What is an expert?

There is an undergoing discussion on how do we define an 'expert'. Here it is my view on the topic:

(Note: originally posted on http://bit.ly/cTydOg)

The basic assumption behind the concept of being an ‘expert’ — which, based from the above comments, it seems everyone agrees — is that there should be a *learning process* that makes such entity distinguished from the ones who haven’t acquired the same level of knowledge or skill.

Thus, one can conclude that an ‘expert’ is a system who have improved its performance based on its learning experience. Note that this is exactly the standard definition one would find by studying “Machine Learning”, a subarea of AI and statistical pattern recognition. However, how does one assess the ‘performance’ of a system? Although that’s not so simple, it’s not that difficult, as there are a variety of metrics one can use to precisely measure the ‘reliability’ of a diverse range of claims made by an ‘expert’.

In this sense, I fully agree with @Openworld comment above in that we should focus on the outcome of the process itself. That is, pick a definite preformance metric and we won’t need to rely on ‘common sense’ to know whether one really knows what he/her is ‘talking about’.

Everyone can do that at home.

My contribution to "Is Computer Science a Misguided Field?"

This is my answer to Amir Michail recent inquiries in Google Buzz. Amir has posted a nice set of questions that make us stop to wonder whether we're on the right track in terms of CS education. In his original post, he wrote (originally posted on http://bit.ly/dp8WKW):

Computers are interesting because you get to invent new applications that change the world.

By focusing on efficiency and correctness of programs, doesn't computer science completely miss the point as to what is interesting about computers? 

By contrast, consider the field of computer games where game design is a key aspect of study. Why isn't there something like that in the more general field of computer science? 

Where's the application level creativity? Why focus only on implementation issues? 

What do you think?

Here it is my follow-up on this thread:


On what Computer Science (CS) is about (and what it isn't)


Interesting discussion. CS is about complexity theory, computability, algorithms, data structures, automata theory, quantum computation science, formal languages and much more. There's a whole theoretical background that is unique to CS.

Definitely, there should be separated majors, such as Software Engineering or Data and Information Management, in order to avoid misconceptions and to address industry's specific needs.

In short, CS is a basic science in wich we can learn about modelling the complex processes that occur in Nature by using abstract mathematical tools. The models we construct in CS (basically, algorithms) have to (i) be given formal descriptions and proofs; (ii) have its fundamental properties investigated (complexity bounds, completeness, etc.); and much more.

So, CS is a basic science and other sciences can benefit from it by using its outocomes. An example of this is Artificial Intelligence. Although it first got inspiration from early mathematical models of the brain (neuroscience), it became clear thereafter that the 'algorithimic' way of modelling natural processess would be fundamental to AI. Today, AI is commonly regarded as a standard discipline in CS, though still multidisciplinary.

Why not segregate the lots of teaching contents flooding CS students minds into separate majors?

Furthermore, let's not forget the revealing quote of a prominient computer scientist:

"Computer Science is no more about computers than Astronomy is about telescopes" Edsger Dijkstra

That summarizes well what CS is definitely not about.

I believe that multidisciplinarity should raise from a well planned reform in the graduate educational system, instead of requiring that a CS major covers every single topic from software engenieering to computation theory. Both are, separetedly, deep and complex enough disciplines to be studied in their own terms.


See: http://en.wikipedia.org/wiki/Edsger_W._Dijkstra

On Creativity

With regard to creativity, well, I'd say that computer scientists need to be creative as much as mathematicians and physicists do. There's no difference in coming up with a novel algorithm and discovering a fundamental power law in a complex system.

So, yes, you need much creativity to work on CS, no more nor less then in other related areas.


On the role of Game Design and related areas on CS


Now, why do I think game design (GD) does not play a significant role in CS? Because game design is an application of CS. Although there are many CS researchers who focus in GD, they are just aplying the standard tools of CS to modelling new useful algorithms for the problems they are working on in the area. So, again, GD 'uses' the 'know-how' and tools provided by CS.

As a perhaps useful analogy, think about CS as the 'Kernel' of an operating system (OS), which provides all the basic functioning to the system, and GD as one of the many 'services' provided by that OS which uses the basic kernel units. That is, GD is closely related to CS, but there is much more about it than just CS and, thus, I'd say that there's no need to consider studying GD in a basic CS curriculum, though it might be interesting to CS practicioneers, just like other disciplines.

In short: I'd say that GD is on the same level of AI, Database Management, Programming Languages and so on... that is, all those disciplines are 'users' of the core knowledge that exists on CS.

My view on Michael Anissimov's post on "Accelerating Future"

(Note: originally posted on: http://bit.ly/aeTIYZ)



Dear Michael,

I personally believe there's a subtle difference between "existence" and "usefulness". The latter is generally measured by the level of interest within a group of individuals towards new technologies.

The fact is that, virtually, we have now all the capabilities to come up with any innovation we want from RK's list for 2009. Just give us enough time, budget and an experienced team of engineers. So, in terms of "usefulness", I would not take any of RK's predictions for granted but rather point out that the the vast majority of his predictions either have been already implemented (even as incipient prototypes) or are potentially plausible within a few years, albeit those may slightly vary on the fundamental principles governing them.

But then, it will most probably require some marketing efforts to convince people to actually consume such innovations. We still have to demonstrate what the benefits are for those who eventually would be willing to trying such new inventions and radical changes.

