PFN is developing technology to create a society where robots can actively support our daily living activities. Unlike in controlled and regulated environments like factories, robots in the home need to respond flexibly to dynamic and complex situations and communicate naturally with humans.

At its exhibition booth (A060), PFN will demonstrate the new robot system using HSRs (Human Support Robots) developed by Toyota Motor Corporation and showcase their ability to keep a cluttered room neat and tidy. This has been difficult to achieve using conventional technologies based on object recognition and robot control. The deep learning-based robots can recognize various scattered household items like clothes, toys, and stationery, grasp and place them in their designated locations. In the demonstration, PFN will also show that these cleaning robots can be controlled intuitively through verbal and gestural instructions.

For technical details, please visit the website.
https://projects.preferred.jp/tidying-up-robot/en

The fully-autonomous tidying-up robot system has been awarded the Semi-Grand Prix in the Industry/Market category at CEATEC Award 2018, which recognizes innovative technologies, products, and services from among a large number of exhibits at CEATEC JAPAN 2018.

• PFN exhibition booth

・Dates/time：10:00–17:00 Oct. 16–19, 2018

・Location：Booth A060, Total Solutions Area, International Exhibition Hall 2

・Exhibit：Technical demonstration of personal robots “fully-autonomous, tidying-up robot system” (first public exhibition)

In addition, PFN President and CEO Toru Nishikawa will make a keynote speech entitled “Robots for Everyone” on the opening day of the CEATEC exhibition. As well as introducing the outlook on future technologies, he will explain how PFN is applying cutting-edge technologies of machine learning, deep learning, and robotics to solve real-world problems.

• CEATEC Keynote Future
• ・Date/Time： 12:30～13:15 Tuesday on Oct. 16, 2018・Location：Convention Hall, International Conference Hall, Makuhari Messe・Speaker：Preferred Networks President and CEO Toru Nishikawa・Title/outline：”Robots for Everyone”The possibilities of robots are rapidly expanding thanks to the advancement of machine learning technology. Fusing the machine learning technology with robotics is essential for making robots that can flexibly respond to unexpected situations and execute various tasks like a human. Soon, we will begin to see an increasing number of robots helping to perform tasks in many places, working alongside humans. As well as introducing the current technology and PFN’s new initiative, President Nishikawa will provide insight into how we can leverage technology in the new era of these kinds of robots and the outlook on future technologies.

投稿 Preferred Networks unveils a personal robot system at CEATEC Japan 2018, exhibiting fully-autonomous tidying-up robots は Preferred Networks, Inc. に最初に表示されました。

The software will make it possible to construct an inspection system quickly and inexpensively with minimal training datasets

Oct. 11, 2018, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has developed Preferred Networks Visual Inspection,  high-precision, visual inspection software based on deep learning technology. PFN will start licensing the software to partner companies in December 2018. We will announce the new software at a new product seminar (N3-5) on Thursday Oct.18 during the CEATEC Japan 2018 exhibition held in Makuhari Messe near Tokyo.

The use of machine learning and deep learning technologies is spreading rapidly in many areas including the manufacturing floor. However, existing visual inspection systems based on deep learning require as many as several thousand images for training, as well as engineers to annotate the considerable number of images to facilitate the training process. Poorly explained inspection results are also among other issues that have been tackled.

In order to solve these problems, PFN has utilized its technical know-how acquired through the development of the deep learning framework Chainer^(TM) and applications of deep learning to our main business domains – transportation systems, manufacturing, and bio-healthcare – to develop the Preferred Networks Visual Inspection.

• The main features of Preferred Networks Visual Inspection:

1. An inspection line can be set up with a small amount of training data (as few as 100 images of normal products and 20 images of defective products)
2. Plastic, metal, cloth, food, and other materials with various shapes can be handled
3. Results are well-explained through visualized anomalies such as scratches, foreign objects, and stains
4. Training is made easy even for non-engineers with intuitive user interfaces

Preferred Networks Visual Inspection consists of a training support tool and CPU-based defect detection software. Depending on requirements, our licensed partners will install a combination of system components which include training workstations, inspection PCs, photographing equipment, UIs for visualization and operation. GPU-based, fast detection software is also available as an option.

The new product will enable users to build an easy-to-use and highly reliable auto-inspection system at a low cost in a short period of time. This product can be introduced with ease to the manufacturing lines which have been difficult to automate by existing products due to their high costs and inflexibility. In addition, defects are visualized so that its results can be easily explained. This is useful for passing down inspection skills and sharing knowledge with others in the company.

Comparison of Preferred Networks Visual Inspection and the existing solutions

• New product announcement

PFN will announce Preferred Networks Visual Inspection at a New Technologies and Products Seminar (N3-5) entitled “Visual inspection system and picking robot solution based on deep learning” at CEATEC Japan in Makuhari Messe.

• Date & Time: 12:30-13:30 Thursday Oct. 18, 2018
• Venue: Room B (Exhibition Hall 4) in Makuhari Messe, Chiba
  https://regist.ceatec.com/?act=Conferences&func=Detailed&event_id=7&conference_id=551

PFN will continue to promote practical applications of machine learning and deep learning technologies in the real world.

