awesome. And others...
I like this idea of Data Analysis where data is compared to oil.
Data is not just money that rains from the sky; it requires effort to make it useful. Just as you can't immediately use unrefined oil because it has to be changed into something else by chemical processes that turn it into gas, plastics, or other chemicals, so data must undergo significant transformations to acquire value."
"You have a weak AI when the AI shows intelligent behavior but isn't conscious like a human being. A strong AI occurs when the AI can really think as a human."---->this is the goal in the field of AI. It's interesting but am still in the beginning of everything. I'll get there someday. KAIZEN is the key.
"Artificial intelligence aims to find solutions to some difficult problems related to human abilities (such as recognizing objects, or understanding speech or text); robotics aims to use machines to perform tasks in the physical world in a partially or completely automated way."-----> AI more on brain; robotics more on physical.
Keep It Simple, Stupid (KISS) principle is the best idea to keep in mind when it comes to developing AI applications.
After reading the book, the field of Data Analysis, Machine Learning, Deep Learning, and Artificial Intelligence is interesting that it makes me excited and fear how to learn all this stuff. I get the gist of these topics but the question now is HOW TO LEARN THIS STUFF? I am already learning Python and hopefully, that's the start of changing my behavior towards programming. I needed to get this skill and get out of my own version of RAT CYCLE.
As my computer's wallpaper, "Talk is cheap. Show me the code." -Linus Torvalds.
I am showing you now.
I want to clarify when I wanted this field. 1)This is what I learned for 5 years. 2)This is an interesting field that will not bore me and find other stuff to fill my stupid mind i.e. dating, marriage, and society's so-called norms. 3)This is the only solution that I see to end my suffering for my current job.
This entire review has been hidden because of spoilers.
June 6, 2018
A great book overall, particularly for the newcomers to the A.I. Field. The authors cover a variety of topics related to A.I., without getting too technical but without oversimplifying things either. They look at what A.I. is, beyond the hype, its relationship with data, how modern technologies like deep learning have helped it become an everyday term, and how A.I. is bound to affect our world in the years to come.
I particularly liked the clarifications made about what constitutes A.I. and how the whole field is related, but not the same as robots. Also, the authors covered in depth how A.I. is both an interesting scientific field of research and a technology that plays a relevant role in business; this is something refreshingly different to what other A.I. experts write about it, as they tend to focus more on the math and engineering parts.
Although this book won’t make you an expert in A.I., it can provide you with a very good understanding of what the field entails and how it is useful. Whether you are a computer science student, an A.I. engineer, or a business person involved in tech, you’ll have something to learn from this book, which makes the whole field be both very accessible and intriguing.
I particularly liked the clarifications made about what constitutes A.I. and how the whole field is related, but not the same as robots. Also, the authors covered in depth how A.I. is both an interesting scientific field of research and a technology that plays a relevant role in business; this is something refreshingly different to what other A.I. experts write about it, as they tend to focus more on the math and engineering parts.
Although this book won’t make you an expert in A.I., it can provide you with a very good understanding of what the field entails and how it is useful. Whether you are a computer science student, an A.I. engineer, or a business person involved in tech, you’ll have something to learn from this book, which makes the whole field be both very accessible and intriguing.
November 27, 2023
I felt this was a good overview of AI in general, although for an audiobook published in November 2023, it was wildly outdated! I guess they just took many, many years to actually publish the audiobook version. Anyway, like I said, it did provide more historical info and context for a lot of things I'd been piecing together here and there. I appreciated the comprehensive breadth if not much depth—except for the handful of places where it went from barely scratching the surface to sudden intense math and jargon. Still, on the whole, a good overview of a complex and quickly moving topic.
January 22, 2024
Great overview on artificial intelligence, what it is, it's limits, it's future applications. Not too technical.
June 9, 2023
Really shitty in terms of content. This book did not know who its audience was. Chapter 7 was little more than a list of software using AI in medicine, something that felt like a list one could find online, not something where I learned about how AI works. Chapters 2 and 3 felt like they were targeting a totally different audience, those who are experts and are thinking about cases in which they can use AI. This book felt like it had been written by an AI, not a human.
