https://mklab.gr/ Mon, 14 Sep 2026 05:05:41 +0000 MyBB https://mklab.gr/showthread.php?tid=1858 Sat, 05 Sep 2026 16:49:33 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1858 LLMs in Production: From Language Models to Successful Products
Authors: Christopher Brousseau & Matthew Sharp
Publisher: Manning Publications
Publication: February 11, 2025 (trade paperback)
Length: 456 pages

LLMs in Production is a practical guide to moving large language models from experimentation into reliable, real-world applications. Rather than concentrating only on transformer theory or training models from scratch, Brousseau and Sharp focus on the engineering and operational problems that appear once an LLM has to serve actual users. The book introduces the fundamentals of LLMs, dataset preparation, model selection, prompt-based interaction, evaluation, fine-tuning, and the broader idea of LLMOps—the processes required to develop, deploy, monitor, retrain, and maintain language-model systems.

A major theme is deciding how much of the model stack you really need to own. The authors compare using existing foundation models with training or adapting your own, and explain techniques such as LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), and RLHF (Reinforcement Learning from Human Feedback). They also address practical trade-offs involving computational cost, latency, model quality, infrastructure, scalability, benchmarking, and deployment. The emphasis is that a successful LLM product is not simply a good model: it requires an entire production pipeline around that model. 

The book is strongly project-oriented. Readers work through examples including training a custom language model, creating an AI-powered VS Code extension, and deploying a small model on a Raspberry Pi. It also discusses cloud deployment, Kubernetes, commodity and edge hardware, load testing, and optimization. This makes the book particularly suitable for ML engineers, data scientists, and developers who already know Python and basic cloud deployment and want to progress from experimenting with LLM APIs toward building maintainable AI systems. 

Key takeaways

• LLM engineering is much more than prompt engineering: production systems need evaluation, infrastructure, monitoring, deployment, and lifecycle management.
  
• Fine-tuning does not always require retraining everything. Techniques such as LoRA and PEFT can adapt large models far more economically.
  
• Cost, latency, reliability, and model quality must be optimized together rather than treating accuracy as the only objective.
  
• The book bridges the gap between understanding LLMs and actually turning them into deployable products, making it especially useful as an introduction to LLMOps and production AI engineering. 
  

BOOK]]> LLMs in Production: From Language Models to Successful Products
Authors: Christopher Brousseau & Matthew Sharp
Publisher: Manning Publications
Publication: February 11, 2025 (trade paperback)
Length: 456 pages

LLMs in Production is a practical guide to moving large language models from experimentation into reliable, real-world applications. Rather than concentrating only on transformer theory or training models from scratch, Brousseau and Sharp focus on the engineering and operational problems that appear once an LLM has to serve actual users. The book introduces the fundamentals of LLMs, dataset preparation, model selection, prompt-based interaction, evaluation, fine-tuning, and the broader idea of LLMOps—the processes required to develop, deploy, monitor, retrain, and maintain language-model systems.

A major theme is deciding how much of the model stack you really need to own. The authors compare using existing foundation models with training or adapting your own, and explain techniques such as LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), and RLHF (Reinforcement Learning from Human Feedback). They also address practical trade-offs involving computational cost, latency, model quality, infrastructure, scalability, benchmarking, and deployment. The emphasis is that a successful LLM product is not simply a good model: it requires an entire production pipeline around that model. 

The book is strongly project-oriented. Readers work through examples including training a custom language model, creating an AI-powered VS Code extension, and deploying a small model on a Raspberry Pi. It also discusses cloud deployment, Kubernetes, commodity and edge hardware, load testing, and optimization. This makes the book particularly suitable for ML engineers, data scientists, and developers who already know Python and basic cloud deployment and want to progress from experimenting with LLM APIs toward building maintainable AI systems. 

Key takeaways

• LLM engineering is much more than prompt engineering: production systems need evaluation, infrastructure, monitoring, deployment, and lifecycle management.
  
• Fine-tuning does not always require retraining everything. Techniques such as LoRA and PEFT can adapt large models far more economically.
  
• Cost, latency, reliability, and model quality must be optimized together rather than treating accuracy as the only objective.
  
• The book bridges the gap between understanding LLMs and actually turning them into deployable products, making it especially useful as an introduction to LLMOps and production AI engineering. 
  

BOOK]]> https://mklab.gr/showthread.php?tid=1857 Sat, 05 Sep 2026 16:45:17 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1857 The Age of AI: And Our Human Future
Authors: Henry A. Kissinger, Eric Schmidt & Daniel Huttenlocher
Publication date: November 2, 2021
Publisher: Little, Brown and Company


The Age of AI and Our Human Future examines artificial intelligence not primarily as a technical innovation, but as a transformation in the way humanity understands knowledge, reality, power, and itself. Kissinger, Schmidt, and Huttenlocher argue that AI systems are increasingly capable of discovering patterns and producing solutions that humans may be unable to explain intuitively. Examples include AI systems producing unexpected chess strategies, identifying potential medicines, and outperforming humans in complex simulations. The important change, in their view, is therefore epistemological: humans are beginning to rely on machines that can reach useful conclusions without reasoning in ways that are fully comprehensible to us. 

The authors explore how this shift could reshape politics, national security, warfare, economics, online information, medicine, education, and global power. AI-enhanced military systems could alter deterrence and strategic stability, while algorithmic platforms increasingly determine what information people encounter and therefore influence their perception of reality. The book draws a historical parallel with the Enlightenment: just as reason and science transformed humanity's conception of the world, AI may create another fundamental intellectual transition. But this transition is unusual because humans may increasingly share the production of knowledge with non-human intelligences. 

The final concern is human identity and responsibility. If machines become better than humans at prediction, discovery and some forms of decision-making, society must decide which decisions should nevertheless remain human. The authors argue that technological development is advancing faster than the political, ethical and philosophical frameworks required to govern it. Rather than treating AI simply as another tool, they call for a broader conversation involving scientists, governments, philosophers and citizens about limits, accountability, security and what distinctly human judgment should mean in an AI-dominated world. The book is consequently less a technical guide to machine learning than a philosophical and geopolitical attempt to understand the civilization that AI may create. (

Key takeaways

• AI changes how knowledge is produced: machines can discover solutions that humans cannot necessarily explain.
  
• AI could alter the balance of global power, particularly through military systems, cybersecurity and strategic competition.
  
• Algorithms increasingly mediate reality, influencing what people read, watch and believe.
  
• Human society needs new ethical and political frameworks for decisions made jointly by humans and machines.
  
• The central question is not merely “What can AI do?” but “What role should humans retain when AI can do more?”
  
• The authors view AI as a potentially civilization-scale transformation comparable to the Enlightenment or the invention of printing, rather than simply another technological revolution. 
  

BOOK]]> The Age of AI: And Our Human Future
Authors: Henry A. Kissinger, Eric Schmidt & Daniel Huttenlocher
Publication date: November 2, 2021
Publisher: Little, Brown and Company


The Age of AI and Our Human Future examines artificial intelligence not primarily as a technical innovation, but as a transformation in the way humanity understands knowledge, reality, power, and itself. Kissinger, Schmidt, and Huttenlocher argue that AI systems are increasingly capable of discovering patterns and producing solutions that humans may be unable to explain intuitively. Examples include AI systems producing unexpected chess strategies, identifying potential medicines, and outperforming humans in complex simulations. The important change, in their view, is therefore epistemological: humans are beginning to rely on machines that can reach useful conclusions without reasoning in ways that are fully comprehensible to us. 

The authors explore how this shift could reshape politics, national security, warfare, economics, online information, medicine, education, and global power. AI-enhanced military systems could alter deterrence and strategic stability, while algorithmic platforms increasingly determine what information people encounter and therefore influence their perception of reality. The book draws a historical parallel with the Enlightenment: just as reason and science transformed humanity's conception of the world, AI may create another fundamental intellectual transition. But this transition is unusual because humans may increasingly share the production of knowledge with non-human intelligences. 

The final concern is human identity and responsibility. If machines become better than humans at prediction, discovery and some forms of decision-making, society must decide which decisions should nevertheless remain human. The authors argue that technological development is advancing faster than the political, ethical and philosophical frameworks required to govern it. Rather than treating AI simply as another tool, they call for a broader conversation involving scientists, governments, philosophers and citizens about limits, accountability, security and what distinctly human judgment should mean in an AI-dominated world. The book is consequently less a technical guide to machine learning than a philosophical and geopolitical attempt to understand the civilization that AI may create. (

Key takeaways

• AI changes how knowledge is produced: machines can discover solutions that humans cannot necessarily explain.
  
• AI could alter the balance of global power, particularly through military systems, cybersecurity and strategic competition.
  
• Algorithms increasingly mediate reality, influencing what people read, watch and believe.
  
• Human society needs new ethical and political frameworks for decisions made jointly by humans and machines.
  
• The central question is not merely “What can AI do?” but “What role should humans retain when AI can do more?”
  
• The authors view AI as a potentially civilization-scale transformation comparable to the Enlightenment or the invention of printing, rather than simply another technological revolution. 
  

BOOK]]> https://mklab.gr/showthread.php?tid=1856 Sat, 05 Sep 2026 16:41:04 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1856
Hands-On Large Language Models: Language Understanding and Generation
Authors: Jay Alammar & Maarten Grootendorst
Publication date: September 2024
Publisher: O’Reilly Media, Inc.
Length: 428 pages
Level: Beginner to intermediate
ISBN: 9781098150952 

Summary
Hands-On Large Language Models is a practical, visually oriented introduction to how modern large language models work and how they can be used in real applications. The authors deliberately follow an “intuition-first” approach: rather than beginning with heavy mathematics, they build an understanding of concepts such as tokenization, embeddings, attention, Transformers, encoder and decoder architectures, contextual representations, and text generation. The first part explains what happens inside an LLM, including token embeddings, Transformer blocks, positional embeddings, attention mechanisms, context windows, decoding, and techniques for making generation more efficient. 

The second and largest part turns these ideas into practical NLP systems. It covers text classification, clustering, topic modeling, prompt engineering, semantic search, RAG, agents, and multimodal models. Readers learn how embeddings can be used to represent documents mathematically, how dense retrieval and rerankers improve search, and how retrieved documents can be supplied to an LLM through Retrieval-Augmented Generation. The book also introduces BERTopic, LangChain-style chains and agents, ReAct reasoning, CLIP, and multimodal models that connect text and images. 

