DeepMind发布最新《神经网络中连续学习》综述论文！
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/heS6wRSHVMn0YptVVJtnANRduO6oQSa9cyiacHBrHnT1JA0YOn2Ric2G5gVwA8CpFxpiczaJedoSxUP81ClEZwuEw/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></p><span style="color: black;"><span style="color: black;">源自</span>：AI科技评论</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">本文<strong style="color: blue;"><span style="color: black;"><strong style="color: blue;">约3200字</strong></span></strong>，<span style="color: black;">意见</span>阅读<strong style="color: blue;"><span style="color: black;">6</span></strong><strong style="color: blue;"><span style="color: black;"><strong style="color: blue;">分钟</strong></span></strong></p><span style="color: black;">本文重点介绍<span style="color: black;">有些</span>神经网络中最有前途和最有影响力的方向。</span><img src="https://mmbiz.qpic.cn/mmbiz_jpg/cNFA8C0uVPuAqMic9fPYeVvPswqialNOX0pSZqZFHgruibAXvvUzDWAy2ibicQd6Pabql2pTMCeAL6zovMcDwHsB0cw/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><span style="color: black;">在过去的几十年中，人工智能<span style="color: black;">科研</span>取得了巨大<span style="color: black;">发展</span>，<span style="color: black;">然则</span>它<span style="color: black;">重点</span>依赖于固定的数据集和固定的环境。</span><strong style="color: blue;"><span style="color: black;">而<span style="color: black;">连续</span>学习是一个越来越<span style="color: black;">关联</span>的<span style="color: black;">科研</span><span style="color: black;">行业</span>，它关心人工系统<span style="color: black;">怎样</span>像生物系统那样从连续的<span style="color: black;">关联</span>数据流中<span style="color: black;">连续</span>地学习。</span></strong><span style="color: black;"><span style="color: black;">近期</span>，DeepMind在Cell上发布了13页的《神经网络中<span style="color: black;">连续</span>学习》综述论文。</span><img src="https://mmbiz.qpic.cn/mmbiz_png/cNFA8C0uVPv1SX313IJ4UnftmAfMAgicXV6waXsGGamtTdEicql0gwNthHpPwmg0laC6nCDVkK9XODMiaJ6pEUScQ/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><span style="color: black;">本文将<span style="color: black;">连续</span>学习与神经网络的学习动力学联系起来，强调了其必须大大<span style="color: black;">加强</span>数据效率的<span style="color: black;">潜能</span>。</span><span style="color: black;">本文进一步<span style="color: black;">思虑</span>近年来<span style="color: black;">显现</span>的许多受生物学启发的新<span style="color: black;">办法</span>，重点是利用正则化，模块性，记忆（ memory）和元学习的<span style="color: black;">办法</span>，并重点介绍<span style="color: black;">有些</span>最有前途和最有影响力的方向。</span><img src="https://mmbiz.qpic.cn/mmbiz_png/cNFA8C0uVPv1SX313IJ4UnftmAfMAgicX0ibiaPyrB4ZCplltp2ibUialrbETvBBnwAibJ7Kc9PN4RenaJfgGnOuozxQ/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;">
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">1、世界不是一成不变的</span></strong></span></h2><span style="color: black;">人工智能成功的一个<span style="color: black;">平常</span>基准是模仿人类学习的能力。<span style="color: black;">咱们</span><span style="color: black;">测绘</span>人类识别图像、玩游戏和驾驶汽车的能力，<span style="color: black;">而后</span><span style="color: black;">研发</span><span style="color: black;">能够</span>匹配或超过给定训练数据的<span style="color: black;">设备</span>学习模型。</span><span style="color: black;">然而这种范式将重点放在<span style="color: black;">最后</span>结果上，而不是学习过程上，并且忽略了人类学习的一个关键特征：<strong style="color: blue;">对<span style="color: black;">持续</span>变化的任务和连续的经验<span style="color: black;">拥有</span>鲁棒性。</strong></span><span style="color: black;">人类<span style="color: black;">能够</span>以这种方式学习是不足为奇的，毕竟，时间是不可逆转的，世界不是一成不变的，<span style="color: black;">因此呢</span>人类的学习已进化到在动态学习环境中蓬勃发展。