July 20, 2025
in general, i'm a fan of the "for dummies" series, the concept is awesome.
with this, specifically, i was not disappointed.
for someone in a related field, it's great, it really gives you the zero to good enough basic general knowledge. for someone who does not understand probability or even how computer science works, it's possible it may feel too technical, since these things get mentioned a bit...but there should be a computer science and game theory in the "for dummies" series too :)
i thought it was great for general knowledge. i am in computer science but not really in AI, so this was pretty much what I expected, a good intro and also lots of context and applications with examples.
as a note, this was written for 2018 (what I found available on Audible), and I've read it in 2025, it would be great if the author/company expanded the book and published (on Audible too) an updated version to include latest interesting breakthroughs from the field maybe every 5 years, or at least every 10 years...this field is definitely in its "summer" period now, blooming and fruitful.
going for the latest (now 2024 Kindle) edition is probably recommend in this case.
some general notes from the book, and what to expect:
reactive machines
theory of mind
learning from data, learning from examples
problem solving types: symbolism (logical approach, inverse deduction to solve problems, given a conclusion and data find the path to the conclusion, inferring logical relationships), connectionism (back propagation to solve problems, fine-tuning weights to fit a gradient function, adjusting parameters), evolutionism (evolution programming to solve problems, random mutations to solutions and choosing the better outcome at each step), bayesian (probabilistic inference to solve problems, updating probabilities based on evidence), kernel machines to solve problems
neural networks, simulation the mechanism from biology, complex networks of interconnected algorithmic mechanisms called neurons, where each neuron gets a pass/fail function to permit the input to move further in the network, altered, or not, based on the case
inputs which fail in the neuron can be reprocessed in specific ways (feedback or a separate connected network) or ignored further
CNN (convoluted neural networks), RNN (recurrent neural networks)
collecting data, data consistency (is it the data needed or enough), storing data, data reliability (is the data good, omissions, bias, corruption, frame of reference), processing/extracting data (including aligning data from different sources), searching data, analysing data, data retention policies
AI can give worse output with too much data.
making things faster: caching, processor caching, preloading, specialty RAM, multithreading.
complex AI models rely on acquiring and processing their own data
the better the data the better the AI
AI can suffer from data corruption
AI breakthroughs (the end of “AI winter”) happened with the increase in processing power and data quality and quantity
complex AI systems can be made up of several individual AI models meant to specialise in a specific part of the problem, but they do not function completely independently, they influence each other’s inputs and settings
adversarial AI example, image generation: one AI generates, one AI decides if the generated image is good enough and adjusts inputs and/or settings for the other AI until the image generated passed as good enough
deep learning is a term used for complex AI systems, they are not necessarily the same type of system
the general idea behind robotics, drones, self-driving cars, space applications
discussions about AI being able to achieve true intelligence, with notes on the main weaknesses: creativity, emotion and natural attachment, interpersonal awareness, motivation
it seems like a few things contained in the 2024 3rd edition which are not in the 1st edition are: AI powered bot nets application and security, deep learning processors (designed and pre-configurated to work with AI model work), generative AI (also a bit more about CNN and RNN, ChatGPT, self-attention models which help with context for data, NLP processes, word embeddings and semantic similarity, WORD2VEC and FastText, simultaneously applying attention models over multiple parts of the data to build a "transformer" architecture, LLMs and how they can predict the next word based on a prompt, a vast amount of pre-trained context data and what was generated before, diffusion models which work by adding noise or variability into the data and learn to remove it in order to generate new data which is of the same category as the initial input data but not too similar, Deep Q-learning & networks DQL & DQN which aim to calculate the value for the best outcome of a move/step through deep neural networks while comparing it with and adapting based on it a "target network" with the best possible predicted values, applications, deep fakes and implications, AI errors, hallucinations and model drift which limit the true applicability of AI contradictory to popular opinion, the importance of supporting infrastructure to enable AI capabilities), AI ethics, extra applications and examples.
as a comparison, the 1st edition does not feel outdated compared to the (now latest) 3rd edition, just incomplete. obviously, the getting the latest edition is recommended if an option.
with this, specifically, i was not disappointed.
