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Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society
An indispensable look at the next frontier of technological advancement and its impact on our world
Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs. In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society. Professionals, technophiles, and anyone with an interest in the future will need to understand how GenAI is set to redefine jobs, revolutionize business, and question the foundations everything we do.
In this book, Marr sheds light on the most innovative real-world GenAI applications through practical examples, describing how they are moulding industries like retail, healthcare, education, finance, and beyond. You'll enjoy a captivating discussion of innovations in media and entertainment, seismic shifts in advertising, and the future trajectory of GenAI. You
Navigate the complex landscapes of risks and challenges posed by Generative AI Delve into the revolutionary transformation of the job market in the age of GenAI Understand AI's transformative impact on education, healthcare, and retail Explore the boundless potentials in media, design, banking, coding, and even the legal arena Ideal for professionals, technophiles, and anyone eager to understand the next big thing in technology, Generative AI In Practice will equip readers with insights on how to implement GenAI, how GenAI is different to traditional AI, and a comprehensive list of generative AI tools available today.
Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs. In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society. Professionals, technophiles, and anyone with an interest in the future will need to understand how GenAI is set to redefine jobs, revolutionize business, and question the foundations everything we do.
In this book, Marr sheds light on the most innovative real-world GenAI applications through practical examples, describing how they are moulding industries like retail, healthcare, education, finance, and beyond. You'll enjoy a captivating discussion of innovations in media and entertainment, seismic shifts in advertising, and the future trajectory of GenAI. You
Navigate the complex landscapes of risks and challenges posed by Generative AI Delve into the revolutionary transformation of the job market in the age of GenAI Understand AI's transformative impact on education, healthcare, and retail Explore the boundless potentials in media, design, banking, coding, and even the legal arena Ideal for professionals, technophiles, and anyone eager to understand the next big thing in technology, Generative AI In Practice will equip readers with insights on how to implement GenAI, how GenAI is different to traditional AI, and a comprehensive list of generative AI tools available today.
- GenresArtificial Intelligence
304 pages, Hardcover
Published March 25, 2024
26 people are currently reading
91 people want to read
About the author
Bernard Marr
64 books122 followersBest-Selling Author, Keynote Speaker and Leading Business and Data Expert
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Displaying 1 - 8 of 8 reviews
May 27, 2024
The "in practice" part of the title is false advertising; this reads like a summary of news articles.
June 6, 2024
WHAT DOES THIS GenAI GENERATE,
Bernard Marr just published Generative AI in Practice (1), which brings together the matter he has been dealing with on his blogs: Artificial Intelligence from the simple practical point of view of a user along with a systematic question about what can the consequences be for the various jobs these users have? But the book is always schematic when it shows the negative consequences will not compare with the positive consequences, though bad or good, they will require a lot of changes in the way we work, the jobs available, and the mindset of the users. Bernard Marr does not insist so much on the negative sides of GenAI, but he does list them, particularly all sorts of cheating by using GenAI to replace one’s own writing work, all types of misuse of the intellectual property of such mechanical production of text, images, videos or any other copyrightable product that the user presents as his/her own. But he does not enter the details, and thus he remains superficial. The problems of misinformation, hallucination, and bias are a lot less important, even with deep fakes, though Bernard Marr remains superficial on such dangers. He considers these GenAI products more like patentable or trademark problems, which they are but that’s the only side or point of view of the businesses using this technology. At the present moment, lawsuits are emerging on the Intellectual Property front with people getting ready to go to court for unauthorized use of protected data and items within LLMs, or the use of voices, slightly synthesized (hence plagiarism and plain theft) as commercial products sold with an unshared profit. I will concentrate, in the second part, on chapter 10 on education to enter some details I know from the practice of self-learning at many levels of the educational system. (2)
[…]
CONCLUSION
To conclude is difficult because there is no possible conclusion in a discussion that can only end up with a gentleman’s agreement. The topic is open for discussion, the practices have to be open for examination, and even scrutinization. It is urgent to get to some kind of code of good manners and uses for GenAI at the world level, hence a global pact. But we know there will always be a black market and a black internet to produce and commercialize, meaning distribute, all sorts of unethical products. Yet, it is urgent to regulate GenAI and control its use, the use of Virtual Reality beyond, and the merging of the two, with the supplementary technique of holographic avatars in a real or virtual environment.
