The "Extreme Learning Machine." [View all]

[NNadir]

I'm most definitely snowed in today, and am leafing through some issues of one of my favorite journals, Industrial Engineering and Chemistry Research and I came across a cool paper about one of my favorite topics, ionic liquids, that discusses the "Extreme Learning Machine."

Ionic liquids are generally salts of cationic and anionic organic molecules which are liquids at or near room temperature. Because they are generally not volatile, they can eliminate some of the problems associated with other process solvents, specifically air pollution. Although the term "green solvent" is probably over utilized with respect to ionic liquids, their very interesting potential uses have lead to a vast explosion of papers in the scientific literature concerning them. There are, to be sure, almost an infinite number of possible ionic liquids (and related liquids called "deep eutectics".)

My own interest in these compounds is connected with my interest in the separation of fission products and actinides in the reprocessing of used nuclear fuels, as well as an interest in their potential for the treatment of certain biological products, including lignins, a constituent of biomass that is quite different from cellulose, representing a sustainable route of access to aromatic molecules, as well as their possible use as radiation resistant (in some cases) high temperature heat transfer fluids.

Anyway, about the "deep learning machine:" The paper in question, written by scientists at Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China, that I've been reading is this one: Ind. Eng. Chem. Res., 2015, 54 (51), pp 12987–12992

The S_σ‑profile is a quantum mechanical factor describing the charge distribution of the surfaces of molecules and organic ions.

Here's the fascinating text:

As compared to the ANN algorithm, the extreme learning machine (ELM) is a relatively new algorithm which was first developed by Huang et al.^23,24 It can effectively tend to reach a global optimum and only needs to learn a few parameters between the hidden layer and the output layer as compared with the traditional ANN and thus can beused to predict properties because of its excellent efficiency and generalization performance.²⁵ However, to the best of our knowledge, the ELM has not yet been used for predicting the properties of ILs until now. Thus, we employed this relatively new ELM algorithm to predict the heat capacity of ILs in this work.

Reference 24 is" Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70, 489−501.

Hmm...the program needs to "learn" only a few parameters...

I always keep in the back of my mind Penrose's criticism of the concept of "artificial intelligence" (maybe because being a human being, I still want my species to be relevant) but I'm intrigued. Neurocomputing is a journal I've never accessed before, but when I can get out of here after this blizzard, I'm going to take a look at that paper which is apparently available at Princeton University's library.

I guess I'm a dork, but I find it all kind of cool...