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This is related to the "Verified Software" item in NewScientist's list of ideas that will change science. At the link below you'll find the text of the patents being auctioned: http://icapoceantomo.com/item-for-sale/exclusive-license-related-improved-methodology-formally-developing-control-systems :) Patent #7,627,538 ("Swarm autonomic agents with self-destruct capability") makes for quite an interesting read: "This invention relates generally to artificial intelligence and, more particularly, to architecture for collective interactions between autonomous entities." "In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy." "In yet another aspect, an autonomous nanotechnology swarm may comprise a plurality of workers composed of self-similar autonomic components that are arranged to perform individual tasks in furtherance of a desired objective." "In still yet another aspect, a process to construct an environment to satisfy increasingly demanding external requirements may include instantiating an embryonic evolvable neural interface and evolving the embryonic evolvable neural interface towards complex complete connectivity." "In some embodiments, NBF 500 also includes genetic algorithms (GA) 504 at each interface between autonomic components. The GAs 504 may modify the intra-ENI 202 to satisfy requirements of the SALs 502 during learning, task execution or impairment of other subsystems."

Interesting databases to play with data mining algorithms

Interesting compilation of breakthroughs for 2009. Wanna think about a quantum algorithm ?

a nice review on the wonders of Hierarchical Bayesian models. It cites a paper on probabilistic programming languages that might be relevant given our recent discussions. At Hippo's farewell lunch there was a discussion on how kids are able to learn something as complex as language from a limited amount of observations, while Machine Learning algorithms no matter how many millions of instances you throw at them, don't learn beyond some point. If that subject interested you, you might like this paper.

Had an opportunity to listen to JBT and TLG during one summer school.. if they're half as good in writing as they are in speaking, should be a decent read...

Well, well, Google says it. And all is a result of an algorithm..... Among the interesting fincings the algorithm says that technical competence of the boss is not needed..... against their own beliefs!!

nobody in the ACT plays golf yet as far as I know ...

Which is a pity as you have a golf field not that far away :-D Maybe a good idea for a nice team building event??

According to the company's wikipedia page, the computer costs $ 10 million. Can we then declare Quantum Computing has officially arrived?! quotes from elsewhere in the site: "first commercial quantum computing system on the market"; "our current superconducting 128-qubit processor chip is housed inside a cryogenics system within a 10 square meter shielded room" Link to the company's scientific publications. Interestingly, this company seems to have been running a BOINC project, AQUA@home, to "predict the performance of superconducting adiabatic quantum computers on a variety of hard problems arising in fields ranging from materials science to machine learning. AQUA@home uses Internet-connected computers to help design and analyze quantum computing algorithms, using Quantum Monte Carlo techniques". List of papers coming out of it.

Sounds good!

well of course money is trust, and even more loans and credit and even more stock and bond markets. It all represents trust and expectations. However since the first banks 500 years ago and the first loans etc. etc., and as well the fact that bonds and currencies bring down whole countries (Greece lately), and are mainly controlled by large financial centres and (central) banks, banks have always been on the winning side no matter what and that isn't going to change easily. So if you are talking about these new currencies it would be a new era, not just a new currency. So should Greece convert its debt to bitcoins ;P ?

well, from 1936 to 1993 the central bank of france was owned by the state and was supposed to serve the general interest...

well a bit misleading title...

A paper about evolutionary algorithms. Could be something for Dario or Christos. Also has some space things in it.

A really cool and powerful tool for creating flow diagrams. I particularly like the layout organising algorithms, really nifty. Anyway, it's installed on the smart board computer now and maybe you'd all like it on your computers too.

The best post of Slashdot: "Genetic Algorithms are like the AI equivalent of text editors... everybody has spent a weekend writing one at some point."

The latest from Schmidt / Lipson / Eureqa. A significant improvement over their previous work is that now "The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree."

Big data analytic systems are reputed to be capable of finding a needle in a universe of haystacks without having to know what a needle looks like. The very best ways to sort large databases of unstructured text is to use a technique called Latent Dirichlet allocation (LDA). Unfortunately, LDA is also inaccurate enough at some tasks that the results of any topic model created with it are essentially meaningless, according to Luis Amaral, a physicist whose specialty is the mathematical analysis of complex systems and networks in the real world and one of the senior researchers on the multidisciplinary team from Northwestern University that wrote the paper. Even for an easy case, big data analysis is proving to be far more complicated than many of the companies selling analysis software want people to believe.

Most of those companies are using outdated algorithms like this LDA and just apply them like retards on those huge datasets. Of course they're going to come out with bad solutions. No amount of data can make up for bad algorithms.

love this one ... it seems to take physicist to explain to the AI crowd what they are actually doing ... Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression. Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG). RG is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. We construct an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs). We illustrate these ideas using the nearest-neighbor Ising Model in one and two-dimensions. Our results suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.

"Here we report the development of the computer algorithm Melvin which is able to find new experimental implementations for the creation and manipulation of complex quantum states." Published in Physical Review Letters. Researchers target future use more artificial intelligence algorithms, such as reinforcement learning techniques.

Our work sheds light on many interesting directions of future exploration in the development of quantum-inspired algorithms for unsupervised machine learning, which are promisingly possible to realize on quantum devices.

Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. For paper see: https://arxiv.org/pdf/1712.06567.pdf

Interesting pointers in this one! I would like to explore neuroevolution as well, although it seems extremely resource-demanding?

Not necessarily, I think it can be made to be much faster hybridizing it with backprop and Taylor maps. Its one ideas in the closet we still have not explored (Differential Intelligence: accelerating neuroevolution).

advantages: p-bits run on room temperature and are easier to connect than q-bit.

This has been around for over a year. The current trend in deep learning is "deeper is better". But a consequence of this is that for a given network depth, we can only feasibly evaluate a tiny fraction of the "search space" of NN architectures. The current approach to choosing a network architecture is to iteratively add more layers/units and keeping the architecture which gives an increase in the accuracy on some held-out data set i.e. we have the following information: {NN, accuracy}. Clearly, this process can be automated by using the accuracy as a 'signal' to a learning algorithm. The novelty in this work is they use reinforcement learning with a recurrent neural network controller which is trained by a policy gradient - a gradient-based method. Previously, evolutionary algorithms would typically be used. In summary, yes, the results are impressive - BUT this was only possible because they had access to Google's resources. An evolutionary approach would probably end up with the same architecture - it would just take longer. This is part of a broader research area in deep learning called 'meta-learning' which seeks to automate all aspects of neural network training.

Btw that techxplore article was cringing to read - if interested read this article instead: https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html