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AI for Absolute Beginners: A Clear Guide to Tomorrow
Curious about AI but feeling lost at the same time?
Well, look no further! AI for Absolute Beginners is your guide to demystifying complex AI concepts and providing you with a solid foundation to step into and stay ahead in the new AI era.In this accessible and comprehensive book, we start from scratch, assuming no prior knowledge. Each chapter is carefully crafted to guide you through the fundamental concepts and practical applications of AI, including machine learning, generative AI, and deep learning.With clear and plain English explanations, you'll be surprised at how quickly you can start thinking and speaking fluently in the words and mindset of an AI expert!
Key knowledge points you will gain from reading this - The 3 phases of AI development and what's coming next...
- The Missing Middle theory of leveraging what humans and AI do best
- Generative AI and a preview of the Data Wars
- Machine learning and a clear introduction on how it works
- Deep learning and why it unlocks progress in all fields of AI
- Natural language processing explained
- Computer vision and why it's not as easy as you think...
- Recommender systems and how to train them to your advantage
- Privacy & ethical considerations you must know
- The future of work and how to thrive in the new era of knowledge work
- The new role of the Chief Intelligence Officer and how they are guiding AI transformation in organizations
Now is a special time in the history of work and efficiency. Follow your curiosity and step confidently into the new era of AI!
Well, look no further! AI for Absolute Beginners is your guide to demystifying complex AI concepts and providing you with a solid foundation to step into and stay ahead in the new AI era.In this accessible and comprehensive book, we start from scratch, assuming no prior knowledge. Each chapter is carefully crafted to guide you through the fundamental concepts and practical applications of AI, including machine learning, generative AI, and deep learning.With clear and plain English explanations, you'll be surprised at how quickly you can start thinking and speaking fluently in the words and mindset of an AI expert!
Key knowledge points you will gain from reading this - The 3 phases of AI development and what's coming next...
- The Missing Middle theory of leveraging what humans and AI do best
- Generative AI and a preview of the Data Wars
- Machine learning and a clear introduction on how it works
- Deep learning and why it unlocks progress in all fields of AI
- Natural language processing explained
- Computer vision and why it's not as easy as you think...
- Recommender systems and how to train them to your advantage
- Privacy & ethical considerations you must know
- The future of work and how to thrive in the new era of knowledge work
- The new role of the Chief Intelligence Officer and how they are guiding AI transformation in organizations
Now is a special time in the history of work and efficiency. Follow your curiosity and step confidently into the new era of AI!
- GenresArtificial Intelligence
179 pages, Kindle Edition
Published July 18, 2023
89 people are currently reading
26 people want to read
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Displaying 1 - 2 of 2 reviews
July 19, 2024
A very good introductory read on the subject. Can be more elaborative
Below are key takeaways:
- Data science focuses on extracting insights and knowledge from raw data, whereas artificial intelligence aims to simulate and embed human intelligence into machines. However, in many cases, AI systems will leverage insight derived from data science to enable machines to learn and make intelligent decisions.
- According to the Turing Test, a machine can be considered intelligent if it can engage in a task without being detected as a machine, such as holding a conversation with a human.
- History:
- The history of AI has experienced a recurring pattern of inflated expectations and hibernation periods known as *AI winters*, characterized by periods of decreased funding and interest. These different cycles highlight the importance of maintaining realistic expectations and a measured approach to AI's capabilities.
- The resilience of AI, seen throughout its history, can be partly attributed to the Lindy effect, which suggests that the longer an idea or technology survives, the longer its future life expectancy becomes. AI's ability to overcome periods of skepticism, funding cuts, and technological limitations underlines its resilience and reinforces the notion that the longer AI continues to advance, the more likely it is here to stay.
- The advent of powerful graphics processing units played a crucial role in the modern era of AI by enabling the parallel processing required for complex computations. This ability significantly reduces the time to train a model and has facilitated the development of deep learning, pushing the boundaries of what AI can achieve.