</span><span style="color: black;">这种鲁棒性与现代最强大的<span style="color: black;">设备</span>学习<span style="color: black;">办法</span>形<span style="color: black;">成为了</span>鲜明的对比，后者仅在呈现经过精心shuffle，平衡和同质化的数据时<span style="color: black;">才可</span>表现良好。</span><span style="color: black;">这些模型不仅在有所变化的数据<span style="color: black;">情况</span>时表现<span style="color: black;">不良</span>，<span style="color: black;">况且</span>在某些<span style="color: black;">状况</span>下会完全失效，或因<span style="color: black;">初期</span>学习的任务而<span style="color: black;">引起</span>性能急剧下降，这被<span style="color: black;">叫作</span>为<span style="color: black;">劫难</span>性遗忘。</span><span style="color: black;"><strong style="color: blue;"><span style="color: black;"><span style="color: black;">经过</span><span style="color: black;">研发</span>像人类<span style="color: black;">同样</span><span style="color: black;">连续</span><span style="color: black;">持续</span>学习的神经网络模型，<span style="color: black;">能够</span>得到什么？</span></strong></span><span style="color: black;"><span style="color: black;">连续</span>学习技术<span style="color: black;">能够</span>使模型<span style="color: black;">得到</span>专门的<span style="color: black;">处理</span><span style="color: black;">方法</span><span style="color: black;">同期</span>不丢掉以前的<span style="color: black;">处理</span><span style="color: black;">方法</span>，从而有可能像人类<span style="color: black;">同样</span>终身学习。</span><span style="color: black;"><span style="color: black;">实质</span>上，<span style="color: black;">连续</span>学习<span style="color: black;">一般</span>被认为是人类水平的人工智能的必要属性之一。</span><span style="color: black;">从<span style="color: black;">基本</span>上讲，<span style="color: black;">经过</span><span style="color: black;">加强</span>学习效率以及实现<span style="color: black;">关联</span>任务之间的知识迁移，<span style="color: black;">连续</span>学习<span style="color: black;">办法</span><span style="color: black;">乃至</span><span style="color: black;">能够</span>在固定环境下为深度神经网络<span style="color: black;">供给</span>巨大<span style="color: black;">优良</span>。</span>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">2、<span style="color: black;">连续</span>学习的<span style="color: black;">基本</span>、定义与要</span></strong><span style="color: black;">求</span></span><strong style="color: blue;"><span style="color: black;">基于生物系统的<span style="color: black;">连续</span>学习<span style="color: black;">基本</span> </span></strong></h2><span style="color: black;">对自然界及其智能物种的<span style="color: black;">科研</span>经常与人工智能<span style="color: black;">科研</span>相交，<span style="color: black;">包含</span><span style="color: black;">连续</span>学习。</span><span style="color: black;">生物学为在<span style="color: black;">繁杂</span>环境中成功进行<span style="color: black;">连续</span>学习<span style="color: black;">供给</span>了存在证据，并暗示了设计<span style="color: black;">办法</span>的原则和成功<span style="color: black;">办法</span>的取舍。</span><span style="color: black;">有多种机制<span style="color: black;">能够</span>使生物系统适应<span style="color: black;">持续</span>变化的环境，而不会产生遗忘。</span><span style="color: black;"><span style="color: black;">因此呢</span>，本节<span style="color: black;">经过</span>类似于其生物学等效物的方式介绍四个连续学习范式，如下图所示：</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/cNFA8C0uVPv1SX313IJ4UnftmAfMAgicXiaRxLU24Cpmib6NAetfLqjW37esxZxCSZZXPtTV2JpHofNIoQfiaBNH8g/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><span style="color: black;">图1 <span style="color: black;">连续</span>学习的范式</span></p>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><strong style="color: blue;"><span style="color: black;"><span style="color: black;">连续</span>学习的定义</span></strong></h2><span style="color: black;"><span style="color: black;">连续</span>学习的问题<span style="color: black;">一般</span>由<span style="color: black;">次序</span>训练协议和<span style="color: black;">处理</span><span style="color: black;">方法</span>预期的功能来定义。与静态数据集或环境的通用<span style="color: black;">设备</span>学习设置相反，<span style="color: black;">连续</span>学习设置<span style="color: black;">知道</span>地专注于动态变化的环境，<span style="color: black;">一般</span>划分为需要<span style="color: black;">次序</span>完成的一组任务。此设置可能在任务转换（平滑或离散），任务长度和重复以及任务类型（例如无监督、监督或强化学习）方面有所<span style="color: black;">区别</span>，<span style="color: black;">乃至</span>可能<span style="color: black;">无</span><span style="color: black;">知道</span>定义的任务。与课程学习比较，学习器<span style="color: black;">没法</span><span style="color: black;">掌控</span>任务的<span style="color: black;">次序</span>。</span><span style="color: black;"><span style="color: black;">连续</span>学习<span style="color: black;">处理</span><span style="color: black;">方法</span><span style="color: black;">一般</span><span style="color: black;">期盼</span>满足许多<span style="color: black;">需要</span>，如下图所示并在方框1中定义。</span><img src="https://mmbiz.qpic.cn/mmbiz_png/cNFA8C0uVPv1SX313IJ4UnftmAfMAgicXpmCvjhI0j85Ofhybh21W6mYO7uuviciayg7fuXpVHT530o7JicRYSRmTw/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><span style="color: black;">图2 在<span style="color: black;">连续</span>学习环境中<span style="color: black;">区别</span>结果的图示</span><strong style="color: blue;"><span style="color: black;"><span style="color: black;">连续</span>学习的<span style="color: black;">需求</span> </span></strong><span style="color: black;">之前任务的最小<span style="color: black;">拜访</span>。该模型<span style="color: black;">无</span>为以前的经验<span style="color: black;">供给</span>无限存储，并且至关重要的是，它<span style="color: black;">没法</span>与以前看到的任务进行交互。</span><span style="color: black;">模型容量和计算的最小<span style="color: black;">增多</span>。该<span style="color: black;">办法</span>必须是可伸缩的：它<span style="color: black;">不可</span>为<span style="color: black;">每一个</span>后续任务添加新模型。</span><span style="color: black;">减少<span style="color: black;">劫难</span>性的遗忘和干扰。对新任务的训练不应显着降低先前学习的任务的性能（图2 A，C）。</span><span style="color: black;">快速适应和恢复。该模型应该能够快速适应新任务或<span style="color: black;">行业</span>转移，并且在呈现过去任务时能够快速恢复。</span><span style="color: black;">保持可塑性。该模型应能够在观察到新任务时<span style="color: black;">连续</span>有效学习（图2 B）。</span><span style="color: black;">最大化向前和向后传输。学习任务应该在学习效率和绩效方面都改善过去和将来的<span style="color: black;">关联</span>任务（图2 D，E）。</span><span style="color: black;">与任务无关的学习。该<span style="color: black;">办法</span><span style="color: black;">不该</span>依赖已知的任务标签或任务边界。</span>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">3<span style="color: black;">、</span>基于梯度的<span style="color: black;">处理</span><span style="color: black;">方法</span></span></strong></span></h2><img src="https://mmbiz.qpic.cn/mmbiz_png/cNFA8C0uVPv1SX313IJ4UnftmAfMAgicX6M3XbMXp346AiavnFaLHQfxibW6SkV7NF7bZ2RictsiaKdwLgTu6XhSlnw/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><span style="color: black;">图3 <span style="color: black;">区别</span>任务的梯度下降优化</span><span style="color: black;">基于梯度的<span style="color: black;">连续</span>学习<span style="color: black;">办法</span><span style="color: black;">能够</span>理解为突触可塑性模型的远亲。