The final section goes deeper into training and fine-tuning. It explains contrastive learning and SBERT for embedding models, fine-tuning BERT-like representation models, few-shot methods such as SetFit, and fine-tuning generative LLMs. Important modern techniques including LoRA, QLoRA, PEFT, supervised fine-tuning, model quantization, RLHF, reward models, and Direct Preference Optimization (DPO) are introduced together with methods for evaluating generative models. The result is a book that bridges the gap between simply calling an LLM API and understanding enough of the underlying machinery to build, adapt, and evaluate sophisticated language-AI systems. 

Key takeaways

• LLMs are more than chatbots. The book emphasizes embeddings, classification, clustering, semantic search, recommendation, retrieval, and multimodal applications alongside text generation. 
  
• Embeddings are a central concept. Much of modern NLP can be understood as representing words, sentences, and documents as vectors and comparing them in high-dimensional spaces.
  
• RAG is one of the most practically important architectures covered:
  documents → embeddings → retrieval → reranking → context → LLM → answer. 
  
• Fine-tuning does not necessarily mean retraining an entire LLM. Techniques such as LoRA, QLoRA, and PEFT allow comparatively efficient adaptation of pretrained models. 
  
• The book is particularly valuable for someone who already knows some Python and basic machine learning but wants to progress from using ChatGPT-like systems toward actually understanding and building LLM applications.
  
• Its strongest feature is the combination of conceptual intuition + visual explanations + executable practical workflows, rather than treating LLMs purely as a theoretical deep-learning topic. 
  

O’Reilly — Hands-On Large Language Models]]>
Hands-On Large Language Models: Language Understanding and Generation
Authors: Jay Alammar & Maarten Grootendorst
Publication date: September 2024
Publisher: O’Reilly Media, Inc.
Length: 428 pages
Level: Beginner to intermediate
ISBN: 9781098150952 

Summary
Hands-On Large Language Models is a practical, visually oriented introduction to how modern large language models work and how they can be used in real applications. The authors deliberately follow an “intuition-first” approach: rather than beginning with heavy mathematics, they build an understanding of concepts such as tokenization, embeddings, attention, Transformers, encoder and decoder architectures, contextual representations, and text generation. The first part explains what happens inside an LLM, including token embeddings, Transformer blocks, positional embeddings, attention mechanisms, context windows, decoding, and techniques for making generation more efficient. 

The second and largest part turns these ideas into practical NLP systems. It covers text classification, clustering, topic modeling, prompt engineering, semantic search, RAG, agents, and multimodal models. Readers learn how embeddings can be used to represent documents mathematically, how dense retrieval and rerankers improve search, and how retrieved documents can be supplied to an LLM through Retrieval-Augmented Generation. The book also introduces BERTopic, LangChain-style chains and agents, ReAct reasoning, CLIP, and multimodal models that connect text and images. 

The final section goes deeper into training and fine-tuning. It explains contrastive learning and SBERT for embedding models, fine-tuning BERT-like representation models, few-shot methods such as SetFit, and fine-tuning generative LLMs. Important modern techniques including LoRA, QLoRA, PEFT, supervised fine-tuning, model quantization, RLHF, reward models, and Direct Preference Optimization (DPO) are introduced together with methods for evaluating generative models. The result is a book that bridges the gap between simply calling an LLM API and understanding enough of the underlying machinery to build, adapt, and evaluate sophisticated language-AI systems. 

Key takeaways

• LLMs are more than chatbots. The book emphasizes embeddings, classification, clustering, semantic search, recommendation, retrieval, and multimodal applications alongside text generation. 
  
• Embeddings are a central concept. Much of modern NLP can be understood as representing words, sentences, and documents as vectors and comparing them in high-dimensional spaces.
  
• RAG is one of the most practically important architectures covered:
  documents → embeddings → retrieval → reranking → context → LLM → answer. 
  
• Fine-tuning does not necessarily mean retraining an entire LLM. Techniques such as LoRA, QLoRA, and PEFT allow comparatively efficient adaptation of pretrained models. 
  
• The book is particularly valuable for someone who already knows some Python and basic machine learning but wants to progress from using ChatGPT-like systems toward actually understanding and building LLM applications.
  
• Its strongest feature is the combination of conceptual intuition + visual explanations + executable practical workflows, rather than treating LLMs purely as a theoretical deep-learning topic. 
  

O’Reilly — Hands-On Large Language Models]]> https://mklab.gr/showthread.php?tid=1855 Sat, 05 Sep 2026 16:36:45 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1855
Artificial Intelligence: A Modern Approach
Book:Artificial Intelligence: A Modern Approach
Authors: Stuart J. Russell and Peter Norvig


Artificial Intelligence: A Modern Approach—usually abbreviated AIMA—is one of the most influential and widely used university textbooks on artificial intelligence. Russell and Norvig organize AI around the idea of intelligent agents: systems that perceive their environment and choose actions intended to achieve goals. From this framework, the book develops the classical foundations of AI, including problem solving through search, heuristic algorithms, constraint-satisfaction problems, adversarial search and game playing, knowledge representation, propositional and first-order logic, planning, and reasoning under uncertainty. It is designed mainly for undergraduate courses but is sufficiently comprehensive to serve as a foundation for graduate-level study as well. 

A major strength of the book is that it connects these symbolic approaches with modern statistical AI. It covers probabilistic reasoning, Bayesian models, machine learning, neural networks, deep learning, reinforcement learning, natural-language-related methods, computer vision, robotics, and multi-agent systems. Rather than presenting AI as synonymous with neural networks, AIMA shows that modern AI is a collection of complementary approaches involving search, optimization, probability, logic, learning, and decision making. The algorithms are generally expressed in pseudocode, while accompanying implementations and exercises are available in languages including Python, Java, Lisp, JavaScript, and Scala.

The book is particularly valuable because it provides a broad conceptual map of the entire AI field. A reader can see how apparently different techniques—such as A* search, Bayesian inference, neural networks, reinforcement learning, and logical reasoning—address different aspects of the same fundamental problem: building agents capable of making good decisions in complex and uncertain environments. This breadth has helped make AIMA a standard reference: Wikipedia reports that by 2023 it was used at more than 1,500 universities worldwide. Four major editions have appeared, in 1995, 2003, 2009, and 2020. 

Key takeaways

• AI is broader than machine learning. Search, logic, probability, optimization, planning, and learning all play important roles.
  
• The central organizing concept is the rational intelligent agent—an entity that observes its environment and selects actions.
  
• The book bridges classical symbolic AI and modern machine learning, including deep learning and reinforcement learning.
  
• It is best regarded as a comprehensive university textbook and reference, rather than a quick practical guide to building applications.
  
• For someone wanting a systematic academic foundation in AI, AIMA remains one of the most important starting points. 
  

BOOK]]>
Artificial Intelligence: A Modern Approach
Book:Artificial Intelligence: A Modern Approach
Authors: Stuart J. Russell and Peter Norvig


Artificial Intelligence: A Modern Approach—usually abbreviated AIMA—is one of the most influential and widely used university textbooks on artificial intelligence. Russell and Norvig organize AI around the idea of intelligent agents: systems that perceive their environment and choose actions intended to achieve goals. From this framework, the book develops the classical foundations of AI, including problem solving through search, heuristic algorithms, constraint-satisfaction problems, adversarial search and game playing, knowledge representation, propositional and first-order logic, planning, and reasoning under uncertainty. It is designed mainly for undergraduate courses but is sufficiently comprehensive to serve as a foundation for graduate-level study as well. 

A major strength of the book is that it connects these symbolic approaches with modern statistical AI. It covers probabilistic reasoning, Bayesian models, machine learning, neural networks, deep learning, reinforcement learning, natural-language-related methods, computer vision, robotics, and multi-agent systems. Rather than presenting AI as synonymous with neural networks, AIMA shows that modern AI is a collection of complementary approaches involving search, optimization, probability, logic, learning, and decision making. The algorithms are generally expressed in pseudocode, while accompanying implementations and exercises are available in languages including Python, Java, Lisp, JavaScript, and Scala.

The book is particularly valuable because it provides a broad conceptual map of the entire AI field. A reader can see how apparently different techniques—such as A* search, Bayesian inference, neural networks, reinforcement learning, and logical reasoning—address different aspects of the same fundamental problem: building agents capable of making good decisions in complex and uncertain environments. This breadth has helped make AIMA a standard reference: Wikipedia reports that by 2023 it was used at more than 1,500 universities worldwide. Four major editions have appeared, in 1995, 2003, 2009, and 2020. 

Key takeaways

• AI is broader than machine learning. Search, logic, probability, optimization, planning, and learning all play important roles.
  
• The central organizing concept is the rational intelligent agent—an entity that observes its environment and selects actions.
  
• The book bridges classical symbolic AI and modern machine learning, including deep learning and reinforcement learning.
  
• It is best regarded as a comprehensive university textbook and reference, rather than a quick practical guide to building applications.
  
• For someone wanting a systematic academic foundation in AI, AIMA remains one of the most important starting points. 
  

BOOK]]> https://mklab.gr/showthread.php?tid=1854 Sat, 05 Sep 2026 16:21:30 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1854
Book Name:Essential Math for AI
Author: Hala Nelson
Publication Date: January 2023
Publisher: O’Reilly Media, Inc.

Essential Math for AI is a broad mathematical guide to the foundations underlying modern artificial intelligence and machine learning. Rather than presenting mathematics as an isolated theoretical subject, Hala Nelson continually connects it to concrete AI applications. The book begins with data, probability distributions, regression, classification, and optimization, then develops the mathematics behind neural networks, gradient descent, regularization, and backpropagation. For example, the standard gradient-descent update appears conceptually as
$\mathbf{w}_{i+1}=\mathbf{w}_i-\eta\nabla L(\mathbf{w}_i)$,
with the mathematics used to explain why neural networks learn rather than simply showing how to call a software library. 

The scope then becomes unusually wide. Nelson covers convolution and CNNs for computer vision; linear algebra, eigenvalues and singular value decomposition for dimensionality reduction and image/text processing; vector representations, embeddings, attention and transformers for NLP; and probabilistic generative models such as VAEs and GANs. Later chapters extend into graph theory and graph neural networks, operations research, optimization, stochastic processes, Markov chains and reinforcement learning, mathematical logic, and even partial differential equations. The result is less a conventional “mathematics prerequisite” book and more a map showing how diverse areas of mathematics connect to different branches of AI. 

