The intersection of cognitive science, AI, and computational neuroscience is discussed. There is a bidirectional flow of ideas between these fields, AI has influenced neuroscience and vice versa. Questions whether imitating the brain is the best way to build AI, or if it is better to find engineering solutions, pointing out the successes and failures of both approaches. There is an importance of incorporating key cognitive ingredients, such as intuitive theories, compositionality, learning-to-learn, and causality, into AI systems to achieve more human-like intelligence. Current AI systems lack these ingredients, highlighting the need for AI researchers to consider higher-level cognitive principles. Cognitive plausibility is encouraged over strict biological accuracy in the pursuit of human-like AI.

Link to article: https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/building-machines-that-learn-and-think-like-people/A9535B1D745A0377E16C590E14B94993

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Link to book: https://www.amazon.com/dp/B0DRT17T5D

Quantum computing is no longer a futuristic concept—it is atransformative technology already making waves across diverse industries. Quantum Solutions: Practical Applications Across Industries explores the foundational principles of quantum computing and its practical applications, providing a roadmap for professionals, researchers, and decision-makers to harness its power.

The book begins with a clear and accessible introduction toquantum computing, unpacking core concepts like qubits, superposition, entanglement, and quantum gates. It then transitions into industry-specific applications, illustrating how quantum computing is being used to tackle real-world challenges.

The book concludes with a forward-looking discussion on the challenges and opportunities in quantum computing. Ethical, technical, and practical considerations are examined alongside the immense potential for innovation, collaboration, and societal benefit.

Why This Book Matters:

Quantum Solutions: Practical Applications AcrossIndustries serves as a bridge between quantum theory and its practical implications. By blending rigorous academic research with actionable insights, it equips readers to navigate the quantum frontier with confidence. Whether you're an industry leader, a researcher, or simply curious about the quantum revolution, this book offers a comprehensive guide to understanding and leveraging quantum computing in the modern world.

With quantum technology poised to redefine industries andaddress global challenges, this book provides an essential foundation for embracing the quantum future.

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This book explores the intersection of quantum computing and machine learning. It details classical machine learning concepts, such as supervised learning, various models (linear, nonlinear, probabilistic), and training methods (gradient descent). It then introduces quantum computing fundamentals, including qubits, quantum gates, and algorithms like Grover's search. The core focus is on how quantum algorithms can be applied to machine learning tasks, including inference and training, examining different encoding techniques (amplitude, basis, Hamiltonian) and their implications for efficiency and feasibility. Finally, it discusses hybrid classical-quantum approaches where classical and quantum computations work together.

Link to book: https://www.amazon.com/Supervised-Learning-Quantum-Computers-Technology/dp/3319964232

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Post-quantum cryptography (PQC) is a field focused on developing cryptographic systems that are secure against attacks from both classical and quantum computers. This is necessary because current public-key algorithms like RSA and ECC, which are widely used to protect the internet, are vulnerable to attacks by quantum computers using Shor's algorithm

•The Quantum Threat: Quantum computers can theoretically solve mathematical problems, such as factoring large numbers and discrete logarithms, much more efficiently than classical computers. This poses a significant threat to current public-key cryptography, which relies on the difficulty of these problems

•Transition to PQC: The development and implementation of PQC is critical to ensure the continued security of digital information. The transition will take time, so it's important to start the process now.
•Real-World Deployment: There are many challenges in getting PQC deployed on the internet, including key distribution and compatibility with existing systems. One approach is to use tools that can be dropped in front of existing modular servers to test the new systems.

•Hybrid approach: A hybrid approach of using both classical and post-quantum algorithms in combination is being considered as a strategy to reduce risk.In the context of internet security, post-quantum cryptography is essential for the future. Current systems are vulnerable to quantum attacks, and the transition to PQC is a complex but necessary undertaking... While PQC is not yet widely deployed, some systems are already showing promise..., and ongoing research aims to address current challenges.Link to book: https://www.amazon.com/Post-Quantum-Cryptography-Daniel-J-Bernstein/dp/3540887016

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Daniel Kahneman's Thinking, Fast and Slow explores the two systems of thinking—System 1, which is fast, intuitive, and emotional; and System 2, which is slower, more deliberate, and logical—and how they interact to shape our judgments and decisions. The book examines cognitive biases, such as the availability heuristic and anchoring effect, which lead to predictable errors in thinking. Kahneman also discusses the concepts of loss aversion, prospect theory, and the differences between the experiencing self and the remembering self. Finally, it explores the limitations of human prediction and the often-superior accuracy of statistical algorithms compared to expert intuition.

