Flock Safety is pitching its police-style drone program to private businesses. It could bring aerial surveillance to shopping centers, warehouses, and hospitals. Flock Safety, whose drones were once reserved for police departments, is now offering them for private-sector security, the company announced today, with potential customers including including businesses intent on curbing shoplifting. Companies in the US can now place Flock's drone docking stations on their premises. If the company has a waiver from the Federal Aviation Administration to fly beyond visual line of sight (these are becoming easier to get), its security team can fly the drones within a certain radius, often a few miles. "Instead of a 911 call [that triggers the drone], it's an alarm call," says Keith Kauffman, a former police chief who now directs Flock's drone program.

When David Fajgenbaum was a twenty-five-year-old medical student, at the University of Pennsylvania, he started to feel so tired that he could barely stand. Fajgenbaum, a former college quarterback, could still bench-press three hundred and seventy-five pounds; he was known for doing pullups on a tree near his workplace. But now he was desperately ill. The lymph nodes in his groin and neck swelled. Small red dots--blood moles--emerged on his chest, and he woke up soaked in sweat. One day, at the hospital where he was doing his rotation, he stumbled down the hall into the emergency room, and doctors told him that his liver, bone marrow, and kidneys were failing. Fluid had leaked out of his blood vessels, into his abdomen and around his heart; bleeding in his retina temporarily blinded him in his left eye.

Large language models have emergent capabilities that come unexpectedly at scale, but we need a theoretical framework to explain why and how they emerge. We prove that language models are actually non-ergodic systems while providing a mathematical framework based on Stuart Kauffman's theory of the adjacent possible (TAP) to explain capability emergence. Our resource-constrained TAP equation demonstrates how architectural, training, and contextual constraints interact to shape model capabilities through phase transitions in semantic space. We prove through experiments with three different language models that capacities emerge through discrete transitions guided by constraint interactions and path-dependent exploration. This framework provides a theoretical basis for understanding emergence in language models and guides the development of architectures that can guide capability emergence.

In this paper, I describe a digital social communication protocol (Gridt) based on Kauffman's NK boolean networks. The main assertion is that a communication network with this topology supports infinitely scalable self-organization of collective action without requiring hierarchy or central control. The paper presents the functionality of this protocol and substantiates the following propositions about its function and implications: (1) Communication via NK boolean networks facilitates coordination on collective action games for any variable number of users, and justifies the assumption that the game's payoff structure is common knowledge; (2) Use of this protocol increases its users' transfer empowerment, a form of intrinsic motivation that motivates coordinated action independent of the task or outcome; (3) Communication via this network can be considered 'cheap talk' and benefits the strategy of players with aligned interests, but not of players with conflicting interests; (4) Absence of significant barriers for its realization warrants a timely and continuing discussion on the ethics and implications of this technology; (5) Full realization of the technology's potential calls for a free-to-use service with maximal transparency of design and associated economic incentives.

It is natural for organizations to flatter their market rivals by copying their successful business strategies. In fact, corporate history is littered with examples of winning ideas being conveniently replicated. Take the now-overcrowded smart home assistant market, for example. Not many years after Amazon debuted the Echo in 2015, rival products such as Google Home and Apple HomePod arrived to compete for market supremacy. In a similar vein, organizations have seemingly understood the power of AI in the last few years.

We approach the question "What is Consciousness?" in a new way, not as Descartes' "systematic doubt", but as how organisms find their way in their world. Finding one's way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. "Possible uses of X to accomplish Y" are "Affordances". The number of uses of X is indefinite (or unknown), the different uses are unordered and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: (1) Artificial General Intelligence based on Universal Turing Machines (UTMs) is not possible, since UTMs cannot "find" novel affordances. (2) Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behavior can be isomorphic to "possible uses". (3) Brain mind must be partly quantum - supported by increasing evidence at 6.0 sigma to 7.3 Sigma. (4) Based on Heisenberg's interpretation of the quantum state as "Potentia" converted to "Actuals" by Measurement, a natural hypothesis is that mind actualizes Potentia. This is supported at 5.2 Sigma. Then Mind's actualizations of entangled brain-mind-world states are experienced as qualia and allow "seeing" or "perceiving" of uses of X to accomplish Y. We can and do jury-rig. Computers cannot. (5) Beyond familiar quantum computers, we discuss the potentialities of Trans-Turing-Systems.

According to the National Oceanic and Atmospheric Administration (NOAA), more than 80% of the ocean "remains unmapped, unobserved, and unexplored" – despite constituting more than 70% of the planet's surface. Now, a pair of Navy veterans are looking to change that with a line of autonomous robot vehicles that will plunge the ocean's depths in search of big data for the company's clients. "The company really started when Joe [Wolfel] and I first got together, which was back in 2004," said Judson Kauffman, who shares the CEO role with Wolfel, in an interview with Datanami. "We met in [Navy] SEAL training together, and ended up being assigned the same unit, and then went into combat together and became very close friends. There, they developed the idea for Terradepth, which "stemmed from some knowledge that we gained in the Navy" – really, Kauffman said, "just of how ignorant humanity is of what's underwater, what's in the sea." "It was shocking to learn how little we know, how little the U.S. Navy knew," he continued – and the more they dug into the issue after their time in the Navy, the more surprised they were.

First, let me tell you how smart I am. My fifth-grade teacher said I was gifted in mathematics and, looking back, I have to admit that she was right. I've properly grasped the character of metaphysics as trope nominalism, and I can tell you that time exists, but that it can't be integrated into a fundamental equation. Most of the things that other people say are only partially true. A paper published in Nature Genetics in 2017 reported that, after analysing tens of thousands of genomes, scientists had tied 52 genes to human intelligence, though no single variant contributed more than a tiny fraction of a single percentage point to intelligence.

We present chemlambda (or the chemical concrete machine), an artificial chemistry with the following properties: (a) is Turing complete, (b) has a model of decentralized, distributed computing associated to it, (c) works at the level of individual (artificial) molecules, subject of reversible, but otherwise deterministic interactions with a small number of enzymes, (d) encodes information in the geometrical structure of the molecules and not in their numbers, (e) all interactions are purely local in space and time. This is part of a larger project to create computing, artificial chemistry and artificial life in a distributed context, using topological and graphical languages.

This essay explores the limits of Turing machines concerning the modeling of minds and suggests alternatives to go beyond those limits. Keywords: computability, mind, Turing, interactions One of the main open questions in science and philosophy since ancient times has been how bodies relate to minds. René Descartes proposed his famous dualism to ontologically separate the physical (res extensa) from the mental (res cogitans). However, with Cartesian dualism one cannot relate bodies and minds. This has led many people to propose ways of reducing minds to the physical realm (e.g.