News

PFN members gave a tutorial on deep learning implementations at AAAI-17

[San Francisco, February 5th] Preferred Networks members, Seiya Tokui and Kenta Oono, gave a tutorial titled “”Deep Learning Implementations and Frameworks (DLIF)” at an international conference (AAAI-17).

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.

[Closed] 2nd Call for application: 2017 summer internship in Tokyo

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-networks.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 and travel cost 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 information:

– Resume / CV (PDF format only. Please DO NOT include any private information e.g. age, personal address, phone number, etc.)
– Name, e-mail address, affiliation
– Github account (optional)
– Linked.in account (optional)


How to apply:

– Please fill the google form. (Application is now closed and no e-mail application will be accepted)
Due: January 20th, 11:59pm Friday (PST)
– No late submission will be accepted
– 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)

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

[Closed] Call for application: 2017 summer internship in Tokyo, Japan

Note: this application is now closed. We are planning to have one more turn early January.

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-networks.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)

Use case of Chainer Playground in Broadband Tower Space Weather Forecast research group

The BroadBand Tower Space Weather Forecast research group at Kyoto University is performing research on predicting solar activities that affect the human civilization every day, by applying Deep Learning technologies to a large amount of astronomical measurement data, collected in a cooperative effort between Kyoto University and people all over the world. (https://www.usss.kyoto-u.ac.jp/bbt.html)

The group has conducted a series of lectures on the prediction of space weather based on Chainer ((http://chainer.org), a Deep Learning framework developed by Preferred Networks. During the lectures, using an early access version of Chainer Playground, a Chainer environment that can be used via a web browser, the students can learn the basics of Deep Learning and the usage of Chainer without installing Python and the various numerical libraries used by Chainer on their laptops.

Takayuki Muranushi, adjunct lecturer at Kyoto University, commented: “This online self-studying environment is very helpful, since it does not require the environmental setup that usually takes a few weeks, and students can use it regardless of the operating system and performance of their laptops.”

PFN recognized as one of most innovative and exciting Japanese startups at 2016 US-Japan Innovation Awards

Toru Nishikawa, CEO, receives “Innovation Showcase” award at the symposium

Preferred Networks recognized as one of most innovative and exciting Japanese startups at 2016 US-Japan Innovation Awards

Palo Alto, CA — July 22nd, 2016 — Preferred Networks, Inc. (PFN) was selected one of the five “Innovation Showcase” companies at 2016 US-Japan Innovation Awards Symposium held in the Stanford University Arrillaga Alumni Center.

US-Japan Innovation Awards is operated by the Japan Society of Northern California in collaboration with the Stanford University US–Asia Technology Management Center. The “Innovation Showcase” is given to most exciting Japanese startup companies that have potential to disruput or transformational globally and to be known to the Sillicon Valley.

 

About Preferred Networks

Preferred Networks Inc. (PFN) is a Tokyo-based startup focusing on applications of latest artificial intelligence technologies to emerging problems in the Internet of Things (IoT). PFN’s vision is the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are intelligent. PFN develops software related to deep learning and IoT, and provides Deep Intelligence in-Motion solutions. PFN collaborates with many world-leading companies in industries, such as FANUC for intelligent robots and Toyota motors for autonomous driving. For more information please visit: www.preferred-networks.jp/en

PFN achieves Second Place at “Pick Task” of Amazon Picking Challenge

 
 
A team of Preferred Networks researchers and engineers participated in the Amazon Picking Challenge from June 29 to July 3, 2016, at Leipzig, Germany, and achieved 2nd place (score tie with 1st place) in the “pick task” and 4th place in the “stow task”.

The Amazon Picking Challenge is a competition with the objective to build a robot that can automate typical warehouse tasks without human intervention. In the “pick task” the objective is to take 12 specified items out of a shelf that also contains around 46 other items and put them into a box. In the “stow task” the objective is to take all 12 items from a box and put them into a shelf that already contains around 34 other items. In these tasks, the robot must operate without any human control. The Challenge combines object recognition, pose recognition, grasp planning, and motion planning.

