Group 1 - Preferred Networks is recognized as a "true AI" company due to its reliance on deep learning, NLP, and generative models, along with its self-developed models and AI frameworks [1][3][4] - The company has a strong product versatility, offering solutions across various sectors including industrial automation, healthcare, and education, with over 435 global patents [5][6] - Despite its initial ambitions for international expansion, Preferred Networks has reverted to a domestic focus, raising concerns for other Japanese tech firms considering overseas ventures [2][10] Group 2 - Preferred Networks was founded in 2014 and developed the deep learning framework Chainer, which was once positioned alongside TensorFlow and PyTorch [3][11] - The company has shifted its strategy to collaborate with major Japanese corporations like Toyota and Nissan, focusing on customized AI systems rather than pursuing a broader international presence [13][18] - The company has established a new subsidiary, Preferred Elements, aimed at foundational technology development, indicating a potential shift towards a more open approach [14][16] Group 3 - PKSHA Technology, another prominent Japanese AI firm, has shown strong profitability with significant revenue growth, serving various industries including retail and finance [24][25][26] - Unlike Preferred Networks, PKSHA retains ambitions for international collaboration, partnering with companies like Microsoft and Tencent [26] - The early establishment of AI companies in Japan, such as PKSHA and Preferred Networks, was driven by a combination of engineering talent and industry demand for automation [28][30] Group 4 - The Japanese AI industry is characterized by a closed-loop system where startups primarily serve large domestic corporations, limiting their growth potential and innovation [44][45] - The government and large companies emphasize project-based AI solutions, which diminishes the drive for exploratory or innovative AI developments [44][45] - Cultural factors contribute to the lack of ambition for developing universal AI platforms, contrasting with the more aggressive approaches seen in other countries [30][43]

文章称，兰姆课题组计划招收2025年秋季以及之后入学的博士生、硕士生，以及访问学生（包括本科实 习生），并优先考虑有机器学习和强化学习研究经历的同学。 此外，在神经信息处理系统大会（NeurIPS）、国际机器学习大会（ICML）或国际表征学习大会 （ICLR）这三大机器学习领域的顶级学术会议上有发表经历，将是申请者有力的加分项。 兰姆的研究聚焦于机器学习，尤其是强化学习和生成模型等方向。他近期的研究重点包括通过交互和无 监督探索来学习策略，从丰富的观察数据中学习抽象世界模型，以及探索新型生成模型和序列模型的训 练方法，以期改进长文本和不确定性建模上的表现。 【文/观察者网 张菁娟】美国持续对教育和科学的攻击，正将科学家和研究人员向外推。 香港英文媒体《南华早报》29日援引两名知情人士的话报道称，微软研究院纽约实验室的高级研究员兰 姆（Alex Lamb）将于即将到来的秋季学期加入新成立的清华大学人工智能学院（College of AI），担 任助理教授。兰姆在一封电子邮件中证实了这一消息。 报道称，兰姆在约翰霍普金斯大学获得应用数学和计算机科学学士学位后，于2015年至2020年在加拿大 蒙特利尔大学攻读计算 ...