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技术专家(ai/大数据)_``我们淹没在数据中'':在专家和AI时代如何思考自己

weixin_26632369 2020-08-23 21:46 211 查看 https://blog.csdn.net/weixin_2

技术专家(ai/大数据)

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Between 2011 and 2017, at least 259 people died while trying to frame the perfect selfie. They fell off cliffs or down waterfalls or out high-rise windows while trying to snap the ideal shot for social media. These tragic deaths come amid rising cases of “death by GPS” and the 1,600,000 accidents per year caused by texting while driving. So, on top of the toll that screens are taking on our attention spans and interpersonal relationships, it’s clear that they’re downright deadly, too.

乙切口白内障手术挽2011年和2017年,至少有259人,而陷害完美的自拍死亡。 他们从悬崖,瀑布或高层窗户上掉下来,同时试图捕捉社交媒体的理想镜头。 这些悲惨的死亡之所以发生,是因为越来越多的“ GPS致死事件”以及每年因驾驶时发短信而导致的1,600,000起事故。 因此,除了银幕正在引起我们的关注范围和人际关系的损失外,很显然它们也是致命的。

These are extreme — and tragic — examples of the blind obedience to technology that Vikram Mansharamani laments in his new book, Think for Yourself: Restoring Common Sense in an Age of Experts and Artificial Intelligence. Mansharamani is a lecturer at Harvard’s School of Engineering and Applied Sciences on decision-making skills, and while he’s not against expertise or technology per se, Mansharamani argues that “today’s interconnected problems demand integrated thinking … what we need is contextualized expertise that complements depth with breadth.”

这些都是极端的和悲惨的例子,Vikram Mansharamani在他的新书《为自己思考:在专家和人工智能时代恢复常识》中感叹对技术的盲目服从。 曼莎拉玛尼(Mansharamani)是哈佛大学工程与应用科学学院的决策技能讲师,尽管他并不反对专业知识或技术本身,但曼莎拉玛尼认为“当今相互联系的问题需要综合思考……我们需要的是情境化的专业知识,可以补充深度知识。宽度。”

His book is an effort to explain how we ended up in a situation where most of us are dependent on technology and experts to navigate our daily lives. Mansharamani says his book can “empower readers with tools and strategies to escape from it.”

他的书旨在解释我们如何最终陷入大多数人依赖技术和专家来度过日常生活的情况。 曼莎拉玛尼(Mansharamani)表示,他的书“可以使读者从中逃脱的工具和策略”

OneZero caught up with Mansharamani to discuss how and why we’ve given our minds over to algorithms, how we might reclaim them, and why we should be skeptical of even the most benign uses of A.I., like when it’s used to offer diagnoses in health care.

OneZero赶上了Mansharamani,讨论了我们如何以及为何将思想投入算法,我们如何回收它们,以及为什么我们应该甚至对AI的最良性使用都持怀疑态度,例如何时将AI用于健康诊断。关心。

This interview has been edited and condensed for clarity.

为了清楚起见,本次采访已经过编辑和整理。

OneZero: You argue that people should break away from blindly following technology. Given how addictive many platforms are — especially social media — how can we expect people to regain focus?

OneZero:您认为人们应该摆脱盲目追随技术。 鉴于许多平台(尤其是社交媒体)的吸引力如何,我们如何期望人们重新获得关注?

Mansharamani: We’re drowning in data. There’s so much information and choices — and the result is we need help to filter that information and to optimize our choices because when we have that many choices, we think there must be a perfect one. We’re always living with this low-grade fever known as FOMO. And there’s this anxiety that there’s a better, more perfect choice out there, and we need to find it. So we turn to technologies to help us filter through the noise, to experts to help us make better choices. And in that process, we’re giving up some control by letting others choose where we focus and manage our information flow by being our filters.

Mansharamani:我们淹没在数据中。 信息和选择太多了-结果是我们需要帮助来过滤该信息并优化选择,因为当我们有很多选择时,我们认为必须有一个完美的选择。 我们一直生活在这种称为FOMO的低度发烧中。 出于这种焦虑,有一个更好,更完美的选择,我们需要找到它。 因此,我们寻求技术来帮助我们过滤掉噪音,寻求专家来帮助我们做出更好的选择。 在此过程中,我们通过让其他人选择我们作为过滤器的方式来集中精力和管理信息流,从而放弃了某些控制权。

I described focus as a double-edged sword, where one edge allows you to get deep knowledge by being focused and targeted in where you’re paying attention. But the other side of focus [might be termed] “broadly ignoring.” And those are two sides of the same coin, right? The more you focus, the more you ignore. And so the question is, what are we losing? The selection of what to ignore and what to focus upon is, in fact, a give-up of our autonomy. Increasingly, technology is framing what we see and how we see it.

