An outline for how to unify relativity and quantum physics, treating all of reality as an unbroken and undivided whole.
This book is short, but expansiAn outline for how to unify relativity and quantum physics, treating all of reality as an unbroken and undivided whole.
This book is short, but expansive. It's basically a theory of everything, though an unusual one, in that it explicitly states that a single coherent account of all of reality is fundamentally impossible. By bringing any facet into focus well enough to describe it with mechanistic law, we necessarily push other facets out of focus and distort them. Still, this way of thinking can explain a lot about the nature of reality. It's not a complete theory, but the sketch of how to create one. It also interrogates how we make physical theories, what they mean, and how those abstract theories relate to what is. It's an ambitious book, spanning physics, life, and consciousness, frequently using technology and philosophy of science as a lens through which to understand it all. It's written very precisely and scientifically, and is sometimes very technical, but it is also quite accessible (you don't need to know much about physics to appreciate it) and even poetic.
This book is a fascinating intersection. Bohm was one of the clique of scientists who codified quantum theory, and knows it extraordinarily deeply. He also seems to have had a mystical spiritual awakening at some point, coming to see all of reality as a sort of universal consciousness. What's so interesting is that he saw these ideas not only as compatible, but as illuminating one another. Although this book is a scientific account of physical reality and not overtly spiritual, I'd also consider it a kind of mystical philosophy. It's a beautiful, mind expanding book, though at times strained. Bohm had clearly glimpsed some deeper truth, but was unable to manifest it as a complete scientific theory. This book seems like an attempt to get his partial work out there, so it would not be lost with him. He struggles to convey deeply spiritual ideas within the scientific frame, in a way that can be a bit awkward and unnatural at times, but I'd say this is part of the book's charm.
I'd recommend this book if you're interested in relativity, quantum theory, and how they might be reconciled. I'd also recommend this book if you'd like to understand how mind and matter can emerge in the universe, can be understood as fundamentally the same, and part of one undivided whole. This is a great complement to On the Origin of Time, which is a very different sort of book approaching the problem of reality from a different angle, but resulting in very compatible conclusions....more
A quick, thorough, and enjoyable overview of symbiosis in nature.
This book explores why symbiosis is important, how it arises and persists, how symbioA quick, thorough, and enjoyable overview of symbiosis in nature.
This book explores why symbiosis is important, how it arises and persists, how symbiotic partners find each other and manage their relationships, and how this all relates to research in biology and medicine. It's chock full of specific examples from all over the world / tree of life, as well as integrative theories meant to characterize symbiosis generally. The author doesn't shy away from biology jargon and the technical details of various studies, but never for long, and she always gives accessible paraphrases and summaries. It's a short book overall, touching on many points briefly and tying them together into a coherent story.
I really enjoyed this book. It's very practical, short and to the point, focused on facts and theory with very little fluff or romance. Despite this, though, it's still easy to read and enjoyable, mostly because of the well written prose and fascinating subject matter. It's just deep enough to really delve into what makes symbiosis so interesting, and the many complexities and unique phenomena that it creates, without becoming tedious or narrowly relevant. I really appreciated the balance of general theory and specific examples, with an emphasis on how symbiosis defies expectations and opens up whole new realms of behavior. It highlights many interesting phenomena, lines of research, and open questions that the reader might follow up on.
This book is perfect for someone looking to quickly get a sense of what symbiosis is all about, either out of curiosity or as the starting point for deeper research....more
An exploration of the origin of the Universe, how the science of cosmology developed, and Steven Hawking's legacy.
This book is a mix of memoir, historAn exploration of the origin of the Universe, how the science of cosmology developed, and Steven Hawking's legacy.
This book is a mix of memoir, history of science, and cosmological theory. It lays down a system of ideas, one at a time, exploring where they came from and the people involved, especially Steven Hawking who is an inspiring character. It shows how the story of our Universe has evolved and become more complex from pre-scientific times through Newton, Einstein, the Quantum revolution, and beyond. It especially focuses on the problem of fine tuning: why does our Universe have the particular rules and shape that it does, which just so happen to make life possible? Hawking's theory presents a new model for how it might have happened, in a sort of quantum cosmological evolution.
