企业在机器学习方面有哪些误解

如果要选2016年的热词，“机器学习”肯定当仁不让。似乎每家公司都在自我介绍里沾点机器学习的边，而且确实效果不错。

据云安全公司CloudPassage的卡森·斯威特介绍，很多企业都想用机器学习的工具解决问题，虽然并不十分了解工具的作用。

不久前在旧金山，斯威特跟另外两家网络安全公司管理者在结构安全论坛上发言，解释了一些机器学习方面常见的误解。其中之一就是将机器学习等同于“人工智能”（也是今年热词榜的大热门）。

威胁监测公司Sqrrl的马克·特伦佐尼解释说，人工智能相当于造一个大脑，但并不能产生确定结果（即产生可预期的结果），所以恶作剧的人通过故意挑逗微软旗下的人工智能聊天软件，能让机器人说出种族歧视言论。

另一方面，机器学习会产生可控的回应和有效的预测。机器学习能从海量数据中找出规则，甚至能将结果以可视化图标方式呈现并突出最重要的信息。

但机器学习也有重要限制，其中最明显的一个就是仍然需要人类提出合适的问题。

“机器学习锋利如矛之尖，但得先建立模型才能为安全分析师所用，”特伦佐尼表示。

凯文·马哈菲任职于移动安全公司Lookout（曾找出臭名昭著的iPhone漏洞），他也表示企业要在机器学习算法中输入“干净的数据”。如果把一堆堆的随机信息扔进去，结果只会是“垃圾进去，垃圾出来”。

回答论坛主持人《财富》杂志的乔纳森·瓦尼恩提问时，马哈菲还解释了“机器学习”与“深度学习”的区别。关键在于规模：深度学习是指最近计算机能力方面的突破，实现深度学习的花费巨大，可供机器学习工具利用成百上千万的参数探索可能性。

不过马哈菲也提醒道，虽然深度学习是一项精深的技术，很多企业还是应该先了解些机器学习的基础。

“我们已经开始问‘今早的冰沙里要加多少甘蓝’，而多数企业还停留在起床抽根烟的阶段，”马哈菲打趣说。（财富中文网）

译者：Pessy

审校：夏林

If you had to pick a tech industry buzzword for 2016, “machine learning” would be a good choice. Every other company, it seems, is packing the phrase into their pitches, and it’s having an effect.

According to Carson Sweet of cloud security firm CloudPassage, many companies are asking for machine learning tools to solve problems—even if they don’t have a clear idea of what these tools can do.

Speaking at the Structure Security conference on Tuesday in San Francisco, Sweet and executives from two other cyber-security firms explained some common misconceptions about machine learning. One of these is that machine learning is the same thing as “artificial intelligence” (another top candidate for buzzword of the year).

As Mark Terenzoni of threat detection firm Sqrrl explained, AI is like building a brain, but one that is unable to produce deterministic outcomes (ones that will produce a predictable outcome) — that’s why mischief makers were able to manipulate Microsoft’s AI chat bot into spewing racist comments.

Machine learning, on the other hand, results in predictable responses and useful predictions. It can detect patterns in giant amounts of data and even present the results in visual graphics that highlight the most salient information.

But there are important limits to machine learning, and the biggest of these is that it still requires humans to frame the right question.

“Machine learning is the tip of spear, but you have to do a lot of curating to create a model that makes sense to a security analyst,” said Terenzoni.

Kevin Mahaffey of mobile security firm Lookout (which helped expose that notorious iPhone bug) likewise noted that firms need “clean data” to feed machine learning algorithms. Simply shoveling random stacks of information, he said, will produce a “garbage in, garbage out” result.

Mahaffey, in response to a question from moderator Jonathan Vanian of Fortune, also clarified the difference between “machine learning” and “deep learning.” It turns to be a question of scale: deep learning describes the recent breakthroughs in computer power and cost that makes it possible for machine learning tools to explore millions of parameters.

Mahaffey, however, cautioned that while deep learning represents a remarkable technology, many firms still need to learn the basics of machine learning.

“We’re asking ‘how many grams of Kale do you want in your smoothies this morning’—while most organizations are still smoking a pack of cigarettes a day,” joked Mahaffey.