for someone in a related field, it's great, it really gives you the zero to good enough basic general knowledge. for someone who does not understand probability or even how computer science works, it's possible it may feel too technical, since these things get mentioned a bit...but there should be a computer science and game theory in the "for dummies" series too :)
i thought it was great for general knowledge. i am in computer science but not really in AI, so this was pretty much what I expected, a good intro and also lots of context and applications with examples.
as a note, this was written for 2018 (what I found available on Audible), and I've read it in 2025, it would be great if the author/company expanded the book and published (on Audible too) an updated version to include latest interesting breakthroughs from the field maybe every 5 years, or at least every 10 years...this field is definitely in its "summer" period now, blooming and fruitful.
going for the latest (now 2024 Kindle) edition is probably recommend in this case.
some general notes from the book, and what to expect:
reactive machines
theory of mind
learning from data, learning from examples
problem solving types: symbolism (logical approach, inverse deduction to solve problems, given a conclusion and data find the path to the conclusion, inferring logical relationships), connectionism (back propagation to solve problems, fine-tuning weights to fit a gradient function, adjusting parameters), evolutionism (evolution programming to solve problems, random mutations to solutions and choosing the better outcome at each step), bayesian (probabilistic inference to solve problems, updating probabilities based on evidence), kernel machines to solve problems
neural networks, simulation the mechanism from biology, complex networks of interconnected algorithmic mechanisms called neurons, where each neuron gets a pass/fail function to permit the input to move further in the network, altered, or not, based on the case
inputs which fail in the neuron can be reprocessed in specific ways (feedback or a separate connected network) or ignored further
CNN (convoluted neural networks), RNN (recurrent neural networks)
collecting data, data consistency (is it the data needed or enough), storing data, data reliability (is the data good, omissions, bias, corruption, frame of reference), processing/extracting data (including aligning data from different sources), searching data, analysing data, data retention policies
AI can give worse output with too much data.
making things faster: caching, processor caching, preloading, specialty RAM, multithreading.
complex AI models rely on acquiring and processing their own data
the better the data the better the AI
AI can suffer from data corruption
AI breakthroughs (the end of “AI winter”) happened with the increase in processing power and data quality and quantity
complex AI systems can be made up of several individual AI models meant to specialise in a specific part of the problem, but they do not function completely independently, they influence each other’s inputs and settings
adversarial AI example, image generation: one AI generates, one AI decides if the generated image is good enough and adjusts inputs and/or settings for the other AI until the image generated passed as good enough
deep learning is a term used for complex AI systems, they are not necessarily the same type of system
the general idea behind robotics, drones, self-driving cars, space applications
discussions about AI being able to achieve true intelligence, with notes on the main weaknesses: creativity, emotion and natural attachment, interpersonal awareness, motivation
it seems like a few things contained in the 2024 3rd edition which are not in the 1st edition are: AI powered bot nets application and security, deep learning processors (designed and pre-configurated to work with AI model work), generative AI (also a bit more about CNN and RNN, ChatGPT, self-attention models which help with context for data, NLP processes, word embeddings and semantic similarity, WORD2VEC and FastText, simultaneously applying attention models over multiple parts of the data to build a "transformer" architecture, LLMs and how they can predict the next word based on a prompt, a vast amount of pre-trained context data and what was generated before, diffusion models which work by adding noise or variability into the data and learn to remove it in order to generate new data which is of the same category as the initial input data but not too similar, Deep Q-learning & networks DQL & DQN which aim to calculate the value for the best outcome of a move/step through deep neural networks while comparing it with and adapting based on it a "target network" with the best possible predicted values, applications, deep fakes and implications, AI errors, hallucinations and model drift which limit the true applicability of AI contradictory to popular opinion, the importance of supporting infrastructure to enable AI capabilities), AI ethics, extra applications and examples.
as a comparison, the 1st edition does not feel outdated compared to the (now latest) 3rd edition, just incomplete. obviously, the getting the latest edition is recommended if an option.
March 28, 2021
The book provides some good examples where AI can be applied and at the same time describes its limitations.
However, the book is a bit repetitive and introductory chapter has plenty of technical information which a personally skipped.