The ideas in this book are as beautiful and profound as they are complicated. There are a lot of concepts from theoretical physics to cover before Hawking's final theory can be understood. Hertog does a good job of explaining each of them in very simple, clear terms. The writing style is careful, deliberate, gentle, and repetitive to give the ideas their best chance to take hold in the reader's mind. Still, this book will be challenging for many, especially folks who aren't already familiar with some of the pieces to this puzzle. In this light, the passages that are more history or memoir are a relief and play an important role in keeping the book engaging. Every time your eyes start to glaze over from too much math, Hertog brings up something more human to change the pace and motivate the heady ideas.
I really love the idea that the fabric of our Universe is an emergent property of quantum fields tangling and constraining one another. It makes it possible to imagine everything, even human life and all our cultural creations, as part of a single continuous process of constraint building on constraint evolving ever greater complexity. The novel twist of seeing all of reality as a superposition of possible Universes, and seeing the origin of time as an illusion that might reflect the holographic nature of our reality is... inspiring, mind-blowing, and hard to comprehend. It's a brilliant idea (whether it's right or not), and one that meshes very nicely with the simulation hypothesis without being fundamentally computational, which is also quite interesting.
If you're curious about cosmology, quantum physics, the origin of life, Steven Hawking, or just how scientists work to make sense of the world, this is a wonderful book. It may not all make sense on the first read, but that's fine. The story is a delight and the big concepts are powerful, even if some details may be too slippery to fully grasp for a non-expert....more
A powerful philosophical guide for the nature / nurture debate.
This book explores how scientists talk about the origins of behavior, often in terms ofA powerful philosophical guide for the nature / nurture debate.
This book explores how scientists talk about the origins of behavior, often in terms of the innate nature each creature is born with, and the effects of the environment, experience, and learning that challenge or build upon that base. Oyama's point is that this way of thinking is fundamentally wrong, that science has long come to a consensus on that, and yet these ideas still persist stubbornly, often in subtle, unnoticed ways. Oyama goes into great depth discussing how and why this view is wrong, why it matters, the many ways dualism and essentialism slip into scientific discourse anyway, and better ways to talk about these ideas that respect the full complexity of evolution, development, and learning as we know them.
This is a book about biology, but even more so it's a book about how scientists investigate and write about biology. It's a careful work of philosophy, teasing apart the subtle nuances of developmental and evolutionary biology, and the ways certain phrases or frames of reference can obscure or distort important aspects of those phenomena. It's full of examples of how to discuss these things well or poorly, delivering pointed critiques at some very big names in these fields. It's a technical and precise academic work, yet well written and easy to read. It assumes a basic grounding in biology, but focuses on a very narrow slice of it, and reviews the evidence and dialog around that slice quite thoroughly, so it's easy for a novice to join the conversation. The tone of the book is of someone fed up with this debate, venting her frustrations in the most respectful and constructive way she can, and trying her best to help everyone move on from arguing about the wrong things to arguing about the right ones.
This is an essential read for anyone studying genetics, evolution, or how those things influence behavior. It's eye-opening, and a practical toolkit for thinking about this stuff. Also recommended for anyone interested in the philosophy of science....more
Speculation about how the concepts of cybernetics relate to human thinking and human society.
Norbert Wiener is one of the scientists who pioneered theSpeculation about how the concepts of cybernetics relate to human thinking and human society.
Norbert Wiener is one of the scientists who pioneered the theory of signal processing, decision making, feedback and control in both animals and machines. He gives a brief overview of these ideas, then shows how those ideas relate to a wide variety of social issues. He discusses human language and how it is similar and different to communication in the realm of machines, the role of the scientist in society, the function of national militaries, various political ideologies, and the consequences of building machines that can replace human thinking.
