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找到相关的专题报道信息 - 机器人与枪
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  • Instagram被曝将引入AI聊天机器人拥有30种人物性格

    Instagram被曝将引入AI聊天机器人拥有30种人物性格

    6月7日消息,Meta正在加速采用生成式人工智能技术,并将其整合到包括广告在内的各种平台上。现在,该公司正在Instagram上测试一项新功能...

    2023-06-07 07:40:29
  • 日本:敞开训练AI不用管版权,LeCun:机器学习天堂

    日本:敞开训练AI不用管版权,LeCun:机器学习天堂

    西风 发自 凹非寺量子位 | 公众号 QbitAI大模型狂潮下,日本政府坐不住了。直接宣布:不会对训练AI所使用的数据实施版权保护。这也就意味着,训练AI可以使用任何数据。不管是非营利还是商业目的,不论是否是复制以外的行为,只要你能搞得到,你就可以用,甚至从非法网站获得的数据也可以用。连Yann LeCun看到消息后都发文表示:日本已经成为了机器学习的天堂。确实,不少日本学术界和商界人士那是敲锣打鼓。毕竟想要在全球大模型舞台上分得一杯羹,数据本就是一大关键要素。但这引起了动漫和艺术创作者的担忧。早在四个月前,日本参议院议员、职业漫画家赤松健在YouTube上发布了一个讲用AI进行图像生成的视频,就有提到过侵权问题,以及漫画师的担忧。在那时,就有日本画友认为:希望能开发出超越“Novel AI”的AI插图服务。当然,这个AI服务的训练数据集中不能使用未经授权的转载图片。但现在,对训练AI所使用的数据集的版权限制已放开。用无视版权的方式“弯道超车”其实,日本并不是第一次在版权保护上有所松懈。日本曾经因为过于严苛的版权保护,导致版权转让极为不便,需要遵循诸多条条框框,盗版问题也很严重。之后,日本在2019年修正了版权保护制度。即便如此,这次日本被爆在训练AI方面的大尺度政策,还是令人惊讶。Technomancers.ai报道称:似乎日本认为版权问题,尤其是与动漫和其它视觉媒体相关的版权问题,阻碍了该国在人工智能技术方面的进展。作为回应,日本决定全力以赴,选择无版权的方式来保持竞争力。尽管日本目前是世界第三大经济体,但自1990年代以来,日本的经济增长一直缓慢。此时,若人工智能可以得到有效发展,就有可能在短时间内将日本的GDP提高50%或者更多。对于日本来说,这是一个令人兴奋的前景。日本还试图把手伸向西方国家的数据库,这也是日本发展人工智能的关键。相较而言,日本的日语训练数据量明显少于西方可用的英语数据资源。日本拥有很受欢迎动漫等文化内容。在这点上,日本的立场似乎很明确:如果西方利用日本文化来训练AI,那么西方文化资源也应该可供日本训练AI。AI监管辩论还在继续TB级别的训练数据是维系大模型进化的一大核心要素。毕竟可用的高质量训练数据越多,AI 模型就越好。而关于大模型训练数据不透明等问题一直是大家讨论的热点。大模型的野蛮生长势必要带来人们对其安全合规的考量,侵权问题就是需要监管的问题之一。而此次日本这一举动也为监管辩论增添了转折。针对版权问题,美国版权局(USCO)在三月份也发布了新规。但并不是对版权进行保护,而是称:人工智能(AI)自动生成的作品不受版权法保护。因为,和人工参与创作的Photoshop作品不同,通过Midjourney、Stability AI、ChatGPT等平台生成的作品完全由AI自动完成,而且训练的数据是基于人类创作的作品,因此不受版权法保护。对此,你怎么看?参考链接:[1]https://technomancers...

    2023-06-03 10:08:55
  • 奇富科技机器人团队语音论文入选INTERSPEECH2023

    奇富科技机器人团队语音论文入选INTERSPEECH2023

    6月1日消息,近日,奇富科技机器人团队论文《Eden-TTS:一种简单高效的非自回归“端到端可微分”神经网络的语音合成架构》(Eden-TTS: A Simple and Efficient Parallel Text-to-speech Architecture with Collaborative Duration-alignment Learning)被全球语音与声学顶级会议INTERSPEECH 2023接收。据悉,INTERSPEECH是由国际语音通讯协会(International Speech Communication Association, ISCA)创办的语音信号处理领域顶级旗舰国际会议,是全球最大的综合性语音信号处理盛会,在国际上享有盛誉并具有广泛的学术影响力。奇富科技的论文研究成果为需要文本转语音的应用场景提供了创新的解决方案,提出了一种端到端可微的非自回归神经网络语音合成模型架构。基于文本音素时长与alignment的密切关系,论文提出了一种简单高效的alignment学习方式:首先采用一种新的energy-modulated注意力机制得到guided alignment,然后利用guided alignment计算音素的时长信息,最后通过音素的时长信息构建monotonic alignment。本方法无需外部的alignment信息,无需引入额外的alignment损失函数。对业务提效而言,这种端到端可微的方法使得各个模块可以方便地替换为各种类型的神经网络模块,从而具有良好的扩展性和稳定性。相比于主流的自回归模型,推理速度提升了10倍以上,能够满足实时语音合成的需求。根据进行的多人MOS评测,该方法的MOS分值达到了4.32分(满分为5分),合成语音的自然流畅程度接近于目前最优的自回归模型,显著优于同类型的非自回归模型。此外,与同类型方法相比,该方法可以节约50%以上的训练时间,显著提升模型训练效率。奇富科技在对话机器人领域一直坚持投入、坚持自研。就在两个月前,奇富科技另一篇音频论文《基于多粒度Transformer的多模态情绪识别》(Multilevel Transformer for Multimodal Emotion Recognition)被第48届IEEE声学、语音与信号处理国际会议(IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023)接收。“我们很高兴在理解用户、优化表达方面取得了关键性的成果。随着奇富GPT对于公司各业务层的重构,我们已经极大提升了对于用户在文本上的理解能力,从语音到文本,再从文本回到语音,更好的识别是为了更好的表达和输出,我们将持续投入,用前沿技术重塑用户体验。”奇富科技首席算法科学家费浩峻表示。(一橙) ...

    2023-06-01 12:40:34
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