Category: Community Feeds

Wolfram|Alpha is going life in two months

Some time ago I was told about the project Wolfram|Alpha by the creator of Mathematica and the author of a new kind of science (NKS), Stephen Wolfram. This project aims at going beyond the typical process of search engines by proposing a system that computes the answers of user questions. That is, instead of […]

Some time ago I was told about the project Wolfram|Alpha by the creator of Mathematica and the author of a new kind of science (NKS), Stephen Wolfram. This project aims at going beyond the typical process of search engines by proposing a system that computes the answers of user questions. That is, instead of going to the data an retrieve information by the syntactic similarity with the user question, the new architecture will try to figure it out the answer, which may not be explicitly written in the web documents, by processing the data. For this purpose, Wolfram proposes to use Mathematica and the NKS to explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable. In addition, there must be the help of human experts to formalize each domain.

Therefore, a new approach, very different to that of natural language processing, that promises to make knowledge computable. Fortunately, I will need to wait only two monts to answer all the questions that arose after reading the Wolfram blog.

Medical applications as a growth area for genetic and evolutionary computing

Within the last week we’ve been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

Our special issue on “Medical Applications of Genetic and Evolutionary Computation” (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue — for example we published “Use of genetic programming to diagnose venous thromboembolism in the emergency department” by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 (“Genetic programming for medical classification: a program simplification approach” by Mengjie Zhang and Phillip Wong, and “Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks” by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: “Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis” by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.
I think that there’s  a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.

Within the last week we’ve been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

Our special issue on “Medical Applications of Genetic and Evolutionary Computation” (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue — for example we published “Use of genetic programming to diagnose venous thromboembolism in the emergency department” by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 (“Genetic programming for medical classification: a program simplification approach” by Mengjie Zhang and Phillip Wong, and “Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks” by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: “Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis” by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.
I think that there’s  a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.

Videos from the first week are now available

The videos from the first week are now available online:

Course Introduction (pdf, video)
Data Mining (pdf, video)
Machine Learning for Data Mining (pdf, video)
Data Representation (pdf, video)
Association Rules Basics (pdf, video)

Note that the first three videos are rather blurred. Unfortunately I did not realize that the videocamera had autofocusing problems with the screen. That’s why in the […]

The videos from the first week are now available online:

Note that the first three videos are rather blurred. Unfortunately I did not realize that the videocamera had autofocusing problems with the screen. That’s why in the next lectures I positioned the videorecording equipment differently.

Another post will follow with the link to the audio/video podcast.

The next generation of neural networks

Some few days ago, while preparing my lectures about neural networks, I ran into the video “The next generation of neural networks” by Geoffrey Hinton, one of the pioneers in machine learning and in the field of neural networks in particular.
Hinton starts the talk by presenting the first generation of neural networks, which included systems […]

Some few days ago, while preparing my lectures about neural networks, I ran into the video “The next generation of neural networks” by Geoffrey Hinton, one of the pioneers in machine learning and in the field of neural networks in particular.

Hinton starts the talk by presenting the first generation of neural networks, which included systems such as perceptrons  (which could not adapt), and the second generation of neural networks, which included methods to let the network to adapt, such as Backpropagation. After referring to Backpropagation as a huge disappointment, Hinton quickly shifts to other systems such as support vector machines (a clever perceptron according to Hinton),  restricted Boltzmann machines, and some particular machines that can do amazing things.

In summary, a video really worth watching, which presents neural networks in a nice way  that can be easily understood for non-experts in this field as me.