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没带罩子让他吃了一天的没事吧 - 不怕雨淋,教你轻松应对突如其来的暴风雨
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没带罩子让他吃了一天的没事吧 - 不怕雨淋,教你轻松应对突如其来的暴风雨

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  新浪财经北美站康路

  在大模型进入深水区、AIAgent走向爆发的2025年,企业对“AI+业务”的落地路径关注前所未有。

  近日,新浪财经在NYU中美高峰论坛期间,对话阿里巴巴通义实验室自然语言处理方向负责人黄非,围绕模型能力演进、Agent范式变革、多模态翻译出海、代码智能化等趋势,展开交流。阿里通义实验室是国内最早从事大模型研究的团队之一,研发的通义系列模型和产品在大模型开源社区和产业界学术界广受好评。黄非分享了阿里通义实验室在大模型与AIAgent方向上的实践路径与战略判断。

  “第一条是围绕模型能力本身的突破。”黄非指出,“比如我们做通义千问的时候,一条主线就是探索大模型的智能边界。这条线并不是以应用驱动为主,而是以‘能力上限’为目标,比如说在各种benchmark(基准)任务上不断提升效果,研究怎么选更优的数据、更有效的训练策略、更强的架构。这其实就是往AGI的方向去推。”

  但与此同时,通义也在更贴近业务场景的方向发力。

  “第二条就是更贴近应用端的路径,也就是你说的‘业务系统协同’。像通义晓蜜这样的智能对话助手长期以来服务包括电商、政务、运营商等各行各业,还有我们开发的通义灵码(代码生成助手),目前在国内的市场占有率是第一的。我们也在研发法律助手通义法睿、角色陪伴类的大模型通义星尘等产品。这类应用其实就是模型能力和业务场景的结合。”

  针对Agent的方向,黄非表示,传统Agent通过人工设定流程来完成任务,但泛化能力不强。“我们现在想做的是让模型自己学会‘什么时候该调哪个工具’,比如用计算器、查天气、调地图API,这些其实都是Agent的工具调度逻辑。这方面我们正在做系统级探索。”

  在谈到多模态翻译如何服务阿里全球化时,黄非举例指出,通义已经在跨语种直播带货中取得实质进展。中国主播在直播带货过程中,可以实时将内容转成英语、西语、阿拉伯语等多种语言,大幅降低了语言壁垒。

  而在电商商品出海过程中,通义也已从“翻译”走向“本地生成”。黄非解释道,当一件中国商品要卖到西语市场,不能只把文字翻译过去,还要重写产品描述、重新生成营销图片和短视频,让当地用户觉得内容就是为他们量身打造的。

  “这背后其实是一整套跨语言、跨模态的智能生成链条。”黄非说。

  作为通义自然语言方向的长期负责人,黄非分享了如何判断某个AI方向具有“长期势能”的思考框架,“我们的判断逻辑也很清晰:第一,看这个方向是不是具有系统性价值,能不能通过解决一类问题带来巨大的业务价值,因为专注才能突破;第二,看它是不是有技术壁垒,不容易被别人复制。如果只是一个“谁都能做”的方向,门槛比较低,那我们的技术优势和团队人才优势就不够彰显。”

  通义一直坚持开源开放的路线。“我们相信AI技术的发展是在不断开放的基础上持续进行,开源开放一方面可以降低千行百业使用AI大模型的门槛,同时也可以和产业界和学术界的专家学者们共同合作推进AI技术的不断发展,推进智能的上限。科技普惠,AI向善,让技术呵护人间烟火也是我们的持续目标。”黄非最后指出了有温度又有人情味的AI的愿景。

  附对话实录:

  新浪财经:当前业界正在从“大语言模型”快速转向“AIAgent”范式。在阿里体系中,Agent是更强调增强对话智能,还是强调与业务系统的深度协同?

  黄非:我觉得是两条路径。

  第一条是围绕模型能力本身的突破。比如我们做通义千问的时候,一条主线就是探索大模型的智能边界。这条线并不是以应用驱动为主,而是以“能力上限”为目标,比如说在各种benchmark(基准)任务上不断提升效果,研究怎么选更优的数据、更有效的训练策略、更强的架构。这其实就是往AGI的方向去推。

  第二条就是更贴近应用端的路径,也就是你说的“业务系统协同”。像通义晓蜜这样的智能对话助手,还有我们做的通义灵码(代码生成助手),目前在国内的市场占有率是第一的。我们也在研发法律助手通义法睿、角色陪伴类的大模型通义星尘等产品。这类应用其实就是模型能力和业务场景的结合。

  你可以把前一条理解成“模型本身有多聪明”,而后一条是“聪明的大脑怎么完成具体任务”。这里面就包括你提到的Agent方向,比如说流程化任务建模、工具调用,甚至解决那些模型以前处理不了的开放性问题。

  举个例子,Manus是通过对人工预先设定流程解决问题的,但泛化性有待加强。现在我们希望模型通过学习掌握这些流程,实现更通用的Agent能力。什么时候用计算器,什么时候查天气、调地图API,这些其实都是Agent的工具调度逻辑。总的来说,这两条路径我们是同时在走的:一方面持续打磨底层模型能力,另一方面在具体场景中做系统级的落地。

  新浪财经:多模态翻译如何支撑阿里“国际化业务”的技术根基?是否可以分享几个典型应用场景,如商品跨语种描述、电商直播?

  黄非:电商直播就是一个非常典型的例子。我们其实早在2020年就上线过一个面向电商直播的实时翻译系统,虽然当时技术还不成熟,还有很多提升空间。但到了现在,大模型技术发展得非常快,比如通义现在可以支持接近100多种语言,其中主流的几种语言——英、中、西、法、德、日、韩、俄、印尼语和阿拉伯语等语言的理解生成翻译能力都处于业界一流。

  这意味着,一个中国主播在用中文讲解商品的时候,可以实时把语音或文字内容翻成不同语言,大幅降低语言壁垒。

  再比如商品本地化。很多中国商品在淘宝上展示的内容是中文图文,但当我要把它们卖到西语市场的时候,就需要重新生成贴合当地用户审美和语言习惯的商品描述、营销图片和介绍视频。

  这就不仅是翻译的问题,还要包括图片生成、营销文案自动生成等,我们希望让西语用户在电商平台上看到这些内容时,觉得是“本地的”,而不是“翻译过来的”。这背后其实是一个跨语言、跨模态的智能生成链条。

  新浪财经:作为技术负责人,您如何判断一个AI方向具有“长期势能”?

  黄非:这个问题特别重要。我觉得核心是:你要清楚今天模型能做什么,未来一两年又会发展到什么程度。这个判断要基于对技术本质的理解、对演化趋势的敏感度和对落地场景的把握。

  比如我们团队特别早就判断代码生成是一个值得投入的方向。两年前我们就开始做通义灵码,现在它是国内做得最好、用户数最多的代码助手。很多厂商到现在才意识到代码生成的重要性,但我们已经有了先发优势,能跑得更快。

  我们还在很早就押注了像联网搜索、RAG(检索增强生成)这样的方向。大模型的训练数据都是静态的,那要获取实时信息,就必须让模型学会怎么查询互联网、怎么判断信息质量、怎么把它整合成可靠答案。这是大模型必需的一环,我们很早就组织人力去做了。

  我们的判断逻辑也很清晰:第一,看这个方向是不是具有系统性价值,能不能解决一类问题,因为专注才能突破;第二,看它是不是有技术壁垒,不容易被别人复制。如果只是一个“谁都能做”的方向,门槛比较低,那我们的技术优势和团队人才优势就不够彰显。

  新浪财经:为什么代码你觉得是很重要的底层能力?

  黄非:有两个原因。

  第一,代码场景的学习反馈机制特别好。就像数学一样,编程的正确与否可以被立即验证。你写了一段代码,要么成功执行,要么直接报错,这种强反馈对模型训练是非常理想的。

  第二,代码本身就是连接现实世界的桥梁。它可以调用数据库、控制硬件、构建前端页面调动后端服务……几乎所有复杂系统都需要代码作为接口。

  更重要的是,我们的目标并不是让程序员更高效,而是让“不是程序员的人”也能写代码。比如一个产品经理、运营、记者甚至小学生,如果他们能通过自然语言对话让大模型生成代码或者应用,解决实际问题,他们就有了更强大的驱动数字世界和物理世界的能力。

  现在在美国有个概念叫“VibeCoding”,就是说你通过自然语言让大模型生成代码应用,把需求变成结果。我们觉得这是很有未来感的方向,大模型可以变成每个人的数字开发助手。

  新浪财经:年轻一代如何在AI浪潮中选择参与路径:技术、产品还是创业?

  黄非:这个就得看个人的背景和兴趣了。

  如果你是计算机相关专业的研究生、博士生,已经掌握了机器学习、深度学习这些基本能力,而且有一定算力条件,那你当然可以选择做底层模型研究,这是门槛高但回报大的路径。

  如果你不是技术出身,那也没关系。现在很多应用场景其实都可以用大模型来解决痛点。关键是你能不能发现“哪些问题是大模型现在能解决的”,解决之后又能创造什么价值。如果你能理解这些机会,你就可以去做产品,甚至创业。

  如果你还不确定方向,那我建议最起码要学会使用大模型。比如我女儿现在学习遇到不会的题目,我不是让她直接问答案,而是问模型怎么解、用什么知识点、能不能再出一道类似的题再练一遍。这其实就是把大模型当成AI助教。

  我相信大模型的使用会越来越普及。你越早上手,越能知道它能做什么、不能做什么,越能知道自己哪些能力可能被替代,哪些能力是AI帮你强化的。这种觉察,对未来每个人都特别重要。

  新浪财经:通义开源了一系列大模型模型,也支持建设了国内领先的AI模型开源社区魔搭。通义在选择开源和闭源方面有什么考量?

  黄非:通义一直坚持开源开放的的路线。随着一系列全尺寸多模态大模型的开源,通义千问的系列模型超越Llama成为全球最广泛衍生的开源大模型家族。我们也在魔搭社区开源了数百个传统AI的NLP,语音,视觉相关的模型。因为我们相信AI技术的发展是在不断开放的基础上持续进行,我们把最好的模型开放出来,一方面可以降低产业界千行百业使用AI大模型的门槛,普惠大众,同时也可以和学术界的专家学者们共同合作推进AI技术的不断发展,推进智能的上限,也充分理解AI技术的优势和风险。科技普惠,AI向善,让技术呵护人间烟火也是我们的持续目标。

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