Can we accept that any candidate in an election may use such tools, and thus be holographically present in ten (why not a hundred) meetings at the same time and actually intervene in all of them to react to what the public says or does? Can we go as far as giving each holographic avatar its GenAI autonomy so that it can answer questions in the ten (why not a hundred) simultaneous meetings specifically and originally, hence multiply by ten (why not a hundred) the intervention of the candidate in meetings? In France, the time of public intervention of candidates is severely contained and equal for all. Do such 100 simultaneous meetings count for one or have to be multiplied by 100? It is no longer a revolution. It is an apocalyptic transcendental manipulation. The case of General Georges-Ernest-Jean-Marie Boulanger (1837-1891) goes that way. Using the French tradition of Members of Parliament having one deputy each in case the elected MP dies or becomes unfit for the job or resigns, General Boulanger was a candidate in many constituencies, was elected in many, and only kept one, resigning from the others thus enabling his deputies to take the seats. He thus ended up with many seats under his own control. But a love affair made him disappear from the scene, and the National Assembly banned all multiple candidacies after the 1889 elections when Boulanger got 88 elected MPs behind him. Note the problem was only to be formally solved with a law passed on July 7, 2000, banning any multiple candidacies in local elections where they were apparently still possible. (15) The stakes are obviously enormous. Bernard Marr does not really examine such problems. But the book is a good positive X-ray picture of the general intervention of GenAI in our society, provided we globalize the approach. That is a lot of self-learning to do.
(15) General Boulanger, https://fr.wikipedia.org/wiki/Georges... L’affaire Boulanger, https://www.senat.fr/connaitre-le-sen...
Dr. Jacques COULARDEAU
Bernard Marr just published Generative AI in Practice (1), which brings together the matter he has been dealing with on his blogs: Artificial Intelligence from the simple practical point of view of a user along with a systematic question about what can the consequences be for the various jobs these users have? But the book is always schematic when it shows the negative consequences will not compare with the positive consequences, though bad or good, they will require a lot of changes in the way we work, the jobs available, and the mindset of the users. Bernard Marr does not insist so much on the negative sides of GenAI, but he does list them, particularly all sorts of cheating by using GenAI to replace one’s own writing work, all types of misuse of the intellectual property of such mechanical production of text, images, videos or any other copyrightable product that the user presents as his/her own. But he does not enter the details, and thus he remains superficial. The problems of misinformation, hallucination, and bias are a lot less important, even with deep fakes, though Bernard Marr remains superficial on such dangers. He considers these GenAI products more like patentable or trademark problems, which they are but that’s the only side or point of view of the businesses using this technology. At the present moment, lawsuits are emerging on the Intellectual Property front with people getting ready to go to court for unauthorized use of protected data and items within LLMs, or the use of voices, slightly synthesized (hence plagiarism and plain theft) as commercial products sold with an unshared profit. I will concentrate, in the second part, on chapter 10 on education to enter some details I know from the practice of self-learning at many levels of the educational system. (2)
[…]
CONCLUSION
To conclude is difficult because there is no possible conclusion in a discussion that can only end up with a gentleman’s agreement. The topic is open for discussion, the practices have to be open for examination, and even scrutinization. It is urgent to get to some kind of code of good manners and uses for GenAI at the world level, hence a global pact. But we know there will always be a black market and a black internet to produce and commercialize, meaning distribute, all sorts of unethical products. Yet, it is urgent to regulate GenAI and control its use, the use of Virtual Reality beyond, and the merging of the two, with the supplementary technique of holographic avatars in a real or virtual environment.