Link to book: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555

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Rich Sutton's "The Bitter Lesson" argues that in AI research, focusing on general methods that leverage computation, such as search and learning, is ultimately more effective than incorporating human knowledge. Historical examples: in chess, Go, speech recognition, and computer vision demonstrate that while human-centric approaches initially yield progress, they eventually plateau, whereas computationally-driven methods continue to improve as computing power increases. The article emphasizes the importance of scalable algorithms capable of handling increasing complexity and the need to avoid prematurely imposing human-like reasoning into AI systems. Sutton contends that focusing on meta-methods that discover rather than pre-define solutions is crucial for future AI advancements.

Read here: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

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Marc Andreessen's "Techno-Optimist Manifesto" argues that technological advancement, fueled by free markets and human ingenuity, is the key to solving global challenges and achieving unprecedented prosperity. The manifesto champions technological progress as a force for good, rejecting pessimism and arguing that technological innovation continually improves lives and creates abundance. It emphasizes the importance of free markets, intelligence, and energy in driving this progress, while opposing centralized planning and pessimistic ideologies that hinder advancement. Andreessen asserts that technology is a solution to various problems, from poverty to environmental concerns, and that a techno-optimist approach is crucial for building a better future. The piece concludes by identifying the "enemies" of progress, which are primarily detrimental ideologies and institutions that obstruct technological advancement.

Read here: https://a16z.com/the-techno-optimist-manifesto/

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An essay by Dario Amodei, CEO of Anthropic, explores the potentially transformative positive impacts of powerful AI, countering the common focus on its risks. Amodei argues that AI could dramatically accelerate progress in biology and medicine, neuroscience and mental health, and economic development, potentially leading to a world free from many diseases and with vastly improved living standards. He also considers the role AI could play in promoting peace and global governance, while acknowledging challenges in ensuring equitable access and maintaining meaningful human work. Ultimately, Amodei presents a hopeful, albeit ambitious, vision for the future shaped by advanced AI.

Read here: https://darioamodei.com/machines-of-loving-grace

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Link to book: https://www.amazon.com/dp/B0DRHKXRCQ

AI Demystified: The Fundamentals and Beyond offers acomprehensive exploration of artificial intelligence (AI), presenting its foundational principles, technological advances, and real-world applications while addressing its ethical implications and future potential. This book is designed for readers seeking to understand AI's transformative role in society and its implications across industries.

The book begins with an introduction to AI, tracingits historical evolution from philosophical musings to a vibrant field of study and innovation. Drawing on seminal works like Russell and Norvig's Artificial Intelligence: A Modern Approach, this section outlines AI's definitions, major milestones, and foundational concepts such as machine learning, natural language processing, and knowledge representation.

In "Types of AI," the book categorizes AI systems based on their capabilities and applications, from narrow AI—designed for specific tasks like facial recognition—to the aspirational goal of general AI, which would match or exceed human-level intelligence. Discussions are enriched by insights from Goodfellow et al.'s Deep Learning and LeCun's research, emphasizing the role of neural networks and advanced machine learning algorithms in pushing AI frontiers.

The "Key Concepts and Technologies" chapterdelves into the technical foundations of AI, including statistical models, probabilistic reasoning, and computational methods that underpin modern AI systems. Drawing from resources like Bishop's Pattern Recognition and Machine Learning and Hastie's The Elements of Statistical Learning, this section explains the mathematics and algorithms that power AI applications in areas such as predictive analytics, pattern recognition, and decision-making.

In "AI in Various Fields," the book showcases how AI is reshaping industries like healthcare, finance, education, and entertainment. Drawing on Topol's Deep Medicine, Brynjolfsson and McAfee's The Second Machine Age, and Davenport's Only Humans Need Apply, this section illustrates how AI augments human capabilities,improves decision-making, and creates opportunities for innovation while addressing challenges such as job displacement and ethical dilemmas.

The book concludes with "Future Trends," anexploration of emerging developments in AI, including the pursuit of General AI, the societal impact of automation, and the implications of the technological singularity as envisioned by thinkers like Kurzweil in The Singularity is Near. It also examines AI's role in solving global challenges such as climate change and healthcare disparities, emphasizing the importance of ethical governance and international collaboration to harness AI for the betterment of humanity.

AI Demystified: The Fundamentals and Beyond balancestechnical depth with accessibility, offering readers a nuanced understanding of AI's current landscape and future trajectory. Whether you are a student, professional, or curious enthusiast, this book equips you with the knowledge and perspectives needed to engage thoughtfully with AI and its impact on our world.

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