The PFN Team used state-of-the-art Deep Learning algorithms and Chainer, a Python-based Open Source Deep Learning framework, on input data obtained from image and 3D location sensors for object detection and localization of the best approach position for each object. On the hardware side, the team used two FANUC robot arms of type M-10iA, equipped with self-built specialized end effectors to grab items of various characteristics reliably, then built the robot motion control from scratch.

Expertise in these technologies enabled Preferred Networks to build a robot that can compete with the world’s leading robotics research groups and companies in only three months’ time and is an important milestone on the way to revolutionize the industrial IoT and robotics.

References:

PFN will participate in Amazon Picking Challenge 2016

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.

Manufacturing Automation Leaders Collaborate: Optimizing Industrial Production through Analytics

TOKYO, — April 18, 2016 —FANUC CORPORATION, the world’s leading supplier of robotics and factory automation, is collaborating with Cisco, the worldwide leader in IT-enabled digitization, as well as Rockwell Automation, the world’s largest company dedicated to industrial automation and information solutions, and Preferred Networks, a leading provider of Artificial Intelligence solutions. The companies will work together on the development and deployment of the FANUC Intelligent Edge Link and Drive (FIELD) system, a platform that connects not only Computer Numerical Control machines (CNCs) and robots, but also peripheral devices and sensors – to deliver analytics that optimize manufacturing production.

The FIELD system will be a platform for the delivery of advanced analytics for FANUC CNCs, robots, peripheral devices, and sensors used in automation systems. It will drive improved machine reliability, quality, flexibility, and speed – elevating the Overall Equipment Efficiency (OEE) and increasing manufacturing profitability. It will also provide advanced machine learning and deep learning capabilities. By working with Cisco, Rockwell Automation, and Preferred Networks – FANUC will offer a complete solution including network and compute infrastructure, applications, and an enabling middleware platform. With this open platform, application developers, sensor and peripheral device makers, system integrators and others can build solutions that improve equipment efficiency, manufacturing output, and quality.

Extension of Current Successes
The FIELD system extends the success of the existing FANUC ZDT (Zero Downtime) connected robots project; ZDT is built with Cisco cloud, IOT data collection software, and end to end security capabilities. The companies are working together to implement systems for major automotive manufacturers by leveraging the Allen-Bradley Stratix Ethernet switches from Rockwell Automation to connect robots to a Cisco Unified Computing System (UCS) – all of this running on FANUC and Cisco’s ZDT Data Collection software. Automotive customers who have implemented this system are quickly realizing a significant decrease in production downtime as well as increased cost savings.

Intelligent Manufacturing
The FIELD system will bring the power of advanced machine learning and artificial intelligence into the hands of customers and application developers — driving manufacturing to a new level of productivity and efficiency. FANUC and Preferred Networks have established these new technologies for applications such as Bin Picking robots, anomaly detection, and failure prediction. The FIELD system combines both artificial intelligence and edge-computing technologies – enabling distributed learning. Data is generated from robots and machines and is processed in real time at the edge of the network. This allows those devices to intelligently coordinate and collaborate in a flexible manner – resulting in sophisticated manufacturing practices not before possible.

 

Who provides What for the FIELD System?

FANUC: Provides CNCs and robots with embedded sensors to track key variables required to improve machine reliability, quality, and speed.

FANUC, Cisco and Preferred Networks: Provides enabling middleware Platform Software, as well as security and application lifecycle management applications. It will also leverage Preferred Networks’ open deep learning framework (Chainer), IoT Stream Engine (SensorBee) – and other advanced machine learning libraries within its Deep Intelligence in-Motion (DIMo) platform.

Cisco and Rockwell Automation: Provides networking, compute and security infrastructure to connect the robots, CNCs, and other cell equipment to the FIELD applications. Based on the Rockwell Automation and Cisco collaboratively developed Converged Plantwide Ethernet (CPwE) architecture – this further drives improved security, connectivity, flexibility, and scalability – all of which allows connection from a single, small cell to large factory with hundreds of cells.

