Posts on Mar 2018

Call for applications for PFN summer internship 2018

Preferred Networks (PFN) is looking for enthusiastic interns who can work with us in our Tokyo office this summer. Students who participated in the previous programs are also eligible to apply. We welcome students who want to help us develop new technologies, software, and services in a wide range of computer science areas including machine learning.  

 

Important notice:

Note that this program is only for students who already have visa eligibility to work as an intern this summer in Japan. We are not accepting applications from students who need support for obtaining the designated activity visa to work as an intern because the due date for processing such applications has already passed.

 

Guidelines for applicants

 

● Characteristics of PFN Internship

  • Over the two-month period, PFN engineers will be assigned to work with each of you as a mentor. You will have opportunities to discuss and study your theme with specialists in various fields including deep learning, computer vision, natural language processing, robotics, bio-healthcare, reinforcement learning, and distributed processing.
  • After the internship, you can make public your research result by writing a paper or making it OSS, to the extent possible.  

 

● Period

Start date:Between late July and early August depending on your schedule

End date:Friday, Sept.21, 2018

※You can choose to continue to work in the week of Sept. 24-28 under the same terms and conditions.

Note that this year’s internship will end on Sept. 21 in consideration of the fact that many schools start their fall semester in late September. If you need more time to finalize your research or want to spend more time with our staff, you can continue to work until the end of September under the same terms and conditions. We understand you may have school or family commitments during the internship which might range from lab activities to attending academic conferences, to returning home. We are very flexible about your need to take days of absence due to these reasons.

 

● Key Qualifications

PFN is seeking highly motivated and skillful individuals who can develop applications, tools, etc. on your own. Having knowledge or development experience in the themes listed below is a plus but not a must. Minimum requirements are:

  • Currently enrolled in high school, technical college, university, or graduate school. Negotiable for those attending other higher educational institution
  • Fluency in Japanese or English
  • Strong communication skills
  • Prior experience in programming (any language)
  • Willingness to come to work in our Tokyo office on weekdays

Do not hesitate to apply even if you don’t have prior experience in full-scale development.

 

【Important notes before you apply】

  • We are not accepting applications from students who need support for obtaining the designated activity visa to work as an intern because the due date for processing such applications has already passed.
  • You need to let us know in advance for any administrative work required for receiving academic credit from your school. Please note that depending on the complexity of the work, PFN may not be able to accommodate your request.

 

● Place of work

PFN Tokyo Office

Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004

 

● Basic working conditions and benefits

  • Salaries:2,500JPY an hour for a technical college, university, graduate school students. 2,000JPY an hour for high school students
  • Work hours:Eight work hours in principle. Five days a week excluding Saturdays, Sundays, public holidays.  
  • Commuting fee support:PFN will pay for your daily commute to and from office in an approved route.
  • Travel cost:For students traveling a long distance by plain or Shinkansen bullet train to participate in the internship, PFN will support a round trip to relocate to the Tokyo area.
  • Accommodation support:For students coming from distant parts who would take roughly 60 minutes or longer to commute, PFN will provide a housing allowance of 5,000JPY a day covering the entire period of your internship. You need to arrange a place to stay by yourself. Reasonable weekly rental apartments are available near PFN office ranging from 100,000 to 150,000 JPY a month. Please note that the accommodation allowance is taxable.

 

● How to Apply

Go to: Application form

※ Click the above form to apply. To access the application form, you will need to log in with a Google account.

Deadline:By 23:59 Monday, April 30, 2018, Japan time

For inquiries:Send us an email at intern2018@preferred.jp

 

※About your portfolio

Summarize your skills and qualifications freely in a A4 paper to pitch yourself and highlight and showcase some of your best work such as software you have developed, a list of published papers, awards or prizes you have received, programming contests you have participated in, your blog, twitter account, and other social media sites.

 

● Themes

Let us know which of the following areas of study you would like to work on during the internship. We will decide your theme after speaking with you. You must choose your 1st and 2nd preferences in the application form. If you have more than two areas of interest, select the 3rd preference, which is optional.

