Artificial intelligence is all the rage, but using swarm intelligence might be the best way to solve the world's biggest problems. Dr. Louis Rosenberg is the Founder & CEO of Unanimous AI, an artificial intelligence company that amplifies human intelligence by building "hive minds" modeled after biological swarms. Learn how swarm intelligence can combine the brainpower of humans and computers to solve humanity's biggest problems. Stream or download the podcast using the player below or find the episode everywhere podcasts are found, including iTunes, Stitcher, and Gretta.

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.

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"Reweighting rewards in embodied evolution to achieve a balanced distribution of labour," in Proceedings of the 14th European Conference on Artificial Life ECAL 2017 (Cambridge, MA: MIT Press), 44–51.

We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches.

In this paper, we present a method to determine the global horizontal irradiance (GHI) from the power measurements of one or more PV systems, located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. The precise assessment of solar irradiance is pivotal for the forecast of the electric power generated by photovoltaic (PV) plants. However, on-ground measurements are expensive and are generally not performed for small and medium-sized PV plants. Satellite-based services represent a valid alternative to on site measurements, but their space-time resolution is limited. Results from two case studies located in Switzerland are presented. The performance of the proposed method at assessing GHI is compared with that of free and commercial satellite services. Our results show that the presented method is generally better than satellite-based services, especially at high temporal resolutions.

Evolutionary algorithms are a heuristic-based approach to solving problems that cannot be easily solved in polynomial time, such as classically NP-Hard problems, and anything else that would take far too long to exhaustively process. When used on their own, they are typically applied to combinatorial problems; however, genetic algorithms are often used in tandem with other methods, acting as a quick way to find a somewhat optimal starting place for another algorithm to work off of. The premise of an evolutionary algorithm (to be further known as an EA) is quite simple given that you are familiar with the process of natural selection. An EA contains four overall steps: initialization, selection, genetic operators, and termination. These steps each correspond, roughly, to a particular facet of natural selection, and provide easy ways to modularize implementations of this algorithm category.

AI is no longer some abstract dream for the future. It is here, now and bringing change across industries. According to the Forrester AI Readiness Study, 40 percent of the 717 businesses surveyed said they were planning to use intelligent recommendation solutions and 43 percent were planning to use AI-enhanced advanced analytics. With breakthroughs coming thick and fast in machine learning, especially deep learning models, the AI advantage is becoming far more compelling and is spanning across a wider set of applications. However, there are still several roadblocks to wide-scale implementation of AI which are important to recognize as the appetite within the industry to integrate the technology continues to grow.

Evolution results from expected effects, such as selection driving alleles toward fixation, and stochastic effects, such as unusual environmental variation and genetic drift. To determine the potential to predict evolutionary change, Nosil et al. examined three naturally occurring morphs of stick insects (see the Perspective by Reznik and Travis). They wanted to determine which selective parameters could be used to foresee changes, despite varying environmental conditions. One morph fit a model of negative frequency-dependent selection, likely owing to predation, but changes in other morph frequencies remained unpredictable. Thus, for specific cases, we can forecast short-term changes within populations, but evolution is more difficult to predict when it involves a balance between multiple selective factors and uncertainty in environmental conditions.

My interest in genetic programming began in 2015 when I studied the iterated ultimatum game. More recently, I have been using genetic algorithms to optimize parameters in a risk management system at work. In this short article, I will discuss the high level idea and the necessary ingredients to build your own genetic algorithm. I will also give a simple implementation of a genetic algorithm used to optimize the heuristic function for a general game playing AI agent using alpha beta pruning and minimax with iterative deepening. Finally, I will discuss several drawbacks to genetic programming in AI. Genetic programming (GP) is a type of evolutionary algorithm that can compute solutions to general problems which humans do not know how to solve directly.