我将焦点描​​述为一把双刃剑,其中的一条优势可以让您通过集中精力和专注于要关注的地方来获得深入的知识。 但是关注的另一面(可能被称为)“大范围忽略”。 那是同一枚硬币的两个侧面,对吗? 您越专注,就越会忽略。 所以问题是,我们在失去什么? 实际上,选择忽略什么和关注什么实际上是放弃我们的自主权。 技术越来越构成我们所看到的东西以及我们如何看待它。

You write that “in the course of our now habituated blind obedience to the people, tech, and systems … our intellectual self-reliance skills have withered.” But something like, say, a calculator, could help free up space for higher-level thinking. So what level of task should we outsource to technology, and when should we think on our own?

您写道:“在我们如今习惯于对人员,技术和系统的盲目服从过程中……我们的智力自给自足技能已经枯竭。” 但是,例如计算器之类的东西可以帮助腾出空间进行更高级的思考。 那么,我们应该将什么水平的任务外包给技术,何时应该自己考虑呢?

Well, I don’t even think of it that way. What I think about it is, it’s not necessarily the level of tech to outsource, it’s the significance of the decision. And tech is a tool, and like other tools and experts and other inputs that we could use, they all have a role and a time and a place. So I’m going to flip it and say, when the stakes of a decision are really high, we want to make sure that we are thinking for ourselves, actually understanding the assumptions that are going into the expertise that’s being offered, whether it’s from a human or a technology. We want to actually think about it and not blindly defer to it.

好吧,我什至不这么认为。 我认为的是,外包不一定是技术水平,而是决定的重要性。 技术是一种工具,就像其他工具和专家以及我们可以使用的其他投入一样,它们都有自己的作用,时间和地点。 因此,我要说的是,当一项决策的风险非常高时,我们要确保我们正在为自己思考,实际上是在理解所提供专业知识的假设,无论这些假设来自人或技术。 我们要真正考虑它,而不是盲目地服从它。

I’m not suggesting deference to experts is bad, if you proactively and mindfully do it. In fact, in the introduction to the book, I talk about a Stanford professor and his wife, who was diagnosed with cancer. It’s about knowing when it’s best to get in the backseat or give up the driver’s seat. They said, “We’ve decided emotionally making decisions around a cancer diagnosis are just too overwhelming. We spent a lot of time finding the doctor. We found one we trust. We’re blindly deferring, having mindfully chosen who to blindly defer to.” It’s a mindfulness argument, at some level, and it has to do with the stakes of the decision.

如果您主动而认真地做到这一点,我并不建议尊重专家。 实际上,在本书的引言中,我谈到了一位斯坦福大学教授和他的妻子,他被诊断出患有癌症。 这是关于知道何时最好进入后座或放弃驾驶员座位。 他们说:“我们已经决定,在情感上围绕癌症诊断做出的决定太过压倒性的。 我们花了很多时间找医生。 我们找到了我们信任的人。 我们盲目地推迟,已经明智地选择了谁盲目地服从谁。” 在某种程度上,这是一种正念论点,它与决策的利害关系有关。

You argue that there are dangers to outsourcing our decision-making process to A.I., citing medical technologies used to diagnose patients as one example. What is the threat here?

您认为,将用于诊断患者的医疗技术作为一个例子,将我们的决策流程外包给AI就有危险。 这里有什么威胁?

Where it’s really useful to use technologies and to check diagnostically is where you can “catch the rabbits before they leave the pen.” But unfortunately, that process of doing more early diagnosis and more technology and more screening and more looking has resulted in more and more finding. And the problem is when we find cancer, we’re having trouble identifying whether it’s the fast-moving kind that could kill you or the slow-moving kind that may never affect you.

