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AI research is so hot, why can I only face a sky full of formulas to make no sense? The vast sea of books, so many fields, where should I start, and what should I learn? When those big guy read undergraduate course, thesis can hair top meeting, why I read a year master/doctor now, connect the edge of scientific research all did not touch? How can I find the scientific research path that suits me? Don’t worry, even MIT PHDS have gone through the same journey.

The following article is about the research experience of Tom Silver, a second-year PhD student at MIT. Tom Silver is a Computer science and mathematics undergraduate from Harvard University. He has worked as an intern at Sabeti Lab, Google, Vicarious and other well-known AI LABS or companies. I believe his scientific research experience will be able to give you some inspiration.

The following is translated by AI Technology Base camp:

My friend is about to start his AI research, and I happen to be two years ahead of him, so he has been asking me about his research experience. This article is a summary of my two-year research experience, which includes both ordinary life sentiment and research skills. I hope it will be of some help to readers.

▌ introduction

Find the right person to Ask “Dumb Questions”

When I first started doing research, I was often afraid to ask my colleagues for advice, for fear of sounding unprofessional and being looked down upon. This went on for several months before easing, but I was still very careful not to let my shyness slip. But now I have a few confidantes with whom I can discuss problems directly. I wish I could have known them earlier!

In the past, I had to go straight to Google for questions. I was often confused by a screen full of links and information. But now when I have a problem, I can bring it up and discuss it with others, instead of trying to solve it all by myself.

Look for research inspiration in different places

Deciding what to do next can often be the most difficult part of many people’s scientific careers. Here are a few strategies that researchers use:

  • Talk to people in different fields of study. Ask them questions that interest them, and try to rephrase them in computer jargon. Ask them if they have any data sets they would like to analyze that are difficult to solve with existing technology. Much of the most influential work in machine learning comes from the collision of computers with biology/chemistry/physics, the social sciences, or pure mathematics. For example, Matthew Johnson et al., in a study published in NIPS 2016 (Composing graphical models with neural networksfor structured representations) And fast inference) from a dataset of mouse behavior. Take Justin Gilmer et al. ‘s paper at ICML 2017 (Neural Message Passing for Quantum Chemistry), which applies machine learning methods to Quantum Chemistry.

  • Write a simple baseline code to get a feel for the problem. For example, try writing some code that controls an inverted pendulum and calibrates it carefully, or see if you can implement a word bag model on a natural language dataset. When WRITING baseline, I often run into something unexpected — a mental model or a bug in the code. Even when my baseline code is working, I usually try a few other ideas to gain a deeper understanding of the problem.

  • Expand the lab section of your favorite paper. Read carefully the methods and results of those papers. Try to find the most valuable parts. First we can consider some of the simplest extensions and ask ourselves whether the methods in the paper are applicable. Then consider the baseline approach, which is not discussed in this article, and think about where those approaches might fail.

Master visual tools and skills

When writing code, my usual strategy is to start by creating visual scripts. After writing the rest of the code, the visual script helps me quickly verify that the code matches the mental model. More importantly, good visualization often makes it easier for me to spot errors in my thinking or code than any other method. Another reason is self-motivation: every time I finish a piece of code, I can pull out a nice chart or video to show off!

Of course, getting the visualization right for the problem at hand can be tricky. For iterative optimization models (such as deep learning), you can start by drawing a loss function curve. In addition, many techniques can also be used to visualize and interpret the learned weights of neural networks (especially convolution), such as guided back propagation.

In reinforcement learning and planning, the obvious things that need to be visualized are the behavior of an agent in an environment, such as an Atari game, a robotic task, or a simple Grid World (such as the environment in OpenAI Gym). With different Settings, we can also visualize the value function and its changes during training (as shown below), or visualize the traversed state tree.

When working with graph models, much information can be obtained by visualizing the change in distribution of one-dimensional or two-dimensional variables during inference (as shown below). One way to measure the effectiveness of visualization techniques is to estimate the amount of information you need to know in advance in mind each time you analyze a visualization. A bad visualization will require a detailed review of the code you’ve written, while a good visualization will make the conclusion obvious.

Tensorboard is a popular GUI for visualizing Tensorflow deep learning models

Plotting the distribution as evidence accumulation makes debugging of the graph model easier (Wikimedia).

Value functions learned through Q-learning can be visualized in the Grid World it represents (by Andy Zeng).

Learn to identify the basic starting point for the researcher and the paper

While many researchers would present at the same conferences, use the same terminology, and all claim to work in the field of artificial intelligence, their motivations would likely be diametrically opposite. Some even want to rename the field to solve the problem (Michael Jordan) Just a recent post called for the field to be renamed https://medium.com/@mijordan3/ artificial-Intelligence-the-Revolution-Hasnt-Happened – Yet-5e1d5812e1e 7). There are at least three main approaches to this field: “mathematical,” “engineering,” and “cognitive.”