A particularly valuable feature is that the book goes beyond the standard linear-algebra/calculus/probability trio found in many machine-learning introductions. Topics such as random matrices, causal modeling, optimal control, graph algorithms, mathematical logic, PDEs, and AI ethics show how broad the mathematical ecosystem of AI really is. Python examples and supplementary Jupyter notebooks make the material more practical for engineers, data scientists, and students. Its breadth is also its main limitation: at over 600 pages and covering fourteen major topics, many subjects cannot receive the depth they would in dedicated textbooks. It is therefore best viewed as a mathematical survey and bridge to more advanced study, especially for readers who already possess some mathematical maturity. 

Key Takeaways

• AI is deeply mathematical: probability, linear algebra, calculus, optimization, graph theory, logic, and differential equations all play important roles.
  
• The book emphasizes applications: mathematical ideas are connected directly to neural networks, computer vision, NLP, generative AI, reinforcement learning, and optimization. 
  
• Its coverage is exceptionally broad, extending from basic regression to GANs, transformers, random matrices, Markov decision processes, causal inference, and PDE-based AI.
  
• Best suited to mathematically inclined AI learners: it provides an excellent roadmap for understanding why AI algorithms work and identifying which mathematical subjects deserve deeper study. 
  

Essential Math for AI — O’Reilly]]>
Book Name:Essential Math for AI
Author: Hala Nelson
Publication Date: January 2023
Publisher: O’Reilly Media, Inc.

Essential Math for AI is a broad mathematical guide to the foundations underlying modern artificial intelligence and machine learning. Rather than presenting mathematics as an isolated theoretical subject, Hala Nelson continually connects it to concrete AI applications. The book begins with data, probability distributions, regression, classification, and optimization, then develops the mathematics behind neural networks, gradient descent, regularization, and backpropagation. For example, the standard gradient-descent update appears conceptually as
$\mathbf{w}_{i+1}=\mathbf{w}_i-\eta\nabla L(\mathbf{w}_i)$,
with the mathematics used to explain why neural networks learn rather than simply showing how to call a software library. 

The scope then becomes unusually wide. Nelson covers convolution and CNNs for computer vision; linear algebra, eigenvalues and singular value decomposition for dimensionality reduction and image/text processing; vector representations, embeddings, attention and transformers for NLP; and probabilistic generative models such as VAEs and GANs. Later chapters extend into graph theory and graph neural networks, operations research, optimization, stochastic processes, Markov chains and reinforcement learning, mathematical logic, and even partial differential equations. The result is less a conventional “mathematics prerequisite” book and more a map showing how diverse areas of mathematics connect to different branches of AI. 

A particularly valuable feature is that the book goes beyond the standard linear-algebra/calculus/probability trio found in many machine-learning introductions. Topics such as random matrices, causal modeling, optimal control, graph algorithms, mathematical logic, PDEs, and AI ethics show how broad the mathematical ecosystem of AI really is. Python examples and supplementary Jupyter notebooks make the material more practical for engineers, data scientists, and students. Its breadth is also its main limitation: at over 600 pages and covering fourteen major topics, many subjects cannot receive the depth they would in dedicated textbooks. It is therefore best viewed as a mathematical survey and bridge to more advanced study, especially for readers who already possess some mathematical maturity. 

Key Takeaways

• AI is deeply mathematical: probability, linear algebra, calculus, optimization, graph theory, logic, and differential equations all play important roles.
  
• The book emphasizes applications: mathematical ideas are connected directly to neural networks, computer vision, NLP, generative AI, reinforcement learning, and optimization. 
  
• Its coverage is exceptionally broad, extending from basic regression to GANs, transformers, random matrices, Markov decision processes, causal inference, and PDE-based AI.
  
• Best suited to mathematically inclined AI learners: it provides an excellent roadmap for understanding why AI algorithms work and identifying which mathematical subjects deserve deeper study. 
  

Essential Math for AI — O’Reilly]]> https://mklab.gr/showthread.php?tid=1766 Mon, 31 Aug 2026 01:10:34 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1766 Book name:Data Modeling: From Physical Processes to Machine Learning
Author: Elmar Rueckert
Publication date: 1 August 2026 (eBook); 3 August 2026 (softcover)
Publisher: Springer Singapore
Edition: 1st edition
Pages: XIII + 114
ISBN: 978-981-95-7592-3 (eBook); 978-981-95-7591-6 (print)
Main areas: Data Science, Machine Learning, Information Theory, Artificial Intelligence, Data Analysis 

Summary

Data Modeling is an introductory textbook that explains how real-world physical processes are converted into data and ultimately into predictive models. Rather than beginning immediately with machine-learning algorithms, Elmar Rueckert follows the complete data lifecycle: observing a physical phenomenon, deciding what and how frequently to measure, collecting measurements with sensors, representing and storing the resulting data, extracting information from it, analysing it, and finally using it in machine-learning systems. This makes the book particularly useful for understanding where machine-learning data actually comes from rather than treating datasets as ready-made objects. 

The early chapters introduce the fundamentals of data modeling, followed by physical processes, data granularity, and sensors. An important theme is that measurements are only approximations of real processes: choices about sampling frequency, resolution, sensor accuracy, and representation determine what information can later be extracted. The book then moves to the concept of data itself and introduces information theory, providing a conceptual bridge between raw measurements and meaningful information. A substantial section is devoted to data analysis and to the methods used to explore and interpret collected datasets. 

The final part introduces the foundations of machine learning, particularly how data must be organised before it can be used for learning. Rueckert then presents selected machine-learning applications, showing how predictive models emerge from the data-processing pipeline developed in the earlier chapters. The approach is deliberately accessible: Springer describes the book as intended for first-semester undergraduate students, and it is accompanied by interactive Jupyter Notebooks, fact sheets, programming tutorials, and practical exercises. Thus, it functions less as a comprehensive mathematical treatment of machine learning and more as a compact introduction connecting physical measurement, data science, information theory, and AI. 

Main contents
The core progression of the book is:
Physical processes → Sensors → Measurements → Data → Information → Analysis → Data organization → Machine learning → Predictions
Its principal chapters cover Fundamentals of Data Modeling, Processes and Sensors, Data and Information Theory, and Machine Learning Fundamentals, including sections on data granularity, sensors, information theory, analysis, data organization, and selected ML applications. 

Key takeaways

• Data modeling begins before machine learning: deciding what to measure and how to represent it strongly affects every later result.
  
• Sensors connect physical reality to computational data, making measurement and data granularity fundamental parts of ML systems.
  
• Information theory helps explain the difference between merely collecting data and extracting useful information from it.
  
• Good machine learning depends heavily on data organization, representation, and analysis, not only on the choice of algorithm.
  
• The book provides a concise, practical introduction to the entire pipeline from physical processes to predictive models, supported by Jupyter-based exercises. 
  

Springer book page]]> Book name:Data Modeling: From Physical Processes to Machine Learning
Author: Elmar Rueckert
Publication date: 1 August 2026 (eBook); 3 August 2026 (softcover)
Publisher: Springer Singapore
Edition: 1st edition
Pages: XIII + 114
ISBN: 978-981-95-7592-3 (eBook); 978-981-95-7591-6 (print)
Main areas: Data Science, Machine Learning, Information Theory, Artificial Intelligence, Data Analysis 

Summary

Data Modeling is an introductory textbook that explains how real-world physical processes are converted into data and ultimately into predictive models. Rather than beginning immediately with machine-learning algorithms, Elmar Rueckert follows the complete data lifecycle: observing a physical phenomenon, deciding what and how frequently to measure, collecting measurements with sensors, representing and storing the resulting data, extracting information from it, analysing it, and finally using it in machine-learning systems. This makes the book particularly useful for understanding where machine-learning data actually comes from rather than treating datasets as ready-made objects. 

The early chapters introduce the fundamentals of data modeling, followed by physical processes, data granularity, and sensors. An important theme is that measurements are only approximations of real processes: choices about sampling frequency, resolution, sensor accuracy, and representation determine what information can later be extracted. The book then moves to the concept of data itself and introduces information theory, providing a conceptual bridge between raw measurements and meaningful information. A substantial section is devoted to data analysis and to the methods used to explore and interpret collected datasets. 

The final part introduces the foundations of machine learning, particularly how data must be organised before it can be used for learning. Rueckert then presents selected machine-learning applications, showing how predictive models emerge from the data-processing pipeline developed in the earlier chapters. The approach is deliberately accessible: Springer describes the book as intended for first-semester undergraduate students, and it is accompanied by interactive Jupyter Notebooks, fact sheets, programming tutorials, and practical exercises. Thus, it functions less as a comprehensive mathematical treatment of machine learning and more as a compact introduction connecting physical measurement, data science, information theory, and AI. 

Main contents
The core progression of the book is:
Physical processes → Sensors → Measurements → Data → Information → Analysis → Data organization → Machine learning → Predictions
Its principal chapters cover Fundamentals of Data Modeling, Processes and Sensors, Data and Information Theory, and Machine Learning Fundamentals, including sections on data granularity, sensors, information theory, analysis, data organization, and selected ML applications. 

Key takeaways

• Data modeling begins before machine learning: deciding what to measure and how to represent it strongly affects every later result.
  
• Sensors connect physical reality to computational data, making measurement and data granularity fundamental parts of ML systems.
  
• Information theory helps explain the difference between merely collecting data and extracting useful information from it.
  
• Good machine learning depends heavily on data organization, representation, and analysis, not only on the choice of algorithm.
  
• The book provides a concise, practical introduction to the entire pipeline from physical processes to predictive models, supported by Jupyter-based exercises. 
  

Springer book page]]> https://mklab.gr/showthread.php?tid=1765 Mon, 31 Aug 2026 01:07:09 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1765 Authors: Sunil Karforma, Subhajit Adhikari, Indrajit De, Siddhartha Bhattacharyya, Jan Platos
Publication date: July 2026
Publisher: De Gruyter
Series: De Gruyter Textbook
Pages: 358
ISBN: 978-3-11-914470-4 (print); 978-3-11-221739-9 (eBook) 

Summary:

Machine Learning with Python is a broad introductory-to-intermediate textbook designed to take the reader from basic Python programming through modern machine learning, neural networks, large language models, and generative AI. It begins with the main paradigms of supervised, unsupervised, and reinforcement learning, while also introducing the practical Python ecosystem—Jupyter, Google Colab, Anaconda, Kaggle, NumPy, Pandas, Scikit-learn, and Matplotlib. A substantial section is devoted to preparing real datasets: missing values, outliers, normalization, standardization, categorical encoding, feature engineering, feature selection, and training/test splitting. The classical machine-learning chapters then cover linear and polynomial regression, logistic regression, decision trees, random forests, K-nearest neighbors, support-vector machines, Naive Bayes, K-means and hierarchical clustering, PCA, and introductory reinforcement learning. 