This book is mostly about Wiener's thoughts and opinions. He explores the control-theory view of a few social and industrial mechanisms, sometimes abstractly waving his hand and sometimes digging into the minutia. It can be a little tedious at times, but provides a fascinating view into a brilliant mind. What's most interesting is Wiener's perspective from the year 1950. This book was written after the industrial revolution and the rapid development of automated weapons and computing technologies for WWII, but before the mass production of computers and their thorough integration into industry and then home life. Wiener makes many predictions that turned out true, but also discusses a wide range of political issues (including fascism, capitalism, white supremacy, censorship, and prison reform) which are still quite relevant today, from a technical and surprisingly modern perspective.
This book is a quick read that offers a fascinating look at how computing reflects human nature and impacts human life, from a pivotal moment in history just before the explosive growth of this technology....more
A guide for developing and optimizing programs for NVidia GPUs.
This book goes into great depth about how GPUs work, how to write efficient GPU-accelerA guide for developing and optimizing programs for NVidia GPUs.
This book goes into great depth about how GPUs work, how to write efficient GPU-accelerated software, and a wealth of tips and tricks for getting the job done. It's pretty heavy on theory, explanations, and low-level details, but also has plenty of code examples to draw from. There are whole chapters that go into depth on key concepts and technologies, but also a step-by-step optimization guide in chapter 9 that pulls all those ideas together in a practical context.
This book is over 550 pages long, but it doesn't need to be. Many ideas are explained repeatedly. This is helpful if you're having a hard time understanding, or want to jump around a lot, but can otherwise be a little tedious. There are also a lot of numbers, data tables, charts, and screenshots that don't add a lot of value, in my opinion. Often these data compare performance of some code across all of the author's test devices, but don't say a lot about the performance characteristics code itself or what you might expect for your project / hardware. This is particularly frustrating because this book was written in 2012, and covers outdated hardware extensively. For instance, it talks a lot about the differences between CC1 and CC2 devices, when the latest GPUs support CC9.
Despite these shortcomings, I still highly recommend this book. It's loaded with useful information. The author explains complex topics in very precise detail, with helpful metaphors and motivating examples. They have decades of parallel programming, and have filled this book with all sorts of useful insights, tips, and tricks for every circumstance. There's probably more in this book than you'll ever need, but on the flip side, it's got everything. It's worth at least touching on all these ideas so that if / when they come up later, you might remember them and pull out this book to get a refresher on all the details.
It's interesting to compare this book to Programming in Parallel with CUDA: A Practical Guide. That book starts with the assumption that CUDA is easy, encouraging the reader to just start coding, pointing out gotchas and important performance optimizations along the way. In contrast, this book assumes that CUDA is hard. Unless you learn the low-level details, think long and hard about design, and master all the tricks, you'll write bad code and waste much of what the GPU has to offer. Both of these perspectives are true, in a sense, and which is best for you depends on your needs. If you're working on a project and you want to try out making it faster by using GPUs, read Programming in Parallel with CUDA. If you want to become a CUDA expert and squeeze every ounce of power out of your hardware, this book is the better choice....more
A short cell biology text describing the transition from single-cellular to multi-cellular life, and the complex evolutionary dynamics at play in thatA short cell biology text describing the transition from single-cellular to multi-cellular life, and the complex evolutionary dynamics at play in that moment.
In this book, Leo Buss gives his take on how to deal with the "units of selection" problem in evolution. The traditional story of evolution focuses on the individual organism as the place where selection happens. But Buss points out how "individuals" as we think of them didn't exist at first, and had to evolve from single cells, so selective pressures on individuals can't possibly explain that transition. That story of evolution also can't explain many important quirks of multicellular organisms, such as their vast (yet limited) diversity, many strange and elaborate forms of sexual reproduction, and complex multi-stage lifestyles.