投稿 Preferred Networks releases deep learning-based, high-precision, visual inspection software は Preferred Networks, Inc. に最初に表示されました。

• CEATEC Keynote Future

・Date/time：12:30-13:15 on Tuesday, Oct. 16, 2018

・Venue：Makuhari Messe international convention complex

・Speaker： President and CEO Toru Nishikawa, Preferred Networks, Inc.

・Title and outline：

Robots for Everyone
The advancement of machine learning technology is rapidly expanding the possibilities of what robots can do. Fusing the machine learning technology with robotics is essential for making robots that can flexibly respond to unexpected situations and execute various tasks much like a human. Shortly, we will begin to see an increasing number of robots helping perform tasks in many places, working alongside humans. PFN President Nishikawa will talk about how we can leverage current technologies in the new era of robots, provide some insight into what lies ahead, and introduce PFN’s new initiative.

• PFN booth

・Duration： Tuesday, Oct. 16 to Friday, Oct. 19, 2018

・Exhibit zone：Total Solutions （Booth No. : A060）

・What will be exhibited?：PFN’s new initiative (to be shown to the public for the first time)

投稿 Preferred Networks will exhibit at CEATEC JAPAN 2018 with CEO Toru Nishikawa scheduled to make a keynote speech titled “Robots for Everyone” は Preferred Networks, Inc. に最初に表示されました。

Chainer, an open source deep learning framework, won the award this year, in the recognition of its dynamic and flexible neural network definition by “define-by-run”.

Chainer is evaluated for the award as follows:
Chainer strives to “bridge the gap between algorithms and deep learning implementations” in its flexible and intuitive Python-based framework for neural networks. Chainer was the first framework to provide the “define-by-run” neural network definition which allows for dynamic changes in the network. Since flexibility is a significant part of the foundations of Chainer, the framework allows for customization that similar platforms do not so easily provide and supports computations on either CPUs or GPUs.

https://opendatascience.com/odsc-east-2018-open-source-data-science-project-award-winner-the-chainer-framework/

About the Open Data Science Conference (ODSC)

ODSC is a conference for people to connect with the data science community and contribute to the open source applications they use every day. Its goal is to bring together the global data science community to help foster the exchange of innovative ideas and encourage the growth of open source software.

About the Chainer Open Source Deep Learning Framework

Chainer is a Python-based deep learning framework developed mainly by PFN, which has unique features and powerful performance that allow for designing complex neural networks easily and intuitively, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.
Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field. （http://chainer.org/)

投稿 Chainer awarded the Open Source Data Science Project Award Winner at ODSC East 2018 は Preferred Networks, Inc. に最初に表示されました。

Kazuo Furukawa, Chairman of IOI 2018 JAPAN Organizing Committee

The support from PFN is a great help in our endeavor to ensure that IOI 2018 JAPAN in which we welcome students from various countries and regions around the world will be held smoothly. Receiving support from companies with employees who have participated in the previous contests such as PFN is quite encouraging for future contestants as well. I expect all the participants who represent the next generation to take this opportunity to expand their network and hope that a new wave of technological innovation will be created through the contest.

Toru Nishikawa, President and CEO of Preferred Networks

PFN has six employees who participated in the previous IOI contests. Acquiring such high-level skills as problem analysis, design of algorithms, and programing will be a great advantage after entering the world of business. I hope IOI2018 will be a wonderful opportunity for students to feel the great joy of programming and improve their skills through friendly competition with both Japanese and international friends.

About International Olympiad in Informatics

International Olympiad in Informatics is one of the international science olympiads that focuses on the field of informatics. Selected groups of students in secondary education from more than 80 countries and regions participate in IOI held every year.
Contestants design algorithms to solve assigned tasks and compete to get the best score based on the performance of their algorithms such as efficiency and quality as well as the programming skill needed to implement the algorithms properly. One of the primary objectives of IOI is to nurture talent who have a network of personal connections around the world and play major roles in the future of advanced IT society by bringing together young students in the same generation gathered from all over the world.

投稿 Preferred Networks support the 30th International Olympiad in Informatics held in Japan は Preferred Networks, Inc. に最初に表示されました。

Based on the fact that using software frameworks is fundamental in deep learning applications, the purpose of this tutorial is to help users to select an appropriate deep learning framework by describing the basics of implementation, design choices, and comparison of the features of the existing frameworks including Chainer,

The presentation slides and sample code can be found here.

AAAI has more than 30 years history as a prestigious academic conference in artificial intelligence. It hosted 24 tutorials this year, with diverse topics from machine learning theory to AI applications to IoT or robotics. The DLIF tutorial attracted the largest number of pre-registrants out of them. This work was co-organized by Dr. Atsunori Kanemura of AIST (National Institute of Advanced Industrial Science and Technology in Japan), also under supervision from Dr. Toshihiro Kamishima and Dr. Hideki Asoh.