The book places considerable emphasis on evaluating and improving models, explaining accuracy, precision, recall, $F_1$ score, ROC-AUC, cross-validation, Grid Search, Randomized Search, Bayesian optimization, overfitting, underfitting, and $L_1$/$L_2$ regularization. It then moves into deep learning, covering the perceptron, feed-forward networks and backpropagation, CNNs, MLPs, RBF networks, RNNs, LSTMs, GRUs and deep belief networks, with TensorFlow, Keras and PyTorch alongside tools such as XGBoost and LightGBM. This gives the reader a reasonably complete progression from traditional statistical learning techniques to contemporary neural-network architectures. 

The final part expands beyond conventional ML into NLP, large language models, generative AI and real-world applications. NLP topics include tokenization, stemming, text classification and sentiment analysis. Separate chapters discuss LLMs and generative-AI systems, followed by big-data concepts and applications in agriculture, biological computation, e-commerce, healthcare, education and security. The book therefore functions less as a narrowly specialized treatment of machine-learning theory and more as a wide-ranging survey and practical course covering the modern AI landscape. It appears particularly suitable for students who know some programming but want one volume connecting Python, data preprocessing, classical ML, deep learning, NLP and newer generative-AI technologies. 

Key takeaways

• Broad coverage: progresses from Python fundamentals and data preparation to classical ML, deep learning, NLP, LLMs and generative AI.
  
• Practical orientation: emphasizes Python libraries, model implementation, preprocessing, evaluation and hyperparameter optimization rather than purely mathematical theory.
  
• Modern scope: unusually for an introductory textbook, entire chapters are devoted to large language models and generative AI. 
  
• Best suited for: undergraduate or graduate students, programmers, and newcomers seeking a structured overview of the contemporary machine-learning ecosystem rather than an advanced theoretical treatment.
  

De Gruyter book page]]> Authors: Sunil Karforma, Subhajit Adhikari, Indrajit De, Siddhartha Bhattacharyya, Jan Platos
Publication date: July 2026
Publisher: De Gruyter
Series: De Gruyter Textbook
Pages: 358
ISBN: 978-3-11-914470-4 (print); 978-3-11-221739-9 (eBook) 

Summary:

Machine Learning with Python is a broad introductory-to-intermediate textbook designed to take the reader from basic Python programming through modern machine learning, neural networks, large language models, and generative AI. It begins with the main paradigms of supervised, unsupervised, and reinforcement learning, while also introducing the practical Python ecosystem—Jupyter, Google Colab, Anaconda, Kaggle, NumPy, Pandas, Scikit-learn, and Matplotlib. A substantial section is devoted to preparing real datasets: missing values, outliers, normalization, standardization, categorical encoding, feature engineering, feature selection, and training/test splitting. The classical machine-learning chapters then cover linear and polynomial regression, logistic regression, decision trees, random forests, K-nearest neighbors, support-vector machines, Naive Bayes, K-means and hierarchical clustering, PCA, and introductory reinforcement learning. 

The book places considerable emphasis on evaluating and improving models, explaining accuracy, precision, recall, $F_1$ score, ROC-AUC, cross-validation, Grid Search, Randomized Search, Bayesian optimization, overfitting, underfitting, and $L_1$/$L_2$ regularization. It then moves into deep learning, covering the perceptron, feed-forward networks and backpropagation, CNNs, MLPs, RBF networks, RNNs, LSTMs, GRUs and deep belief networks, with TensorFlow, Keras and PyTorch alongside tools such as XGBoost and LightGBM. This gives the reader a reasonably complete progression from traditional statistical learning techniques to contemporary neural-network architectures. 

The final part expands beyond conventional ML into NLP, large language models, generative AI and real-world applications. NLP topics include tokenization, stemming, text classification and sentiment analysis. Separate chapters discuss LLMs and generative-AI systems, followed by big-data concepts and applications in agriculture, biological computation, e-commerce, healthcare, education and security. The book therefore functions less as a narrowly specialized treatment of machine-learning theory and more as a wide-ranging survey and practical course covering the modern AI landscape. It appears particularly suitable for students who know some programming but want one volume connecting Python, data preprocessing, classical ML, deep learning, NLP and newer generative-AI technologies. 

Key takeaways

• Broad coverage: progresses from Python fundamentals and data preparation to classical ML, deep learning, NLP, LLMs and generative AI.
  
• Practical orientation: emphasizes Python libraries, model implementation, preprocessing, evaluation and hyperparameter optimization rather than purely mathematical theory.
  
• Modern scope: unusually for an introductory textbook, entire chapters are devoted to large language models and generative AI. 
  
• Best suited for: undergraduate or graduate students, programmers, and newcomers seeking a structured overview of the contemporary machine-learning ecosystem rather than an advanced theoretical treatment.
  

De Gruyter book page]]> https://mklab.gr/showthread.php?tid=1764 Mon, 31 Aug 2026 01:02:31 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1764 Generative AI: Innovations and Applications Across Industries
Subtitle:Exploring the Influence on Healthcare, Education, Finance, and Beyond
Authors: Brijesh Kumar Karna, Suryabhan Singh, Pethuru Raj Chelliah, Akshitta R. Chaudhary
Publication date: 26 August 2026 (eBook)
Publisher: Apress, Berkeley, CA
Edition: 1st
Length: 527 pages
ISBN: 979-8-8688-2912-3
Topics: Artificial Intelligence, Machine Learning, Generative AI, Large Language Models, Python 

Summary

Generative AI: Innovations and Applications Across Industries is a broad, application-oriented survey of generative artificial intelligence and the ways it is reshaping professional and industrial activity. The book begins with the foundations of generative AI, introducing technologies such as deep neural networks, variational autoencoders (VAEs), generative adversarial networks (GANs), transformers, and large language models. Rather than concentrating only on how the models work mathematically, the authors emphasize how these technologies can be incorporated into real systems and organizations. 

The central part of the book examines generative AI sector by sector. Creative applications include automated writing, visual art, and music; business and marketing applications include content generation, personalization, and new forms of customer engagement. Healthcare chapters discuss applications such as drug discovery, personalized medicine, and medical imaging, while the education chapter examines personalized learning and AI-generated interactive educational material. Other chapters extend the discussion to entertainment, law, finance, industrial maintenance, agriculture, environmental sustainability, recruitment, and talent acquisition. 

A particularly substantial part of the book is devoted to the broader consequences of deploying these systems. The authors examine ethical, legal, governance, privacy, bias, and societal questions alongside the technical possibilities of generative AI. The concluding section uses case studies and expert perspectives to connect the earlier examples and consider future developments. The result is less a specialist textbook on building foundation models and more a wide-ranging guide to where generative AI can be used, what benefits it may provide, and what problems organizations must manage when adopting it. It is aimed primarily at AI professionals, machine-learning engineers, software developers, and readers interested in applying generative AI across business domains. 

Key takeaways

• Generative AI is becoming a general-purpose technology: its uses extend well beyond chatbots into medicine, education, finance, manufacturing, agriculture, recruitment, media, and creative work.
  
• LLMs are only one part of the field: the book places them alongside GANs, VAEs, deep learning, transformers, and other generative techniques.
  
• The main challenge is increasingly deployment rather than mere model capability: organizations need integration strategies, governance, reliability, privacy safeguards, and appropriate human oversight.
  
• Ethical and legal questions are treated as central rather than secondary: bias, accountability, privacy, regulation, intellectual property, and responsible deployment become increasingly important as generative systems move into consequential domains. 
  

Springer/Apress book page]]> Generative AI: Innovations and Applications Across Industries
Subtitle:Exploring the Influence on Healthcare, Education, Finance, and Beyond
Authors: Brijesh Kumar Karna, Suryabhan Singh, Pethuru Raj Chelliah, Akshitta R. Chaudhary
Publication date: 26 August 2026 (eBook)
Publisher: Apress, Berkeley, CA
Edition: 1st
Length: 527 pages
ISBN: 979-8-8688-2912-3
Topics: Artificial Intelligence, Machine Learning, Generative AI, Large Language Models, Python 

Summary

Generative AI: Innovations and Applications Across Industries is a broad, application-oriented survey of generative artificial intelligence and the ways it is reshaping professional and industrial activity. The book begins with the foundations of generative AI, introducing technologies such as deep neural networks, variational autoencoders (VAEs), generative adversarial networks (GANs), transformers, and large language models. Rather than concentrating only on how the models work mathematically, the authors emphasize how these technologies can be incorporated into real systems and organizations. 

The central part of the book examines generative AI sector by sector. Creative applications include automated writing, visual art, and music; business and marketing applications include content generation, personalization, and new forms of customer engagement. Healthcare chapters discuss applications such as drug discovery, personalized medicine, and medical imaging, while the education chapter examines personalized learning and AI-generated interactive educational material. Other chapters extend the discussion to entertainment, law, finance, industrial maintenance, agriculture, environmental sustainability, recruitment, and talent acquisition. 

A particularly substantial part of the book is devoted to the broader consequences of deploying these systems. The authors examine ethical, legal, governance, privacy, bias, and societal questions alongside the technical possibilities of generative AI. The concluding section uses case studies and expert perspectives to connect the earlier examples and consider future developments. The result is less a specialist textbook on building foundation models and more a wide-ranging guide to where generative AI can be used, what benefits it may provide, and what problems organizations must manage when adopting it. It is aimed primarily at AI professionals, machine-learning engineers, software developers, and readers interested in applying generative AI across business domains. 

Key takeaways

• Generative AI is becoming a general-purpose technology: its uses extend well beyond chatbots into medicine, education, finance, manufacturing, agriculture, recruitment, media, and creative work.
  
• LLMs are only one part of the field: the book places them alongside GANs, VAEs, deep learning, transformers, and other generative techniques.
  