</span><span style="color: black;">哺乳动物的大脑在突触级<span style="color: black;">拥有</span><span style="color: black;">繁杂</span>的机制，<span style="color: black;">能够</span>防止新旧知识之间的干扰，<span style="color: black;">乃至</span>是事实和技能之间的冲突。虽然突触可塑性<span style="color: black;">已然</span><span style="color: black;">科研</span>了数十年，<span style="color: black;">然则</span>许多核心功能仍然难以捉摸。</span><span style="color: black;">基于梯度的<span style="color: black;">办法</span>的一个弱点是它们依赖于要达到的<span style="color: black;">目的</span>的近似值，这<span style="color: black;">寓意</span>着一旦模型<span style="color: black;">显现</span>了<span style="color: black;">非常多</span>任务或序列中的任务更加多样化，它们就很可能失败。</span>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><strong style="color: blue;"><span style="color: black;">4、</span></strong><strong style="color: blue;"><span style="color: black;">模块化架构</span></strong></h2>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">模块化是人工系统用来实现<span style="color: black;">连续</span>学习的另一个范式。</span></strong></span><span style="color: black;">从进化的<span style="color: black;">方向</span>来看，在日益<span style="color: black;">繁杂</span>的环境中成功的<span style="color: black;">存活</span>与神经系统的<span style="color: black;">剧烈</span>分化和专一性<span style="color: black;">关联</span>并非偶然。</span><span style="color: black;"><span style="color: black;">实质</span>上，生物大脑是模块化的，<span style="color: black;">拥有</span><span style="color: black;">区别</span>但相互<span style="color: black;">功效</span>的子系统（如用于记忆或运动<span style="color: black;">掌控</span>）。就稀疏激活和分层组织而言，模块化的证据已从解剖学特征扩展到功能分离。</span></h2><span style="color: black;">模块化神经网络体系结构是对<span style="color: black;">连续</span>学习中的干扰和<span style="color: black;">劫难</span>性遗忘问题的一种自然而有效的<span style="color: black;">处理</span><span style="color: black;">方法</span>。</span><strong style="color: blue;"><span style="color: black;">模块化<span style="color: black;">亦</span><span style="color: black;">能够</span>表现为层次结构。</span></strong><span style="color: black;">理解模块化的另一种<span style="color: black;">办法</span>是从稀疏性的<span style="color: black;">方向</span>出发，它已从压缩和效率<span style="color: black;">方向</span>进行了广泛的探索，它<span style="color: black;">亦</span>对强化学习和<span style="color: black;">掌控</span><span style="color: black;">拥有</span>适用性。</span><span style="color: black;">从<span style="color: black;">连续</span>学习的<span style="color: black;">方向</span>来看，稀疏<span style="color: black;">暗示</span>和渐变都会<span style="color: black;">引起</span>较少的干扰和遗忘，<span style="color: black;">由于</span>将有较少的任务<span style="color: black;">敏锐</span>参数或较少的有效参数更改。稀疏性<span style="color: black;">亦</span>可能<span style="color: black;">引起</span>模块的<span style="color: black;">显现</span>，而无需预定义的模块化体系结构，<span style="color: black;">咱们</span>将其视为<span style="color: black;">将来</span><span style="color: black;">科研</span>的重要途径。</span><span style="color: black;">模块化的范式，尤其是在与稀疏约束和层次抽象结合时，<span style="color: black;">供给</span>了一种实用而强大的<span style="color: black;">办法</span>来<span style="color: black;">处理</span><span style="color: black;">连续</span>学习的问题。</span><strong style="color: blue;"><span style="color: black;">展望<span style="color: black;">将来</span>，人类级人工智能将需要具备专业能力和技能组合能力，模块化的<span style="color: black;">连续</span>学习描述了通往两者的道路。</span></strong>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><strong style="color: blue;"><span style="color: black;">5、</span></strong><strong style="color: blue;"><span style="color: black;">人工学习系统的记忆</span></strong></h2><span style="color: black;">大脑中的记忆系统对人类学习是至关重要的，并且是人工神经网络中基于记忆的<span style="color: black;">连续</span>学习的灵感<span style="color: black;">源自</span>。</span><span style="color: black;">尽管神经科学尚未完全描述突触可塑性、情景记忆和语义记忆之间的<span style="color: black;">繁杂</span>相互<span style="color: black;">功效</span>，但很<span style="color: black;">显著</span>，记忆是<span style="color: black;">守护</span>人类学习和适应<span style="color: black;">各样</span>经历的<span style="color: black;">长时间</span>生命的堡垒。</span><span style="color: black;">借鉴生物学的启发，本文<span style="color: black;">思虑</span>了一个更雄心勃勃的<span style="color: black;">处理</span><span style="color: black;">连续</span>学习问题的<span style="color: black;">办法</span>：<strong style="color: blue;">实现一个<span style="color: black;">能够</span>对知识或经验进行编码，存储和回忆的神经网络存储器。</strong></span><span style="color: black;">人工存储器<span style="color: black;">针对</span><span style="color: black;">长时间</span>召回可能<span style="color: black;">拥有</span>更大的可伸缩性，<span style="color: black;">然则</span>它带来了额外的挑战，即以一种跨任务的方式来设计或最好学习用于编码，<span style="color: black;">查找</span>和编写信息的框架。</span><span style="color: black;">用于<span style="color: black;">连续</span>学习的最简单的记忆实现<span style="color: black;">一般</span><span style="color: black;">叫作</span>为回放或排练：其想法是维持观察的历史，<span style="color: black;">而后</span>从该缓冲区（以及当前的观察）中采样学习<span style="color: black;">目的</span>，从而<span style="color: black;">经过</span>连续排练来避免<span style="color: black;">劫难</span>性的遗忘以前看过的任务（图1 D）。</span><span style="color: black;">在这种类别下，还有情节记忆<span style="color: black;">办法</span>，它们<span style="color: black;">区别</span>于排练<span style="color: black;">办法</span>，<span style="color: black;">由于</span>它们不仅<span style="color: black;">运用</span>回放记忆进行训练，<span style="color: black;">况且</span>还用到了推理。</span><span style="color: black;">排练和情景记忆很简单，并且在减少遗忘方面非常有效，但它们的伸缩性<span style="color: black;">欠好</span>。</span><strong style="color: blue;"><span style="color: black;">无论面临什么挑战，记忆框架<span style="color: black;">针对</span><span style="color: black;">连续</span>学习显然都是有价值的，<span style="color: black;">况且</span>最<span style="color: black;">广泛</span>的端到端模型有<span style="color: black;">潜能</span>在该<span style="color: black;">行业</span>开拓新的<span style="color: black;">行业</span>，但前提是<span style="color: black;">咱们</span>必须克服训练此类系统的挑战。</span></strong>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><strong style="color: blue;"><span style="color: black;">6、</span></strong><strong style="color: blue;"><span style="color: black;">元学习：<span style="color: black;">发掘</span>用于<span style="color: black;">连续</span>学习的归纳偏差</span></strong></h2><strong style="color: blue;"><span style="color: black;">用于<span style="color: black;">连续</span>学习的元学习是一种<span style="color: black;">办法</span>，该<span style="color: black;">办法</span>受大脑在有限的经验之后合成新颖<span style="color: black;">处理</span><span style="color: black;">方法</span>的能力的激励。</span></strong><span style="color: black;"><span style="color: black;">经过</span>应用<span style="color: black;">设备</span>学习来优化学习<span style="color: black;">办法</span>本身，<span style="color: black;">因此呢</span>用来学习“学习”本身，元学习<span style="color: black;">期盼</span>实现与生物系统所展示的那种快速、通用的适应能力。</span><span style="color: black;">到<span style="color: black;">日前</span>为止讨论的所有<span style="color: black;">处理</span><span style="color: black;">方法</span>都规定了手动设计的机制或体系结构，归纳偏差，以进行<span style="color: black;">连续</span>学习。</span><span style="color: black;"><span style="color: black;">每一个</span>归纳偏见都在<span style="color: black;">巴望</span>之间产生了<span style="color: black;">区别</span>的权衡，例如，良好的知识<span style="color: black;">保存</span>与基于内存的<span style="color: black;">办法</span>中的正向转移。