Research Activities at Preferred Networks
Hello, I am Takuya Akiba, a newly appointed corporate officer doubling as chief research strategist. I would like to make an inaugural address as well as sharing my view on research activities at PFN.
What does research mean at PFN?
It is very difficult to draw a line between what is research and what is not, and it is not worthwhile to go out of your way to define it. Research means to master something by sharpening one’s thinking. It is usually understood that research is to deeply investigate into and study a subject in order to establish facts and reach new conclusions about it.
Almost all projects at PFN are challenging, entail great uncertainty, and require no small amount of research. In most cases, research and development of core deep learning technologies, not to mention their applications, does not go well without selecting an appropriate method or devising a nontrivial technique according to a task or data. We are also dealing with unknown problems that arise when trying to combine technologies in multiple fields such as robotics, computer vision, and natural language processing. In addition to that, when we design a cluster, manage its resources, and work on a deep learning framework, there are many things to consider and solve by trial and error in order to make them useful and highly efficient while satisfying requirements that are specific to deep learning at the same time.
Among them, especially the following projects involve a great deal of research:
- Academic research whose findings are worthy to be published in a paper
- Prepare and perform a demonstration at an exhibition
- Participation in competitions
- Solve open social problems that have been left unsolved
We have already started producing excellent results in these activities, with our papers continuously being accepted by a wide range of top conferences, including ICML, CVPR, ACL, and CHI. We are not only publishing more papers than before, but our papers are receiving global attention. One of our researchers won the Best Paper Award on Human-Robot Interaction at ICRA’18 while another researcher was chosen as Oral at ICLR’18 recently. With regards to demonstrations, we displayed our work at several exhibitions including CES 2016 and ICRA 2017. We also took part in many competitions and achieved great results at Amazon Picking Challenge 2016, IPAB drug discovery contest, and the like.
Why does PFN do research？
What is the point of researching what doesn’t seem to bring immediate profits to a business like PFN? For example, writing a research paper means that the researcher will need to spend a good amount of his/her precious time in the office, and publishing it would be tantamount to revealing technology to people outside the company. You may be wondering whether activities like academic research and paper writing have a negative impact on the company.
At PFN, however, we highly value such activities and will even continue to increase our focus on them. It is often said that the “winner takes all” in the competitive and borderless world of computer and AI businesses. In order to survive in this harsh business environment, we need to obtain a world-class technological strength through these activities and retain a competitive edge to stay ahead of rivals. Building a good patent portfolio is practically important as well.
Also, I often hear some say, “Isn’t it more efficient to focus on practical applications of technologies in papers published by others?” It is certain, however, that leading organizations in the world will be far ahead by the time those papers come out and catch our eyes. Besides, the information we can get from reading papers is very limited. Often times, we need to go through a process of trial and error or ask authors before successfully reproducing the published result or need to apply it to other datasets to learn its negative aspect that is not written in the paper. These would take an incredible amount of time. Alan Kay, who is known as the father of personal computers, once said: “The best way to predict the future is to invent it.” Now that we have made one great achievement after another in multiple research fields, his words are beginning to hit home. They carry a great sense of reality.
Furthermore, we not only research within the company but also place great importance on presenting our study results to contribute to the community. This not only helps make our presence felt both in and out of Japan but will eventually accelerate the advances of the technology necessary to realize our objectives if we can inspire other professionals in the world to undertake follow-on research based on the techniques we publish. This is why we are very active in making the codes and data used in our research open to the public as well as releasing software as an OSS. Our researchers also peer-review papers in academic journals in work hours as part of our contributions to the academic community.
What kind of research are we promoting?
We are working on an extensive range of research fields, centering around deep learning. They include computer vision, natural language processing, speech recognition, robotics, compiler, distributed processing, dedicated hardware, bioinformatics, and cheminformatics. We will step up efforts to further promote these research activities based on the following philosophy.
Any research should be conducted not only by looking at the world today but also with an eye for the future. The value of research shouldn’t be judged using only the common knowledge now, either. An unpractical method that requires heavy computation or a massive process that no one dares to do in today’s computing environment is not necessarily negative. For example, we succeeded in a high-profile project where we completed training an image recognition model within minutes through distributed processing on 1,024 GPUs last year. Not only the unprecedentedly high speed that we achieved was extraordinary but the scale of the experiment itself – we used 1,024 GPUs all at once – was out of the ordinary. It may not be realistic to use 1,024 GPUs for ordinary training. Then, is research like this not worth conducting?
Computational speed is yet continuing to improve. Especially for deep learning, people are keen to develop a chip dedicated to it. According to an analysis released by OpenAI, the computational power used in large-scale deep learning training has been doubling every 3.5 months. Such settings seem incredible now but may become commonplace and widely available to use in several years. Knowing what will happen and what will be a problem at that time and thinking how to solve them and what we will be able to do – to quickly embark on this kind of far-sighted action is extremely important. The experiment using 1,024 GPUs mentioned above was the first step in our endeavor to create an environment that would make such large-scale experiments nothing out of the ordinary. We are taking advantage of having a private supercomputer and a team specializing in parallel, distributed computing to realize this.