• The main challenge is increasingly deployment rather than mere model capability: organizations need integration strategies, governance, reliability, privacy safeguards, and appropriate human oversight.
  
• Ethical and legal questions are treated as central rather than secondary: bias, accountability, privacy, regulation, intellectual property, and responsible deployment become increasingly important as generative systems move into consequential domains. 
  

Springer/Apress book page]]> https://mklab.gr/showthread.php?tid=1763 Mon, 31 Aug 2026 00:59:41 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1763 Book name:AI Explained: A Guide for Non-Technical Readers
Authors: Wendy Hall and Pete Rai
Publication date: September 2026 — retailers list September 9, 2026
Publisher: Wiley
Edition: 1st edition
Pages: 256
ISBN: 978-1-394-43114-4 

Summary

AI Explained is designed for readers who need to understand artificial intelligence without becoming programmers or machine-learning engineers. Rather than beginning with modern systems such as ChatGPT, the book builds AI from its conceptual foundations. It moves from rule- and logic-based AI through statistical approaches, neural networks and machine learning, eventually reaching generative AI and large language models. The central question is not how to program these technologies, but how AI systems arrive at the outputs and decisions they produce. 

The authors connect the technical ideas to practical applications through use cases from areas such as healthcare, law and business. This makes the book particularly suitable for professionals who encounter AI in their work but do not have a computer-science background. Neural networks, machine-learning algorithms and generative models are presented conceptually rather than through mathematics or programming, allowing readers to develop a mental model of what different types of AI can and cannot do. 

The book also treats AI as more than a technical subject. It discusses ethics, regulation, public policy and societal consequences, reflecting the authors' experience advising governments and international organizations. The intended readership includes educators, policymakers, lawyers, journalists, healthcare professionals and business leaders. In this respect, the book aims to provide the kind of AI literacy increasingly necessary for people who must evaluate, regulate, teach about or make decisions involving AI, rather than build the systems themselves. 

Key takeaways

• AI is broader than generative AI. The book places today's LLMs within the longer development of symbolic, statistical and machine-learning approaches.
  
• Understanding matters more than coding. Its purpose is to explain why and how AI works, not teach Python or model development.
  
• Applications are used to clarify theory. Healthcare, law and business examples connect abstract AI concepts to real decisions.
  
• AI literacy includes ethics and policy. Regulation, societal impact and responsible use are treated as essential parts of understanding the technology. 
  

BOOK]]> Book name:AI Explained: A Guide for Non-Technical Readers
Authors: Wendy Hall and Pete Rai
Publication date: September 2026 — retailers list September 9, 2026
Publisher: Wiley
Edition: 1st edition
Pages: 256
ISBN: 978-1-394-43114-4 

Summary

AI Explained is designed for readers who need to understand artificial intelligence without becoming programmers or machine-learning engineers. Rather than beginning with modern systems such as ChatGPT, the book builds AI from its conceptual foundations. It moves from rule- and logic-based AI through statistical approaches, neural networks and machine learning, eventually reaching generative AI and large language models. The central question is not how to program these technologies, but how AI systems arrive at the outputs and decisions they produce. 

The authors connect the technical ideas to practical applications through use cases from areas such as healthcare, law and business. This makes the book particularly suitable for professionals who encounter AI in their work but do not have a computer-science background. Neural networks, machine-learning algorithms and generative models are presented conceptually rather than through mathematics or programming, allowing readers to develop a mental model of what different types of AI can and cannot do. 

The book also treats AI as more than a technical subject. It discusses ethics, regulation, public policy and societal consequences, reflecting the authors' experience advising governments and international organizations. The intended readership includes educators, policymakers, lawyers, journalists, healthcare professionals and business leaders. In this respect, the book aims to provide the kind of AI literacy increasingly necessary for people who must evaluate, regulate, teach about or make decisions involving AI, rather than build the systems themselves. 

Key takeaways

• AI is broader than generative AI. The book places today's LLMs within the longer development of symbolic, statistical and machine-learning approaches.
  
• Understanding matters more than coding. Its purpose is to explain why and how AI works, not teach Python or model development.
  
• Applications are used to clarify theory. Healthcare, law and business examples connect abstract AI concepts to real decisions.
  
• AI literacy includes ethics and policy. Regulation, societal impact and responsible use are treated as essential parts of understanding the technology. 
  

BOOK]]> https://mklab.gr/showthread.php?tid=1762 Mon, 31 Aug 2026 00:55:56 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1762 Book:AI and Ethics Handbook
Editor: Larry Medsker
Publication date: August 15, 2026 (hardcover, according to Springer’s bibliographic record)
Publisher: Springer, Cham / Springer Nature Switzerland AG
Edition: 1st
Length: XV + 991 pages
ISBN: 978-3-032-12200-1 (hardcover); 978-3-032-12201-8 (eBook)
Fields: Artificial Intelligence, Engineering Ethics, Philosophy of Technology 

Summary

AI and Ethics Handbook is a large interdisciplinary reference work examining the ethical, legal, regulatory, and social problems created by artificial intelligence. Edited by Larry Medsker, it is deliberately written to be accessible to both technical and non-technical readers. Its central concern is not merely whether AI systems can perform particular tasks, but how they should be designed, governed, deployed, and controlled so that their benefits do not produce unacceptable social harms. Particular attention is paid to both intentional and unintended misuse, and to the interaction between technology, law, institutions, and society. 

The handbook begins with fundamental problems of responsible AI, including fairness and bias, privacy and data protection, misinformation and disinformation, transparency, accountability, safety, cybersecurity, trustworthy and explainable AI, human oversight, accessibility, generative-AI misuse, the future of work, education, data governance, data provenance and algorithmic decision-making. Rather than treating ethics as an abstract philosophical add-on, many chapters consider how ethical principles can be incorporated directly into the development and management of AI systems. One chapter, for example, explores “ethics-by-design”, while another develops the concept of organizational AI-ethics maturity as a way for institutions such as universities to assess whether responsible-AI principles are actually embedded in their practices. 

The later sections broaden the discussion to the effects of AI across society and specific industries. Topics include regulation and compliance, elections, healthcare, social media, children and young people, autonomous vehicles and weapons, robotics, sustainability and AI's environmental impact, government, manufacturing, agriculture, digital twins, quantum computing, the Internet of Things, law, intellectual property, finance, marketing, entertainment, religion, and artificial general intelligence. The result is less a single sustained argument than a comprehensive map of the emerging AI-ethics landscape. Its major message is that responsible AI cannot be achieved through better algorithms alone: trustworthy AI requires technical safeguards together with governance, reliable data, human accountability, institutional procedures, regulation, and continuing public debate. 

Key takeaways

• AI ethics is a systems problem. Bias, privacy, safety and accountability depend on data, algorithms, organizations, regulation and human decision-makers rather than on the model alone.
  
• Governance must accompany technological development. Standards, auditing, regulation, human oversight and institutional responsibility are recurring themes throughout the handbook. 
  
• Data provenance and quality are ethical issues. Knowing where training data originated, whether it is valid, and how it was obtained is fundamental to trustworthy AI.
  
• Generative and increasingly autonomous AI intensify existing problems. Misinformation, accountability, intellectual property, employment and misuse become more difficult as systems gain greater capabilities.
  
• The book is primarily a reference work. At roughly 1,000 pages and covering dozens of areas, it is best used as a handbook for researchers, educators, policymakers and AI practitioners rather than read as a conventional introductory textbook. 
  

Overall:AI and Ethics Handbook provides a broad, contemporary survey of responsible AI. Its particular strength is its interdisciplinary approach: it connects the technical questions surrounding AI with philosophy, law, public policy, organizational governance and concrete applications. It should be especially useful as a starting point for investigating a particular ethical issue—such as algorithmic bias, explainability, generative-AI governance, AI in education, or regulation—because Springer explicitly positions the handbook's extensive references as gateways to further research. 


Springer – AI and Ethics Handbook]]> Book:AI and Ethics Handbook
Editor: Larry Medsker
Publication date: August 15, 2026 (hardcover, according to Springer’s bibliographic record)
Publisher: Springer, Cham / Springer Nature Switzerland AG
Edition: 1st
Length: XV + 991 pages
ISBN: 978-3-032-12200-1 (hardcover); 978-3-032-12201-8 (eBook)
Fields: Artificial Intelligence, Engineering Ethics, Philosophy of Technology 

Summary

AI and Ethics Handbook is a large interdisciplinary reference work examining the ethical, legal, regulatory, and social problems created by artificial intelligence. Edited by Larry Medsker, it is deliberately written to be accessible to both technical and non-technical readers. Its central concern is not merely whether AI systems can perform particular tasks, but how they should be designed, governed, deployed, and controlled so that their benefits do not produce unacceptable social harms. Particular attention is paid to both intentional and unintended misuse, and to the interaction between technology, law, institutions, and society. 

The handbook begins with fundamental problems of responsible AI, including fairness and bias, privacy and data protection, misinformation and disinformation, transparency, accountability, safety, cybersecurity, trustworthy and explainable AI, human oversight, accessibility, generative-AI misuse, the future of work, education, data governance, data provenance and algorithmic decision-making. Rather than treating ethics as an abstract philosophical add-on, many chapters consider how ethical principles can be incorporated directly into the development and management of AI systems. One chapter, for example, explores “ethics-by-design”, while another develops the concept of organizational AI-ethics maturity as a way for institutions such as universities to assess whether responsible-AI principles are actually embedded in their practices. 

The later sections broaden the discussion to the effects of AI across society and specific industries. Topics include regulation and compliance, elections, healthcare, social media, children and young people, autonomous vehicles and weapons, robotics, sustainability and AI's environmental impact, government, manufacturing, agriculture, digital twins, quantum computing, the Internet of Things, law, intellectual property, finance, marketing, entertainment, religion, and artificial general intelligence. The result is less a single sustained argument than a comprehensive map of the emerging AI-ethics landscape. Its major message is that responsible AI cannot be achieved through better algorithms alone: trustworthy AI requires technical safeguards together with governance, reliable data, human accountability, institutional procedures, regulation, and continuing public debate. 

Key takeaways

• AI ethics is a systems problem. Bias, privacy, safety and accountability depend on data, algorithms, organizations, regulation and human decision-makers rather than on the model alone.
  
• Governance must accompany technological development. Standards, auditing, regulation, human oversight and institutional responsibility are recurring themes throughout the handbook. 
  