Can we accept that any candidate in an election may use such tools, and thus be holographically present in ten (why not a hundred) meetings at the same time and actually intervene in all of them to react to what the public says or does? Can we go as far as giving each holographic avatar its GenAI autonomy so that it can answer questions in the ten (why not a hundred) simultaneous meetings specifically and originally, hence multiply by ten (why not a hundred) the intervention of the candidate in meetings? In France, the time of public intervention of candidates is severely contained and equal for all. Do such 100 simultaneous meetings count for one or have to be multiplied by 100? It is no longer a revolution. It is an apocalyptic transcendental manipulation. The case of General Georges-Ernest-Jean-Marie Boulanger (1837-1891) goes that way. Using the French tradition of Members of Parliament having one deputy each in case the elected MP dies or becomes unfit for the job or resigns, General Boulanger was a candidate in many constituencies, was elected in many, and only kept one, resigning from the others thus enabling his deputies to take the seats. He thus ended up with many seats under his own control. But a love affair made him disappear from the scene, and the National Assembly banned all multiple candidacies after the 1889 elections when Boulanger got 88 elected MPs behind him. Note the problem was only to be formally solved with a law passed on July 7, 2000, banning any multiple candidacies in local elections where they were apparently still possible. (15) The stakes are obviously enormous. Bernard Marr does not really examine such problems. But the book is a good positive X-ray picture of the general intervention of GenAI in our society, provided we globalize the approach. That is a lot of self-learning to do.
(15) General Boulanger, https://fr.wikipedia.org/wiki/Georges... L’affaire Boulanger, https://www.senat.fr/connaitre-le-sen...
Dr. Jacques COULARDEAU
December 17, 2024
Bernard Marr’s “Generative AI in Practice” is a comprehensive exploration of the practical applications of this transformative technology. The book effectively positions itself as a roadmap for businesses and individuals seeking to understand and leverage the potential of generative AI.
Marr's approach is commendable in its breadth and depth. He meticulously examines a wide range of industries, from healthcare to finance, providing concrete examples of how generative AI is being used to solve complex problems and create new opportunities. For instance, the exploration of AI-driven drug discovery in healthcare and the use of generative models for personalized financial advice offers valuable insights into the practical applications of the technology.
A particular strength of the book lies in its ability to bridge the gap between technical concepts and business implications. While not delving deep into the mathematical intricacies of generative models, Marr successfully conveys the core principles and benefits of the technology in a manner accessible to a broad audience. This makes the book valuable for both technical and non-technical readers.
Moreover, the author's emphasis on the human element in the AI equation is commendable. He acknowledges the challenges and ethical considerations associated with generative AI, such as bias and job displacement, while also highlighting the potential for human-AI collaboration. This balanced perspective is essential for understanding the full impact of the technology.
“Generative AI in Practice” offers a particularly illuminating exploration of generative AI's impact on the financial sector. Marr deftly navigates the complexities of this industry, demonstrating how AI is revolutionizing areas such as fraud detection, risk assessment, and customer service. The book provides concrete examples of how generative models are being used to analyze vast datasets, identify patterns, and make predictions, ultimately leading to more efficient and personalized financial services.
A noteworthy aspect of Marr's analysis is his focus on the potential of generative AI to enhance financial inclusion. By discussing how AI-powered tools can be used to assess creditworthiness for individuals traditionally underserved by financial institutions, the author highlights the technology's potential for social impact.
In conclusion, “Generative AI in Practice” is a valuable resource for anyone seeking to grasp the practical implications of generative AI. By providing a comprehensive overview of real-world applications, Marr offers a solid foundation for understanding how this technology is reshaping industries and society.
The original book review can be found here: https://igorpejic.substack.com/
Marr's approach is commendable in its breadth and depth. He meticulously examines a wide range of industries, from healthcare to finance, providing concrete examples of how generative AI is being used to solve complex problems and create new opportunities. For instance, the exploration of AI-driven drug discovery in healthcare and the use of generative models for personalized financial advice offers valuable insights into the practical applications of the technology.
A particular strength of the book lies in its ability to bridge the gap between technical concepts and business implications. While not delving deep into the mathematical intricacies of generative models, Marr successfully conveys the core principles and benefits of the technology in a manner accessible to a broad audience. This makes the book valuable for both technical and non-technical readers.