But overall, I would recommend this book if you want to get an idea what AI is and how it works.
However, the book is a bit repetitive and introductory chapter has plenty of technical information which a personally skipped.
But overall, I would recommend this book if you want to get an idea what AI is and how it works.
May 28, 2022
Good introduction to AI, covering the history and development of the field, specific hardware, technical constraints, use cases and possibilities, dangers and opportunities. A good place to start learning about Artificial Intelligence.
July 25, 2024
Eindelijk dit boek uit. Maar het is niet slecht genoeg voor 1 ster, sommige informatie is oprecht interessant te noemen.
Het interessantste wat in het boek benoemd werd, was het Chinese kamer-experiment, wat duidelijk illustreert dat je dingen niet hoeft te begrijpen om iets te kunnen doen. Het laat zien dat computers dus niet noodzakelijk iets begrijpen terwijl ze het uitvoeren. Mooi inzichtelijk.
Verder ook wel goede dingen over de kansen en bedreigingen van AI, uitleg over waarom AI nooit de mens zal "vervangen". Ruimtevaart, zelfrijdende auto's en rijhulpsystemen, allerlei dingen waar je als mens nu of in de toekomst mee te maken kan krijgen.
Qua leesbaarheid is het boek soms veel te technisch met allerlei formules, grafieken en andere wiskundigheden. Daarnaast vind ik het zó vreemd dat bronnen direct IN DE TEKST als hyperlink worden aangegeven. Werk desnoods met voetnoten of een literatuurlijst per hoofdstuk of zoiets, een QR-code of verkorte link naar een website over het boek waarin je de meest recente links kan vinden of zoiets. Dit is echt heel chaotisch en gezien het een vrij actueel onderwerp is, vraag ik me af hoeveel links uit de tekst inmiddels al dood zijn. Sowieso is links midden in een zin plakken not done. Zoiets dóé je niet. Klaar.
De tekst is vrij verouderd en op sommige plekken is er echt wat mis gegaan met het vertalen van het boek. Vertaalfouten, tikfouten, nee, het is geen heel leesbaar boek. Het was een ware worsteling en dat is zonde gezien de inhoud kansen biedt en best interessant is.
Wat interessante noten:
"Als onderdeel van het creëeren van de dataset die je nodig hebt voor analyse, maak je een kopie van de oorspronkelijke gegevens in plaats van die aan te passen. Houd de originele, ruwe data altijd zuiver zodat je die later voor andere analyses kunt gebruiken." ~ bladzijde 36-37
"Het AI-effect houdt in dat succesvolle intelligente computerprogramma's al snel de erkentelijkheid van mensen kwijtraken en stille acteurs worden, terwijl de aandacht verlegd wordt naar AI-problemen die nog steeds om een oplossing vragen." ~ bladzijde 46
"Als je een boek wilt begrijpen, lees je het pagina voor pagina. De sequenties zijn genest. Binnen een pagina is een sequentie van woorden en binnen woorden is een sequentie van letters. Om het boek te begrijpen, moet je de sequentie van letters, woorden en pagina's begrijpen. Een Recurrent Neural Network (RNN) is het antwoord omdat het actuele input verwerkt terwijl het eerdere input naloopt." ~ bladzijde 196
"Robots zijn een relatief recent idee. Het woord is afgeleid van het Tsjechische woord robota, dat dwangarbeid betekent. De term verscheen voor het eerst in het toneelstuk Rossum's Universal Robots uit 1920, van de Tsjechische schrijver Karel Capek." ~ bladzijde 206
Het interessantste wat in het boek benoemd werd, was het Chinese kamer-experiment, wat duidelijk illustreert dat je dingen niet hoeft te begrijpen om iets te kunnen doen. Het laat zien dat computers dus niet noodzakelijk iets begrijpen terwijl ze het uitvoeren. Mooi inzichtelijk.
Verder ook wel goede dingen over de kansen en bedreigingen van AI, uitleg over waarom AI nooit de mens zal "vervangen". Ruimtevaart, zelfrijdende auto's en rijhulpsystemen, allerlei dingen waar je als mens nu of in de toekomst mee te maken kan krijgen.