• Data provenance and quality are ethical issues. Knowing where training data originated, whether it is valid, and how it was obtained is fundamental to trustworthy AI.
  
• Generative and increasingly autonomous AI intensify existing problems. Misinformation, accountability, intellectual property, employment and misuse become more difficult as systems gain greater capabilities.
  
• The book is primarily a reference work. At roughly 1,000 pages and covering dozens of areas, it is best used as a handbook for researchers, educators, policymakers and AI practitioners rather than read as a conventional introductory textbook. 
  

Overall:AI and Ethics Handbook provides a broad, contemporary survey of responsible AI. Its particular strength is its interdisciplinary approach: it connects the technical questions surrounding AI with philosophy, law, public policy, organizational governance and concrete applications. It should be especially useful as a starting point for investigating a particular ethical issue—such as algorithmic bias, explainability, generative-AI governance, AI in education, or regulation—because Springer explicitly positions the handbook's extensive references as gateways to further research. 


Springer – AI and Ethics Handbook]]> https://mklab.gr/showthread.php?tid=1752 Mon, 31 Aug 2026 00:08:20 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1752 Book:AI Agents: The Definitive Guide
Author: Nicole Koenigstein
Publication date: September 2026
Publisher: O’Reilly Media, Inc.
Level: Intermediate to advanced
Length: 378 pages (O'Reilly Media)

Summary

AI Agents: The Definitive Guide is an engineering-focused treatment of how to move AI agents beyond impressive prototypes into reliable production systems. Koenigstein begins with the conceptual transition from static LLM applications to stateful agents, using finite and hierarchical state machines to explain how agents maintain state, reason about goals, and determine their next actions. The book then develops major agent architectures—including reasoning-and-action patterns, planning, reflection, hierarchical agents, supervisor architectures, and multi-agent “swarms.” It also explores more advanced ideas such as reinforcement learning, search, test-time compute, Monte Carlo Tree Search, and techniques for improving agents through experience. 

A major strength of the book is its emphasis on the engineering infrastructure surrounding the LLM rather than treating the model itself as the entire agent. Koenigstein discusses model selection, mixture-of-experts and reasoning models, LoRA, quantization, structured outputs, Pydantic, tool calling, and the Model Context Protocol (MCP). She then addresses the difficult problems that emerge when agents enter production: sandboxing tool execution, Docker-based isolation, monitoring, fallbacks, inference infrastructure, evaluation, benchmarking, memory management, and computational cost. Agent memory is treated as an architectural component, with distinctions among episodic, semantic, procedural, short-term, and long-term memory and discussion of how memory should be organized across multi-agent systems. 

The final sections place particular emphasis on reliability and security. Agents need evaluation not only before deployment but continuously once they operate in real environments. The book therefore covers behavioral stress testing, production traces, custom benchmarks, long-horizon reasoning evaluation, threat modeling, red teaming, tool governance, layered defenses, and secure execution boundaries. Overall, the central message is that production-grade agentic AI is primarily a systems-engineering problem: useful agents require careful orchestration of models, state, memory, tools, infrastructure, evaluation, security, monitoring, and cost management rather than simply better prompting. 

Key takeaways

• An AI agent is a system, not merely an LLM. State, planning, memory, tools, execution environments, monitoring, and feedback mechanisms are fundamental parts of the architecture.
  
• Production reliability matters more than impressive demos. The book focuses heavily on contracts, structured outputs, testing, observability, fallbacks, sandboxing, and evaluation.
  
• MCP and tool integration are becoming important infrastructure. Agents increasingly need standardized ways to discover and interact safely with external tools and services.
  
• Security and evaluation must be architectural concerns from the beginning. Autonomous tool use increases the consequences of mistakes, making isolation, governance, red teaming, and continuous evaluation essential. 
  

Overall: This looks particularly useful for developers who already understand LLMs and basic agent frameworks and want to understand how serious agent systems should actually be engineered and deployed. It is less a “build your first chatbot” book and more a guide to the architecture and operational discipline required for dependable autonomous AI. 

O’Reilly book page]]> Book:AI Agents: The Definitive Guide
Author: Nicole Koenigstein
Publication date: September 2026
Publisher: O’Reilly Media, Inc.
Level: Intermediate to advanced
Length: 378 pages (O'Reilly Media)

Summary

AI Agents: The Definitive Guide is an engineering-focused treatment of how to move AI agents beyond impressive prototypes into reliable production systems. Koenigstein begins with the conceptual transition from static LLM applications to stateful agents, using finite and hierarchical state machines to explain how agents maintain state, reason about goals, and determine their next actions. The book then develops major agent architectures—including reasoning-and-action patterns, planning, reflection, hierarchical agents, supervisor architectures, and multi-agent “swarms.” It also explores more advanced ideas such as reinforcement learning, search, test-time compute, Monte Carlo Tree Search, and techniques for improving agents through experience. 

A major strength of the book is its emphasis on the engineering infrastructure surrounding the LLM rather than treating the model itself as the entire agent. Koenigstein discusses model selection, mixture-of-experts and reasoning models, LoRA, quantization, structured outputs, Pydantic, tool calling, and the Model Context Protocol (MCP). She then addresses the difficult problems that emerge when agents enter production: sandboxing tool execution, Docker-based isolation, monitoring, fallbacks, inference infrastructure, evaluation, benchmarking, memory management, and computational cost. Agent memory is treated as an architectural component, with distinctions among episodic, semantic, procedural, short-term, and long-term memory and discussion of how memory should be organized across multi-agent systems. 

The final sections place particular emphasis on reliability and security. Agents need evaluation not only before deployment but continuously once they operate in real environments. The book therefore covers behavioral stress testing, production traces, custom benchmarks, long-horizon reasoning evaluation, threat modeling, red teaming, tool governance, layered defenses, and secure execution boundaries. Overall, the central message is that production-grade agentic AI is primarily a systems-engineering problem: useful agents require careful orchestration of models, state, memory, tools, infrastructure, evaluation, security, monitoring, and cost management rather than simply better prompting. 

Key takeaways

• An AI agent is a system, not merely an LLM. State, planning, memory, tools, execution environments, monitoring, and feedback mechanisms are fundamental parts of the architecture.
  
• Production reliability matters more than impressive demos. The book focuses heavily on contracts, structured outputs, testing, observability, fallbacks, sandboxing, and evaluation.
  
• MCP and tool integration are becoming important infrastructure. Agents increasingly need standardized ways to discover and interact safely with external tools and services.
  
• Security and evaluation must be architectural concerns from the beginning. Autonomous tool use increases the consequences of mistakes, making isolation, governance, red teaming, and continuous evaluation essential. 
  

Overall: This looks particularly useful for developers who already understand LLMs and basic agent frameworks and want to understand how serious agent systems should actually be engineered and deployed. It is less a “build your first chatbot” book and more a guide to the architecture and operational discipline required for dependable autonomous AI. 

O’Reilly book page]]> https://mklab.gr/showthread.php?tid=1711 Thu, 20 Aug 2026 19:22:26 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1711 Foundations of Machine Learning and AI: Geometry, Probability and Optimization
Authors: Pradeep Singh, Balasubramanian Raman
Publisher: Springer Cham
Series:Studies in Big Data, Vol. 199
Publication: eBook — 17 August 2026; Hardcover — 18 August 2026
Edition: 1st
Pages: XXX + 558
DOI: 10.1007/978-3-032-30336-3
Subjects: Machine Learning, Artificial Intelligence, Data Science, Linear Algebra, Probability, Statistics 

Summary
Foundations of Machine Learning and AI is a mathematically oriented textbook designed to build the theoretical foundations needed to understand modern machine learning rather than simply use ML libraries as black boxes. Its central idea is that two mathematical viewpoints underpin much of contemporary AI: geometry, mainly through linear algebra, and uncertainty, through probability and statistics. The authors develop these themes together, showing how concepts such as inner products, projections, eigenvalues, singular-value decomposition, probability distributions, estimation and generalization lead naturally to practical machine-learning algorithms. 

The first half develops the necessary linear algebra and geometry: Euclidean spaces $\mathbb{R}^n$, norms, inner products, projections, linear transformations, matrices, spectral theory, SVD, PCA and numerical linear algebra. Importantly, these subjects are connected directly with ML applications—for example, PCA for denoising and dimensionality reduction, matrix conditioning in regression, and randomized/sketching methods for solving very large least-squares problems. 

The second half moves into probability, statistics and optimization. It covers random variables, expectation, moments and concentration inequalities, then statistical estimation through MLE, MAP and Bayesian methods, hypothesis testing and resampling. These foundations lead to regression and classification, kernels, dimensionality reduction and techniques for high-dimensional data. The final chapter develops optimization methods specifically for statistical learning. The overall progression is therefore approximately
$$
\text{Linear Algebra}
\rightarrow
\text{Geometry}
\rightarrow
\text{Probability}
\rightarrow
\text{Statistics}
\rightarrow
\text{Machine Learning}
\rightarrow
\text{Optimization}.
$$

The emphasis throughout is on deriving algorithms from first principles and understanding why they work, rather than merely presenting recipes. Springer explicitly positions examples around real ML problems such as regularization selection, PCA denoising, regression instability and scalable least-squares computation. 

Key takeaways

• Strong mathematical foundation for ML: it connects linear algebra, probability, statistics and optimization into one continuous treatment rather than teaching them as unrelated subjects.
  
• Geometry plays a central role: projections, eigenvectors, spectra, SVD and PCA provide a geometric interpretation of many machine-learning methods.
  
• Probability explains uncertainty and learning: estimation, concentration and generalization provide the theoretical basis for understanding what a model can infer from finite data.
  
• Theory is tied to algorithms: the book repeatedly connects mathematical results to regression, classification, kernels, dimensionality reduction and large-scale computation. 
  

BOOK]]> Foundations of Machine Learning and AI: Geometry, Probability and Optimization
Authors: Pradeep Singh, Balasubramanian Raman
Publisher: Springer Cham
Series:Studies in Big Data, Vol. 199
Publication: eBook — 17 August 2026; Hardcover — 18 August 2026
Edition: 1st
Pages: XXX + 558
DOI: 10.1007/978-3-032-30336-3
Subjects: Machine Learning, Artificial Intelligence, Data Science, Linear Algebra, Probability, Statistics 

Summary
Foundations of Machine Learning and AI is a mathematically oriented textbook designed to build the theoretical foundations needed to understand modern machine learning rather than simply use ML libraries as black boxes. Its central idea is that two mathematical viewpoints underpin much of contemporary AI: geometry, mainly through linear algebra, and uncertainty, through probability and statistics. The authors develop these themes together, showing how concepts such as inner products, projections, eigenvalues, singular-value decomposition, probability distributions, estimation and generalization lead naturally to practical machine-learning algorithms. 