  • From a “mathematical” perspective: What are the basic properties and limitations of an intelligent system?

  • From an “engineering” perspective: How can we develop intelligent systems that are better at solving real problems?

  • From a “cognitive” perspective: How should we model the natural intelligence found in humans and other animals?

These starting points are not in conflict, and many of the interesting aspects of AI are interesting from all angles. In addition, individual researchers are often touched by different perspectives, which contributes to the intersection of the AI field.

Of course, the starting point may be different. I have friends and colleagues who are clearly focused on the “engineering” Angle, while others are primarily interested in “biology”. Engineers are likely to be interested in an article showing that some clever combination of existing technologies can exceed the current optimal level at baseline, while cognitive scientists may not be interested at all, or even scoff at it. An article that illustrates biological plausibility or cognitive connections but remains theoretical or has no serious results may receive opposite responses from the two types of researchers.

Good papers and researchers state their starting point at the outset, but the underlying motivation can go very deep. If the starting point is not obvious, it can be helpful to analyze the article from multiple angles.

▌ Learn from the research community

Looking for a paper

There are tons of AI papers on arXiv, and they’re free to view. In addition to the rapid growth in the number of papers, the large number of active users in the community has made it easier to find high-quality articles. Andrej Karpathy, one of Li’s students, has set up arXiv Sanity PreServer to help us sort, search and filter related articles. Miles Brundage often posts a carefully selected list of arXiv papers on Twitter every night; Much of this was done by the Brundage Bot. Many other Twitter users also share interesting references from time to time – I suggest you follow researchers on Twitter who are of interest to you.

If you like to use Reddit, consider r/MachineLearning, but these posts tend to be more suitable for MachineLearning engineers than academic researchers. Jack Clark publishes a weekly community newsletter called “Import AI” (https://jack-clark.net/), and Denny Britz has a column called “The Wild Week in AI”.

Some of the AI conference paper collections are also worth watching. The top three conferences in machine learning are NIPS, ICML and ICLR. Other meetings include AAAI, IJCAI and UAI. There will be more specific sessions for each sub-discipline in the field. Conferences in the field of computer vision include CVPR, ECCV and ICCV; Conferences in the field of natural language processing are ACL, EMNLP and NAACL; Conferences in the field of robotics include CoRL, ICAPS, ICRA, IROS and RSS; For more theoretical work, follow conferences such as AISTATS, COLT, and KDD. These conferences are currently the main channel for AI publications, as well as some journals. JAIR and JMLR are the two most important journals in the field. Occasionally more advanced articles appear in scientific journals such as Nature and Science.

It’s also important to look up classic papers, but it’s also more difficult. The names of classic papers are often included in references to many articles or on recommended reading lists for graduate programs. Another way to find classic papers is to start with senior professors in the field and look for their early work, their research tracks, or email those professors for more references (though don’t mind if they’re too busy to reply). For older articles, Google Scholar is a good way to search for keywords.

How much time should I spend reading the paper?

I often hear two kinds of advice about how much time people should spend learning about previous research. First, if you’re just getting started, read all the papers! It is often said that the first semester or first year of graduate students should only read papers. The second suggestion is not to spend too much time reading papers after you have had an initial understanding of the field of study! The latter is based on the idea that it is easier for researchers to construct and solve problems in creative ways if they are not influenced by previous methods.

Personally, I agree with the first suggestion and disagree with the second. In my opinion, researchers should read as many papers as possible on the premise of ensuring time for original research. “If I’m not familiar with what others have tried, it’s much easier for me to devise a new, better way to solve a difficult problem.” Such a thing seems unlikely and a little too arrogant. Yes, it’s important to see things from a fresh perspective, and many stories about amateurs come from thinking outside the box. But as professional researchers, we can’t rely on this element of luck alone to arrive at a solution to a problem without much thought. For the vast majority of our research careers, we have been solving problems patiently, step by step, methodically. Reading papers is an effective way to understand where we are and what to try next.

Of course, in the case of reading as many papers as possible, I need to be reminded that taking time to digest papers is just as important as reading them. It’s better to read a few papers first, and then carefully note and reflect on each one, rather than rambling through it one by one.

