Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose this second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, participants compete under a limited environment sample-complexity budget to develop systems which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform where they are trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. In this follow-up iteration to the NeurIPS 2019 MineRL Competition, we implement new features to expand the scale and reach of the competition. In response to the feedback of the previous participants, we introduce a second minor track focusing on solutions without access to environment interactions of any kind except during test-time. Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.

Preferred Networks, as a research-oriented AI startup, participates every year in NeurIPS, the world's biggest machine learning conference. This post highlights our accomplishments and activities at NeurIPS 2019. We are very excited to be a part of it & looking forward to seeing top ML researchers from all over the world there! This year, four papers from Preferred Networks have been accepted for poster presentation. Three of them are based on ex-intern's work and we are very proud of their dedication and high-quality research.

Automation has become part of the global manufacturing line, where robots take on repetitive jobs, like filling boxes or welding a car frame in the same way, day after day. But what if robots could step away from their limited range of tasks, and start to problem solve in complex operational situations, like spotting a malfunction on the assembly line or identifying a better compound for a part? And how could robots enabled with "deep learning" – where algorithms learn from large amounts of data collected via experience – begin to share insights with other robots, to increase innovation in all kinds of settings, from factories to self-driving cars on the road to early cancer detection and drug discovery in hospitals? These questions are the focus of Preferred Networks, a cutting-edge artificial intelligence company founded in 2014. The Tokyo-based firm, which is worth roughly $2 billion, according to CB Insights, is a symbol of Japan's sweeping strategic innovation initiative, where AI and robotics are viewed as keys to both solving social issues and achieving new economic growth.

PFN to work with PyTorch and the open-source community to develop the framework and advance MN-Core processor support. Preferred Networks, Inc. (PFN, Head Office: Tokyo, President & CEO: Toru Nishikawa) today announced plans to incrementally transition its deep learning framework (a fundamental technology in research and development) from PFN's Chainer to PyTorch. Concurrently, PFN will collaborate with Facebook and the other contributors of the PyTorch community to actively participate in the development of PyTorch. With the latest major upgrade v7 released today, Chainer will move into a maintenance phase. PFN will provide documentation and a library to facilitate the migration to PyTorch for Chainer users.

For Preferred Networks, building tech for self-driving cars and smart factories is the daily routine. One of its biggest opportunities is to devise a business model that complements its technology. If you live outside Japan or work outside of the machine learning community, you may not have heard of Preferred Networks (PFN). This Tokyo-based start-up has been incrementally realising the potential of AI to reshape the internet of things (IoT) – ever since shifting focus from search engines to deep learning (and dropping its original moniker, Preferred Infrastructure) in 2014. But some of the company's biggest breakthroughs appear modest at first glance.

The companies said that by combining their respective technologies and know-how, Toyota and PFN would develop service robots capable of learning in typical living environments and executing a variety of tasks. Toyota's robotic development has for 15 years included a variety of different applications besides automotive manufacturing, including social, medical and rehabilitation. Its Human Support Robot, first developed in 2012, is intended to support independent living, including for the elderly and disabled. Toyota is working to improve robotic capabilities in tasks such as picking up and carrying objects, to potential uses in applications such as health management. According to the company, HSRs continue to undergo trials at elderly-care facilities; it has also been used by research and development at 49 organizations in 13 countries.

TOKYO (Reuters) - Toyota Motor Corp has designs on making robot helpers for your home, and has enlisted a Japanese startup that specializes in artificial intelligence to jump-start its plan. Japan's biggest automaker and Tokyo-based Preferred Networks Inc will carry out joint research to develop so-called service robots that are "capable of learning in typical living environments", the companies said in statements on Wednesday. The two firms have already been collaborating on driverless vehicles since 2014. Eighty-year-old manufacturing giant Toyota is trying to transform itself and adapt to technology, such as ride-hailing and automated driving, that is disrupting the auto industry. Toyota sees robots as part of that effort, particularly in Japan, where it aims to have them in homes and hospitals to support one of the world's fastest ageing populations.

Toyota is enlisting the help of startup Preferred Networks, a Japanese company founded in 2014 with a focus on artificial intelligence and deep learning, to help move forward its goal of developing useful service robots that can assist people in everyday life. The two companies announced a partnership today to collaborate on research and development that will use Toyota's Human Support Robot (HSR) robotics platform. The platform, which Toyota originally created in 2012 and has been developing since, is a basic robot designed to be able to work alongside people in everyday settings. Its primary uses involve offering basic car and support assistance in nursing and long-term care applications. Equipped with one arm, a display, cameras and a wheeled base, it can collect and retrieve items, and provide remote control and communication capabilities.

In pop culture, the combination of business interests and artificial intelligence is something to be feared. It brings to mind Skynet, the malevolent neural network from the Terminator movies that goes to great lengths to destroy its human makers. The reality is different, though. We take advantage of it every time we check out new products recommended by Amazon.com, We have fun with it when we browse Netflix, which uses AI to predict what viewers might like to watch next. We're also increasingly likely to encounter it at work, since businesses of all types are finding ways to use it in industrial, retail, and service operations.

A Japanese tech start-up is using deep learning to teach a pair of machines a simple job for a human, but a surprisingly tricky task for a robot - cleaning a bedroom. Though it may seem like a basic, albeit tedious, task for a human, robots find this type of job surprisingly complicated. A Japanese tech start-up is using deep learning to teach AI how to deal with disorder and chaos in a child's room. Deep learning is where algorithms, inspired by the human brain, learn from large amounts of data so they're able to perform complex tasks. Some tasks, like welding car chassis in the exact same way day after day, are easy for robots as it is a repetitive process and the machines do not suffer with boredom in the same way as disgruntled employees.