Most of this text is a deep dive into the minutia of how differentiated multicellular life evolved. Buss talks about specific ancestral species, their biological quirks and limitations, and how these severely constrained life's search for possible lifestyles going forward, establishing precedents that persist to this day. He points out that multicellular life is, in fact, quite unnatural for cells and was not obviously beneficial in the very beginning. There were particular challenges to overcome, and Buss explores the biology of many species to show how life solved those problems, and to find patterns that suggest what sorts of solutions were possible or not. The story that emerges is one of collaboration by competition, aligned incentives, and mutual constraint. Each multicellular individual is the result of a tenuous and actively managed truce between cell lines and the individual they make up. Evolution is the force that built up this mechanism, and also a force to be managed, lest it break the truce and cause multicellular life to dissolve back into colonies of single cells.
I found this book equally challenging and engaging. It's quite short, but it's also incredibly dense with cell biology and highly technical discussion of evolutionary dynamics. On the other hand, Buss's theory of evolution is refreshingly new and eye-opening. It gives a powerful new perspective on a familiar problem, much like the Selfish Gene. However, Buss makes a solid argument for why his perspective is more enlightening and practically useful for evolutionary biologists. In short, these two models are compatible. The selfish gene theory lets us justify why certain counterintuitive evolutionary innovations were able to take hold. But Buss's hierarchical selection model also lets us predict what sort of innovations are possible (or not), and gives us new tools for analyzing life's great transitions.
This book blew my mind repeatedly. The world of cells is so alien and wildly complex. The challenges of survival and evolutionary pressures are totally different from what we experience at our scale. And yet, we are made of autonomous cells and we evolve by our cells evolving. There's something very powerful in understanding how and why cells came together in the first place, and how that change reshaped the evolutionary landscape on this planet.
This book gives a breathtaking new perspective on strange and complex phenomena in the microscopic world, and how they shape life at our scale. It's a technical, complicated, and mind-boggling journey, but if you stick with it, you will gain a transformative new perspective on evolution and life itself....more
A CUDA programming guide emphasizing practical examples and studying source code.
This book gives a general overview of GPU-based parallel computing anA CUDA programming guide emphasizing practical examples and studying source code.
This book gives a general overview of GPU-based parallel computing and practical considerations when writing or optimizing code. It explores several common CUDA use cases such as image-processing, Monte Carlo simulations, and data analysis pipelines. Rather than focusing on theory or algorithms, it focuses on code. The author walks through many examples line-by-line, with explanations for why he wrote the code that way and what alternatives are worth considering. Throughout the book, the author emphasizes the hardware constraints that impact performance and how the code relates to what the GPU device is actually doing at the lowest level. He demonstrates practical methods for analyzing and comparing the performance of parallel algorithms for iterative optimization.
Where this book shines is in its discussions about why you might use various CUDA features, when they're worth while, and how to apply them effectively in specific common use cases. This is something I find to be lacking in the otherwise excellent official CUDA Programming Guide. It's extremely useful to see the author's process of deciding which optimizations to try and weighing the costs and benefits. There's also a fair amount of discussion about style choices and design patterns that make CUDA code more readable and less error-prone. I didn't always agree with the author's style, but his explanations made it obvious what details impact performance and which are just a matter of taste.
Not all of this book is relevant to everyone (especially the chapter on PET scanners), but it's easy to skip around and revisit topics as needed. One distracting shortcoming of the first edition hardcover is that it has lots of typos. Hopefully this will be corrected in future editions. That said, none of the errors I noticed were particularly problematic; it was generally pretty clear what the author meant.
This is a great book for an established C++ programmer who's transitioning from beginner to intermediate CUDA development. It makes an excellent complement to the official docs from Nvidia. By giving explanations, examples, and analysis in the context of realistic code, it makes the complicated and abstract ideas from the CUDA programming model much more tangible.
