Česká verze příspěvku

Since I was a little boy, I was astonished how weird sport biathlon is. I couldn’t imagine how could someone possible invent a combination of cross-country skying and shooting. It blew my mind when I found out there is even weirder combination of disciplines called modern pentathlon. With this preparation, it was no surprise that there exist annual competitions in machine translation and many other even weirder and more specialized tasks in computer science.

The AI combats

There are many computer science competitions in fields that could be called artificial intelligence and more and more appear every year. The (cynical) reason is simple – more and more computer scientists (and scientists in general) in the world demand to satisfy their urge to compete with each other. The more competitions and more specialized, the bigger is your chance to win. Not every competition looks like when Deep Blue or AlphaGo try to beat the champions of the most difficult board games, which typically ends with exaltation and jubilation of the engineers in front of journalists’ cameras in a room conspicuously similar to NASA mission control center.

The competitions are less spectacular and dramatic. While announcing a competition, organizer typically provide the competing teams with the training data (system inputs and expected outputs), which they can train the system on, and some test data and evaluation methodology (or software) they can be used for an independent test of the system performance. The data that will be used for the final evaluation are kept in secret, so the teams cannot cheat by tailoring the system to the test data (or even memorizing the expected outputs).

In case of the already mentioned machine translation, the training data are publicly available bodies of text, so called parallel corpora – files of millions or even billions sentence pairs which are translations of each other. They are either crawled automatically from the web or they can be proceedings of various European or international institutions. The test data are newspaper stories collected and translated for the particular year of the competition.

Machine translation is not the only task computer scientists compete in. Some of them are very specialized tasks, hardly comprehensible of a non-expert. On the other hand, there some practical tasks and some them were able to achieve quite a lot media attention. In 2009, video streaming service provider Netflix held a competition in predicting user rating for movies based on the user’s history and preferences of other users. Quite popular are also competition in robotic football or DARPA Grand Challenge, a competition of autonomous car. By the way, in the first round, in 2004, all the cars get lost or broken and none of them was able to beat the 240 km long route in Mohave desert. Today’s autonomous car already start to be capable of orienting in urban traffic.

To compete or not to compete?

Holding the competitions have many apparent advantages. Most of the research papers focus on narrow and isolated aspects of the problems and tend to neglect the overall view on the problems. A typical conclusion of a paper utilizing machine learning is: we invented a smart model which improves performance of a baseline model in some aspects. A mutual influence of the individual improvements is not usually considered to be interesting from a research perspective as inventing a new model, you can just try it and see (pure engineering, no science in there). There are also things that “everybody knows”, so there is need to write about it. For instance a combination of more slightly different models usually leads to better results because they also have slightly different distribution of errors. It has been proven long time ago, so there is no need to spend the valuable researchers time on trying it.

On the other hand, if it is a competition, every trick that improves the performance comes handy. No one no longer cares about the novelty of the approach – the only goal is to use the know-how obtained by reading and publishing research papers to do the best engineering job possible.

The competitions have drawbacks as well. If the researchers would accept their rank in a competition as a main measure of their success, it could happen that they their institute would gradually exchange research for pure development. Even though they could win the competitions, the scientific impact of their work would be rather low.

Another problem the competitions suffer with is the fact that no metric is perfect and in many cases such as sentiment analysis or word similarity, a good metric cannot exist in principle, still you need to measure the performance somehow. You can gain an advantage in the competition by greedily optimizing towards the competition metric, even though it may not correlate with how people perceive the task. In machine translation, it even led to creating another competition, not in the automatic translation itself, but in measuring the quality of the automatic translation. The participants try to develop such a metric that correlates the most with how humans perceive translation quality – which needs to be measured as well and it quite unclear what is the best way! Luckily, there is no competition in measuring the quality of measuring the quality of machine translation (yet).

Finding a weakness of the competition evaluation process can lead to success even though it totally misses the objective the competition tries to follow. An excellent example could be the Turing Test which has been for a long time considered to be criterion of AI being really intelligent. A system passes the Turing Test if it is able to chat with a human in such a way that the human is not able to tell whether he or she talks to another human or a computer program. The first and the only one system that passed this test was a project of two Russian programmers in 2014. It fooled the judges by pretending, it was a thirteen-years-old boy from Ukraine who was not a native speaker of English. Obviously, winning the competition in this way shows more intelligence of the authors than intelligence of the program itself.

When a competition gets established in the scientific community with its standard datasets and standard metrics, researchers may continue to take part even though, the problem does have to be as relevant as in the time the competition started. This is in my point of view the case of Dialog State Tracking Challenge. The goal of the competing systems is to find out what is the goal of a user of a spoken dialog system (e.g., find out when the next tram arrives). The design of the task assumes a decomposition of the dialog system which was invented ten years ago and is quite different from what the current smart assistants like Google Home or Amazon Echo look like. Ideas about the systems design has changed with the advent of deep learning and with experience from production deployment of such systems, however new rounds of the challenge start every year.


This maybe surprising competitiveness of AI researchers and engineers has been used by company Kaggle with a very interesting business model. It runs a server that holds competitions in various machine learning problems open for broad public. Kaggle’s clients are businesses that can come up with a machine learning problem and list a reward for the best solvers.

These could be problems like automatic labeling of YouTube videos or searching satellite pictures for places where the Amazon rain forest is being destroyed. In addition to commercially motivated problems, there are also competitions on the server to help programmers learn how to work with machine learning, as well as problems motivated more academically.

Kaggle was founded in 2010 and since that time, it gathered a community of 536 thousand registered users and supposedly earned 12.5 million dollars. The company was recently bought by Google and became a part of the Alphabet holding.

An infinite list of competitions

I spent 30 minutes by googling to get an idea how many AI competitions for scientific community are out there. I was certainly biased towards natural language processing (moreover other fields do not use the term shared task which makes the search easier), so most of the are language oriented. Here is what I found. (By the way, in the Olympic Games, there are just 28 sports with 300 disciplines).

And by the way, chess-box is an interesting sport as well: