… is in Kioloa, Australia from March 3 to March 14. It’s a great chance to learn something about Machine Learning and I’ve enjoyed several previous Machine Learning Summer Schools.
The website has many more details, but registration is open now for the first 80 to sign up.
How many papers do you remember from 2006? 2005? 2002? 1997? 1987? 1967? One way to judge this would be to look at the citations of the papers you write—how many came from which year? For myself, the answers on recent papers are:
This spectrum is fairly typical of papers in general. There are many reasons that citations are focused on recent papers.
- The number of papers being published continues to grow. This is not a very significant effect, because the rate of publication has not grown nearly as fast.
- Dead men don’t reject your papers for not citing them. This reason seems lame, because it’s a distortion from the ideal of science. Nevertheless, it must be stated because the effect can be significant.
- In 1997, I started as a PhD student. Naturally, papers after 1997 are better remembered because they were absorbed in real time. A large fraction of people writing papers and attending conferences haven’t been doing it for 10 years.
- Old papers aren’t on the internet. This is huge effect for any papers prior to 1995 (or so). The ease of examining a paper greatly influences the ability of an author to read and understand it. There are a number of journals which essentially have “internet access for the privileged elite who are willing to pay”. In my experience, this is only marginally better than having them stuck in the library.
- The recent past is more relevant to the present than the far past. There is a lot of truth in this—people discover and promote various problems or techniques which take off for awhile, until their turn to be forgotten arrives.
Should we be disturbed by this forgetting? There are a few good effects. For example, when people forget, they reinvent, and sometimes they reinvent better. Nevertheless, it seems like the effect of forgetting is bad overall, because it causes wasted effort. There are two implications:
- For paper writers, it is very common to overestimate the value of a paper, even though we know that the impact of most papers is bounded in time. Perhaps by looking at those older papers, we can get an idea of what is important in the long term. For example, looking at my own older citations, simplicity is it. If you want a paper to have a long term impact, it needs to have a simple algorithm, analysis method, or setting. Fundamentally, only those things which are teachable survive. Was your last paper simple? Could you teach it in a class? Are other people going to start doing so? Are the review criteria promoting the papers which a hope of survival?
- For conference organizers, it’s important to understand the way science has changed. Originally, you had to be a giant to succeed at science. Then, you merely had to stand on the shoulders of giants to succeed. Now, it seems that even the ability to peer over the shoulders of people standing on the shoulders of giants might be helpful. This is generally a good thing, because it means more people can help on a very hard task. Nevertheless, it seems that much of this effort is getting wasted in forgetting, because we do not have the right mechanisms to remember the information. Which is going to be the first conference to switch away from an ordered list of papers to something with structure? Wouldn’t it be great if all the content at a conference was organized in a wikipedia-like easy-for-outsiders-to-understand style?
When presenting part of the Reinforcement Learning theory tutorial at ICML 2006, I was forcibly reminded of this.
There are several difficulties.
- When creating the presentation, the correct level of detail is tricky. With too much detail, the proof takes too much time and people may be lost to boredom. With too little detail, the steps of the proof involve too-great a jump. This is very difficult to judge.
- What may be an easy step in the careful thought of a quiet room is not so easy when you are occupied by the process of presentation.
- What may be easy after having gone over this (and other) proofs is not so easy to follow in the first pass by a viewer.
These problems seem only correctable by process of repeated test-and-revise.
- When presenting the proof, simply speaking with sufficient precision is substantially harder than in normal conversation (where precision is not so critical). Practice can help here.
- When presenting the proof, going at the right pace for understanding is difficult. When we use a blackboard/whiteboard, a natural reasonable pace is imposed by the process of writing. Unfortunately, writing doesn’t scale well to large audiences for vision reasons, losing this natural pacing mechanism.
- It is difficult to entertain with a proofÃ¢â‚¬â€there is nothing particularly funny about it. This particularly matters for a large audience which tends to naturally develop an expectation of being entertained.
Given all these difficulties, it is very tempting to avoid presenting proofs. Avoiding the proof in any serious detail is fairly reasonable in a conference presentation—the time is too short and the people viewing are too heavily overloaded to follow the logic well. The “right” level of detail is often the theorem statement.
Nevertheless, avoidance is not always possible because the proof is one of the more powerful mechanisms we have for doing research.