- AI Building Blocks
- Classification and regression are two common categories of algorithms used in AI. Classification algorithms assign input data to specific categories or classes based on their features, while regression algorithms predict continuous values.
- Sorting algorithms arrange data into a specific order, facilitating easier understanding. Clustering algorithms, on the other hand, group similar data points together without predefined labels, allowing for the identification of new patterns and relationships.
- Transparency and interpretability of algorithms are important considerations. Transparent algorithms have clear and understandable steps, making their decision-making process easily interpretable. Black box algorithms, on the other hand, lack transparency and their internal workings are difficult to trace.
- Datasets play a crucial role in AI systems. The quality, diversity, and relevance of the data used to train AI models significantly impact their performance. Inaccurate, biased, or incomplete data can lead to poor or biased predictions. Preprocessing and cleaning the data are also important steps for ensuring data quality.
- Libraries promote convenience and consistency. By using established libraries, developers adhere to standardized practices, reducing the risk of errors and improving the replicability of code for future use.
- The choice of programming language should consider factors such as computational efficiency, library availability, community support, integration with existing systems, data handling capabilities, and parallel computing support.
- Python is currently the most widely used language in AI and machine learning, offering simplicity, readability, and a rich ecosystem of libraries.
- 3 stages of AI development
- Narrow AI, general AI, and superintelligent AI form the three potential stages of AI development. Narrow AI is designed for specific tasks, while general AI possesses broad cognitive capabilities similar to humans, and superintelligent AI surpasses human intelligence.
- There are serious concerns regarding the control and alignment of superintelligent AI to ensure that it aligns with human goals and values.
- It is difficult to discuss and imagine the future of AI without confronting the issue of the Singularity, which predicts a future point where the human race is overtaken and potentially overrun by AI agents.
- It’s important to discuss the potential implications of superintelligence and introduce early precautionary measures despite the absence of scientific proof.
- Machine Learning
- Machine learning allows systems to learn from data and make predictions without explicit programming.
- Training involves feeding a machine learning algorithm a dataset to learn patterns and relationships. The goal is to find a model that accurately captures the underlying patterns without overfitting the training data.
- There are three primary types of machine learning models:
- Supervised learning uses labeled data with known explanatory variables and a known target variable to train the model.
- Unsupervised learning deals with known explanatory variables and aims to discover hidden patterns and structures within the data to create a new target variable.
- Reinforcement learning involves learning through trial and error in an environment with rewards and punishments in order to achieve a predefined target.
- Model selection depends on the type of data you have available and the problem you are attempting to solve.
- Deep Learning
- Deep learning is an advanced subfield of machine learning that uses artificial neural networks with deep and multiple layers to learn and model complex patterns in data.
- Deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer networks. Each has its own strengths and common use cases.
- CNNs enable machines to see and interpret visual data (Computer vision)
- RNNs are renowned for their inherent ability to remember. Central to Natural language processing (NLP), Speech recognition and time series prediction
- Transformer Networks: Represent a significant leap forward in dealing with sequential data and especially in natural language processing. By focusing on the parts of the data that matter most and processing sequences more efficiently, they have opened a new paradigm in deep learning.
- Due to the complexity of the neural networks and the large amount of data they handle, deep learning models require substantial computational resources. Deep learning models can also suffer from limitations such as the black box nature of their decision-making mechanisms and the risk of overfitting patterns in the training data.
- Natural Language Processing (NLP)
- Natural language processing is a multidisciplinary field that empowers computers to process, understand, and generate human language.
- NLP can be divided into natural language understanding and natural language generation. NLU concentrates on extracting meaning, sentiment, and other semantic features from the text, while NLG focuses on generating coherent and contextually appropriate text.
- Challenges in NLP include understanding the intricacies of human language, such as irony and cultural references, language subtlety, addressing bias, ensuring model interpretability, protecting data privacy, and developing NLP models for low-resource languages.
- Generative AI
- Generative AI differs from traditional predictive AI through its ability to create, innovate, and generate new outputs.