使用技术和进行诊断检查真正有用的地方是您可以“在兔子离开笔之前抓住它们”。 但是不幸的是,进行更多早期诊断,更多技术,更多筛选和更多查找的过程导致了越来越多的发现。 问题是,当我们发现癌症时,我们很难确定是哪种快速移动的药物会杀死您,还是慢速移动的药物永远不会影响您。

As for technology being able to help decipher between those two types of cancers, I think that would be hugely valuable. My understanding is that we don’t have great progress yet, on that front. So I think technology as a tool, if deployed towards the areas of confusion and questions that we have, could be really valuable. But we can’t lose track of the fact — and this is a really critical point — we can’t lose track of the fact that technologies, algorithms, artificial intelligence are all designed by humans.

至于能够帮助破译这两种癌症的技术,我认为这将是非常有价值的。 我的理解是,在这方面我们还没有取得很大进展。 因此,我认为,将技术作为一种工具,如果将其部署到我们所遇到的困惑和问题领域,可能会非常有价值。 但是,我们不能忘记这个事实-这是一个非常关键的点-我们不能忘记技术,算法和人工智能都是由人类设计的事实。

Humans set the initial conditions. We may not know where those official conditions take the technology with machine learning. But we know we taught it how to learn, so to say, or we set the initial parameters, and we’re finding that in many of those cases, the algorithms are producing biased outcomes.

人类设定了初始条件。 我们可能不知道这些官方条件将技术与机器学习结合起来的地方。 但是我们知道我们教过它如何学习,可以这么说,或者我们设置了初始参数,并且我们发现在许多情况下,这些算法都会产生有偏差的结果。

And those algorithms are being written by experts, as you point out.

正如您所指出的那样,这些算法是由专家编写的。

What I’m talking about is expertise, and expertise can be embodied in multiple formats. Expertise can be embodied in an individual. Expertise could be embodied in the technology via an algorithm, embodied in a checklist that says, “Hey, we know these are the most important things to do at this time and this way — check, check, check.” And so you can call those protocols or rules generally in the management of bureaucracies that sort of are embedded or extend expertise, if you will.

我说的是专业知识,专业知识可以多种形式体现。 专业知识可以体现在个人中。 可以通过算法在技术中体现专业知识,该算法体现在清单中:“嘿,我们知道这些是目前和这种方式下最重要的事情-检查,检查,检查。” 因此,如果可以的话,您可以在官僚机构的管理中通常称呼这些协议或规则,这些官僚机构具有嵌入或扩展的专业知识。

But broadly speaking, “experts” have taken a hit lately. Many people are hesitant to trust experts or anyone they perceive to be “elite,” which, as we’re seeing with the Covid19 crisis, can be a serious problem. Why shouldn’t we listen to experts?

但从广义上讲,“专家”最近受到打击。 许多人不愿信任专家或任何他们认为是“精英”的人,正如我们在Covid19危机中所看到的那样,这可能是一个严重的问题。 我们为什么不听专家的话?

It’s a great issue. A lot of people ask me the question in this particular logic, which is that the U.S. commander in chief seems to think for himself. Is that necessarily a good thing?

这是一个很大的问题。 很多人问我这个特殊逻辑的问题,那就是美国总司令似乎在想自己。 那一定是一件好事吗?

People ask, “Vikram, are you suggesting we dismiss experts?” And the answer is absolutely not. What I’m suggesting is that unfortunately we humans tend to bounce like a ping pong ball between complete dismissal of experts and blind deference to experts.

人们问:“维克拉姆,您是在建议我们解雇专家吗?” 答案绝对不是。 我的意思是,不幸的是,在完全解雇专家与盲从专家之间,我们人类倾向于像乒乓球一样弹跳。

But there’s a middle ground. The idea here is that we generate enough insight from experts. We extract the value that experts are able to produce, literally tapping into their expertise, but don’t give up our autonomy. So it’s a slightly more nuanced way to think about it. I don’t want to say, “All experts are bad.” A lot of progress in human society over hundreds of years has been because of specialization and expertise. I’m really flipping it around and saying that how we, as individuals, use experts is a problem. We need to listen to them, but we need to not be blindly deferential to them.

但是有一个中间立场。 这里的想法是我们从专家那里获得足够的见解。 我们从专家的角度上挖掘专家能够产生的价值,但不会放弃我们的自主权。 因此,这是一种稍微微妙的思考方式。 我不想说:“所有专家都是坏人。” 数百年来,人类社会取得了许多进步,这要归功于专业化和专业知识。 我的意思是说,我们个人如何使用专家是个问题。 我们需要听取他们的意见,但我们不必盲目尊重他们。

We all definitively seek out confirmatory evidence when we’re trying to make a tough choice. We have an inclination. We see confirmatory evidence, and we end up down this path where we only find data that endorses our existing view. And so one of the strategies I suggest to help combat some of these biases, for instance, is to say we should employ a devil’s advocate in our process.

