Theta Labs CEO:人工智能的下一波浪潮来自移动领域

比推2024-09-29 tarihinde yayınlandı2024-09-29 tarihinde güncellendi

作者:Mitch Liu,CoinDesk;编译:白水,金色财经

人工智能对资源的需求是无穷无尽的。它消耗大量的电力和数据,预计 2022 年将消耗 460 太瓦时,到 2026 年将急剧增加到 620 至 1,050 太瓦时。但是,它最迫切的需求是计算:支持复杂模型训练、海量数据集分析和大规模推理执行的处理能力。

这种对计算的渴求重塑了我们的许多专业领域。2024 年,全球人工智能市场规模超过 1840 亿美元,预计到 2030 年可能会超过 8000 亿美元——这一价值与波兰目前的 GDP 相当。业内最著名的产品 ChatGPT 在 2022 年 11 月推出后仅两个月就拥有了 1 亿活跃用户。

然而,随着 ChatGPT 等人工智能产品的增多和发展,我们对人工智能运作方式的看法很快就过时了。人工智能的流行形象——庞大的数据中心、巨额的电费账单、由科技巨头控制——已不再能说明全部情况。这种观点让许多人认为,有意义的人工智能开发是资金雄厚的公司和大型科技公司的专属领域。

人工智能的新愿景正在出现,它着眼于我们口袋中尚未开发的潜力。这种方法旨在通过利用全球数十亿部智能手机的集体力量来实现人工智能的民主化。我们的移动设备每天闲置数小时,其处理能力处于休眠状态。通过利用这一巨大的未使用计算能力库,我们可以重塑人工智能格局。人工智能开发可以不再仅仅依赖中心化的企业基础设施,而是由全球日常设备网络提供支持。

尚未开发的潜力

智能手机和平板电脑代表着一个巨大的、尚未开发的全球计算能力宝库。仅 2024 年,预计出货量就将达到 12.1 亿台,这提供的闲置计算的真正潜力很难计算。

像 Theta EdgeCloud 这样的移动计划旨在利用这种分布式消费级 GPU 网络进行人工智能计算。从中心化计算到边缘计算的转变是一种技术革命,能够彻底改变人们与人工智能模型交互和为人工智能模型提供支持的方式。

通过在移动设备上本地处理数据,该行业有望实现更低的延迟、增强的隐私和减少的带宽使用。这种方法对于自动驾驶汽车、增强现实和个性化人工智能助手等实时应用尤为重要。边缘是新的人工智能用例将起飞的地方,尤其是那些供个人使用的用例。为这些程序提供支持不仅会变得更加实惠,而且还会变得更加反应灵敏和可定制,这对消费者和研究人员来说都是双赢的。

区块链是为这个分布式人工智能生态系统完美设计的。它们的去中心化特性与利用全球数百万台设备的闲置计算能力的目标完美契合。通过利用区块链技术,我们可以创建一个安全、透明且有激励机制的计算资源共享框架。

这里的关键创新是使用链下验证。虽然链上验证会在数百万台并行设备的网络中造成瓶颈,但链下方法允许这些设备无缝协作,而不受单个连接问题的影响。这种方法可以创建一个无需信任的系统,设备所有者可以在不损害其安全性或隐私的情况下为 AI 开发做出贡献。

该模型借鉴了“联合学习”的概念,这是一种分布式机器学习方法,可以扩展到移动设备上的大量数据,同时保护用户隐私。区块链既为该网络提供了基础设施,也提供了奖励参与者的机制,从而激励广泛参与。

区块链和边缘 AI 之间的协同作用正在培育一个比传统中心化模型更具弹性、更高效、更具包容性的新生态系统。它使 AI 开发民主化,允许个人直接从他们的移动设备参与 AI 革命并从中受益。

克服技术挑战

AI 训练和推理可以在多种 GPU 类型上进行,包括移动设备中的消费级 GPU。自智能手机上市以来,支持我们移动设备的硬件一直在稳步改进,并且没有放缓的迹象。行业领先的移动 GPU,如 Apple 的 A17 Pro 和 Qualcomm 的 Adreno 750(用于三星 Galaxy 和 Google Pixel 等高端 Android 设备)正在重新定义可以在移动设备上完成的 AI 任务。

现在,正在生产专门为消费者 AI 计算而设计的新芯片,称为神经处理单元 (NPU),可在管理移动设备的热量和电池电量限制的同时实现设备上的 AI 用例。添加智能系统设计和架构,可以将作业路由到最适合该作业的硬件,并且创建的网络效应将非常强大。

虽然边缘 AI 的潜力巨大,但它仍然面临着一系列挑战。针对各种移动硬件优化 AI 算法、确保在不同网络条件下保持一致的性能、解决延迟问题以及维护安全性都是关键障碍。然而,AI 和移动技术的持续研究正在稳步解决这些挑战,为这一愿景成为现实铺平了道路。

企业对社区

关于人工智能发展的最大抱怨之一,也是最公正的抱怨,就是它消耗了惊人的电量。大型数据中心还需要大片土地来建设物理基础设施,以及维持在线状态所需的惊人电量。移动模式可以通过使用现有设备中的备用 GPU(而不是依赖集中式数据中心的 GPU)来减轻许多此类环境影响,从而提高效率,并减少碳排放。它对我们的环境的潜在影响不容小觑。

人工智能向边缘计算的转变也将从根本上改变谁可以参与支持人工智能网络以及谁可以从中获利。拥有数据中心的企业将不再是封闭的。相反,大门将敞开,个人开发者、小型企业甚至业余爱好者将能够访问人工智能网络。

赋予更大的用户和支持者权力也将使发展更加快速和开放,有助于遏制备受讨论和担忧的行业停滞不前。可访问性的提高还将带来更多样的应用,解决原本可能被忽视的小众问题和服务不足的社区。

这种转变的经济影响将是深远的。通过允许个人和中小型组织将其设备的闲置计算能力货币化,新的收入来源将源源不断。它还为消费级人工智能硬件和边缘优化软件开辟了新的市场。

人工智能创新的未来不在于建立更大的数据中心,而在于利用我们口袋和家中已经存在的力量。通过将重点转移到边缘计算,可以出现一个更具包容性、更高效、更具创新性的人工智能生态系统。这种分散的方法不仅使人工智能民主化,而且符合全球可持续发展目标,确保所有人都能享受人工智能的好处,而不仅仅是少数特权阶层。

说明: 比推所有文章只代表作者观点,不构成投资建议

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