- Generative AI presents challenges and ethical considerations, including the misuse of technology for generating deep fakes, spam, fake news, or phishing emails. There are also issues regarding the originality, ownership, and copyright of AI-generated content.
- The Data Wars refer to the battle between corporations' data collection efforts and the demand for privacy protections. Stricter regulations will limit data accessibility and companies are building walled gardens to control and monetize their data.
- Recommender Systems
- Content-based filtering recommends items based on their characteristics and a user's preferences. It relies on item descriptions and user profiling, matching similar items to those the user has liked or browsed in the past. Content-based filtering is effective for recommending new items but may lack variety and struggle with recommending items to new users.
- Collaborative filtering recommends items based on the preferences of similar users with shared interests. It leverages the wisdom of the crowd and can suggest items that users may not have discovered otherwise.
- The hybrid approach is a combination of collaborative and content-based filtering techniques. Hybrid recommender systems can operate by running as a unified model or by separating content-based and collaborative filtering and then combining their predictions.
- Understanding the type of recommender system used by a platform is essential for marketers, content creators, and general users. Properly labeling items, associating them with popular items, and leveraging paid advertising can train recommender systems to enhance accurate targeting and reach relevant audiences.
- Computer Vision
- Computer vision involves training computers to see and understand visual information. This includes image classification, object detection, and image segmentation.
- Challenges in computer vision include dealing with variations and complexity in real-world scenarios, acquiring and processing large and diverse datasets, managing computational requirements, and addressing legal and ethical dilemmas.
- Organizations engaging in computer vision must invest heavily in data, computational resources, and human expertise—and even then, it may not go as smoothly as planned!
- Privacy & Ethical Considerations
- AI systems can unintentionally replicate and amplify biases present in their training data or the biases of their human creators, leading to unfair outcomes.
- Privacy is a major concern as AI relies on vast amounts of personal data, raising questions about individual privacy. Legal frameworks like GDPR and CCPA aim to protect privacy rights, but AI's complexity also presents challenges to upholding these regulations.
- Legal frameworks, ethical guidelines, software design, and human resources are essential for creating an environment where AI technologies can evolve while safeguarding privacy rights.
- The Future of Work
- The fear of job displacement isn’t new and while AI is expected to create new roles and demand new skills, we could see a drastic shakeup to knowledge work. This includes humans working together with AI agents as copilots to enhance productivity and efficiency.
- Resistance is common in the face of technological innovation, with individuals and organizations often going through a series of emotional responses akin to the seven stages of grief.
- AI technology has the potential to empower smaller companies and individuals to test new ideas at a lower cost.
- AI-powered service providers can offer faster and low-cost services, challenging traditional business models.
- Appointing a Chief AI Officer can help organizations navigate AI strategies, identify opportunities, and align AI efforts with overall business objectives.
- The proliferation and ubiquity of AI technology could pave the way for a new form of digital trade or commerce carried out by humans, without AI involvement.
Below are key takeaways:
- Data science focuses on extracting insights and knowledge from raw data, whereas artificial intelligence aims to simulate and embed human intelligence into machines. However, in many cases, AI systems will leverage insight derived from data science to enable machines to learn and make intelligent decisions.
- According to the Turing Test, a machine can be considered intelligent if it can engage in a task without being detected as a machine, such as holding a conversation with a human.
- History:
- The history of AI has experienced a recurring pattern of inflated expectations and hibernation periods known as *AI winters*, characterized by periods of decreased funding and interest. These different cycles highlight the importance of maintaining realistic expectations and a measured approach to AI's capabilities.
- The resilience of AI, seen throughout its history, can be partly attributed to the Lindy effect, which suggests that the longer an idea or technology survives, the longer its future life expectancy becomes. AI's ability to overcome periods of skepticism, funding cuts, and technological limitations underlines its resilience and reinforces the notion that the longer AI continues to advance, the more likely it is here to stay.