It's interesting to compare this book to CUDA Programming: A Developer's Guide to Parallel Computing with GPUs. That book starts with the assumption that CUDA is hard. Unless you learn the low-level details, think long and hard about design, and master all the tricks, you'll write bad code and waste much of what the GPU has to offer. In contrast, this book assumes that CUDA is easy, encouraging the reader to just start coding, pointing out gotchas and important performance optimizations along the way. Both of these perspectives are true, in a sense, and which is best for you depends on your needs. If you want to become a CUDA expert and squeeze every ounce of power out of your hardware, check out CUDA Programming. If you're working on a project and you want to try out making it faster by using GPUs, this book is the better choice....more
A book about searching for good outcomes without rigid expectations of what "good" means.
This book describes Artificial Intelligence research into "noA book about searching for good outcomes without rigid expectations of what "good" means.
This book describes Artificial Intelligence research into "novelty search," and attempts to generalize that idea to many different domains, like choosing a career, scientific research, technological innovation, and evolving life. The key idea is that setting an objective up front and steadily working towards that goal often leads to getting stuck in dead ends and missing less obvious paths that might lead to better outcomes.
The ideas in this book are powerful, important, and expressed clearly in simple terms. If anything, the authors may have made this too easy to read. It spells out a lot that for me was obvious, and repeats its main points over and over. It's a short book, but it's still twice as long as it needs to be. It's also unclear what this book is meant to be. Is it a Computer Science book discussing the novelty search algorithm? A study of how the concept of "objectives" shapes our collective ontology? A manifesto on how AI research should be done? A self-help book? It seems like all of the above. That said, the meandering writing style is worth it for such an inspiring premise and the interesting examples that motivate it.
This book is a quick, easy read. It may be eye-opening or obvious, depending on your starting point. If you're interested in surprising discoveries, the chaotic path to innovation, and how to pursue something really new and interesting, definitely give this a read!...more
A hands-on guide to writing genetic algorithms in Python.
This book is organized around a collection of practice problems. Each chapter presents a hardA hands-on guide to writing genetic algorithms in Python.
This book is organized around a collection of practice problems. Each chapter presents a harder problem, requiring some new genetic programming techniques to solve. Sheppard leads the reader through framing each problem effectively, coding up a solution, and fleshing out a genetic algorithm framework along the way which gets reused throughout the book. The text is light on theory and explanations. It mostly just introduces ideas, provides implementations, and leaves further research to the reader if they want it.
I love this book's structure and hands-on approach. I quickly learned the skills and concepts I was after, so the book served its purpose well. However, there's a lot I dislike about the author's code style and the design of his framework. It's gross, frankly. As an experienced Python programmer, I just rewrote everything to my satisfaction as I went along, which was actually a big help for mastering the core ideas. On the other hand, a Python novice might have a hard time following along, or might pick up some bad coding habits.
I recommend this book for getting up and running with genetic algorithms fast, just so long as you don't mind learning from messy, tricky code with no comments.
This book focuses on one particular kind of GA, a few of its variations, and a small set of practice problems. For a more general overview of GA theory and a thorough exploration of their many forms and applications, check out An Introduction to Genetic Algorithms....more
An overview of Genetic Algorithm theory and examples from the first few decades of research.
This book starts by explaining what Genetic Algorithms areAn overview of Genetic Algorithm theory and examples from the first few decades of research.
This book starts by explaining what Genetic Algorithms are, what they're used for, and how they perform. It explores selected examples of GAs for practical problem solving and scientific modeling, explaining the design and performance of each GA in detail and explaining what makes them interesting to the field in general. Lastly, the book explores general theory, common techniques, and important design decisions for implementing GAs. Unfortunately, the book was published in 1996 and has not been updated, so it doesn't cover any of the progress made in the field since then.
As someone interested in designing novel GAs, I found this book fascinating and tremendously useful. Mitchell does a great job of summarizing the enormous breadth and variation within this field. She strikes a good balance between describing published work in rigorous detail, and discussing the theoretical implications, value, shortcomings, and possible future directions of that work. Her enthusiasm for the field is clear, and it helps hold the reader's interest and keep the book flowing. This book is short and to the point, cherry-picking only a modest number of examples to discuss, and providing lots of leads for further research to the reader. It's rather technical, and expects the reader to have a solid background in Computer Science.