FANUC, Rockwell Automation and Preferred Networks: Delivers the initial Application Software on top of the FIELD middleware and infrastructure platform. This will extend the LINK-i and ZDT applications currently deployed, along with additional deep learning applications from the FANUC and Preferred Networks partnership. It is planned that Rockwell Automation manufacturing software products (including FactoryTalk View, FactoryTalk VantagePoint, and FactoryTalk Production Center) will seamlessly integrate with the FIELD system to speed deployment.

 

LEADERSHIP QUOTES

Sujeet Chand, Senior VP and CTO, Rockwell Automation
“By collaborating with world class companies, Rockwell Automation helps maximize manufacturers’ investments by leveraging the data from their intelligent devices they are using today to drive an enterprise-wide analytics strategy. With a secure scalable compute approach to analyze this data – from device to the enterprise – users can improve operations and make more informed decisions tailored to meet the needs of their organizations.”

Rowan Trollope, SVP IoT and Applications, Cisco
This collaboration represents a historic shift in the industry, with IoT, industrial automation and machine learning coming together to make the factory of the future a reality. It’s been talked about for years, but now it is really happening. Cisco couldn’t be more thrilled to be a part of this effort, one that will be key to our positioning in other industries that want to realize the benefits of digitization.”

Toru Nishikawa, CEO, Preferred Networks
“PFN is excited that this collaboration will further accelerate the advancement of the manufacturing industry. Since the start of our work with FANUC, leveraging machine learning and artificial intelligence has been aimed not only at making machines and robots smarter, but also towards a continuous improvement of manufacturing productivity through intelligent real-time coordination and collaboration between robots and machines. We are confident that FIELD will play a central role in making that vision a reality.

  

About FANUC CORPORATION
FANUC CORPORATION, headquartered at the foot of Mt. Fuji, Japan, is the global leader and the most innovative manufacturer of Factory Automation, Robots and ROBOMACHINE’s in the world. With 252 offices in 46 countries, FANUC provides world-class service and support to customers globally. Since its inception in 1972, FANUC has contributed to the automation of machine tools as a pioneer in the development of computer numerical control equipment. FANUC technology has been a leading force in a worldwide manufacturing revolution, which evolved from the automation of a single machine to the automation of entire production lines. For more information visit: http://www.fanuc.co.jp/eindex.htm.

About Cisco
Cisco (NASDAQ:CSCO) is the worldwide leader in IT that helps companies seize the opportunities of tomorrow by proving that amazing things can happen when you connect the previously unconnected.
For ongoing news, please go to http://thenetwork.cisco.com.

About Preferred Networks
Preferred Networks Inc. (PFN) is a Tokyo-based startup focusing on applications of latest artificial intelligence technologies to emerging problems in the Internet of Things (IoT). PFN’s vision is the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are intelligent. PFN develops software related to deep learning and IoT, and provides Deep Intelligence in-Motion solutions. PFN collaborates with many world-leading companies in industries, such as FANUC for intelligent robots and Toyota motors for autonomous driving. For more information please visit: www.preferred-networks.jp/en

PFN & PFI Summer Internship Program

As goes the tradition, Preferred Infrastructure (PFI) and Preferred Networks (PFN) will be organizing the joint internship program this summer too. We are looking forward to receiving students who want to join us in creating new technologies and services. Students who previously applied are also welcome to try again this year.

Application Guideline
Qualification requirements and emplyment conditions are same as PFN and PFI, except that you must be as fluent in Japanese as natives if you want to work on PFI’s themes. To which company candidates will be assigned depends on the themes they will work on.

Period
August 1st 2016 – September 30th 2016
(The period of the internship can be flexibly arranged.)

