Toru Nishikawa

Preferred Networks develops deep learning technology applied to robotics, autonomous driving, and industrial applications, building neural network architectures for real-time edge inference in robot control, factory automation, and connected vehicle systems. The Tokyo company gained international recognition for early competition victories in autonomous navigation and robot manipulation challenges and developed its own deep learning framework, Chainer, which influenced the design of PyTorch before Chainer was eventually retired.\n\nThe company raised approximately $350 million including a landmark $105 million Series A from Toyota Motor Corporation and other strategic investors, making it one of the most valuable AI startups in Japan at the time of its fundraising. Preferred Networks collaborates with Toyota on autonomous driving AI, with NTT on communications AI, and with Fanuc on factory robot intelligence, creating a portfolio of deep technology industrial partnerships that provide both funding and deployment scale for its AI research.\n\nPreferred Networks operates in Japan industrial AI market where established relationships with major manufacturing and automotive companies provide a defensible position that international AI startups find difficult to penetrate through conventional sales approaches. The company research focus on edge AI inference for robotics aligns with Japan competitive strengths in manufacturing automation and precision robotics, markets where AI-enhanced robot intelligence is being adopted to address labour shortages and quality requirements that purely mechanical automation cannot satisfy.