  1. Theoretical study of Machine learning/deep learning
  2. Computer vision
  3. Deep reinforcement learning
  4. Robotics
  5. Bio-healthcare
  6. HPC and distributed data management for distributed deep learning/deep learning
  7. Natural language processing
  8. Speech processing
  9. VR/AR
  10. Human computer interaction, human machine interaction
  11. Applications of deep learning to animation, creator support
  12. Development of Chainer
  13. Development of area-specific libraries on Chainer
  14. R&D of machine learning algorithms such as anomaly detection
  15. Information visualization tool and front-end development for machine learning
  16. Machine learning research support, cluster management, experiment management system development
  17. Development of dedicated accelerator/processor for deep learning
  18. Development of compiler/optimizer for deep learning
  19. Development of IoT/Edge Heavy Computing platform
  20. Other
  21. (New) R&D of automatic tuning methods for deep learning
  22. (New) Video analytics (sports, etc…)

 

● Selection process

▼First screening

After sending the application by April 30, you will receive two tests: (1) Online self-interview (recording) (2) Coding test. Deadline for completing these tests is  May 14 (subject to change).

▼Interview

An interview will be scheduled sometime during the two weeks starting from June 5. For students living in distant areas, PFN will arrange a video chat such as Skype.

▼Letter of acceptance (by late June)

Preferred Networks to Launch “MN-1b” Private Sector Supercomputer Adopting NVIDIA Tesla V100 32GB GPUs Will expand NTT Com Group’s multi-node GPU platform

TOKYO, JAPAN — Preferred Networks, Inc. (PFN), a provider of IoT-centric deep learning systems, NTT Communications Corporation (NTT Com), the ICT solutions and international communications business within the NTT Group, and NTT Com subsidiary NTT PC Communications Incorporated (NTT PC) announced today that PFN will launch an expanded version of its MN-1 private sector supercomputer equipped with NTT Com and NTTPC’s next-generation GPU platform by July. The new MN-1b supercomputer will adopt the NVIDIA(R) Tesla(R) V100 32GB, that was announced at GTC 2018 on March 27, 2018 (U.S. time).

PFN plans to enhance MN-1 by adding 512 NVIDIA Tesla V100 32GB GPUs and have them up and running by July, with the added GPUs having a theoretical peak performance of about 56 PetaFLOPS1, a massive 56,000 trillion floating-point operations per second, based on a mixed precision floating-point operation2 used in deep learning. This means the expansion alone will contribute to a roughly threefold increase from the current peak.

PFN expects the new supercomputer’s extra high speed and massive processing environment leveraging the latest GPUs will accelerate the real-world applications of its research and development in deep learning and related technologies and thereby strengthen PFN’s global competitiveness. NTT Com and NTT PC will build and operate the multi-node platform leveraging the latest GPUs that meets PFN’s requirements, using their knowledge of intra-GPU communication and waste heat processing.

“We are truly honored that Preferred Networks has chosen NVIDIA Tesla V100 32GB, most advanced data center GPU with 2X the memory, for its next-generation private supercomputer’s computation environment, “MN-1b”. With NTT Com Group’s experience of establishing and managing highly reliable data center services, combined with NVIDIA’s latest high-speed GPUs for deep learning, we sincerely look forward to R&D results in the fields of transportation systems, manufacturing and biotech/healthcare.”
said Masataka Osaki, Vice President of Corporate Sales and NVIDIA Japan Country Manager.

Emmy Chang, Board Director, Supermicro KK and VP of Strategic Sales, Supermicro said
“Preferred Networks is the first in the world to deploy our SuperServer(R) 4029GP-TRT2 equipped with the latest version of Intel(R) Xeon(R) Scalable processors and supporting eight NVIDIA Tesla V100 32GB GPU accelerators,” “Preferred Networks has developed the world-class private supercomputer through cooperative work with NTT Com Group, and Supermicro continues to support them with our latest innovative hardware and solutions.  We are confident that Preferred Networks will achieve new heights with its new private supercomputer.”

 

PFN will use the new MN-1b to raise the speed of its ChainerTM open source deep-learning framework and further accelerate its research and development in fields that require a huge amount of computing resources, namely transportation systems, manufacturing, bio-healthcare, and creativity.