Time & Place
8 hours/day, 5 days/week (excluding holidays)
Otemachi-Bldg. 2F 1-6-1, Otemachi, Chiyoda-ku,Tokyo, 100-1004

Salary
High school: 1200Yen/hour
Technical college/Undergraduate/Graduate: 1500Yen/hour
Transportation expenses (up to 10000Yen/month) are also covered.

Why join the PFN internship program?
・You will be collabolating and be mentored by experts in various fields including information retrieval, natural language processing, deep learning, algorithms, distributed processing, computer vision, etc.
・You will experience every aspect of a startup company. It is valuable to those who want to work in or to be a founder of startups.
・You can make public the results of your work during the internship program, as OSS or paper, etc. (Some restrictions might apply)

Qualification requirements:
We are looking for highly motivated people who have Software development capabilities. Expertise in the fields mentioned above, or prior development experience are taken into consideration, but are not a must. Application requirements are as follows:
・Currently a students 18 of age or older (High school, technical college, college, graduate students, others could also be discussed.)
・Have programming skills (regardless of the programming language)
・Able to work fulltime on weekdays at our Tokyo office
・If you want to work on PFI’s themes, you must be as fluent in Japanese as natives (fluency in English and Chinese are welcome).
# We will prepare accommodation for those who live far from Tokyo.
# Women and Non-Japanese speakers are also very welcome
# You can still apply even if you are not a fully-fledged application developer

How to apply:
Please send the application documents below to intern-apply@preferred.jp
Questions about the internship program are also accepted by this address.

Application documents:
・Resume (Name, address, contact information, background)
・Proof of skills; explain your strengths and expertise fields, etc. (one A4 page)
Ex: List of papers, received awards, developed/used Software&Services, programming contests participation history, personal website/blog, twitter account, etc.
・Motivation letter (Please include your interest field/theme and your expectations from the internship.)
# This is a very important for both the admission procces, and the internship theme selection, so please describe in as much detail as possible.

Application Deadline
May. 8th, 2016 (sun.) 23:59 (JST)

Selection process:
Selection process:
1. Application documents screening: Starts on May 8th. Results are announced to applicants via email in a week.
2. Pre-interview task: Applicants who passed receive a task via email.
3. Interviews: First interview starts in June (Skype is used in case of remote applicants)
4. Final results: Result of the selection processes is announced to applicants in the beginning of July.

Themes
@PFI
1.Technology field: Natural Language Processing for Japanse documents
・Automatic document classification
・Named entity recognition
・Synonym Extraction
・Automatic summarization
・Similarity search

2. Technology field: Speech Recognition for Japanese speech
・Speech recognition by deep learning.
・Document recognition by deep learning.
・Speaker identification
・Noise filtering

@PFN

Application fields
・Image recognition
・Anomaly detection
・Robotics (bipedal walking, car control)
・Genomics, Epi-genomics, proteomoics
・Art generation (Generation of images, videos, sounds etc.)
・Application of deep learning to embedded system
Stream processing for IoT

Research fields
・Machine learning with limited labels (One-shot, Weakly superivsed, Semi-supervised Learning)
・Distributed algorithm, Distributed deep learning
・Deep generative model
・Machine learning using simulators

Change of Address

This is to inform you that we will be moved to the below address as of April 25, 2016.

[New location]
Otemachi-Bldg.2F
1-6-1, Otemachi
Chiyoda-ku,Tokyo, 100-1004

From April 25th, we will do normal operation.
Thank you for your understanding and support.

FEB18 Shohei Hido Speaks at JAPAN-UK Robotics and Artificial Intelligence Seminar @ London

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.

TOYOTA and PFN announce capital alliance

PFN is at International Robot Exhibition 2015

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/

R&D partnership deal with NVIDIA

PFN and NVIDIA agreed on a partnership deal in research and development of deep learning technologies which are used to optimize various industry applications.