The first half develops the necessary linear algebra and geometry: Euclidean spaces $\mathbb{R}^n$, norms, inner products, projections, linear transformations, matrices, spectral theory, SVD, PCA and numerical linear algebra. Importantly, these subjects are connected directly with ML applications—for example, PCA for denoising and dimensionality reduction, matrix conditioning in regression, and randomized/sketching methods for solving very large least-squares problems. 

The second half moves into probability, statistics and optimization. It covers random variables, expectation, moments and concentration inequalities, then statistical estimation through MLE, MAP and Bayesian methods, hypothesis testing and resampling. These foundations lead to regression and classification, kernels, dimensionality reduction and techniques for high-dimensional data. The final chapter develops optimization methods specifically for statistical learning. The overall progression is therefore approximately
$$
\text{Linear Algebra}
\rightarrow
\text{Geometry}
\rightarrow
\text{Probability}
\rightarrow
\text{Statistics}
\rightarrow
\text{Machine Learning}
\rightarrow
\text{Optimization}.
$$

The emphasis throughout is on deriving algorithms from first principles and understanding why they work, rather than merely presenting recipes. Springer explicitly positions examples around real ML problems such as regularization selection, PCA denoising, regression instability and scalable least-squares computation. 

Key takeaways

• Strong mathematical foundation for ML: it connects linear algebra, probability, statistics and optimization into one continuous treatment rather than teaching them as unrelated subjects.
  
• Geometry plays a central role: projections, eigenvectors, spectra, SVD and PCA provide a geometric interpretation of many machine-learning methods.
  
• Probability explains uncertainty and learning: estimation, concentration and generalization provide the theoretical basis for understanding what a model can infer from finite data.
  
• Theory is tied to algorithms: the book repeatedly connects mathematical results to regression, classification, kernels, dimensionality reduction and large-scale computation. 
  

BOOK]]> https://mklab.gr/showthread.php?tid=1710 Thu, 20 Aug 2026 19:15:43 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1710 Book:Artificial General Intelligence: Principles and Practices
Editors: T. Saravanan, P. Preethi, Sumaya Sanober, N. Thillaiarasu & S. Balamurugan
Publication date: July 2026
Publisher: Wiley–Scrivener
Edition: 1st edition
ISBN: 978-1-394-42267-8
Length: approximately 472–480 pages 

Summary

Artificial General Intelligence: Principles and Practices is a large edited collection examining what would be required to move from today's narrow AI, which performs specialized tasks, toward Artificial General Intelligence (AGI) capable of learning, reasoning, adapting, and transferring knowledge across many domains. Rather than treating AGI simply as a matter of scaling neural networks, the book approaches intelligence from cognitive, mathematical, symbolic, neural, and computational perspectives. Its opening sections discuss definitions of general intelligence, cognitive architectures, Bayesian reasoning, algorithmic learning, symbolic versus neural approaches, and especially neuro-symbolic systems that attempt to combine explicit logical reasoning with the pattern-recognition strengths of neural networks. 

A major theme is generalization: an AGI should not merely solve tasks seen during training but learn how to learn. Consequently, several chapters examine self-supervised learning, transfer learning, meta-learning, continual learning, common-sense reasoning, memory, and adaptation to changing environments. The book also considers explainability, proposing architectures in which reasoning can be inspected rather than remaining entirely inside a neural-network “black box.” Reinforcement learning and decision-making under uncertainty receive separate treatment, while multimodal chapters investigate how an intelligent system could combine vision, language and sound into a unified representation of its environment. 

The final sections move from fundamental AGI questions toward applications and possible future technologies. Topics include multimodal perception, anomaly detection, computer vision, agricultural AI, assistive systems for visually impaired users, quantum computing and brain–computer interfaces. The concluding chapter speculates about the progression from narrow AI through increasingly autonomous systems toward AGI and potentially superintelligence, while also mentioning security and ethical issues. 

Main topics

• Foundations of AGI: What general intelligence actually means and how it might be formally characterized.
  
• Cognitive architectures: Memory, reasoning, planning, learning and goal-directed behavior.
  
• Neuro-symbolic AI: Combining neural networks with symbolic logic and explicit knowledge representation.
  
• Learning and generalization: Self-supervised learning, meta-learning, transfer learning and lifelong learning.
  
• Common-sense reasoning: Giving AI systems background knowledge and inference abilities closer to human reasoning.
  
• Explainable AGI: Making decisions and reasoning processes interpretable.
  
• Reinforcement learning: Model-based versus model-free approaches.
  
• Multimodal intelligence: Integrating language, images, sound and contextual information.
  
• Future AGI: Quantum computing, brain–computer interfaces, autonomous learning and possible post-AGI systems. 
  

Key takeaways

1. AGI is presented as an architectural problem, not merely a scaling problem. The book repeatedly argues that general intelligence will require memory, reasoning, adaptation, knowledge representation and learning to operate together.
   
2. Hybrid neuro-symbolic approaches receive considerable attention. The editors appear to regard the combination of neural learning and symbolic reasoning as an important route toward systems capable of both flexibility and reliable logical reasoning.
   
3. Learning to generalize is central to AGI. Meta-learning, transfer learning, self-supervised learning and continual learning are treated as mechanisms that could allow an AI to reuse knowledge instead of being retrained separately for every problem.
   
4. This is more a research collection than a unified textbook. The 19 chapters are written by different contributors, and some later chapters—such as plant-disease detection, industrial anomaly detection and web-spyware classification—are conventional applied machine-learning topics whose connection to AGI is relatively indirect. 
   

Overall assessment
This looks most useful as a broad survey/reference volume for postgraduate students and researchers who want exposure to many of the approaches currently associated with AGI rather than a single rigorous theory of general intelligence. The publisher explicitly targets engineering students, researchers, IT professionals and AI/deep-learning specialists. 
Its greatest strength is breadth: cognitive architectures, mathematical foundations, neuro-symbolic reasoning, meta-learning, multimodality and future technologies are gathered in one volume. Its weakness is the same breadth—the table of contents suggests that the book sometimes drifts from AGI itself into general contemporary machine-learning applications. I would therefore regard it as an AGI overview and research anthology, rather than a definitive technical textbook explaining how AGI can actually be built.


BOOK]]> Book:Artificial General Intelligence: Principles and Practices
Editors: T. Saravanan, P. Preethi, Sumaya Sanober, N. Thillaiarasu & S. Balamurugan
Publication date: July 2026
Publisher: Wiley–Scrivener
Edition: 1st edition
ISBN: 978-1-394-42267-8
Length: approximately 472–480 pages 

Summary

Artificial General Intelligence: Principles and Practices is a large edited collection examining what would be required to move from today's narrow AI, which performs specialized tasks, toward Artificial General Intelligence (AGI) capable of learning, reasoning, adapting, and transferring knowledge across many domains. Rather than treating AGI simply as a matter of scaling neural networks, the book approaches intelligence from cognitive, mathematical, symbolic, neural, and computational perspectives. Its opening sections discuss definitions of general intelligence, cognitive architectures, Bayesian reasoning, algorithmic learning, symbolic versus neural approaches, and especially neuro-symbolic systems that attempt to combine explicit logical reasoning with the pattern-recognition strengths of neural networks. 

A major theme is generalization: an AGI should not merely solve tasks seen during training but learn how to learn. Consequently, several chapters examine self-supervised learning, transfer learning, meta-learning, continual learning, common-sense reasoning, memory, and adaptation to changing environments. The book also considers explainability, proposing architectures in which reasoning can be inspected rather than remaining entirely inside a neural-network “black box.” Reinforcement learning and decision-making under uncertainty receive separate treatment, while multimodal chapters investigate how an intelligent system could combine vision, language and sound into a unified representation of its environment. 

The final sections move from fundamental AGI questions toward applications and possible future technologies. Topics include multimodal perception, anomaly detection, computer vision, agricultural AI, assistive systems for visually impaired users, quantum computing and brain–computer interfaces. The concluding chapter speculates about the progression from narrow AI through increasingly autonomous systems toward AGI and potentially superintelligence, while also mentioning security and ethical issues. 

Main topics

• Foundations of AGI: What general intelligence actually means and how it might be formally characterized.
  
• Cognitive architectures: Memory, reasoning, planning, learning and goal-directed behavior.
  
• Neuro-symbolic AI: Combining neural networks with symbolic logic and explicit knowledge representation.
  
• Learning and generalization: Self-supervised learning, meta-learning, transfer learning and lifelong learning.
  
• Common-sense reasoning: Giving AI systems background knowledge and inference abilities closer to human reasoning.
  
• Explainable AGI: Making decisions and reasoning processes interpretable.
  
• Reinforcement learning: Model-based versus model-free approaches.
  
• Multimodal intelligence: Integrating language, images, sound and contextual information.
  
• Future AGI: Quantum computing, brain–computer interfaces, autonomous learning and possible post-AGI systems. 
  

Key takeaways

1. AGI is presented as an architectural problem, not merely a scaling problem. The book repeatedly argues that general intelligence will require memory, reasoning, adaptation, knowledge representation and learning to operate together.
   
2. Hybrid neuro-symbolic approaches receive considerable attention. The editors appear to regard the combination of neural learning and symbolic reasoning as an important route toward systems capable of both flexibility and reliable logical reasoning.
   
3. Learning to generalize is central to AGI. Meta-learning, transfer learning, self-supervised learning and continual learning are treated as mechanisms that could allow an AI to reuse knowledge instead of being retrained separately for every problem.
   
4. This is more a research collection than a unified textbook. The 19 chapters are written by different contributors, and some later chapters—such as plant-disease detection, industrial anomaly detection and web-spyware classification—are conventional applied machine-learning topics whose connection to AGI is relatively indirect. 
   