（From right to left, Seiya Tokui of PFN, Kenta Oono of PFN and Dr.Atsunori Kanemura of AIST ）

Preferred Networks will continue contributing to academia through open source software, research papers, and tutorial talks.

投稿 PFN members gave a tutorial on deep learning implementations at AAAI-17 は Preferred Networks, Inc. に最初に表示されました。

Preferred Networks will be organizing internship programs next summer in Tokyo. In order to make the process smooth for the students outside of Japan, we open an early bird application opportunity for the first time. We are looking forward to welcoming students who want to join us in creating new technologies and services. Note that similar programs will follow for the students both in & outside of Japan, also after this first call.

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.jp/en/about

Period & Compensation:

– The period of the internship can be flexibly arranged though it is usually held during July & August
– We require minimum of two month (40 business days), in order to be able to tackle a challenging task,
– Interns are paid a competitive salary
– Residence is to be provided

Requirements:

– Formally enrolled in university or research institute outside of Japan during 2017-2018 school year
– Fluent in English (or Japanese)
– Good programming skill (any programming language)
– Computer science basics
– Able to work full-time on weekdays at our Tokyo office during the period

Preferred experience & skills:

– Machine learning and deep learning basics
– Experience with software & service development
– Experience with team development
– Contribution to open source projects

Candide themes (subject to change)

1. Technology areas: Sub-field of machine learning, such as
Deep learning theory
Reinforcement learning
Computer vision
Parallel distributed learning
Weakly supervised learning
Transfer learning
Anomaly detection
Deep Generative Model
Others
2. Application areas: Advanced IoT applications, such as
Image recognition
Robotics & machine control
Life science & medicine
Machine Learning Framework development (Incl. OSS such as Chainer)
Others

(FYI) Projects of 2016 summer interns
DQN with Differentiable Memory Architectures
Multi-modal Deep Generative Model for Anomaly Detection
CNN based robotic grasping for randomly placed objects by human demonstration
Anomaly Detection by ADGM / LVAE
Imitation Learning for Autonomous Driving in TORCS
3D Volumetric Data Generation with Generative Adversarial Networks
Bayesian Dark Knowledge and Matrix Factorization
Automatically Fusing Functions on CuPy
Generation of 3D-avatar animation from latent representations
Response Summarizer: An Automatic Summarization System of Call Center Conversation
Product marketing in conversations

Application documents:

– Resume (CV)

How to apply:

– Application has been closed
– Due: November 23rd, 11:59pm (PST)
– The interview process takes about 2-4 weeks after application submission
– Usually, getting visa support in Japan takes up to 3 months so that the preparation must be done in advance of the internship period

Interview process:

1. Document review
2. Skype interviews in English or Japanese (multiple times if necessary)

投稿 [Closed] Call for application: 2017 summer internship in Tokyo, Japan は Preferred Networks, Inc. に最初に表示されました。

Preferred Networks will participate in the Amazon Picking Challenge 2016 (http://amazonpickingchallenge.org/) from June 29 to July 3 in Leipzig, Germany.

The Amazon Picking Challenge is a competition with the objective to build a robot that can recognize and take out items of various shapes and materials from a shelf and put them into a box – and the other way around – without human intervention. Our team will use Deep Learning technology for visual object recognition in conjunction with multiple robot arms that have specialized sensors and end effectors.

Preferred Networks is conducting research and development on Deep Learning to revolutionize the industrial IoT, and participating in this challenge is one step on the way to achieve this goal.

投稿 PFN will participate in Amazon Picking Challenge 2016 は Preferred Networks, Inc. に最初に表示されました。

Shohei Hido, Chief Research Officer of Preferred Networks, participated in a Japan-UK Robotics and Artificial Intelligence Seminar 2016 that was held on February 18th in London. The event was co-organized by the British Embassy in Tokyo and the Japan Embassy in London in order to discuss and enhance the collaboration between the research communites and govermental agencies in Japan and the United Kingdom.

From Japan, a number of distinguished researchers including Prof. Ishiguro from Osaka University attended the seminar. Hido presented the combination of artificial intelligence technologies with applications in automotive and industrial robotics, and showed a demo as an example of new business innovations in the “Socio-economic Impact” session. After the presentation, he was contacted by the audience including British agencies and local consulting firms for future cooperations.

During the stay in London, the Japan group officially visited the newly-established Alan Turing Institute and several start-up companies. Hido also gave a one-hour talk as a public seminar at Imperial College of London and made connections with attendees including faculty members.

Preferred Networks will continue to increase presense not only in the UK but also world-wide for business collaborations and recruitment activities.

投稿 FEB18 Shohei Hido Speaks at JAPAN-UK Robotics and Artificial Intelligence Seminar @ London は Preferred Networks, Inc. に最初に表示されました。

Preferred Networks, Inc. is pleased to announce its participation in the International Robot Exhibition 2015, as a part of FANUC’s exhibition, in Tokyo, Japan, December 2nd-5th.

International Robot Exhibition 2015 Official Web
http://biz.nikkan.co.jp/eve/irex/english/

投稿 PFN is at International Robot Exhibition 2015 は Preferred Networks, Inc. に最初に表示されました。