I'd highly recommend this book to anyone with a Computer Science education who wants to learn what Genetic Algorithms are all about.
This book does contain practice problems for the reader, but they weren't the focus and I didn't find them particularly useful. For a practical introduction to writing GAs to solve problems, I'd recommend a book like Genetic Algorithms with Python instead....more
A collection of philosophical papers exploring the relationship between language, mind, and reality.
The chapters in this book are standalone publicatiA collection of philosophical papers exploring the relationship between language, mind, and reality.
The chapters in this book are standalone publications that discuss and critique specific theories and arguments from other philosophers. Putnam uses this as a way to explore his own ideas, gradually building up a coherent set of theories known as "functionalism." This book touches on the philosophy of mind, language, science, epistemology, and more. The main themes which come up repeatedly are what the "meaning" of a word is, when a person "knows" that meaning, and what research into cybernetics and computation can tell us about the mind / body problem.
I'm interested in how the mind works, so the long stretches where Putnam talks instead about schools of philosophy, thinkers, and ideas that he rejects did get tedious at times. Although there's some build up of ideas, most readers should probably just read the chapters that interest them. Unfortunately, it's a little hard to tell which ones are interesting until you dig into them. Personally, I really enjoyed The meaning of 'meaning' and the later chapters about how Turing Machines relate to the mind / body problem, dualism, and the nature of mental activity. I was very excited to see how well Putnam's ideas about meaning fit with modern AI systems that work with natural language. The ideas he wrote in the 70's were surprisingly prescient.
This is an important and thought-provoking work exploring the philosophy of functionalism and many adjacent ideas. It's a long, dense, read, but for those interested in what cognition really is and how humans collectively understand reality, it explores these ideas from several angles and paints a compelling picture....more
An attempt to integrate two models of the brain: symbol manipulator and neural network.
Gary Marcus explores our understanding of how the mind works inAn attempt to integrate two models of the brain: symbol manipulator and neural network.
Gary Marcus explores our understanding of how the mind works in terms of linguistics and symbol manipulation and compares it with a class of simple neural networks ("multi-level perceptrons"). Rather than continue the debate of "which model is right," Marcus tries to find ways to make them compatible. He highlights places where MLPs seem to be unable to reproduce human-like behavior. Then he discusses how adding some symbol-manipulating capacity could fix those issues, and ways to do that which don't seem to violate the spirit of these connectionist models.
I found this book fascinating, especially the way he thinks about embedding symbol-manipulation algorithms within the context of a neural network. Like Marcus, I suspect this is what the brain really does! Unfortunately, this book hasn't aged very well. It was published in 2003, and the state of the art in machine learning has advanced dramatically since then. Most notably, there are large sections of this book critiquing specific papers and models which feel quite dated. Those models are no longer the best examples to critique, as newer models are substantially more human-like in their performance (though, still lacking in particular ways). If anything, I see those sections as the start of a citation trail, to be followed by anyone who wants to see where the debate has gone since then. Despite this, I believe the theoretical discussions and the way of thinking presented in this book are still very relevant.
Whether you believe neural networks are the ultimate answer to ML, or whether you're troubled by the bizarre and inhuman mistakes they make, this book has some excellent food for thought....more
A collection of essays published in 1985 about the nature of mind, from a computational perspective.
In this book, Marvin Minsky lays out a theory abouA collection of essays published in 1985 about the nature of mind, from a computational perspective.
In this book, Marvin Minsky lays out a theory about how the mind works. Rather than a single, comprehensive narrative, this is a network of many loosely connected essays explaining specific phenomena. It covers a wide range of topics, putting human experience into perspective, pointing out important quirks and nuances of how thought works, and suggesting algorithms that might explain our behaviors and experiences. Written before the success of neural networks, it presents a very different (but totally compatible) view of AI than what is popular today.