- The advent of powerful graphics processing units played a crucial role in the modern era of AI by enabling the parallel processing required for complex computations. This ability significantly reduces the time to train a model and has facilitated the development of deep learning, pushing the boundaries of what AI can achieve.
- AI Building Blocks
- Classification and regression are two common categories of algorithms used in AI. Classification algorithms assign input data to specific categories or classes based on their features, while regression algorithms predict continuous values.
- Sorting algorithms arrange data into a specific order, facilitating easier understanding. Clustering algorithms, on the other hand, group similar data points together without predefined labels, allowing for the identification of new patterns and relationships.
- Transparency and interpretability of algorithms are important considerations. Transparent algorithms have clear and understandable steps, making their decision-making process easily interpretable. Black box algorithms, on the other hand, lack transparency and their internal workings are difficult to trace.
- Datasets play a crucial role in AI systems. The quality, diversity, and relevance of the data used to train AI models significantly impact their performance. Inaccurate, biased, or incomplete data can lead to poor or biased predictions. Preprocessing and cleaning the data are also important steps for ensuring data quality.
- Libraries promote convenience and consistency. By using established libraries, developers adhere to standardized practices, reducing the risk of errors and improving the replicability of code for future use.
- The choice of programming language should consider factors such as computational efficiency, library availability, community support, integration with existing systems, data handling capabilities, and parallel computing support.
- Python is currently the most widely used language in AI and machine learning, offering simplicity, readability, and a rich ecosystem of libraries.
- 3 stages of AI development
- Narrow AI, general AI, and superintelligent AI form the three potential stages of AI development. Narrow AI is designed for specific tasks, while general AI possesses broad cognitive capabilities similar to humans, and superintelligent AI surpasses human intelligence.
- There are serious concerns regarding the control and alignment of superintelligent AI to ensure that it aligns with human goals and values.
- It is difficult to discuss and imagine the future of AI without confronting the issue of the Singularity, which predicts a future point where the human race is overtaken and potentially overrun by AI agents.
- It’s important to discuss the potential implications of superintelligence and introduce early precautionary measures despite the absence of scientific proof.
- Machine Learning
- Machine learning allows systems to learn from data and make predictions without explicit programming.
- Training involves feeding a machine learning algorithm a dataset to learn patterns and relationships. The goal is to find a model that accurately captures the underlying patterns without overfitting the training data.
- There are three primary types of machine learning models:
- Supervised learning uses labeled data with known explanatory variables and a known target variable to train the model.
- Unsupervised learning deals with known explanatory variables and aims to discover hidden patterns and structures within the data to create a new target variable.
- Reinforcement learning involves learning through trial and error in an environment with rewards and punishments in order to achieve a predefined target.
- Model selection depends on the type of data you have available and the problem you are attempting to solve.
- Deep Learning
- Deep learning is an advanced subfield of machine learning that uses artificial neural networks with deep and multiple layers to learn and model complex patterns in data.
- Deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer networks. Each has its own strengths and common use cases.
- CNNs enable machines to see and interpret visual data (Computer vision)
- RNNs are renowned for their inherent ability to remember. Central to Natural language processing (NLP), Speech recognition and time series prediction
- Transformer Networks: Represent a significant leap forward in dealing with sequential data and especially in natural language processing. By focusing on the parts of the data that matter most and processing sequences more efficiently, they have opened a new paradigm in deep learning.
- Due to the complexity of the neural networks and the large amount of data they handle, deep learning models require substantial computational resources. Deep learning models can also suffer from limitations such as the black box nature of their decision-making mechanisms and the risk of overfitting patterns in the training data.
- Natural Language Processing (NLP)
- Natural language processing is a multidisciplinary field that empowers computers to process, understand, and generate human language.
- NLP can be divided into natural language understanding and natural language generation. NLU concentrates on extracting meaning, sentiment, and other semantic features from the text, while NLG focuses on generating coherent and contextually appropriate text.