值得<span style="color: black;">思虑</span>的是，<span style="color: black;">是不是</span><span style="color: black;">能够</span><span style="color: black;">经过</span>从数据中学习<span style="color: black;">处理</span><span style="color: black;">方法</span>而不是依靠人类的独创性来设计<span style="color: black;">处理</span><span style="color: black;">方法</span>，从而实现更好的折衷。</span><span style="color: black;">从历史上看，许多“元学习”或“从学习到学习”的<span style="color: black;">办法</span>都<span style="color: black;">显示</span>，<span style="color: black;">能够</span><span style="color: black;">经过</span>自动学习归纳性偏见（例如体系结构，数据和学习参数）来改善<span style="color: black;">处理</span><span style="color: black;">方法</span>，否则这些就需要手动进行。</span>
<h2 style="color: black; text-align: left; margin-bottom: 10px;"><strong style="color: blue;"><span style="color: black;">7、结语和<span style="color: black;">将来</span>方向</span></strong></h2><span style="color: black;">人类<span style="color: black;">拥有</span>快速学习和强大概括的非凡能力，正是这种能力<span style="color: black;">能够</span>使人类在科学，艺术和工业<span style="color: black;">行业</span>取得成就，但<span style="color: black;">咱们</span>并不经常提及人类在<span style="color: black;">一辈子</span>的教育和经验中<span style="color: black;">持续</span>学习的能力。</span><strong style="color: blue;"><span style="color: black;">本文不仅试图强调<span style="color: black;">连续</span>学习的重要性，<span style="color: black;">况且</span>试图揭露现代神经网络在这方面的局限性。</span></strong><strong style="color: blue;"><span style="color: black;"><span style="color: black;">经过</span>调查，本文确定了几种学习范式，这些范式在神经科学和生物学系统中都有很强的<span style="color: black;">类似</span>性。</span></strong><span style="color: black;"><strong style="color: blue;"><span style="color: black;">基于梯度的<span style="color: black;">办法</span>直接修改了神经网络的优化，并且<span style="color: black;">已然</span>证明<span style="color: black;">能够</span>减少<span style="color: black;">劫难</span>性的遗忘。</span></strong></span><span style="color: black;"><strong style="color: blue;"><span style="color: black;">模块化体系结构<span style="color: black;">经过</span>技能和知识的分层重组为<span style="color: black;">劫难</span>性遗忘<span style="color: black;">供给</span>了务实的<span style="color: black;">处理</span><span style="color: black;">方法</span>。</span></strong></span><span style="color: black;"><strong style="color: blue;"><span style="color: black;">端到端记忆模型可能是用于<span style="color: black;">长期</span>学习的可扩展<span style="color: black;">处理</span><span style="color: black;">方法</span>，而元学习<span style="color: black;">办法</span>可能会完全超过手工设计的算法和体系结构。</span></strong></span><span style="color: black;">最后，<span style="color: black;">连续</span>学习<span style="color: black;">处理</span><span style="color: black;">方法</span><span style="color: black;">能够</span><span style="color: black;">经过</span><span style="color: black;">加强</span>学习算法的<span style="color: black;">长时间</span><span style="color: black;">靠谱</span>性以及<span style="color: black;">研发</span><span style="color: black;">保证</span>从边界的体系结构来减轻<span style="color: black;">危害</span>。</span><span style="color: black;">编辑：于腾凯</span><span style="color: black;">校对：汪雨晴</span><span style="color: black;"><img src="https://mmbiz.qpic.cn/mmbiz_jpg/heS6wRSHVMmmmsXvnS4ogHoibobq5TLiaoE6PPwClydEOpYqmXavDAMADkpdPyxib6eO0G4pWVlMITYIbGl96ARnw/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></span>
认真阅读了楼主的帖子，非常有益。
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