Group items tagged

Stephanie Forrest awarded $3.2 million by DARPA to further develop her work on automated software repair through evolutionary computing (papers)

If you get suspicious text you can have an automated check. However, I suggest to remove any references chapter since they boost the amount of hits. My last Ariadna report has a plagiarism rate of 8.7%. Anyone higher?

These guys from caltech work on a minituarized, automated microscope. Could be interesting for any type of human space exploration.

Statistical methods of text analysis have become increasingly sophisticated over the years. A good example is automated topic analysis using latent models, two variants of which are Probabilistic latent semantic analysis and Latent Dirichlet Allocation.

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."

A simple, but neat helper for coding: ML gives idiomatic usage patterns to semi-automate the daily development work.

Machine learning to write better machine learning code...count me in haha

New results in NLP might allow to automate scientific discoveries by data mining of papers. Work considers 3.3M abstracts from material science, physics and chemistry and claims to discover new materials before they are published later on.

ACT did that from diigo post digging in the retreat of 2014! Still without NLP.

That's cool! Didn't know.

While robots began assisting and replacing assembly line workers in automobile and airplane factories years ago, humans still reign supreme in satellite manufacturing. But that's slowly starting to change.

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

What makes us different is the particulars of our history, which gives us our notions of purpose and goals. That's a long way of saying when we have the box on the desk that thinks as well as any brain does, the thing it doesn't have, intrinsically, is the goals and purposes that we have. Those are defined by our particulars-our particular biology, our particular psychology, our particular cultural history. The thing we have to think about as we think about the future of these things is the goals. That's what humans contribute, that's what our civilization contributes-execution of those goals; that's what we can increasingly automate.

Echoing this, in 2009 Google researchers Alon Halevy, Peter Norvig, and Fernando Pereira penned an article under the title The Unreasonable Effectiveness of Data. In it, they describe the surprising insight that given enough data, often the choice of mathematical model stops being as important - that particularly for their task of automated language translation, "simple models and a lot of data trump more elaborate models based on less data." If we make the leap and assume that this insight can be at least partially extended to fields beyond natural language processing, what we can expect is a situation in which domain knowledge is increasingly trumped by "mere" data-mining skills. I would argue that this prediction has already begun to pan-out: in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research.

Nice link and clear overview! I like the point raised about 1000 man in space, even when including automation (as far as they could envision at the time). Now that is a future of the space industry and permanent habitation of near-earth orbits! In fact, I can envision just two reasons maybe, power and the hotel industry

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."

A handy feature that allows linking of over 300 online apps. For example, generating Trello cards from calendar events automatically (and vice versa).

I mainly see neural networks in this... maybe some evolutionary stuff :))

A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space

And Paul, it includes neural networks!

I kept telling you guys but you just laughed and laughed :))

Let an AI build up your web page for just 96$ per year!

Amazing! Not even coding jobs are safe from AI anymore! AI programming will be the last bastion in the struggle against automation. Get ready :)