- Challenges in NLP include understanding the intricacies of human language, such as irony and cultural references, language subtlety, addressing bias, ensuring model interpretability, protecting data privacy, and developing NLP models for low-resource languages.
- Generative AI
- Generative AI differs from traditional predictive AI through its ability to create, innovate, and generate new outputs.
- Generative AI presents challenges and ethical considerations, including the misuse of technology for generating deep fakes, spam, fake news, or phishing emails. There are also issues regarding the originality, ownership, and copyright of AI-generated content.
- The Data Wars refer to the battle between corporations' data collection efforts and the demand for privacy protections. Stricter regulations will limit data accessibility and companies are building walled gardens to control and monetize their data.
- Recommender Systems
- Content-based filtering recommends items based on their characteristics and a user's preferences. It relies on item descriptions and user profiling, matching similar items to those the user has liked or browsed in the past. Content-based filtering is effective for recommending new items but may lack variety and struggle with recommending items to new users.
- Collaborative filtering recommends items based on the preferences of similar users with shared interests. It leverages the wisdom of the crowd and can suggest items that users may not have discovered otherwise.
- The hybrid approach is a combination of collaborative and content-based filtering techniques. Hybrid recommender systems can operate by running as a unified model or by separating content-based and collaborative filtering and then combining their predictions.
- Understanding the type of recommender system used by a platform is essential for marketers, content creators, and general users. Properly labeling items, associating them with popular items, and leveraging paid advertising can train recommender systems to enhance accurate targeting and reach relevant audiences.
- Computer Vision
- Computer vision involves training computers to see and understand visual information. This includes image classification, object detection, and image segmentation.
- Challenges in computer vision include dealing with variations and complexity in real-world scenarios, acquiring and processing large and diverse datasets, managing computational requirements, and addressing legal and ethical dilemmas.
- Organizations engaging in computer vision must invest heavily in data, computational resources, and human expertise—and even then, it may not go as smoothly as planned!
- Privacy & Ethical Considerations
- AI systems can unintentionally replicate and amplify biases present in their training data or the biases of their human creators, leading to unfair outcomes.
- Privacy is a major concern as AI relies on vast amounts of personal data, raising questions about individual privacy. Legal frameworks like GDPR and CCPA aim to protect privacy rights, but AI's complexity also presents challenges to upholding these regulations.
- Legal frameworks, ethical guidelines, software design, and human resources are essential for creating an environment where AI technologies can evolve while safeguarding privacy rights.
- The Future of Work
- The fear of job displacement isn’t new and while AI is expected to create new roles and demand new skills, we could see a drastic shakeup to knowledge work. This includes humans working together with AI agents as copilots to enhance productivity and efficiency.
- Resistance is common in the face of technological innovation, with individuals and organizations often going through a series of emotional responses akin to the seven stages of grief.
- AI technology has the potential to empower smaller companies and individuals to test new ideas at a lower cost.
- AI-powered service providers can offer faster and low-cost services, challenging traditional business models.
- Appointing a Chief AI Officer can help organizations navigate AI strategies, identify opportunities, and align AI efforts with overall business objectives.
- The proliferation and ubiquity of AI technology could pave the way for a new form of digital trade or commerce carried out by humans, without AI involvement.
August 30, 2024
Simple writing with profound meaning.
I thoroughly enjoyed reading this book, which provided me with a comprehensive background in AI as a branch of computer science. The author explains machine learning, supervised learning, unsupervised learning, and reinforcement learning. He also provided a background in generative AI.
I also have an interest in the future of AI and the ethical considerations that must be followed to ensure fair and unbiased systems.
I thoroughly enjoyed reading this book, which provided me with a comprehensive background in AI as a branch of computer science. The author explains machine learning, supervised learning, unsupervised learning, and reinforcement learning. He also provided a background in generative AI.
I also have an interest in the future of AI and the ethical considerations that must be followed to ensure fair and unbiased systems.
Displaying 1 - 2 of 2 reviews