Qua leesbaarheid is het boek soms veel te technisch met allerlei formules, grafieken en andere wiskundigheden. Daarnaast vind ik het zó vreemd dat bronnen direct IN DE TEKST als hyperlink worden aangegeven. Werk desnoods met voetnoten of een literatuurlijst per hoofdstuk of zoiets, een QR-code of verkorte link naar een website over het boek waarin je de meest recente links kan vinden of zoiets. Dit is echt heel chaotisch en gezien het een vrij actueel onderwerp is, vraag ik me af hoeveel links uit de tekst inmiddels al dood zijn. Sowieso is links midden in een zin plakken not done. Zoiets dóé je niet. Klaar.
De tekst is vrij verouderd en op sommige plekken is er echt wat mis gegaan met het vertalen van het boek. Vertaalfouten, tikfouten, nee, het is geen heel leesbaar boek. Het was een ware worsteling en dat is zonde gezien de inhoud kansen biedt en best interessant is.
Wat interessante noten:
"Als onderdeel van het creëeren van de dataset die je nodig hebt voor analyse, maak je een kopie van de oorspronkelijke gegevens in plaats van die aan te passen. Houd de originele, ruwe data altijd zuiver zodat je die later voor andere analyses kunt gebruiken." ~ bladzijde 36-37
"Het AI-effect houdt in dat succesvolle intelligente computerprogramma's al snel de erkentelijkheid van mensen kwijtraken en stille acteurs worden, terwijl de aandacht verlegd wordt naar AI-problemen die nog steeds om een oplossing vragen." ~ bladzijde 46
"Als je een boek wilt begrijpen, lees je het pagina voor pagina. De sequenties zijn genest. Binnen een pagina is een sequentie van woorden en binnen woorden is een sequentie van letters. Om het boek te begrijpen, moet je de sequentie van letters, woorden en pagina's begrijpen. Een Recurrent Neural Network (RNN) is het antwoord omdat het actuele input verwerkt terwijl het eerdere input naloopt." ~ bladzijde 196
"Robots zijn een relatief recent idee. Het woord is afgeleid van het Tsjechische woord robota, dat dwangarbeid betekent. De term verscheen voor het eerst in het toneelstuk Rossum's Universal Robots uit 1920, van de Tsjechische schrijver Karel Capek." ~ bladzijde 206
This entire review has been hidden because of spoilers.
February 8, 2020
Хубава книжка, бих преслушал две, три глави пак след време. Добро въведение и обобщение на темата. Книгите фор дъмис са супер добри. Но се вижда, че полето е много по-дълбоко от написаното в тази книга. Но и се вижда, че за сега това което наричат изкуствен интелект е просто експертна система.
Храниш го с много примери и той си намира някакъв начин, от пеоба и грешка да ти ги разпознава.
Така както ние упим по патология да разпознаваме различни болести от микроскопски препарати. Гледаш много докато не се научиш.
В бъдеще изглежда изкуствен интелект ще е много полезен на хората за подобни неща. Тип анализиране на ДНК, анализиране на големи данни, мисля си си тезиране на основни теми и идеи от енциклопедии например. Сравняване на 100тина учебника по някъв предмет и намиране на сходствата и правене на обзори.
И всичко свързано с транспорта коли, космически кораби и пр.
Изкуството за мен е преди всичко начин за синтезиране на есенциата от някаква реалност, та в това ИИ може също да помогне със събирането и обработването на данни.
В бъдеще хората ще е добре да имат базова идея ор статистика и дата сайанс и вероятно бейсик програмиране (които аз нямам за сега) за да могат си играят с ИИ.
Вероятно ще се появят и интерфейси за идиоти и програмирането ще стане елементарно.
Трябва да науча малко пайтън и да поразцъкам някой от ИИ играчките със отворен код
Храниш го с много примери и той си намира някакъв начин, от пеоба и грешка да ти ги разпознава.
Така както ние упим по патология да разпознаваме различни болести от микроскопски препарати. Гледаш много докато не се научиш.
В бъдеще изглежда изкуствен интелект ще е много полезен на хората за подобни неща. Тип анализиране на ДНК, анализиране на големи данни, мисля си си тезиране на основни теми и идеи от енциклопедии например. Сравняване на 100тина учебника по някъв предмет и намиране на сходствата и правене на обзори.
И всичко свързано с транспорта коли, космически кораби и пр.
Изкуството за мен е преди всичко начин за синтезиране на есенциата от някаква реалност, та в това ИИ може също да помогне със събирането и обработването на данни.
В бъдеще хората ще е добре да имат базова идея ор статистика и дата сайанс и вероятно бейсик програмиране (които аз нямам за сега) за да могат си играят с ИИ.
Вероятно ще се появят и интерфейси за идиоти и програмирането ще стане елементарно.
Трябва да науча малко пайтън и да поразцъкам някой от ИИ играчките със отворен код
September 2, 2025
Dreadful time waster. Listened to the audiobook version with only 62% finished. DNF. After speeding up the narrator's voice to 1.22% I could not take the hyperlink reading any longer. Why in the world would a narrator feel compelled to spell out each hyperlink in the printed copy of the book? You know: "w-w-w-dot-w-h-y-a-m-i-d-o-i-n-g-t-h-i-s-dot-com-forward-slash-t-h-e-r-e-m-u-s-t-b-e-a-r-e-a-s-o-n-forward-slash-a-d-n-a-u-s-e-u-m...".
Aside from narrator annoyances, the book did not have any heavy AI lifting that I was seeking. It merely states well established or already known facts of AI behaviour with no regard to how AI might have technically achieved that functionality. So I had have enough and will move on the the next book in my AI quest "AI Engineering" by Chip Huyen. Let you know how that goes!
Aside from narrator annoyances, the book did not have any heavy AI lifting that I was seeking. It merely states well established or already known facts of AI behaviour with no regard to how AI might have technically achieved that functionality. So I had have enough and will move on the the next book in my AI quest "AI Engineering" by Chip Huyen. Let you know how that goes!
October 14, 2025
This book is not for me. Apparently I require AI for absolute morons.
The first chapters were quite technical, so I basically read words without being able to take anything in, and then skipped some.
The last half was readable, but I just didn't care so was a DNF.
Probably my fault as I was really looking for an AI overview, so I could see how I could actually use it eg 'this is how you would use prompts to write a letter' 'this is how you can create a book', and show me different things I can do with it, not what NASA can.
No wonder my local library doesn't have a copy! I will see if they want mine.
And my fear of AI still exists!
The first chapters were quite technical, so I basically read words without being able to take anything in, and then skipped some.
The last half was readable, but I just didn't care so was a DNF.
Probably my fault as I was really looking for an AI overview, so I could see how I could actually use it eg 'this is how you would use prompts to write a letter' 'this is how you can create a book', and show me different things I can do with it, not what NASA can.
No wonder my local library doesn't have a copy! I will see if they want mine.
And my fear of AI still exists!
March 8, 2024
Interesting reading for anyone who wants to take the first step towards AI and has little to cero knowledge in the field. This book provides an accessible introduction to Artificial Intelligence, Big Data, and Machine Learning, making it a perfect starting point for anyone with little to no prior knowledge. I enjoyed reading the first half of the book for its ability to explain basic concepts in a simple manner avoiding technical jargon. Although, the last half of the book feels dated.
Definitely not for intermediate or advance AI levels.
Definitely not for intermediate or advance AI levels.
July 3, 2021
foa, i'm a trained computer engineer. though my specialisation is in automation & control instead of AI per se, I'm far from being a noob, so please add salt to suit your situation.
i guess the book can be helpful to someone totally uninitiated on the subject of AI and doesn't intend to go too much deeper but if you want more. Surely there are better introductory books on the subject of AI, or intelligence in general
i guess the book can be helpful to someone totally uninitiated on the subject of AI and doesn't intend to go too much deeper but if you want more. Surely there are better introductory books on the subject of AI, or intelligence in general
July 8, 2023
Essential reading for anyone interested in AI. Tells you what it can and can't do and it's the "can't do" that enthusiasts and other promoters need to read. As an aid and an assistant to everyday tasks and chores, AI is essential and that aspect of things will only increase and get better. But for those who want C-3PO or Robby the Robot around the house, you're going to have to wait, probably for a very long time.