Unfortunately, there is still much room for us to improve socially and politically before we can set up the scenario wherein the different range of plural societies will be able to fully benefit from such technologies. Globalization and economical development would indeed help for that matter. However, this might not be so simple as it seems. We must first solve the conflict of interest which remain among many cultures.

That said, I attribute the small mistakes done by RK in terms of dates (for + or -) to the lack of (or surplus of) interest from all societies on the technologies that we are, without doubt, ultimately and fully capable of manufacturing.

Please, let me know whether this makes any sense for you.

Sincerely,
Carlos R. B. Azevedo
Recife, Brazil

Depreciação do ativo humano científico brasileiro

O governo federal assinou hoje uma MP reajustando o salário mínimo que passará a valer R$ 510,00 a partir de 1º de Janeiro de 2010. Apesar do fato constituir-se uma notícia excelente para a estabilidade econômica, produtiva e social do Brasil, o mesmo, por outro lado, expõe a fragilidade do investimento em pesquisa e tecnologia e o fracasso na formação de recursos humanos no setor científico no país.

Como uma imagem vale mais do que mil palavras, desenvolvo o texto com base em um gráfico aproximado (Figura 1) compilado por mim o qual mostra a evolução da relação dos valores de bolsa de pós-graduação em instituições superiores brasileiras pagas pelo Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) versus o valor do salário mínimo no país.

Figura 1. Relação do valor das bolsas de pós-graduação do CNPq e salário mínimo no Brasil.

A inspeção visual do gráfico demonstra que tal relação vem em tendência de queda desde 1994, quando o valor da bolsa de mestrado era de R$ 724,50; a de doutorado de R$ 940,00 e o salário mínimo equivalia a R$ 70,00. Ou seja,  o valor pago ao estudante brasileiro de doutorado para dedicar-se exclusivamente aos estudos e à pesquisa equivalia a aproximadamente 13 vezes o valor do salário mínimo. Em 2010, com aumento do salário mínimo, um estudante de doutorado brasileiro passará a ganhar 3 salários e meio, enquanto que o de mestrado ganhará menos de 2 salários e meio, o que é uma absoluta vergonha.

Os ínfimos aumentos concedidos na gestão do presidente Lula, apesar de toda a boa vontade do nobre chefe de Estado brasileiro, mostraram-se insuficientes e não condizem com o discurso do governo de se tornar um "parceiro" do setor científico-tecnológico brasileiro. Se a política de investimentos na formação de recursos humanos não mudar nos próximos cinco anos, temo que o tão sonhado despertar do Brasil  no cenário internacional não passe de apenas mais um sonho frustrado. Para construirmos o futuro, devemos edificar os pilares de sustentação e, nesse cenário, ciência e tecnologia provarão serem fundamentais para a soberania nacional.

Enquanto isso, contentar-me-ei com o valor pago atualmente para dedicar-me exclusivamente ao mestrado e assistirei, paralisado, colegas ganharem o triplo (em média) no mercado de trabalho apenas com a graduação. Qual o nosso futuro? Incerto, certamente.

The Intelligent Quotient (IQ) of Evolution

I'm going through the first three chapters of the excellent 1999's "The Age of Spiritual Machines" by Ray Kurzweil. So far, it has been a mix of everything: from cosmology, biology and information theory to computer science, nanoengineering and philosophy. I'm quite amazed on the way Ray was able to deliver all of those breathtaking subjects in less then one hundred pages without making things obscure or even scary to the non-initiated.

Take for instance the discussion going on chapter 2 about the IQ of evolution. He concludes that it should be no better than an infinitesimal quantity greater than zero, given the great lenght of time (in a scale of billions of years) that was needed to evolve some remarkable things such as the human brain. Of course, Ray is agreeing whith many psychologists who evaluate an intelligent process in terms of the elapsed time to reach a solution, i.e., the quicker the better.

The most interesting conclusion drawn here, though, is that all the astonishing complexity observed in Nature is a byproduct of only a slight amount of order encompassed by the emergence of extraordinarily intricate chemical and biological patterns formed apparently by chance.

I'm enjoying it so much!

Conferences vs. Journal publications in CS

This was originally posted as a comment in the GPEMJournal blog:
http://gpemjournal.blogspot.com/2009/08/journal-publication-versus-conference.html

While deadlines, as well as all the follow-up review process which is needed to have a work considered for publication in journals, might directly affect the publishing preferences of many computer scientists towards conferences and workshops, these factors alone don't help me to understand why the same effect does not occur in other related areas. In other words, if these were two of the primary causes behind, we would see the same happening among, say, physicists and mathematicians. Yet this does not seem to be the case.

I sincerely believe that there is one simpler factor that could also be considered in the specific case of CS: the relative lack of maturity of the field regarding consolidated research methodologies and data analysis. Of course, working in a scientific field with less than 60 years do not help us much. We're often borrowing ideas and methodologies from other areas and, still, there's no concensus on, say, which statistical tests are more appropriate.

So, this may perhaps explain why someone in the CS field might think that it's no use considering to fufill journal reviewers and editors' requests - who are mainly (and correctly) concerned with methodological issues - when one can bypass this process and have his/her results published immediately in somewhat respectful and prestigious conferences.

Smart Dust

I've transcribed today a short video about Smart Dust technology. It was quite difficult, given some rapid passages. The accent of Dr. Kris Pisters from Berkeley didn't help much too. I found at least two passages which were challenging to understand.

Anyway, even not being a native speaker, I think I did a good job on this one. The translation to brazilian Portuguese is also done.

I'm enjoying the dotSub transcipt/translation interface very much.

Here it goes: