Archive for November, 2013

29

Nov
2013

Data Science’s Role in Healthcare – The Case of Intelligent Diagnostics


Medical data appears to be exploding in volume lately, a trend that is expected to continue in the future. This makes the existing diagnostics techniques more or less obsolete. Could data science hold the key to managing, processing, and visualizing the data involved? Dr. Ynnerman seems to think so and with the use of easily accessible computer tech he sets off on a journey of information discovery in at attempt to shed some light on the oceans of medical data that doctors nowadays have to deal with.

Professor Anders Ynnerman received a Ph.D. in physics from Gothenburg University. During the early 90s he was doing research at Oxford University and Vanderbilt University. In 1996 he started the Swedish National Graduate School in Scientific Computing, which he directed until 1999. From 1997 to 2002 he directed the Swedish National Supercomputer Centre and from 2002 to 2006 he directed the Swedish National Infrastructure for Computing (SNIC).

Since 1999 he is holding a chair in scientific visualization at Linköping University and in 2000 he founded the Norrköping Visualization and Interaction Studio (NVIS). NVIS currently constitutes one of the main focal points for research and education in computer graphics and visualization in the Nordic region. Ynnerman is currently heading the build-up of a large scale center for Visualization in Norrköping.

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28

Nov
2013

How big data creates havoc in privacy issues, who is to blame and what we can do to fight back

Big data has been viewed as a very rich resource and a very promising revenue source. However, just like everything else, there is a dark side to it. Chomsky and Gellman elaborate on this idea pinpointing the hows and whys of privacy violations due to big data, the culprits, and some potential solutions to the problem. Apparently, things are not all fun and games, but they are not all gloom either. Read this article to find out more about this intriguing topic.

“Big Data is a step forward,” said Chomsky. “But our problems are not lack of access to data, but understanding them. [Big Data] is very useful if I want to find out something without going to the library, but I have to understand it, and that’s the problem.
Image source: Lespritcondos.ca

Image source: Lespritcondos.ca

We at Persontyle promote a healthy balance between the users and the scientists, characterized by a respect for the privacy of the former and a person-centric nourishment of the latter, in an attempt to move towards a better world using data science.

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26

Nov
2013

Can systems be artificially stupid in very useful ways? Artificial Stupidity


  “Birds fly by flapping their wings. Airplanes fly, but their wings don’t move (at least, not in the same way). Are airplanes artificial birds? Not really. Flying is not what makes a bird a bird. Things that fly are not artificial birds. And yet flight is very valuable to us. So why do we care whether or not an airplane is an artificial bird? It’s the flightness of an airplane that’s useful to us, not the birdness. Hence Artificial Stupidity. Being unyoked from Artificial Intelligence, your program can be artificially stupid in very useful ways.”

In the past decades we have observed the flourishing of the Artificial Intelligence field (aka AI) which has found many applications in a variety of fields, including data science. However, AI is not flawless and may never be. There are inherent limitations such as these ones that were first put forward by Dr. Jay Liebowitz [1]:

  • – Ability to possess and use common sense
  • – Development of deep reasoning systems
  • – Ability to vary an expert system’s explanation capability
  • – Ability to get expert systems to learn
  • – Ability to have distributed expert systems
  • – Ability to easily acquire and update knowledge

So, even though AI can be promising it is no panacea, that’s for sure! Yet, today many people in the fascinating field of data science choose to rely on AI, particularly some Machine Learning that mimic the human brain. Although there is nothing inherently wrong with this approach, if one is to rely solely on a machine to perform the various steps of the data science process, one is bound to produce results that may be:

  • – Interesting but inapplicable
  • – Applicable but impossible to interpret
  • – Neither interesting not applicable

This is mainly because a machine, no matter how well trained, lacks the inherent reasoning skills let alone the intuition that are essential in any data science role. Even a junior data scientist exercises his/her judgment and sense of perspective when engineering features and using them to analyze the data and distill useful information from it. Therefore, if one were to rely on an artificial neural network (ANN), a fuzzy classifier, or some other AI system, one is bound to employ not AI but Artificial Stupidity instead. This will not only have negative consequences to that person, but also to the field of data science in general as such blunders may taint the image of the data scientist and reduce the role from “the sexiest profession of the 21st century” to “another hyped term for a data analyst who relies on computers mainly.”

source: http://www.tanyakhovanova.com/Jokes/pictures.html

source: http://www.tanyakhovanova.com/Jokes/pictures.html

We at Persontyle not only foresee the danger of artificial stupidity in both the individual and the collective level, but also take measures to remedy this problem before it manifests. Specifically we provide a variety of data science courses around the world as well as data science services. As a well-known scientist once said “light a candle instead of cursing the darkness.” At Persontyle this is exactly what we encourage people to do. Instead to getting lost in the darkness of ignorance and half-knowledge, try illuminating the space through education and acquisition of practical experience in the field. Would you like to join us?

References
1. Liebowitz, Jay (July 1989). “If There is Artificial Intelligence, Is There Such Thing As Artificial Stupidity?”. SIGART Newsletter 109.
2. http://www.c2.com/cgi/wiki?ArtificialStupidity read more

26

Nov
2013

Together, we can shape a better future using data science


Data Science with a mission to do social good.


Just like any science, data science is in and of itself neutral, i.e. it can be used for good or for evil. I’m sure you have plenty of examples in mind about the latter but what about the former? How can it be used for good? Many researchers usually pinpoint some good applications of data science when they publish their work in an academic medium, but more often than not that’s where this good intention stays. Shouldn’t it take the next step though and become more than just some nice idea? I believe yes and I am willing to bet that you do so as well.

In our data-driven world data is in abundance so there is data out there that is ideal for a humanitarian application. That’s nice, but there are practical issues that need to be overcome before this data can make its way to the hands (or computers) of data scientists and eventually transmute into data products that can make the world a better place. Such data products can manifest in healthcare, systems of third-world countries, etc. But first thing’s first. The data has to be acquired and make its way to a data scientist. And that’s not an easy task.

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Enter the data science for humanity project. This project (which aspires to be much more than that) is basically a framework that makes this whole process simple and cost-effective. It makes sure that there are databases that hold tons of data from various charities, non-profits, and NGOs. Once this data is collected and assessed and a worthwhile objective is defined, it takes the form of a Social Analytical Challenge (SAC). The project contains several SACs, which are then made available to data science volunteers (e.g. people who wish to gain experience in the field and are willing to work for the love of it) for data activism. These volunteers crunch the numbers and whatever else is there in these data-sets and bring about value, impact and insight from the otherwise useless SACs. Take a look at the great and inspiring work DataKind is doing and you’ll understand why people are passionate and interested in doing things which matter. There are people out there who are dreaming of such opportunities and collaborative environments for data activism i.e. learning, doing and changing together.

The aforementioned framework can be implemented by anyone who has the know-how and the good will to do so. At Persontyle, a data science organization whose mission is to promote data literacy and empower you to analyze data scientifically, we make sure that this framework is more than just a nice idea. We ensure that it is some real and applicable. Unlike other similar organizations that tap onto the data science as a service (DSaaS) market, we go one step further; we promote face-to-face communications and collaborations among our members. Because even if this looks hard, in the long run it is an easier and much more efficient option. This is why crowd sourcing and funding initiatives have been such a great success in both America and Europe, as well as some other parts of the world.

Persontyle aspires to give data science a human face and through it, make the world a better place. We are fully aware of the magnitude of this challenge but our people-based philosophy enables us to make steps towards that. I believe it’s worth a shot. Don’t you?

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