Out into the world
You should aspire to lead the world in your research regardless of the research field. Having a technological strength that is cut above the rest of the world can bring great value. Not act too inwardly, but you should look outside the company and take the lead. Publishing a paper that will be highly recognized by global researchers, becoming among the top in a competition, or getting invited to give a lecture on a spotlighted subject – these are the kind of activities you should aim for. In reality, it may be difficult to outdistance the world in every research area. But, when you are conscious of and aiming to reach the top spot, you will know where you stand relative to the most advanced research in the world.
It is also very important to work your way into an international community. If you become acquainted with leading researchers and they recognize you are to be reckoned with, you will be able to exchange valuable information with them. Therefore, PFN is encouraging its members to make a speech outside the company and making sure to publicize those who have made such contributions.
Go all-out to expand
Any research should not be kept behind closed doors but expanded further. For example, compiling a paper on your research is an important milestone, but it’s not the end of your research project. You shouldn’t undertake research just for the sake of writing a paper. In deep learning, a common technique can sometimes work effectively in different application fields. I have high hopes that PFN members will widen the scope of their research for extensive applications by working with other members from different study areas. Having people with a variety of expertise is one of our company’s forte. If possible, you should also consider developing new software or giving feedback to make an in-house software serviceable. It would also be great if your research would result in improving day-to-day business activities. Although I emphasized the importance of the number of research papers accepted by top conferences, I have no intention to evaluate R&D activities solely based on the number of papers or the ranking of a conference by which the paper was accepted.
To break into one of the top places, you need to utilize your skills fully while being highly motivated. Having said that, you don’t need to do everything by yourself. You should positively consider relying on someone who has an ability that you don’t have. This is not only about technical skills but also paper writing. Even if you put a lot of efforts into your research and made interesting findings, your paper could be underestimated, thus not accepted by an academic conference due to misleading wording or other reasons caused by your lack of experience or knowledge of writing a good paper. PFN has many senior researchers with years of experience in basic research who can teach young members not only about paper writing but also how to conduct a thorough investigation as well as the correct way to compare experiments. I will ensure that our junior members can receive the support of these experienced researchers.
The appeal of working on R&D at PFN
What are the benefits of engaging in research and development at PFN for researchers and engineers?
One of the most attractive points is that your superb individual skills as well as organizational technical competence are truly being sought after and can make a big difference in PFN’s technical domains, mainly deep learning. This means that the difference of technical skills, whether they are individual or team, will be hugely reflected on the outcome of research. So, having high technological skills will lead directly to a high value. Your individual skills and the ability to put them to good use in a team are highly regarded. This is particularly a good thing if you are confident about or motivated to improve your technical capability.
It is also worth mentioning that we have flexibility in the way we do research. Some researchers devote 100% of their time to pure basic research, and they have formed a team entirely dedicated to it, which we even plan to expand. Some are handling business-like problems while progressing their main research activities. Joint research with the academia is also actively being carried out. Some members are working part-time to take a doctor’s course in graduate school to polish their expertise.
We are also putting extra effort into enhancing our in-house systems to promote R&D activities. PFN provides full support to its members taking up on new challenges by trusting and giving considerable discretion to them and flexibly dealing with needs to improve such in-house systems or requests for assets that are not available in the company. For example, all PFN members are eligible to spend up to 20% of their work hours at their own discretion. This 20% rule enables us to test our ideas right away. So, I am expecting our motivated members to produce unique ideas and launch new initiatives one after another.
Everything from the algorithm, to software framework, to research supporting middleware, and to hardware is important in deep learning and other technical domains that PFN engages in. It is also one of the appealing points that at PFN you get to chat with experts in a wide range of research fields such as deep learning, reinforcement learning, computer vision, natural language processing, bioinformatics, high-performance computation, distributed system, network, robotics, simulation, data analysis, optimization, and anomaly detection. You can ask them about subjects you’re not familiar with, exchange practical problems, work together on a research subject, and so on.
Finally, let me write a little bit about my personal aspirations. I have been given the honor that is more than I deserve of serving as the corporate officer and chief research strategist at a company where many esteemed professionals are doing splendid work in a wonderful team whose great abilities keep inspiring me everyday. At first, I hesitated whether I should accept this important role that seemed too big for someone like me and I was afraid that I might not be able to live up to their expectations.
I was a researcher in academia before joining PFN and worked as an intern for several corporate labs outside Japan in my university days because I was interested in becoming a researcher in a corporate environment. During one of the internships, they carried out layoffs, and I saw right before my eyes all researchers in the lab, including my mentor, being dismissed. I experienced firsthand the toughness of continuing to make research activities meaningful enough for a company.
Despite the bitter experience, I believe PFN should promote research as a corporate activity and generate value from maintaining it in a healthy state. This is not an easy but very exciting and meaningful task, and this is exactly the area where my experiences and knowledge obtained in various places could be useful. So, I decided to do my best to make contributions in this new role.
I excel at combining several areas of my expertise such as researching, engineering, deep learning and distributed computation into creating a new value as well as elaborating and executing a competitive strategy. I will try to exploit these strong points of mine to the fullest in broader areas.
PFN is looking for researchers and engineers who are enthusiastic about working with us on these research activities.