Background:
PFN owns leading edge technologies in distributed machine learning, focusing on deep learning, in the areas of industrial robotics, autonomous driving and drug discovery, and is leading the transformation of these industries. These technologies require high-performance data processing capabilities.
NVIDIA has been and will be providing high performance computing technologies accelerated by GPUs and advanced software solutions, in order for data scientists and researchers to maximize their uses of deep learning technologies.

Contents:
PFN has been using NVIDIA’s GPUs for its research and development activities in deep learning. Chainer, an Open-Source Software developed by PFN, uses GPUs to accelerate computing in deep learning modeling processes.
It is necessary to process data on edge devices as well as in the cloud to utilize deep learning technologies in industry segments such as IoT. GPUs play an important role for these edge devices to become more intelligent. We believe this partnership will accelerate the industry trends toward the edge optimization.

Oct22 PFN Career information event

On October 22 (Thursday) Preferred Networks will hold a career information event with talks from the PFN founders Nishikawa and Okanohara, as well as researchers and engineers working at PFN. There will also be a Q&A session where you can get in touch with many of our employees.

Please note: This time, all the talks will be in Japanese, but we are planning to have another event aimed at an English-speaking audience soon. Follow us on Twitter (https://twitter.com/PreferredNet) to stay updated!

Time/Date: Oct 22, 2015, 18:30-20:30 (doors open at 18:00)
Location: エムワイ貸会議室 Ochanomizu
Address: Ochanomizu Union Building (4th floor), 2-1-20 Kandasurugadai, Chiyoda-ku, Tokyo 101-0062

PFN is quickly expanding and we are always looking for great talent. Please visit our job page for more information: https://www.preferred-networks.jp/job_en

Sep7-11 Tobias Pfeiffer Speaks at MobiCom Panel@Paris

Tobias Pfeiffer, Software Engineer at Preferred Networks, participated in a panel at the 21st International Conference on Mobile Computing and Networking (MobiCom), one of the top conferences in the field of mobile computing and wireless networking, that was held from September 7-11 in Paris (France).

In the panel called “Big Data, IoT, … Buzz Words For Academia Or Reality For Industry?”
which was chaired by Wenjun Hu (Yale University, USA), Rui Aguiar (Universidade de Aveiro, Portugal), and Hiroshi Esaki (University of Tokyo, Japan), he presented Preferred Networks’ approach to the Internet of Things, based on Edge-Heavy Computing and Deep Learning. Afterwards, the panelists discussed with the audience the state and future development of the IoT, for example questions such as: “How can academic institutions obtain real-world data for their research?” or “What are important developments that need to happen to advance the current state of IoT?”

FANUC and Preferred Networks announce capital alliance

Tokyo, Japan – FANUC CORPORATION (President: Yoshiharu Inaba; hereinafter, FANUC) and Preferred Networks, Inc. (President and CEO: Toru Nishikawa, hereinafter, PFN) announced today that the two companies have reached an agreement on a capital alliance to collaborate on the technical development of advanced intelligence for industrial machines and robots through application of machine learning and deep learning.

1.Overview of investment by FANUC

Amount of finance: 900 million JPY

Acquired stock: 6.0% of the total number of issued stock of PFN

Method of investment: FANUC underwrites the allocation of new stock of PFN

Date of investment: By the end of September 2015 (Expected)

2.Background of strategic and capital alliance
Intelligent IoT machines will drive the manufacturing industry in its evolution towards Industry 4.0 and the Industrial Internet. However, as machines increase in both intelligence and number, the volume of data being generated also increases, making the problem of how best to utilize this big data more and more important. Advanced automation of industrial machines and robots will be the key to increasing production and efficiency in the future, but it will also generate data that require new methods of analysis. FANUC and PFN have begun joint projects using machine learning and deep learning to analyze such data at the edge of the network—in the machines themselves—in real-time. Today, the two companies announce a capital alliance to accelerate this collaboration.