Going forward, NTT Com expects to increasingly support the delivery of AI technologies and related platforms for advanced research and commercialized deep learning, including the AI business initiatives of PFN.

 

Related links:

Chainer:

Enterprise Cloud:

Nexcenter:

 

Notes:

1 A unit measuring computer performance. Peta is 1,000 trillion (10 to the power of 15) and FLOPS is used to count floating-point operations per second. So, 1 PetaFLOPS means that a computer is capable of performing 1,000 trillion floating-point calculations per second.

2 Mixed precision floating-point operation is a method of floating point arithmetic operations with a combination of multiple precisions.

ChainerTM is a trademark or a registered trademark of Preferred Networks, Inc. in Japan and other countries. Other company names and product names written in this release are the trademarks or the registered trademarks of each company.

 

About Preferred Networks, Inc.

Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in three priority business areas: transportation, manufacturing and bio/healthcare. PFN develops and provides Chainer, an open source deep learning framework. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Corporation and the National Cancer Center.

https://www.preferred-networks.jp/

 

About NTT Communications Corporation

NTT Communications provides consultancy, architecture, security and cloud services to optimize the information and communications technology (ICT) environments of enterprises. These offerings are backed by the company’s worldwide infrastructure, including the leading global tier-1 IP network, the Arcstar Universal One™ VPN network reaching over 190 countries/regions, and over 140 secure data centers worldwide. NTT Communications’ solutions leverage the global resources of NTT Group companies including Dimension Data, NTT DOCOMO and NTT DATA.
www.ntt.com | Twitter@NTT Com | Facebook@NTT Com | LinkedIn@NTT Com

 

NTT PC Communications Incorporated

NTTPC Communications Incorporated (NTTPC), established in 1985 is a subsidiary of NTT Communications, is a network service and communication solution provider in Japanese telco market, The company has been the most strategic technology company of the group throughout of years. NTTPC launched the 1st ISP service of the NTT group, so called “InfoSphere” at 1995, and also launched the 1st Internet Data Center and server hosting services of Japan so called “WebARENA” at 1997. NTTPC have always started something new in ICT market.

Preferred Networks’ private supercomputer ranked first in the Japanese industrial supercomputers TOP 500 list

Preferred Networks Launches one of Japan’s Most Powerful Private Sector Supercomputers

Preferred Networks’ automatic coloring service PaintsChainer receives the Excellence Award at the 21st Japan Media Arts Festival

TOKYO, JAPAN — PaintsChainer™, an automatic coloring service developed and provided by Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa), has received the Excellence Award of the Entertainment Division at the 21st Japan Media Arts Festival organized by Agency for Cultural Affairs, Government of Japan.

The Japan Media Arts Festival publicly honors highly creative, artistic works in four fields of media arts: Art, Entertainment, Animation, and Manga. The annual festival was first organized in 1997 and has since recognized and awarded outstanding works. This year, it has received 4,192 entries from 98 countries and regions around the world, the most in its history, and has announced a grand prize winner, four excellence award winners, and three new face award winners in each of the four categories.

PaintsChainer has drawn a huge reaction on Twitter and other social media sites the moment it was released in January 2017 as a free online service that colors black and white sketches automatically. It uses a deep learning technology to recognize faces, clothing, and other background objects on an uploaded sketch image or picture, and color them automatically or based on a specified color. Currently, it provides three different coloring models Tanpopo, Satsuki, and Canna.

https://paintschainer.preferred.tech/index_en.html

The jury cites the significance of providing an automatic coloring platform as a free web service as the main reason for giving the Excellence Award to PaintsChainer.

A commemorative photo of award winners taken at the press conference at the National Art Center, Tokyo. The fourth person from the left in the middle row is Taizan Yonetsuji.

 

PaintsChainer was developed by one of PFN engineers Taizan Yonetsuji, who has commented as follows:

It all started as my personal project, so I could also study deep learning. I feel very honored to receive such an award and am truly grateful to my senior colleagues who have taught me about deep learning, everyone at Preferred Networks who has supported the launch and operation of the service, and all the users who have said PaintsChainer was fun and enjoyed using it. We will make PaintsChainer even greater while continuing to take on other new challenges.

