Spontaneous self-replication in cellular automata has long been considered rare, with most known examples requiring careful design or artificial initialization. In this paper, we present formal, causal evidence that such replication can emerge unassisted -- and that it can do so in a distributed, multi-component form. Building on prior work identifying complex dynamics in the Outlier rule, we introduce a data-driven framework that reconstructs the full causal ancestry of patterns in a deterministic cellular automaton. This allows us to rigorously identify self-replicating structures via explicit causal lineages. Our results show definitively that self-replicators in the Outlier CA are not only spontaneous and robust, but are also often composed of multiple disjoint clusters working in coordination, raising questions about some conventional notions of individuality and replication in artificial life systems.

The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates where life emerges is a marked shift in dynamics when self-replication appears. While there are some hypotheses regarding how self-replicators arose in nature, we know very little about the general dynamics, computational principles, and necessary conditions for self-replicators to emerge. This is especially true on "computational substrates" where interactions involve logical, mathematical, or programming rules. In this paper we take a step towards understanding how self-replicators arise by studying several computational substrates based on various simple programming languages and machine instruction sets. We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise. We demonstrate how this occurs due to random interactions and self-modification, and can happen with and without background random mutations. We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators. Finally, we show a counterexample of a minimalistic programming language where self-replicators are possible, but so far have not been observed to arise.

House Armed Services Committee holds hearing on the Department of Defense using AI. The Pentagon is planning to field thousands of artificial intelligence-enabled autonomous vehicles by 2026 in a bid to keep pace with the Chinese military. The plan, which has been called Replicator, will seek to "galvanize progress in the too-slow shift of U.S. military innovation to leverage platforms that are small, smart, cheap and many," Deputy Secretary of Defense Kathleen Hicks said, according to a report by The Associated Press. While the report notes few details, including how the program will be funded and how fast the Pentagon will truly be able to accelerate the development of the new vehicles, the program represents an ongoing shift in how the U.S. views the future of warfare, especially as China continues to forge ahead with AI programs of its own. Phil Siegel, the founder of the Center for Advanced Preparedness and Threat Response Simulation (CAPTRS), believes the rapid push toward AI weapons is similar to that of a nuclear arms race.

In this paper we examine the relationship between the flow of the replicator dynamic, the continuum limit of Multiplicative Weights Update, and a game's response graph. We settle an open problem establishing that under the replicator, sink chain components -- a topological notion of long-run outcome of a dynamical system -- always exist and are approximated by the sink connected components of the game's response graph. More specifically, each sink chain component contains a sink connected component of the response graph, as well as all mixed strategy profiles whose support consists of pure profiles in the same connected component, a set we call the content of the connected component. As a corollary, all profiles are chain recurrent in games with strongly connected response graphs. In any two-player game sharing a response graph with a zero-sum game, the sink chain component is unique. In two-player zero-sum and potential games the sink chain components and sink connected components are in a one-to-one correspondence, and we conjecture that this holds in all games.

Nvidia CEO Jensen Huang recently hosted yet another spring GTC event that drew more than 200,000 participants. And while he didn't succeed in acquiring Arm for $80 billion, he did have a lot of things to show off to those gathering at the big event. He gave an update on Nvidia's plans for Earth-2, a digital twin of our planet that -- with enough supercomputing simulation capability within the Omniverse –could enable scientists to predict climate change for our planet. The Earth 2 simulation will require the best technology -- like Nvidia's newly announced graphics processing unit (GPU) Hopper and its upcoming central processing unit (CPU) Grade. Huang fielded questions about the ongoing semiconductor shortage, the possibility of investing in manufacturing, competition with rivals, and Nvidia's plans in the wake of the collapse of the Arm deal. He conveyed a sense of calm that Nvidia's business is still strong (Nvidia reported revenues of $7.64 billion for its fourth fiscal quarter ended January 30, up 53% from a year earlier). Gaming, datacenter, and professional visualization market platforms each achieved record revenue for the quarter and year. He also talked about Nvidia's continuing commitment to the self-driving vehicle market, which has been slower to take off than expected. Huang held a Q&A with the press during GTC and I asked him the question about Earth-2 and the Omniverse (I also moderated a panel on the industrial metaverse as well at GTC). I was part of a large group of reporters asking questions. Question: With the war in Ukraine and continuing worries about chip supplies and inflation in many countries, how do you feel about the timeline for all the things you've announced? For example, in 2026 you want to do DRIVE Hyperion. With all the things going into that, is there even a slight amount of worry? Jensen Huang: There's plenty to worry about. I have to observe, though, that in the last couple of years, the facts are that Nvidia has moved faster in the last couple of years than potentially its last 10 years combined. It's quite possible that we work better, actually, when we allow our employees to choose when they're most productive and let them optimize, let mature people optimize their work environment, their work time frame, their work style around what best fits for them and their families. It's very possible that all of that is happening. It's also true, absolutely true, that it has forced us to put a lot more energy into the virtual work that we do. For example, the work around OmniVerse went into light speed in the last couple of years because we needed it. Instead of being able to come into our labs to work on our robots, or go to the streets and test our cars, we had to test in virtual worlds, in digital twins.