Moreover, the author's emphasis on the human element in the AI equation is commendable. He acknowledges the challenges and ethical considerations associated with generative AI, such as bias and job displacement, while also highlighting the potential for human-AI collaboration. This balanced perspective is essential for understanding the full impact of the technology.
“Generative AI in Practice” offers a particularly illuminating exploration of generative AI's impact on the financial sector. Marr deftly navigates the complexities of this industry, demonstrating how AI is revolutionizing areas such as fraud detection, risk assessment, and customer service. The book provides concrete examples of how generative models are being used to analyze vast datasets, identify patterns, and make predictions, ultimately leading to more efficient and personalized financial services.
A noteworthy aspect of Marr's analysis is his focus on the potential of generative AI to enhance financial inclusion. By discussing how AI-powered tools can be used to assess creditworthiness for individuals traditionally underserved by financial institutions, the author highlights the technology's potential for social impact.
In conclusion, “Generative AI in Practice” is a valuable resource for anyone seeking to grasp the practical implications of generative AI. By providing a comprehensive overview of real-world applications, Marr offers a solid foundation for understanding how this technology is reshaping industries and society.
The original book review can be found here: https://igorpejic.substack.com/
October 27, 2024
GIGO: Garbage In, Garbage Out.
This book doesn't add anything meaningful to your knowledge base. It just aggregates hearsay of interviews and news articles from questionable sources, like Forbes, Tech Crunch, Buzzfeed, and Wired. Reads like that kid on the playground that says, "Didja hear? With AI, you can do _____!!!"
Where _____ is basically any blue-sky task a child's wild imagination dreams up. GenAI can improve customer service! GenAI can tell you how much laundry detergent to put in! GenAI can do research for you! GenAI can write code! In the future, GenAI can make decisions for you! Just an endless torrent of wind, without any implementation detail.
It's clear Bernard Marr never tried doing any of this stuff with AI; he just hears people theorizing about it online, then rephrases their quotes for the book. Nothing is firsthand. Everything is hearsay. The book teaches you nothing about actually writing programs or creating products using OpenAI's API. It doesn't compare and contrast the quality of results from Midjourney, Claude, Copilot, or any of the number of bots popping up around the internet; it doesn't even seem like Marr actually tried studying their output himself, or even if he actually made anything on top of asking questions of an AI chatbot like a consumer.
The book says nothing about training your own large language model (LLM), fine-tuning a model for a specific use case, calling into an API from code, setting up your own private server, engineering your prompts, evaluating your results, or any task a basic creator would have to do to actually create value from generative AI algorithms. It namedrops a bunch of services with sentence-long blurbs that describe their capability like you can read on their homepages, but says nothing about how to actually build on top of them, or adopt them in your own product.
Get your hands dirty and walk us through a use case, Bernard! Otherwise, this book is just wind, like the kind you'd feel scrolling through Twitter or Buzzfeed.
This book doesn't add anything meaningful to your knowledge base. It just aggregates hearsay of interviews and news articles from questionable sources, like Forbes, Tech Crunch, Buzzfeed, and Wired. Reads like that kid on the playground that says, "Didja hear? With AI, you can do _____!!!"
Where _____ is basically any blue-sky task a child's wild imagination dreams up. GenAI can improve customer service! GenAI can tell you how much laundry detergent to put in! GenAI can do research for you! GenAI can write code! In the future, GenAI can make decisions for you! Just an endless torrent of wind, without any implementation detail.
It's clear Bernard Marr never tried doing any of this stuff with AI; he just hears people theorizing about it online, then rephrases their quotes for the book. Nothing is firsthand. Everything is hearsay. The book teaches you nothing about actually writing programs or creating products using OpenAI's API. It doesn't compare and contrast the quality of results from Midjourney, Claude, Copilot, or any of the number of bots popping up around the internet; it doesn't even seem like Marr actually tried studying their output himself, or even if he actually made anything on top of asking questions of an AI chatbot like a consumer.
The book says nothing about training your own large language model (LLM), fine-tuning a model for a specific use case, calling into an API from code, setting up your own private server, engineering your prompts, evaluating your results, or any task a basic creator would have to do to actually create value from generative AI algorithms. It namedrops a bunch of services with sentence-long blurbs that describe their capability like you can read on their homepages, but says nothing about how to actually build on top of them, or adopt them in your own product.