Overall assessment
This looks most useful as a broad survey/reference volume for postgraduate students and researchers who want exposure to many of the approaches currently associated with AGI rather than a single rigorous theory of general intelligence. The publisher explicitly targets engineering students, researchers, IT professionals and AI/deep-learning specialists. 
Its greatest strength is breadth: cognitive architectures, mathematical foundations, neuro-symbolic reasoning, meta-learning, multimodality and future technologies are gathered in one volume. Its weakness is the same breadth—the table of contents suggests that the book sometimes drifts from AGI itself into general contemporary machine-learning applications. I would therefore regard it as an AGI overview and research anthology, rather than a definitive technical textbook explaining how AGI can actually be built.


BOOK]]> https://mklab.gr/showthread.php?tid=1709 Thu, 20 Aug 2026 19:11:50 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1709 Algorithms and Programs: An AI-Assisted Approach
Author: Eric Braude
Publication date: August 2026
Publisher: Addison-Wesley Professional
Length: 144 pages
Level: Intermediate
ISBN: 9780135887622 

Summary

Algorithms and Programs: An AI-Assisted Approach presents classical algorithm design from a modern software-development perspective, with particular emphasis on how AI tools can assist in specifying, designing, implementing, testing, and verifying algorithms. Braude argues that simply expressing an algorithm as code or conventional pseudocode often hides its underlying logic. Instead, he advocates creating structured algorithm outlines that describe the outcomes each part of the algorithm is intended to achieve. These outlines form an intermediate layer between the mathematical idea and the final program, making correctness, invariants, and complexity easier to reason about. 

The first part of the book develops this methodology through problem specification, functions and classes, algorithm decomposition, complexity analysis, invariants, and systematic algorithm construction. AI is treated as a development assistant: it can help formulate requirements, generate or implement algorithmic outlines, test code, and inspect whether the resulting implementation actually satisfies the intended conditions. The emphasis is therefore not on letting AI simply “write the program,” but on giving it a sufficiently precise specification so that its output can be checked and reasoned about. 

The later chapters apply this framework to the standard core of an algorithms course: divide-and-conquer, sorting, dynamic programming, greedy algorithms, minimum spanning trees, and shortest-path algorithms. Examples include binary search, mergesort, quicksort, heapsort, radix sort, rod cutting, longest common subsequence, Huffman coding, Prim's and Kruskal's algorithms, Bellman–Ford, and Dijkstra's algorithm. Thus, although AI is prominent in the title, the book is fundamentally an algorithms text that attempts to rethink how algorithms should be designed and explained in the era of generative AI. 

Key Takeaways

• Specification before coding: clearly state what a function or algorithm must accomplish before attempting to implement it.
  
• Algorithm outlines rather than raw pseudocode: Braude proposes describing algorithms through intended intermediate outcomes, making correctness and invariants more transparent.
  
• AI as a collaborator, not an oracle: AI can generate and inspect implementations, but reliable development still requires precise specifications and verification.
  
• Traditional algorithmic thinking remains essential: complexity analysis, divide-and-conquer, dynamic programming, greedy methods, graph algorithms, and invariants remain central even when AI writes part of the code. 
  

BOOK]]> Algorithms and Programs: An AI-Assisted Approach
Author: Eric Braude
Publication date: August 2026
Publisher: Addison-Wesley Professional
Length: 144 pages
Level: Intermediate
ISBN: 9780135887622 

Summary

Algorithms and Programs: An AI-Assisted Approach presents classical algorithm design from a modern software-development perspective, with particular emphasis on how AI tools can assist in specifying, designing, implementing, testing, and verifying algorithms. Braude argues that simply expressing an algorithm as code or conventional pseudocode often hides its underlying logic. Instead, he advocates creating structured algorithm outlines that describe the outcomes each part of the algorithm is intended to achieve. These outlines form an intermediate layer between the mathematical idea and the final program, making correctness, invariants, and complexity easier to reason about. 

The first part of the book develops this methodology through problem specification, functions and classes, algorithm decomposition, complexity analysis, invariants, and systematic algorithm construction. AI is treated as a development assistant: it can help formulate requirements, generate or implement algorithmic outlines, test code, and inspect whether the resulting implementation actually satisfies the intended conditions. The emphasis is therefore not on letting AI simply “write the program,” but on giving it a sufficiently precise specification so that its output can be checked and reasoned about. 

The later chapters apply this framework to the standard core of an algorithms course: divide-and-conquer, sorting, dynamic programming, greedy algorithms, minimum spanning trees, and shortest-path algorithms. Examples include binary search, mergesort, quicksort, heapsort, radix sort, rod cutting, longest common subsequence, Huffman coding, Prim's and Kruskal's algorithms, Bellman–Ford, and Dijkstra's algorithm. Thus, although AI is prominent in the title, the book is fundamentally an algorithms text that attempts to rethink how algorithms should be designed and explained in the era of generative AI. 

Key Takeaways

• Specification before coding: clearly state what a function or algorithm must accomplish before attempting to implement it.
  
• Algorithm outlines rather than raw pseudocode: Braude proposes describing algorithms through intended intermediate outcomes, making correctness and invariants more transparent.
  
• AI as a collaborator, not an oracle: AI can generate and inspect implementations, but reliable development still requires precise specifications and verification.
  
• Traditional algorithmic thinking remains essential: complexity analysis, divide-and-conquer, dynamic programming, greedy methods, graph algorithms, and invariants remain central even when AI writes part of the code. 
  

BOOK]]> https://mklab.gr/showthread.php?tid=1696 Thu, 20 Aug 2026 17:02:35 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1696 A Beginner’s Guide to Generative AI: An Introductory Path to Diffusion Models, ChatGPT, and LLMs
Authors: Deepshikha Bhati, Fnu Neha, Angela Guercio, Md Amiruzzaman & Aloysius Bathi Kasturiarachi
Publisher: Springer, Cham
Series:Synthesis Lectures on Computer Science
Length: XIX + 236 pages
Field: Artificial Intelligence / Machine Learning / Generative AI / NLP 

A Beginner’s Guide to Generative AI is an introductory textbook intended for readers with little or no previous background in artificial intelligence. Its aim is to explain how modern generative systems produce text and images while gradually introducing the underlying machine-learning ideas. The book begins with a general introduction to generative AI and then traces the development of language modelling from earlier neural-network approaches toward large language models (LLMs). Particular attention is given to the Transformer architecture, explaining why attention-based models became the foundation of systems such as ChatGPT. 

The middle chapters focus more closely on LLMs, Transformers, ChatGPT and Google Bard, including an architectural discussion of ChatGPT. The authors then move from language generation to image generation, introducing three major families of generative models: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models. The diffusion-model chapter explains their basic architecture and why diffusion techniques have become particularly effective for realistic image synthesis. 

The final part becomes more practical. There is a short chapter on setting up an environment and implementing LLM-based systems, followed by a substantial discussion of ChatGPT applications in areas such as business, customer service, marketing, content creation, healthcare and other professional settings. Chapters contain summaries and multiple-choice exercises, making the book closer to an introductory course or self-study text than to an advanced research monograph. 

Main topics

1. Introduction to Generative AI
   
2. Evolution from neural networks to large language models
   
3. Transformers and LLMs
   
4. ChatGPT architecture
   
5. Google Bard and related models
   
6. VAEs, GANs and diffusion models for image generation
   
7. Setting up an environment for LLM implementation
   
8. Practical ChatGPT use cases 
   
   

Key takeaways

• Good entry point for beginners: it assumes no substantial previous AI knowledge and builds the concepts progressively. 
  
• It provides a useful conceptual bridge from traditional neural networks → Transformers → modern LLMs rather than discussing ChatGPT only as a black-box application. 
  
• Its coverage is broader than just LLMs: it also explains the major approaches to generative image modelling, particularly VAEs, GANs and diffusion models. 
  
• It is primarily an introductory and educational overview, not a mathematically rigorous treatment of deep learning. For someone wanting to understand what generative AI systems are doing before studying the mathematics and programming in greater depth, that is probably its strongest role. 
  

BOOK]]> A Beginner’s Guide to Generative AI: An Introductory Path to Diffusion Models, ChatGPT, and LLMs
Authors: Deepshikha Bhati, Fnu Neha, Angela Guercio, Md Amiruzzaman & Aloysius Bathi Kasturiarachi
Publisher: Springer, Cham
Series:Synthesis Lectures on Computer Science
Length: XIX + 236 pages
Field: Artificial Intelligence / Machine Learning / Generative AI / NLP 

A Beginner’s Guide to Generative AI is an introductory textbook intended for readers with little or no previous background in artificial intelligence. Its aim is to explain how modern generative systems produce text and images while gradually introducing the underlying machine-learning ideas. The book begins with a general introduction to generative AI and then traces the development of language modelling from earlier neural-network approaches toward large language models (LLMs). Particular attention is given to the Transformer architecture, explaining why attention-based models became the foundation of systems such as ChatGPT. 

The middle chapters focus more closely on LLMs, Transformers, ChatGPT and Google Bard, including an architectural discussion of ChatGPT. The authors then move from language generation to image generation, introducing three major families of generative models: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models. The diffusion-model chapter explains their basic architecture and why diffusion techniques have become particularly effective for realistic image synthesis. 

The final part becomes more practical. There is a short chapter on setting up an environment and implementing LLM-based systems, followed by a substantial discussion of ChatGPT applications in areas such as business, customer service, marketing, content creation, healthcare and other professional settings. Chapters contain summaries and multiple-choice exercises, making the book closer to an introductory course or self-study text than to an advanced research monograph. 

Main topics

1. Introduction to Generative AI
   
2. Evolution from neural networks to large language models
   
3. Transformers and LLMs
   
4. ChatGPT architecture
   
5. Google Bard and related models
   
6. VAEs, GANs and diffusion models for image generation
   
7. Setting up an environment for LLM implementation
   
8. Practical ChatGPT use cases 
   
   

Key takeaways

• Good entry point for beginners: it assumes no substantial previous AI knowledge and builds the concepts progressively. 
  
• It provides a useful conceptual bridge from traditional neural networks → Transformers → modern LLMs rather than discussing ChatGPT only as a black-box application. 
  
• Its coverage is broader than just LLMs: it also explains the major approaches to generative image modelling, particularly VAEs, GANs and diffusion models. 
  
• It is primarily an introductory and educational overview, not a mathematically rigorous treatment of deep learning. For someone wanting to understand what generative AI systems are doing before studying the mathematics and programming in greater depth, that is probably its strongest role. 
  

BOOK]]>