Herself’s Artificial Intelligence

With the advent of smartphone technology, it has become possible to conceive of entirely new classes of applications. Social swarming, in which users armed with smartphones are directed by a central director to report on events in the physical world, has several real-world applications: search and rescue, coordinated fire-fighting, and the DARPA balloon hunt challenge. In this paper, we focus on the following problem: how does the director optimize the selection of reporters to deliver credible corroborating information about an event. We first propose a model, based on common intuitions of believability, about the credibility of information. We then cast the problem posed above as a discrete optimization problem, and introduce optimal centralized solutions and an approximate solution amenable to decentralized implementation whose performance is about 20% off on average from the optimal (on real-world datasets derived from Google News) while being 3 orders of magnitude more computationally efficient. More interesting, a time-averaged version of the problem is amenable to a novel stochastic utility optimization formulation, and can be solved optimally, while in some cases yielding decentralized solutions. To our knowledge, we are the first to propose and explore the problem of extracting credible information from a network of smartphones. source

paper ( pdf )

More information:
Thruthy, Indiana U webpage to test the truthiness of something using datamining
Fast Company story on Truthy

So many claims, it’s difficult to sort the intelligent from the educated. But at some point one or more of these claims will be true.

FOR generations, the Avidians have been cloning themselves quietly in a box. They’re not perfect, but most of their mutations go unnoticed. Then something remarkable happens. One steps forward, and that changes everything. Tens of thousands of generations down the line, some of its descendents will evolve memory.

Avidians are not microbes, or sci-fi alien life forms. They are the digital offspring of Charles Ofria and colleagues at Michigan State University (MSU) in East Lansing. They “live” in a computer world called Avida, and replicate using strings of coded computer instructions instead of DNA. But in many ways they are similar to real life: they compete with each other for resources, replicate, mutate, and evolve. They – or things like them – might eventually evolve to become artificially intelligent life forms. read more Artificial life forms evolve basic intelligence

More information:
HyperNEAT
Avida-ED
Avida-ED user manual ( pdf )
Observing mutations on the genomes of evolving Avidians (pdf)
Harnessing Digital Evolution ( $$$ )
Digital Evolution with Avidian

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed. [download the paper pdf]

More information
Other swarm papers by Xin-She Yang