Nvidia (NVDA) is well-known for its autonomous vehicle efforts, and at the company's GTC 2021 conference, it's rolling out three technologies to support its future self-driving capabilities: Nvidia Drive Hyperion 8, Drive Chauffeur, and Drive Concierge. Taken together, the technologies help Nvidia push deeper into the autonomous car space. What's more, the technologies provide drivers and passengers with their own personal AI assistant while their car drives them down the street. Drive Hyperion 8 combines a series of sensors including 12 cameras, nine radars, 12 ultrasonic sensors, and one front-facing lidar. The whole setup is meant to be modular so automakers can take and leave what they want.

Evolution gave rise to human and animal intelligence here on Earth. We argue that the path to developing artificial human-like-intelligence will pass through mimicking the evolutionary process in a nature-like simulation. In Nature, there are two processes driving the development of the brain: evolution and learning. Evolution acts slowly, across generations, and amongst other things, it defines what agents learn by changing their internal reward function. Learning acts fast, across one's lifetime, and it quickly updates agents' policy to maximise pleasure and minimise pain. The reward function is slowly aligned with the fitness function by evolution, however, as agents evolve the environment and its fitness function also change, increasing the misalignment between reward and fitness. It is extremely computationally expensive to replicate these two processes in simulation. This work proposes Evolution via Evolutionary Reward (EvER) that allows learning to single-handedly drive the search for policies with increasingly evolutionary fitness by ensuring the alignment of the reward function with the fitness function. In this search, EvER makes use of the whole state-action trajectories that agents go through their lifetime. In contrast, current evolutionary algorithms discard this information and consequently limit their potential efficiency at tackling sequential decision problems. We test our algorithm in two simple bio-inspired environments and show its superiority at generating more capable agents at surviving and reproducing their genes when compared with a state-of-the-art evolutionary algorithm.

As a kid watching Star Trek, I was thrilled by this machine that could deliver food on demand. My childhood sweet tooth was delighted with the immediate gratification I witnessed on the Starship Enterprise. If only I had a replicator, I could have candy bars, chocolate cake, and hot cocoa whenever I wanted. Oh, to be a space traveler living in the world of advanced computers, bubble-light transportation, and holographic people. What seemed so far-fetched in my childhood doesn't seem that much of a stretch today.

On the Starship Enterprise, replicators were devices that were used "to dematerialize matter and then reconstitute it in another form," according to Startrek.com. For Captain Picard's hungry crew, in particular, that usually meant nostalgically reconstituting meals on demand to appease a sudden craving. Though we remain a long way away from being able to transmogrify matter into a chocolate sundae on command, a team of real-life researchers has created a 3-D printer that can create entire objects simultaneously instead of creating them one painstaking layer at a time like most printing techniques. The new approach ---- known as Computer Axial Lithography (CAL) ---- carves an object out of a synthetic resin that solidifies when it comes into contact with particular patterns and intensities of light. Using a device dubbed "the replicator," researchers from University of California, Berkeley and the Lawrence Livermore National Laboratory used the technique to create tiny airplanes and bridges, copies of the human jaw, a screwdriver handle and minuscule copies of Rodin's Thinker.

A team of researchers from the University of North Carolina at Chapel Hill have unveiled a 3D printer that uses light to create an entire object at once. It's called the Replicator, named after the machines in the Star Trek universe that can synthesize food, water, air and various objects seemingly out of nothing. Before you get too excited, the researchers didn't quite create an exact replica of that fictional machine, but it still offers a new and promising 3D printing technique. According to the team's paper published in Science, the Replicator works like a reverse CT scan. When a patient undergoes the procedure, an X-ray tube rotates around their body to take multiple photos that a computer can use to create 3D images.