Reading this book in 2023 was an interesting experience, and at times challenging. Minsky is rightfully considered one of the great thinkers in AI, but many of the ideas he states so proudly and confidently as the future of the field have fallen out of favor. I believe some have even been proven wrong. He also offers his perspective on many hot button issues he isn't an expert on, such as religion, art, emotions, and mental disability, some of which I found offensive. That said, I'm glad he didn't censor himself. This book has many very clever insights and beautiful ideas that might have been lost if he had only said what he could prove.
At this point, the field of AI is dominated by deep learning. You will find nothing about deep learning in this book. This may be disappointing to some, or make the book seem irrelevant, but I think it's very much worth reading. To make our deep learning tools more human-like, we may need to design them with structures and biases like what Minksy describes. Or, perhaps such structures can / do emerge spontaneously in neural networks, in which case these theories might help us identify and understand those emergent behaviors. None of what Minsky wrote is incompatible with our ideas on neural networks, and I'd be very interested to see an attempt to reconcile the two perspectives.
This book is a pretty easy read. It gets technical at times, especially when describing the algorithms of thought and defining lots of new technical terms, but Minsky gives explanations in plain English with lots of helpful diagrams. My only complaint is that it could have been organized better and streamlined. He jumps around between ideas quite a bit, sometimes repeating himself, and sometimes leaving gaps in his theory. Some of the essays are much less relevant than others, and are unnecessarily ideological.
I recommend this book to anyone who wants to understand how thinking works. It's relevant both to AI researchers and to people who want to be power users of their own minds. Although parts of this book haven't aged well, there are many powerful ideas here that are still very relevant....more
An exploration of how new evidence is adding layers of complexity to our understanding of evolution.
The main theme of this book is that there's a lot An exploration of how new evidence is adding layers of complexity to our understanding of evolution.
The main theme of this book is that there's a lot more to adaptation and heritability than just genes. It explores the powerful impact of epigenetics, social learning, and human culture on evolution, and the complex ways these factors interact with each other and the genome. This book is chock full of detailed examples of life defying many assumptions of "traditional" evolutionary theory. It makes a compelling case that life evolved to be more adaptable, to learn faster, and make informed guesses rather than relying on chance alone.
This book is meant for a general audience, but large sections require some familiarity with molecular biology. You can skim over these sections and still get value from this book, but if genetics and protein synthesis make your head swim, this is not the book for you. Otherwise, I found it highly accessible, clear, and thought provoking. I particularly enjoyed the introduction, which gives a history of the theory of evolution, how it was constructed, and how the prevalence of different perspectives has shifted over the years. I strongly agree with the ideas presented in this book, and am excited to see them described in such detail and with so much evidence.
If you want a systematic review of mind-bending examples on the frontiers of evolutionary science and a sketch of new ways of thinking that are emerging now, I highly recommend this book.
If you're intimidated by the biology jargon or the sheer size and scope of this book, you might prefer Dance to the Tune of Life, which covers similar territory in a shorter, more accessible form without expecting any prior knowledge of biology. The Triple Helix is another accessible read covering similar territory....more
An eccentric, free-associative journey through the mind of a genius, exploring the intersection of math and minds.
GEB is a study of Gödel's famous incAn eccentric, free-associative journey through the mind of a genius, exploring the intersection of math and minds.
GEB is a study of Gödel's famous incompleteness theorem, what it means, and how it relates to the nature of human minds. In order to explain this proof, Hofstadter gently introduces many difficult concepts from mathematics through the aid of logic puzzles that the reader is invited to solve, and metaphorical references to music and visual art, most notably the works of M.C. Escher and J.S. Bach. Later chapters fold in more ideas from philosophy, genetics, neuroscience, computer science, and artificial intelligence to build up to his notion of the mind as a very particular sort of machine. Each chapter of the book is paired with a whimsical and surreal dialog that explores the concrete ideas from the chapter metaphorically. The overall effect is to assemble an enormous web of complex ideas and help the reader gain an intuitive understanding by examining them all from multiple directions.