Communication >> Video > Paper > Conference presentation

Reading a paper is certainly the easiest way to understand an unfamiliar research calendar, but what is the most efficient way? Different people may have different answers, and for me, talking to someone (ideally, someone who already knows what you’re thinking) is by far the fastest and most effective way to understand. If no one is around, watching videos on the topic — such as the talk the author of this paper was invited to give — is also a great way to understand. When speaking to a live audience, speakers prioritize clarity over accuracy. But in most essay writing, the priorities are reversed. In a paper, the number of words is very important (the author should not take up too much space to clarify a concept), and inaccurate background may suggest a lack of knowledge of the field. Finally, short conference speeches are often more formal than meaningful. Of course, the post-speech interaction with the keynote speaker is also very valuable.

Beware of the hype

Success in the field of ARTIFICIAL intelligence draws public attention and draws more people into the field, a cycle that has mostly benign effects but also has a harmful side effect — hype. The media always wants more clicks; Tech companies want to woo investors and hire more workers. Similarly, researchers want to raise the profile and citations of their papers, which has led to more and more hype. So when we see the headlines of media reports or papers, we should think more about the factors behind them and beware of the clickbait.

During a question-and-answer session at NIPS 2017, hundreds of people in the audience heard a rather famous professor take a microphone and advise the authors of the paper (against the hype) to be careful about using the word “imagination” in the title. I have mixed feelings about this kind of near-public disapproval, and happen to like this particular paper, but I quite understand the professor’s displeasure. One of the most common and yet most repugnant pitches in AI research is to rebrand an old idea with a new term. Be careful with these buzzwords — as a serious researcher, you should judge papers primarily on the basis of their experiments and results.

Scientific research is a marathon

Set measurable goals

In the early days of looking for research projects, I spent a lot of time brainstorming. For me, brainstorming back then meant putting my head down on a desk and hoping that vague intuitions would turn into concrete insights. I often feel tired and frustrated at the end of a day of brainstorming. Is this life scientific research?

Of course, there is no recipe for instant research progress, and fumbling in the dark is part of most people’s research careers. However, I now find that by setting quantifiable goals and then planning my work, I can make my research life easier and more fulfilling. When I don’t know what to do next, I often write down my vague ideas in as much detail as possible. If, as you write down the idea, it doesn’t feel right, write down your reasons for excluding it (rather than scrapping the idea entirely and not measuring your progress). In the absence of any ideas, we can resort to reading articles or communicating with colleagues. At the end of each day, my work finally shows some tangible signs. Even though these ideas have never been used, they have greatly boosted my confidence. I don’t have to worry about wasting time on the same ideas in the future.

Learn to recognize and avoid dead ends

Good researchers spend more time on good ideas because they spend less time on bad ones. Being able to tell good ideas from bad seems to be largely a matter of experience. Nonetheless, researchers at all levels often face such choices. My research idea is flawed or uncertain, so should I try A) further rescue or continue the idea, or B) completely abandon the idea? I personally regret spending time on A) that I should have spent on B). Especially in the beginning, I got stuck in a dead end many times and spent a long time there. My reluctance to give up is probably rooted in the sunk cost fallacy — if I let go of this dead end, I’ll be wasting the time I’ve already spent.

I still feel disappointed every time I give up a dead end. But I’ve been trying to tell myself that backtracking is a step forward, and it’s counterintuitive, but I’ve been internalizing it. The costs already paid are worth it and have not sunk. If I don’t explore this cul-de-sac today, I might get into it again tomorrow. Dead ends are not the end; they are a normal part of scientific life. Hopefully, one of these ideas will stick. If not, there’s Feynman: We try to prove ourselves wrong as soon as possible, because only then can we make progress.

We are trying to prove ourselves wrong as quickly as possible, because only in that way can we find progress 。― Richard Feynman

The pen!

I had the opportunity to ask a very well-known AI researcher for advice early in my career. His advice is simple: Write! In addition to writing blogs and essays, it’s important to write down your thoughts every day. Since taking his advice, I’ve noticed a noticeable difference in the progress I make when I’m actively writing instead of just thinking.

Physical and mental health is the premise of scientific research

Researchers often forget to eat and sleep when they are absorbed in their research, which is a very dangerous sign. I used to aim for this state, and I was often ashamed of myself for not achieving it. I now understand, at least on a rational level, that exercise and mental rest are investments, not distractions. If I spend 8 hours sleeping and 4 hours working, I will be more productive than if I spend 4 hours sleeping and 8 hours working.

Of course, interrupting work on a thorny issue can still be very difficult. Even when I’ve passed the point of exhaustion or frustration and haven’t made any real progress, I don’t rest. I just keep digging. When I finally move forward a bit and can stop and take a deep breath, I always feel genuinely happy that I have persevered. I hope I can keep this drive as I enter the next stage of my research career.

Original author: Tom Silver

The original link:

http://web.mit.edu/tslvr/www/lessons_two_years.html