January 5, 2024
AI is advancing so fast that the tech news in 2023 was all about AI.
This book was published in 2018.
It is now January 2024, so about 4 years have passed.
Thus, the book lacks the freshness of AI information.
Today's news is that the Windows keyboard will have an AI button.
This is the first time in 30 years that the keyboard design has changed.
News feeds are more useful for up-to-date information on AI like this, but essential learning is better learned in texts like this one.
This book was published in 2018.
It is now January 2024, so about 4 years have passed.
Thus, the book lacks the freshness of AI information.
Today's news is that the Windows keyboard will have an AI button.
This is the first time in 30 years that the keyboard design has changed.
News feeds are more useful for up-to-date information on AI like this, but essential learning is better learned in texts like this one.
June 6, 2024
This is not a book on working with AI, but a book about what AI is doing to make our lives better. From self-driving cars to medical breakthroughs, AI has been around since the 50s and has just recently started entering the public eye. Self-driving cars are having much success, but there are still bugs to work out, in medicine, it has allowed for faster diagnoses and quicker responses to life-threatening situations.
August 7, 2022
Part of the book requires some background on computer science and machine learning to understand, so it's not for any ordinary readers. I found it ok because I had such a background, but I sometimes still got confused. Also I found that the last portion of the book (Part 6) is repeating what it already discussed in earlier parts.
Other than that, the book is interesting to read.
Other than that, the book is interesting to read.
March 15, 2023
Provides an overview of AI applications and current limitations from the lens that really only serves the consumer. I may check out the “Machine Learning for Dummies” instead, as I was hoping this book would serve as starting point for a more technical breakdown.
Not poor quality, but wouldn’t recommend.
Not poor quality, but wouldn’t recommend.
June 28, 2023
There were a lot of interesting topics in this book. However there was a ton of repetition of the same ideas, it could have been a lot more concise. It would have been cool to follow more real life stories instead of quick tidbits on articles. I felt myself losing interest and having to force myself to keep moving, but maybe that’s just me.
February 21, 2025
I found it very shallow, unstructured with to much fluff in it, so I quit after maybe half of the book. The book tries to cover everything and because of that it stays pretty much on the surface. Sometimes less is more.
When reading it I didn’t have the impression that the authors are very proficient in the topic, I rather had the feeling that the content was generated with AI.
When reading it I didn’t have the impression that the authors are very proficient in the topic, I rather had the feeling that the content was generated with AI.
July 17, 2025
This is a pretty good primer on AI. I have been playing around with ChatGPT when writing or developing plans for some of my nonprofit board work. I have a 40+ IT degree so I have a basic idea on how it works but I wanted to get a little deeper. This book helped on a few things but still hasn't answered all my questions.
January 7, 2026
I read through to about chapter 6 in this book last year, then put it aside. I decided to move it from currently-reading to abandoned today, since I wasn't getting much out of it, and I don't think I'm going to want to come back to it. Parts of it were interesting, but it wasn't really useful for me.
February 22, 2019
Feels like it was thrown together in a week from a collection of bullet points and web links. Not insightful, poorly edited (there’s a screen shot of a Word document with the spell check squiggly under a word, missing words, etc.), and just tedious to read through. I eventually gave up.
January 10, 2024
This is free on audible. Stopped reading when I realised it is published in 2018. A bit pointless given the progress in AI the last few years.
Also - the narration in audible is horrible. It sounds like it's read by an AI :)
Also - the narration in audible is horrible. It sounds like it's read by an AI :)
June 11, 2024
In my quest to read all the books I could get my hands on on the topic of artificial intelligence, I read this one, not expecting much from it. I was pleasantly surprised to find that it wasn't bad at all, contrary to the opinion I had perhaps unfairly formed of this series.
December 31, 2024
A very nice summary of the overall AI/ML space.
I appreciated the medium depth that this book goes into in a wide array of artificial intelligence and machine learning technologies and applications.
I appreciated the medium depth that this book goes into in a wide array of artificial intelligence and machine learning technologies and applications.
Displaying 1 - 30 of 48 reviews