Genetic programming (another name for evolutionary systems) creates generations of computer programs "using the principles of Darwinian natural selection and biologically inspired operations. The operations include reproduction, crossover (sexual recombination), mutation, and architecture-altering operations patterned after gene duplication and gene deletion in nature."
– Genetic Programming, Inc.
Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI – algorithms modelled on swarms in nature that amplifies human intelligence.
Cities are some of the clearest and well-used examples of a complex system, and whilst we are certainly better than we have been at managing their growth, they are, to a large extent, unmanageable. A recent study by a team of Spanish researchers at the Universidade da Coruna highlights how AI can be used to better understand how cities grow and evolve, at least in a vertical sense. The researchers use an evolutionary algorithm that's trained on historical and economic data of an urban area to predict how the skyline could look in a few years time. The method was successfully deployed in the Minato Ward, in Tokyo. The team believes that cities grow in a similar way to self-organized biological systems.
This is one of only a handful couple of writings that consolidates three fundamental postulations in the investigation of rationale programming: the logic that gives logic programs their extraordinary character: the act of programming viably utilizing the logic; and the productive usage of logic software on PCs.
What is the relationship between machine learning and optimization? On the other hand, what happens when machine learning is used to solve optimization problems? Consider this: a UPS driver with 25 packages has 15 trillion possible routes to choose from. And if each driver drives just one more mile each day than necessary, the company would be losing $30 million a year. While UPS would have all the data for their trucks and routes, there is no way they can run 15 trillion computations per each driver with 25 packages.
If nature knows what it's doing, it sure does a good job hiding it. Like, why would evolution produce an elephant with a shovel for a face? For very good reasons, as it turns out. Natural selection is an astoundingly creative phenomenon, molding species to fit their environments, even if that means turning their faces into shovels. It's also created a galaxy of ways for animals to move about, from walking to crawling to flying.
Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI - algorithms modelled on swarms in nature that amplifies human intelligence.
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effective exploration strategy to deal with sparse or misleading reward scenarios: if they do not experience any state with a positive reward during the initial random exploration, it is very unlikely to solve the problem. Here, we propose a novel model-based policy search algorithm, Multi-DEX, that leverages a learned dynamical model to efficiently explore the task space and solve tasks with sparse rewards in a few episodes. To achieve this, we frame the policy search problem as a multi-objective, model-based policy optimization problem with three objectives: (1) generate maximally novel state trajectories, (2) maximize the expected return and (3) keep the system in state-space regions for which the model is as accurate as possible. We then optimize these objectives using a Pareto-based multi-objective optimization algorithm. The experiments show that Multi-DEX is able to solve sparse reward scenarios (with a simulated robotic arm) in much lower interaction time than VIME, TRPO, GEP-PG, CMA-ES and Black-DROPS.
Formalizing self reproduction in dynamical hierarchies is one of the important problems in Artificial Life (AL) studies. We study, in this paper, an inductively defined algebraic framework for self reproduction on macroscopic organizational levels under dynamical system setting for simulated AL models and explore some existential results. Starting with defining self reproduction for atomic entities we define self reproduction with possible mutations on higher organizational levels in terms of hierarchical sets and the corresponding inductively defined `meta' - reactions. We introduce constraints to distinguish a collection of entities from genuine cases of emergent organizational structures.
The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML "Model Agnostic Meta-Learning" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner.
Here on Earth, human settlements have thrived for so long due to two very important truths: we evolved on this planet and we survived by supporting each other. Our settlement history has not been perfect1. But as humans, our population vitality is a result of many individuals working to support the civilization in one large positive feedback loop of survival. The space-based human settlements of the future, however, will require advanced technology to continue this trend. Settlements on Mars, or even Earth's moon, Luna, will require human-machine teaming between the occupants of the settlement and artificial intelligence, or AI, to augment their skills and knowledge.