3.Future direction of collaboration through capital alliance
Until now, deep learning and machine learning have been used primarily for Internet-based applications and services or for retrospective analysis. However, the powers of deep learning and machine learning have not yet been exploited in applications for automated manufacturing processes in the physical world. Through this collaboration, FANUC and PFN aim to integrate PFN’s cutting-edge deep learning and machine learning technologies with FANUC’s industry-leading industrial machines. The focus of the collaboration will be on giving FANUC machines the ability to learn autonomously. For example, FANUC machines will be able to autonomously learn

-complex and non-repetitive behaviors
-strategies and methods for cooperation with other machines
-methods of predicting and repairing malfunctions.

The collaboration will advance the state-of-the-art in the automation of manufacturing processes, including those in Industry 4.0. The two companies plan to work together to innovate throughout the entire range of FANUC products.

[ Contact ]
Preferred Networks, Inc.
pfn-info@preferred.jp

June 17 Announcement for business alliance with Asian Frontier

Announcement of business alliance of Preferred Networks Inc. and Asian Frontier Co., Ltd.
June 17th 2015

Preferred Networks Inc.(PFN) and Asian Frontier Co., Ltd.(Asian Frontier) came to the basic agreement on their business alliance in appointing Asian Frontier as a controlling partner of PFN’s partner companies, who wish to apply PFN’s machine learning and deep learning technologies into their solutions.

[Background]
PFN’s machine learning and deep learning technology have broad deployment potentiality and innovation capability in any business sectors under today’s evolutionary advanced network infrastructure. But, same as many other advanced technologies, deploying such technologies into the business scene, it requires to clarify each business goals and accelerate the development of partner’s business applications properly, which normally took time in the past.
To respond to this issue, PFN appointed Asian Frontier, who has deep experience in Business and IT consultation globally, to efficiently drive the deployment of PFN technology into their partner companies’ business solutions.

[Contents]
PFN plans to establish “Partner Program” for those companies who wish to apply PFN’s machine learning and deep learning technologies into their business solutions.
While PFN keeps focus on the development of his own advanced technology, under this business alliance with Asian Frontier, who brings deep Business and IT consultation know-how, “Partner Program” will benefit partner companies in accelerating their development efficiently.

This “Partner Program” will be started from providing a package software called “DIMo(Deep Intelligence in Motion)”. DIMo provides a platform where each partner company to be able to develop their dedicated solution over the machine learning and deep learning technology.

Asian Frontier, as a controlling partner of this “Partner Program”, will enhance partner companies’ solution development in efficient and timely manner.

* About Asian Frontier
Company Name: Asian Frontier Co., Ltd.
Address: 1-11-28, Nagatacho, Chiyoda-ku, Tokyo, JAPAN
Representative Director: Miguel Angel ESTEVEZ ABE
Founded: September 5th 2007

[Contact]
Preferred Networks Inc.
e-mail: pfn-info@preferred.jp

Asian Frontier Co., Ltd.
http://asian-frontier.com
e-mail:toiawase@asian-frontier.com

June 9-10 Interop Tokyo 2015

Toru Nishikawa, CEO, and Daisuke Okanohara, Executive Vice Present, gave keynote talks at Interop Tokyo 2015 held on Feb 9~10th in Tokyo. On the 10th, PFN announced parthenships with Panasonic and FANUC.

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Please refer to the news links below for more details on the partnerships announced.

NEWS
Partnership with Panasonic announcement
Partnership with FANUC announcement

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YouTube
A video demonstration of the technology can be viewed at the YouTube link below:
Robot control using Distributed Deep Reinforcement Learning (7:13)
Preferred Research Blog
The technology used in the demo was developed by Eiichi Matsumoto. He explains it in more details at the Research blog below:
Preferred Research Blog (Japanese)

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PFN also announced the release of a new deep learning framework “Chainer” as Open Source Software. Seiya Tokui, the Chainer project leader, explains more details about the new framework at the research blog below:

Official site
Chainer.org
NEWS
Release Announcement of “Chainer” : Open Source Deep Learning Framework
Preferred Research Blog
Preferred Research Blog(Japanese)

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The announcement were covered in several Japanese and Global Medias:

Wall Street Journal 
http://www.pressreader.com/china/the-wall-street-journal-asia/20150610/281586649216941/TextView
http://www.wsj.com/articles/panasonic-in-deal-to-help-computers-learn-on-the-job-1433840398
http://blogs.wsj.com/cfo/2015/06/10/the-morning-ledger-startups-employ-novel-metrics-to-lure-investment/

The Nikkei Newspaper:(Japanese)
http://www.nikkei.com/article/DGXLASDZ10HRX_Q5A610C1TJC000/
http://www.nikkei.com/article/DGXLASDZ11HJK_R10C15A6TJC000/

The Nikkan Kogyo Newspaper:(Japanese)
http://www.nikkan.co.jp/news/nkx0120150611aaam.html

IT Pro(Japanese)
http://itpro.nikkeibp.co.jp/atcl/news/15/061001951/?top_nhl

June 10 Announcement for R&D alliance with FANUC Corporation

FANUC Corporation. and Preferred Networks Inc. (PFN) came to the basic agreement on their alliance in the research and development utilizing machine learning and deep learning technologies, which will enable machine tools and robotics to be highly intelligent.

Background:
IoT has attracted a lot of attention as a key technology to support next generation manufacturing technologies such as Industry 4.0 or Industrial Internet. With the rapidly increasing amount of data, question on how to utilize big data, or how to process the data in real time, remain unsolved. To resolve this question, we focus on machine learning and deep learning technologies which intelligently process big data at edge, at real time and enable high level of automation at manufacturing sites such as machine tools and robotics .

Contents:
Application of machine learning and deep learning has been limited to cyber space. It has not been applied to machine tools and robotics in physical manufacturing sites. We will combine PFN’s expertise in machine learning and deep learning, and FANUC’s numerous technologies in the machines and robotics. We aim best in class automation processes in many layers of manufacturing sites including those where Industry 4.0 applies. The alliance with PFN will cover FANUC’s operations as a whole. Here are some examples that we are aiming for;
Machine Tools and Robotics perform the followings
– Self learning
– Learn to cooperate by themselves
– Self detection of deficiencies and supplement each other
Therefore, it will bring the following results.
– Highly optimized operations of machines and robotics
– Advanced level of protective maintenance
– Non stop factory operations

June 10 Announcement for R&D alliance with Panasonic Corporation

Nishikawa, CEO of PFN Inc. announced in his keynote speech at Interop 2015 Tokyo that PFN will enter into research and development alliance with Panasonic Corporation.

In this alliance, we will aim to integrate Panasonic’s forefront hardware technologies with our machine learning and deep learning technologies which we believe most advanced. Panasonic expects the research outcomes to be applied to automotive area and will also improve functionality of its digital AV devices.

PFN will expand the application of technology elements which we will obtain through the alliance, mainly in deep learning area, into other industry segments.

Release Announcement of”Chainer”: Open Source Deep Learning Framework

Preferred Networks, Inc. and Preferred Infrastructure Inc. have released Chainer, a Deep Learning Framework. We are proud to release this Powerful, Flexible, and Intuitive Framework as an open source software.
We will provide latest news on Chainer through the following media:

Official Website: http://chainer.org
Twitter:@ChainerOfficial
Google group:Chainer User Group

We will also post announcements to Google+ Deep Learning Community

For design concepts and technical characteristics of Chainer, please refer to Preferred Research Blog (Japanese)

PFN is at Cisco Live! at San Diego

Preferred Networks, Inc. (PFN) is pleased to announce its participation in Cisco Live! 2015 in San Diego, California, June 8th-11th.

 

 

 

PFN is providing a live demo of surveillance video analytics based on its product, Deep Intelligence™ in Motion (DIMo) v1.0, which is designed to realize network-wide intelligence for IoT. In addition, PFN is demonstrating its latest results using deep learning for autonomous optimization of machine behaviors. A new demo video is being shown for the first time that shows how virtual race cars learn to control themselves using deep reinforcement learning.

