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One of the primary objectives of this book is to highlight the profound difference between two types of AI that pursue distinct goals: emulative AI, which seeks to build machines whose output is similar to, or even superior to, that of the human brain, and investigative AI, whose purpose is to make invisible information within data visible by uncovering the laws through which individual behaviors self-organize into collective behaviors. The former is better known, as it serves as a useful tool for automating human labor and generating market profits; the latter is less widely recognized but is more scientifically oriented towards saving lives (in the medical field), explaining otherwise inexplicable phenomena (in the geophysical field), and enhancing our understanding of the material and abstract world. Both are valuable yet distinct: the emulative approach generates immediate profits and creates illusions of human-like power, while the investigative approach enhances fundamental scientific research and will yield its greatest benefits over time. The investigative approach presented in this volume seeks to rebuild the bridge between humanity and nature.