The near book-length title essay in this collection looks at the deep processes of evolution and shows that it has (in a certain sense) progressively become faster and more efficient. Evolution is a spontaneous adaptive process that finds solutions to environmental challenges through slow processes of brute-force trial and error. Brains also discover adaptive responses to challenges but do so much more intelligently. Creatures equipped with brains surpass mere trial and error because they learn as they go along and acquire the ability to generalize from past experiences. But are these two forms of adaptive problem-solving completely different? Newly emerging insights suggest otherwise. Richard A. Watson and other researchers in the field of computational evolutionary biology have found a deep similarity between long-term evolutionary processes and the neural-network learning processes that occur in brains and artificial neural network machine-learning systems. Their work suggests that evolution, too, has moved past mere trial and error and can now make intelligent guesses. Evolution proceeds not only through mutations in genes but also through shifts in the systems that regulate gene expression. Watson’s analysis shows that these “gene regulatory networks” transform over evolutionary time in much the same way that the neural networks in our brains transform as we learn through life experience. In the language of artificial neural network machine-learning systems, evolving gene regulatory networks undergo a form of unsupervised learning. When faced with novel environmental challenges—such as anthropogenic climate change—populations of organisms can effectively generalize from past adaptations to “propose” novel variations that have an elevated likelihood of surviving. Evolvability has itself evolved. Both neural networks and gene regulatory networks operate by constructing models of the world. The second essay, based on the fertile, surprising, and sometimes head-spinning ideas of physicist Yoshitsugo Oono, attempts to answer a deep question: How is it that the world can be modeled at all? The third essay builds on elements of the first two to propose a deflationary account of such metaphysical concepts as consciousness, sentience and self-awareness. The final essay shows how our intuitive models of the world can easily go astray, as demonstrated by the counterintuitive results of double-blind studies. Editorial Reviews of Title Essay "Why are organisms so good at evolving - and can they become even better? In this excellent essay, Steven Bratman guides the reader through the innovative and interdisciplinary research that tries to answer these questions. Elegantly written, rigorous and accessible, 'What Evolution Learns' explains how models of gene regulatory networks shed light on emerging ideas in evolutionary biology. Bratman successfully brings the evolution of evolvability to a wider audience. If you didn't know this theory existed, or found the whole thing confusing, this is the place to start." — Tobias Uller, PhD., Professor of Evolutionary Biology, co-author of Evolution Evolving: The Developmental Basis of Adaptation and Biodiversity and Evolutionary Causation: Biological and Philosophical Reflections "Many have noted a similarity between learning and evolution. However, when learners like us solve problems, we can do so intelligently, using knowledge gained from past experience to make educated guesses. In contrast, evolution through natural selection is just random variation and selection, plodding away blindly and inexorably. Or, at least, that's the traditional view. A new body of work suggests that evolutionary systems are not necessarily as blind as we thought. Evolving gene-regulation networks change their connections over evolutionary time in much the same way that learning mechanisms alter the synaptic connections of the brain's neural networks during a single lifetime. This means that biological evolution can get smarter over time, learning to evolve better with experience.These are new ideas that have not previously been published in non-technical form. In this essay, Steven Bratman does a fantastic job of gathering them, explaining how they fit together, and discussing what this means. His writing shows both deep technical understanding and an ability to convey ideas in an accessible way, with examples and analogies that utilize easy, familiar concepts. This takes great skill—to be accurate while still presenting ideas in a way that makes them seem easy. The result is an excellent and generous service to science that a broad audience can appreciate."— Richard A. Watson, PhD ., Institute for Life Sciences/ Department of Electronics and Computer Science (Agents, Interaction and Complexity group), University of Southampton, UK. I had planned write a normal-length essay on Kirschner and Gerhart's theory of Facilitated Variation, but along the way I discovered new ideas that