 

 

In addition to developing video analytics products, PFN is also focusing on research and development for revolutionizing industrial IoT areas including manufacturing and smart transportation/cities. These new methods use cutting-edge deep learning technologies to combine information extracted from multiple types of sensors. PFN’s demonstration is located at booth #3131 in the World of Solutions hall, as part the booth for Cisco Entrepreneur in Residence, the incubation program in which PFN is participating. Please come to our booth and enjoy our new technologies.

 

 

PFN&PFI Software Engineering Summer Internship

Winning ITpro EXPO AWARD 2014

From October 15th, we have showed a demonstration system of ” Video analytics solution using Deep learning” in the Intel pavilion at ITpro EXPO 2014. We proudly announce that our system won the IT pro EXPO AWARD 2014. We appreciate the audience who stopped by our booth during the three days.
The demonstration was based on the latest prototype for retail applications. It detects humans, estimate their locations inside the booth, and show them on the display as points on the floor map. Since multiple cameras work together, one can be detected even if one camera cannot see her or him due to obstacles or occlusion. Some characteristics of detected humans, such as gender, age, and wearing-jacket-or-not, are recognized using deep learning models, and the results can be visualized as proportion by area or funnel analysis. Based on this technology, we continue to develop video analytics products and solutions for retail industry.

Note: ITpro EXPO AWARD is given to advanced products/services and appealing demonstrations, based on the reviews by Nikkei BP IT-press editors.

October 7-11: CEATEC JAPAN 2014

On October 7th-11th, Preferred Networks, Inc. showed a video analytics demonstration system based on Jubatus in a booth of the NEDO (New Energy and Industrial Technology Development Organization) at CEATEC Japan 2014.
CEATEC is one the biggest business showcases of technology companies in Japan. During the 5 days we had a lot of attendees visited our booth seeing the live demonstration to find people and recognize their characteristics based on machine learning technologies. The vice minister of the Ministry of Economics, Trade and Industry also stopped by our booth hearing to the explanation by our chief strategy officer, Hasegawa.
At October 10th morning, our chief research officer, Hido gave a presentation titled “Data analytics technologies for realizing intelligent IoT” talking about the IoT applications today and the vision of our company at the internal stage.
We continue doing research and development on advanced IoT applications including this demo.

> Ceatec JAPAN 2014

PFN Media Coverage: October 15

Nikkei Electronics
“IoT特化の機械学習で新会社,NTTも出資”

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Cloud Watch
“NTTとPFNが資本・業務提携、IoT向け次世代ビッグデータ技術の確立目指す”

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Converge! Network digest
“NTT Backs a Start-up for Distributed Processing and Machine Learning”

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telecompaper
“NTT, PFN to develop Big Data technologies for IoT”

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The Wall Street Journal

“NTT, Toyota Seek ‘Deep Learning’ Expertise”

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IT pro by Nikkei Computer

“PFIが深層学習専業の「Preferred Networks」を設立、NTTが出資しトヨタと共同研究も”

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Business capital tie-up contract with NTT

Joint R&D with Toyota on Self-driving Cars

Preferred Networks, Inc. (HQ: Tokyo, President & CEO: Toru Nishikawa) today announced that the company starts a joint research and development project with Toyota Motor Corporation (HQ: Aichi, CEO: Akio Toyoda) on self-driving cars to examine the feasibility of its machine learning and deep learning technologies.

October 2: CSO Junichi Hasegawa to speak at OECD’s Global Forum

October 16: Tobias Pfeiffer to speak at 5th EU-Japan Symposium in ICT Research and Innovation in Brussels

October 7: CEO Toru Nishikawa to speak at ICT Innovation forum 2014

The Nikkei Quotes the Business of PFN

The Nikkei mentioned our performance of visual recognition technologies on Sep 29 in the article of “AI”.
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Website update announcement

We are pleased to announce the release of our new corporate website.
Please keep in touch!