2018 年 4 个需要关注的人工智能趋势
AI 人工智能

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> 今年人工智能决策将变得更加透明？
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![](/data/attachment/album/201801/26/043645wxtyp3r32vef2xj2.png)


无论你的 IT 业务现在使用了多少[人工智能](https://enterprisersproject.com/tags/artificial-intelligence)，预计你将会在 2018 年使用更多。即便你从来没有涉猎过 AI 项目，这也可能是将谈论转变为行动的一年，德勤董事总经理 David Schatsky 说。他说：“与 AI 开展合作的公司数量正在上升。”


看看他对未来一年的AI预测：


### 1、预期更多的企业 AI 试点项目


如今我们经常使用的许多现成的应用程序和平台都将 AI 结合在一起。 Schatsky 说：“除此之外，越来越多的公司正在试验机器学习或自然语言处理来解决特定的问题，或者帮助理解他们的数据，或者使内部流程自动化，或者改进他们自己的产品和服务。


“除此之外，公司与人工智能的合作强度将会上升。”他说，“早期采纳它的公司已经有五个或略少的项目正在进行中，但是我们认为这个数字会上升到十个或有更多正在进行的计划。” 他说，这个预测的一个原因是人工智能技术正在变得越来越好，也越来越容易使用。


### 2、人工智能将缓解数据科学人才紧缺的现状


人才是数据科学中的一个大问题，大多数大公司都在努力聘用他们所需要的数据科学家。 Schatsky 说，AI 可以承担一些负担。他说：“数据科学的实践，逐渐成为由创业公司和大型成熟的技术供应商提供的自动化的工具。”他解释说，大量的数据科学工作是重复的、乏味的，自动化的时机已经成熟。 “数据科学家不会消亡，但他们将会获得更高的生产力，所以一家只能做一些数据科学项目而没有自动化的公司将能够使用自动化来做更多的事情，虽然它不能雇用更多的数据科学家”。


### 3、合成数据模型将缓解瓶颈


Schatsky 指出，在你训练机器学习模型之前，你必须得到数据来训练它。 这并不容易，他说：“这通常是一个商业瓶颈，而不是生产瓶颈。 在某些情况下，由于有关健康记录和财务信息的规定，你无法获取数据。”


他说，合成数据模型可以采集一小部分数据，并用它来生成可能需要的较大集合。 “如果你以前需要 10000 个数据点来训练一个模型，但是只能得到 2000 个，那么现在就可以产生缺少的 8000 个数据点，然后继续训练你的模型。”


### 4、人工智能决策将变得更加透明


AI 的业务问题之一就是它经常作为一个黑匣子来操作。也就是说，一旦你训练了一个模型，它就会吐出你不能解释的答案。 Schatsky 说：“机器学习可以自动发现人类无法看到的数据模式，因为数据太多或太复杂。 “发现了这些模式后，它可以预测未见的新数据。”


问题是，有时你确实需要知道 AI 发现或预测背后的原因。 “以医学图像为例子来说，模型说根据你给我的数据，这个图像中有 90％ 的可能性是肿瘤。 “Schatsky 说，“你说，‘你为什么这么认为？’ 模型说：‘我不知道，这是数据给的建议。’”


Schatsky 说，如果你遵循这些数据，你将不得不对患者进行探查手术。 当你无法解释为什么时，这是一个艰难的请求。 “但在很多情况下，即使模型产生了非常准确的结果，如果不能解释为什么，也没有人愿意相信它。”


还有一些情况是由于规定，你确实不能使用你无法解释的数据。 Schatsky 说：“如果一家银行拒绝贷款申请，就需要能够解释为什么。 这是一个法规，至少在美国是这样。传统上来说，人类分销商会打个电话做回访。一个机器学习模式可能会更准确，但如果不能解释它的答案，就不能使用。”


大多数算法不是为了解释他们的推理而设计的。 他说：“所以研究人员正在找到聪明的方法来让 AI 泄漏秘密，并解释哪些变量使得这个病人更可能患有肿瘤。 一旦他们这样做，人们可以发现答案，看看为什么会有这样的结论。”