This book has some beautiful, powerful, and mind-bending ideas in it, but it is a challenge to read. It's very long and continuously shifts tone and topic in a way that can be hard to follow. It seems like Hofstadter was unable to disentangle this complex system of ideas, and so he shared them all, at once, in all their tangled self-referential glory. Perhaps his later and much shorter book I am a Strange Loop succeeds in teasing the threads apart? Although there's lots about this book that I liked, there's also lots I didn't. Perhaps I would have enjoyed it more if I read it as a teenager, when I was less familiar with these ideas and had more patience for playful puzzles. I hope his explanations are accessible to a novice, but I found them hard to follow as an expert. Hofstadter attempted to simplify the math and make it more accessible by translating it into his own invented languages. I often felt like I had to re-learn what I already knew, but in Hofstadter's quirky style, just to follow-along.
This book is one of the great works about philosophy of mind and artificial intelligence. It's just as brilliant and mathematically rigorous as it is strange and artistic. It's the kind of book that many have on their shelves, but few have fully read and understood. Although AI has come a long way since this book was published, the problems Hofstadter focuses on are precisely the ones the AI field hasn't solved, so his points are still very relevant today....more
A critique on functionalism as a model of how the mind works.
This is a short philosophical text that argues we cannot assume there is anything like a A critique on functionalism as a model of how the mind works.
This is a short philosophical text that argues we cannot assume there is anything like a "common mental representation" for ideas that is consistent between people, then explores the consequences that follow from that. Putnam discusses how vocabulary can't possibly be innate (in contrast to Chomsky's thinking), and how any attempt to provide a mathematical account of the meaning of language or of how that meaning is constructed will lead to unsatisfying results.
I found this book useful because it made two important concepts vividly clear and intuitive for me: 1) Humans do not perceive or talk about reality, only their mental approximation of reality. 2) Interpreting language is an arbitrarily complex problem requiring the fullness of human reasoning.
I quite like Putnam's style which is precise without being too dense or obtuse, and lightly humorous especially when he gets a little sassy critiquing other philosopher's work.
If you're interested in philosophy of mind and language, this book has some useful ideas, but it's probably best understood in the larger context of Putnam's work and the field generally....more
A low-level explanation for how life (in all its sentient and purposeful complexity) emerged spontaneously from matter.
I love this book, but can only A low-level explanation for how life (in all its sentient and purposeful complexity) emerged spontaneously from matter.
I love this book, but can only recommend it with a pretty big caveat. The ideas and reasoning are brilliant, but the writing is not.
Deacon walks the reader through his reasoning step by step, through several challenging and profound perspective shifts about life and physics, providing in-depth discussions of all the necessary concepts along the way. Unfortunately, it's a very difficult read. It's long, dense, and highly technical. Deacon invents several new words and a layered system of new concepts the reader must master just to follow along. The narrative structure and pacing are inconsistent and weak in spots, making the book that much harder to follow and comprehend.
Personally, I found the effort worthwhile. Other works tell a similar story, but usually from a 10,000 ft perspective and with a lot of hand waving. Deacon's version is special in that it is solidly grounded in science and reason and doesn't skip any steps. The argument is laid out in full, with specific claims that can be validated or challenged. He spends a great deal of time exploring the concepts of "purpose" and "meaning" which are generally taboo in science, but critically important for understanding life. Incredibly, Deacon ties these abstract concepts back to thermodynamics in order to explain how they emerged, why we need them to explain life, and how they play a central role in shaping the world (despite the apparent contradiction with the Universe's bottom-up, mechanical nature).
If you want to go deep on the emergence of life and purpose, this book is the book for you. I strongly recommend taking notes to make sure you understand and retain the information. If you want an overview that covers similar territory (and more) that's much easier to swallow, check out From Bacteria to Bach and Back by Daniel Dennett....more