当我们试图做出艰难的选择时,我们都会明确地寻找确认性证据。 我们有一个倾向。 我们看到了确凿的证据,最后我们只找到能支持我们现有观点的数据。 因此,例如,我建议用来帮助克服其中一些偏见的策略之一就是说,我们应该在流程中雇用恶魔的拥护者。

So whether it’s a friend, a family member, or if it’s in a corporate setting, find any person on your team whose job is to actually take the opposite perspective, regardless of the merits. Someone who’s given the time, energy, and resources to produce the contrary case. A healthy way to make sure we find disagreement, and actually seek evidence, and think hard about evidence that’s opposite to what our natural confirmatory bias might lend itself towards.

因此,无论是朋友,家庭成员还是公司背景,无论其功绩如何,都请团队中的任何人实际采取相反的观点。 有人花了时间,精力和资源来提出相反的情况。 一种健康的方式来确保我们发现分歧,并实际寻求证据,并认真思考与我们的自然确认性偏见可能正相反的证据。

Let’s seek disagreement rather than allowing our natural instincts to run amok.

让我们寻求分歧,而不是让我们的本能直言不讳。

You argue that specialization has become popular, but that’s it’s a problem and that it prevents the kind of systems thinking we actually need right now.

您认为专业化已变得很流行,但这是一个问题,它阻止了我们现在实际需要的那种系统思维。

I’m a big fan of being a generalist. I think that connecting dots is as important, if not more important, than generating dots. And that fits into this idea of experts on tap, not on top. If you’re saying someone’s a great expert, let’s take their dot, but we need to paint our mosaic, and so let’s take those tiles, and put them into our picture, for our context. And we have to just remember that experts live in silos, almost definitionally — they’re narrow and deep. That means there’s a large domain outside of their area of expertise that may have an impact on us.

我是通才的忠实粉丝。 我认为连接点与生成点同样重要,甚至更重要。 这符合专家的想法,而不是放在首位。 如果您说某人是一位出色的专家,那么让我们以他们的观点为准,但是我们需要绘制马赛克,因此根据我们的情况,让我们将这些图块并放入我们的图片中。 我们只需要记住,专家几乎毫无疑问地生活在筒仓中,他们既狭narrow又深厚。 这意味着他们的专业领域之外还有很大的领域可能会对我们产生影响。

It’s critical that we retain a focus on the context because we’re the ones who know the context. It’s not malicious, it’s not intentional. It is structural. Deep expertise means narrow focus, which implies broad ignoring, which means living in that silo. Where the boundaries of that silo are matter. So in order to do that, in order to manage experts, we have to see the big picture — zoom out and see where and how all of these dots come together. So it’s really a call for integrated thinking. More systems thinking, where we see the connections across the silos. And in fact find that some of the value for us as individuals is in fact crossing these silos or connecting these silos.

至关重要的是,我们必须专注于上下文,因为我们是了解上下文的人。 这不是恶意的,不是故意的。 它是结构性的。 深入的专业知识意味着狭窄的关注点,这意味着广泛的无视,这意味着生活在孤岛中。 那个筒仓的边界很重要。 因此,为了做到这一点,为了管理专家,我们必须看到大局—缩小并查看所有这些点在哪里以及如何结合在一起。 因此,这实际上是对集成思维的呼唤。 更多的系统思考,我们可以看到各个孤岛之间的联系。 实际上,我们发现作为个人的某些价值实际上正在跨越这些孤岛或与这些孤岛联系。

Seeking disagreement is essential. If there were easy answers, we wouldn’t be stressing or thinking about these things. It’s when they’re nonobvious answers that disagreement can help us navigate through it.

寻求分歧至关重要。 如果有简单的答案,我们就不会在强调或思考这些事情。 只有当他们不是很明确的答案时,分歧才能帮助我们解决问题。

翻译自: https://onezero.medium.com/were-drowning-in-data-how-to-think-for-yourself-in-the-age-of-experts-and-a-i-a41990a834

技术专家(ai/大数据)

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