Get your hands dirty and walk us through a use case, Bernard! Otherwise, this book is just wind, like the kind you'd feel scrolling through Twitter or Buzzfeed.
July 14, 2024
📕 Why (Not) to read this book (Target Audience)
Insightful book that brings the essence off generative ai.
👀 How this book changed my daily live (Takeaways)
Challenges and risks - Ethical and Societal concerns
• Overly Dependence of technology
○ Loosing vital human skills
• Propagation of false information: Disinformation
○ Ai generated clickbait
○ Content farms
○ Ai inbreed
• Copyright
○ Who owns generated content?
§ Providers of training Data - Sourced from
§ Ai System - Generated by
§ End users - Prompted by
○ Was the system trained on licensed data?
○ No learning (ladder) on the job as basic skills have been replaced by Ai
• Privacy
○ Disclosing data during prompting
○ Data should be anonymized when presented as input
• Environmental impact
○ Energy
○ Rare earth materials
⁉ Spoiler Alerts (Highlights)
Evolution
Generative AI has real time creation off content: Patern creation
Discriminative AI prediction off outcomes: Patern recognition
Neural models:
• LLM - relation between words
• GAN - Generative adversarial networks
• Variational autoencoder
• Diffusion -
• Foundation -
1950 Turing Test
Deepmind - WaveNet - Google TTS
Google - Lambda - Lemoine declares sentient AI
GenAi can be used to sustain disappearing knowledge (example icelandic language)
Job Risks & Exposure - Ai Delegation - All you need to do is ask
No learning (ladder) on the job as basic skills have been replaced by Ai
High Level Skills - More time for (Not replaced)
• Critical thinking
• Strategic thinking
• Interpersonal Skills
• Curiosity
• Empathy
Low level Skills - Liberated time (Replaced by)
• Generation of output on existing information
○ Repetitive
○ Predictable
○ No intuition
See - Google Coding
Insightful book that brings the essence off generative ai.
👀 How this book changed my daily live (Takeaways)
Challenges and risks - Ethical and Societal concerns
• Overly Dependence of technology
○ Loosing vital human skills
• Propagation of false information: Disinformation
○ Ai generated clickbait
○ Content farms
○ Ai inbreed
• Copyright
○ Who owns generated content?
§ Providers of training Data - Sourced from
§ Ai System - Generated by
§ End users - Prompted by
○ Was the system trained on licensed data?
○ No learning (ladder) on the job as basic skills have been replaced by Ai
• Privacy
○ Disclosing data during prompting
○ Data should be anonymized when presented as input
• Environmental impact
○ Energy
○ Rare earth materials
⁉ Spoiler Alerts (Highlights)
Evolution
Generative AI has real time creation off content: Patern creation
Discriminative AI prediction off outcomes: Patern recognition
Neural models:
• LLM - relation between words
• GAN - Generative adversarial networks
• Variational autoencoder
• Diffusion -
• Foundation -
1950 Turing Test
Deepmind - WaveNet - Google TTS
Google - Lambda - Lemoine declares sentient AI
GenAi can be used to sustain disappearing knowledge (example icelandic language)
Job Risks & Exposure - Ai Delegation - All you need to do is ask
No learning (ladder) on the job as basic skills have been replaced by Ai
High Level Skills - More time for (Not replaced)
• Critical thinking
• Strategic thinking
• Interpersonal Skills
• Curiosity
• Empathy
Low level Skills - Liberated time (Replaced by)
• Generation of output on existing information
○ Repetitive
○ Predictable
○ No intuition
See - Google Coding
July 29, 2024
One of the few decent books that has enough content, although at a very high level
October 20, 2025
Outdated if you're reading it right now, it may have resonated back in 2024 but most of the content is no longer valid
July 26, 2024
Reading this book about generative AI feels like reading a book written by generative AI. But there are good parts in the end about how to approach the topic from enterprise perspective.
Displaying 1 - 8 of 8 reviews