 

● Summary

 

● 21st Japan Media Arts Festival Exhibition of Award-winning Works

All the award-winning works that represent the media arts of the modern times will be shown to the public during the exhibition, which also includes symposia, artists’ talks, workshops, and other related events.

 

※PaintsChainer™ is the registered trademark of Preferred Networks, Inc. in Japan and other countries. Other company names and product names written in this release are the trademarks or the registered trademarks of each company.

Call for applications for PFN AI residency program 2018-2019 in Tokyo

Preferred Networks (PFN) will be organizing an AI residency program in Tokyo for students from outside of Japan in 2018-2019.

We are a growing startup with about 120 members based in Tokyo, Japan, focusing on applying deep learning to industrial problems such as autonomous driving, manufacturing, and bio-healthcare. We are actively developing the deep learning framework Chainer.

We are looking for brilliant students who have expertise in various topics, such as deep learning, reinforcement learning, computer vision, bioinformatics, natural language processing, distributed computing, simulation, etc.

In previous years, by selecting highly capable interns and encouraging them to tackle challenging and important problems, some of our interns were able to have their work published at top conferences such as ICML, ICCV,  ICRA, and ICLR.

This year, we would like to expand our reach to attract talented students around the world and collaborate with them to tackle challenging problems in AI for a longer period, by introducing this AI residency program.

During the residency program, you will have a unique opportunity to collaborate with our excellent research team members at PFN and work on real-world applications of deep learning, while living in Tokyo; one of the most attractive cities in the world.

 

We are looking forward to receiving your applications! Please see below for the instructions.

●Target of this program

  • Students outside of Japan

 

●Work time & Location:

  • Business hours:
    8 hours/day, 5 days/week (excluding national holidays)
  • Location: Center of Tokyo
    Preferred Networks Tokyo office: Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
    https://www.preferred-networks.jp/en/about

 

●Period & Compensation:

  • The period of the residency program can be flexibly arranged between September 2018 and August 2019, the minimum term is 6 months
  • AI residency program students are paid a competitive salary
  • We will cover residence and travel cost

 

●Requirements:

  • We can only accept Ph.D. students or new graduates who have been accepted for Ph.D. program starting 2018 Fall
    • Applicants are responsible for negotiating with their university for one year leave or deferring the admission to next year
  • Experience in at least one of the technology areas (listed below) other than only attending lectures
    e.g., published a paper, won a competition, part-time work, open source contribution
  • Strong programming skill (any programming language)
  • Fluent in either English or Japanese
  • Able to work fulltime on weekdays at our Tokyo office during the period

 

●Preferred experience & skills:

  • Machine learning and deep learning
  • Experience with NumPy / SciPy / deep learning frameworks
  • Experience with software & service development
  • Experience working with shared codebases (e.g. GitHub / bitbucket / etc)
  • Contribution to open source projects

 

●Candidate themes (subject to change)

   1. Technology areas: Sub-field of machine learning, such as

a. Deep learning theory

b. Reinforcement learning

c. Computer vision

d. Natural language processing

e. Parallel / distributed computing

 

   2. Application areas: Advanced applications, such as

a. Object detection / tracking / segmentation from image / video

b. Robotics / factory automation / predictive maintenance

c. Life science / healthcare / medicine

d. Human machine interaction

e. Design / content creation / visualization

f. Deep learning software (Chainer, CuPy, ChainerMN/CV/RL, etc)

g. Optimization for deep learning hardware

 

●Application information:

  • Resume / CV (PDF format only. Please DO NOT include any personal or private information [e.g., age, race, nationality, religion, personal address, phone number] except name, email address, affiliation)
  • Github account (optional)

 

●How to apply:

  • Please fill the google forms and submit
  • Due: March 20th, 11:59 pm Tuesday (PDT)

  • No late submission will be accepted
  • The review process takes about 6-8 weeks after submission
  • Usually, getting a visa for working in Japan takes up to 3 months

 

●Interview process:

  1. Document review
  2. One-way video interview (webcam, recording)
  3. Skype interview in English or Japanese (multiple times if necessary)

 

If you have questions, please contact us at hr-pfn@preferred.jp (Sorry but no late application is accepted for fairness)