他说，这意味着人工智能的发现和决定可以用在许多今天不可能的领域。 “这将使这些模型更加值得信赖，在商业世界中更具可用性。”




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via: <https://enterprisersproject.com/article/2018/1/4-ai-trends-watch>


作者：[Minda Zetlin](https://enterprisersproject.com/user/minda-zetlin) 译者：[Wuod3n](https://github.com/Wuod3n) 校对：[wxy](https://github.com/wxy)


本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出


OK
However much your IT operation is using [artificial intelligence](https://enterprisersproject.com/taxonomy/term/426) today, expect to be doing more with it in 2018. Even if you have never dabbled in AI projects, this may be the year talk turns into action, says David Schatsky, managing director at [Deloitte](https://www2.deloitte.com/us/en.html). “The number of companies doing something with AI is on track to rise,” he says.

Check out his AI predictions for the coming year:

## 1. Expect more enterprise AI pilot projects

Many of today’s off-the-shelf applications and platforms that companies already routinely use incorporate AI. “But besides that, a growing number of companies are experimenting with machine learning or natural language processing to solve particular problems or help understand their data, or automate internal processes, or improve their own products and services,” Schatsky says.

**[ What IT jobs will be hot in the AI age? See our related article, 8 emerging AI jobs for IT pros. ]**

“Beyond that, the intensity with which companies are working with AI will rise," he says. "Companies that are early adopters already mostly have five or fewer projects underway, but we think that number will rise to having 10 or more pilots underway.” One reason for this prediction, he says, is that AI technologies are getting better and easier to use.

## 2. AI will help with data science talent crunch

Talent is a huge problem in data science, where most large companies are struggling to hire the data scientists they need. AI can take up some of the load, Schatsky says. “The practice of data science is increasingly automatable with tools offered both by startups and large, established technology vendors,” he says. A lot of data science work is repetitive and tedious, and ripe for automation, he explains. “Data scientists aren’t going away, but they’re going to get much more productive. So a company that can only do a few data science projects without automation will be able to do much more with automation, even if it can’t hire any more data scientists.”

## 3. Synthetic data models will ease bottlenecks

Before you can train a machine learning model, you have to get the data to train it on, Schatsky notes. That’s not always easy. “That’s often a business bottleneck, not a production bottleneck,” he says. In some cases you can’t get the data because of regulations governing things like health records and financial information.

Synthetic data models can take a smaller set of data and use it to generate the larger set that may be needed, he says. “If you used to need 10,000 data points to train a model but could only get 2,000, you can now generate the missing 8,000 and go ahead and train your model.”

## 4. AI decision-making will become more transparent

One of the business problems with AI is that it often operates as a black box. That is, once you train a model, it will spit out answers that you can’t necessarily explain. “Machine learning can automatically discover patterns in data that a human can’t see because it’s too much data or too complex,” Schatsky says. “Having discovered these patterns, it can make predictions about new data it hasn’t seen.”

The problem is that sometimes you really do need to know the reasons behind an AI finding or prediction. “You feed in a medical image and the model says, based on the data you’ve given me, there’s a 90 percent chance that there’s a tumor in this image,” Schatsky says. “You say, ‘Why do you think so?’ and the model says, ‘I don’t know, that’s what the data would suggest.’”

If you follow that data, you’re going to have to do exploratory surgery on a patient, Schatsky says. That’s a tough call to make when you can’t explain why. “There are a lot of situations where even though the model produces very accurate results, if it can’t explain how it got there, nobody wants to trust it.”

There are also situations where because of regulations, you literally can’t use data that you can’t explain. “If a bank declines a loan application, it needs to be able to explain why,” Schatsky says. “That’s a regulation, at least in the U.S. Traditionally, a human underwriter makes that call. A machine learning model could be more accurate, but if it can’t explain its answer, it can’t be used.”

Most algorithms were not designed to explain their reasoning. “So researchers are finding clever ways to get AI to spill its secrets and explain what variables make it more likely that this patient has a tumor,” he says. “Once they do that, a human can look at the answers and see why it came to that conclusion.”

That means AI findings and decisions can be used in many areas where they can’t be today, he says. “That will make these models more trustworthy and more usable in the business world.”

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