英文赏析：Will a Chat Bot Be Your Next Learning Coach?
By Margie Meacham
Eighty percent of major companies expect to be using artificial intelligence by 2020, but their training departments are likely to be the last places you’ll find it. We need to fix that.
A recent survey of Millennials revealed that 40 percent of them interact with a chat bot, a program that simulates a human conversation, on a daily basis; another survey indicates that many people prefer chat bots over humans for certain types of customer support transactions.
While other industries are already developing AI, the learning industry seems to be lagging behind. It’s pretty hard to implement something you don’t understand, so let’s start there.
Artificial Intelligence
Artificial intelligence, or AI, is a branch of computer science that aims to create intelligent machines, capable of performing problem-solving, pattern recognition, and learning without explicit programming.
AI requires vast amounts of data to create intelligent machines, and Big Data requires intelligent machines to perform the massive calculations necessary to find meaningful patterns and connections. For this reason, you will often find Big Data and AI are employed together and support each other.
Big Data
“Big Data” refers to data sets that are so voluminous and complex that traditional data processing application software packages are inadequate to deal with them. Big Data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.
Big Data analytics examines these massive, varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that drives artificial intelligence.
3 Dimensions of Big Data
There are three dimensions to Big Data: velocity, variety, and volume.
Velocity
Data is coming at us from all directions, and it is coming faster every day. To benefit from Big Data insights, companies must be able to capture, analyze, and use this massive amount of information as quickly as it is coming in. Human beings alone could never keep up with this firehose of information, so Big Data solutions must include strategies to control and keep up with the speed of incoming data. Bring in the smart machines!
Variety
Consider your own experience as a digital consumer. In a single hour, you may read an email on your PC, send a text on your phone, download a podcast, watch a video, and post a tweet. Each requires different strategies for capture and analysis—and these are only a few examples of the diversity of data available online today.
VolumeHere is just a snapshot of the sheer volume of data that came at us every day of 2017:
456,000 tweets on Twitter
50,926 videos viewed on Buzzfeed
3,607,080 Google searches.
The amount of data coming from your learning management system (LMS) and performance management software is puny compared to the onslaught coming from social media; but it is part of the Big Data mosaic, and most of us are simply not taking advantage of the information we have readily available.
Machine Learning
Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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In other words, machine learning focuses on the development of computer programs that can access large amounts of data and change their behavior or programming based on that information, without human intervention. Uses for machine learning in talent development include:
Assess and predict job performance.
Predict the competencies that will be needed in 10 years so learners can develop relevant skills today.
Provide personalized conversation about new information, performance coaching, or motivation on a 24-hour basis, without the need for a human coach.
Identify learner competencies and gaps to make better training and education suggestions that are truly personalized to the individual.
Examples of AI in Talent Development
Here are just a few examples of education-focused AIs that are already in use. Many early adopters are in the higher education arena, but the ideas work equally well in corporate training or K-12 education.
Jill Watson, the virtual teaching assistant at the University of Georgia, communicates with students via email.
Virtual tutors can help each learner move at a pace that is right for them.
Penn State is using chat bots to help teachers gain confidence handling difficult conversations, like bullying or hate language in class.
Think grading essays requires the human touch? Think again! At Stanford, an AI grading system achieved an 81 percent accuracy rate when compared to essays graded by humans.
Beware These Beginner Mistakes
Because some AI applications are still in the early days on the hype cycle, I interviewed an AI expert at one of my client organizations to find out what common mistakes she sees in chat bot projects led by early adopters.
Here’s a summary of her list.
Garbage In/Garbage Out (GIGO)
Many projects fail because project managers forget to check data quality, or do not have the right approach to identify and resolve these issues. When we analyze incomplete or “dirty” data sets, our AI ends up making decisions and recommendations based on a poor foundation.
Apples and Oranges
Comparing unrelated data sets or data points will result in inferring relationships or similarities that do not exist.
Overly Narrow Focus
Some projects are designed to consider one data set without considering other data points that might be crucial for the analysis. For example, a project set up to analyze learner pass/fail rates while ignoring the course completion rate may inflate performance results.
Cool but Useless
Some AI projects are quick to deliver but fail to make a significant impact on the learner’s everyday experience. Ensure that you have the right strategy to deliver the most value to your learners, and avoid giving them something cool that doesn’t really help them learn.
Getting Started
My advice is to just get on with it. Make a point of learning something about AI and machine learning every day, always with an eye to how you might be able to use it in your own organization. Here are a few suggestions:
Check out datascience.com for a huge list of data science resources.
Take this course from Google on Udacity—it’s free, and quite well done.
Brainstorm some ideas with colleagues. There are some great ideas here, and even more ideas here.
Build a Bot
There are dozens of platforms that let you create free chat bots for specific messaging apps without any special skills or coding knowledge. Snatchbot, for example, can be used on Facebook Messenger, Slack, WeChat, Skype, and more. It’s easy to use, and the interface is probably already familiar to many of your users. And Botsify has a variety of bot templates to get you started, including a whole list of education bots. Looking for more do-it-yourself tools? Here’s a nice list from business2community. com.
Engage With Colleagues
You might be surprised how many of your colleagues are eager to test the waters with a chat bot or other educational AI application. You won’t find them unless you join the conversation. One place to start is by attending the ATD 2018 International Conference & Exposition (for example, Elliott Masie will talk about some innovations changing workplace learning during the session, Learning Trends, Disrupters, and Hype in 2018) or any of our other conferences designed to educate, engage, and inspire you.
Will You Be Replaced by a Chat Bot?
While there is a vast difference of opinion on how AI is shaping the very near future of work and learning, one thing I know for sure: Those of us who are not part of the disruption will become lost in the dust that the disruptors kick up. I plan on being in front of it
Margie Meacham is an adult learning expert with a master of science in learning technologies and more than 15 years of experience in the field. A self-described “scholar-practitioner,” Margie collaborates with like-minded instructional designers to find practical applications of neuroscience to instructional design.
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2018年02月19日
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AI 人工智能中对HR的常见术语解释，没事可以了解下在工作环境中准备人工智能对于技术厌恶的招聘团队来说是一个令人望而生畏的。
对于那些探索他们的业务意味着什么，这里有8个基本的解释开始：
算法：算法是在解决问题或计算中应遵循的一组规则。算法需要在招聘过程中生成大量数据，并将其转换为HR可用于候选人选择的信息。在之前的一项研究中，通过算法招聘的候选人比人力资源招聘的人员长15％。算法有助于提高候选人的选择并减少不良招聘的可能性。
人工智能（AI）：人工智能（AI）通常被称为“第四次工业革命”，它是一种能够模仿智能人类行为的机器，其中包括做决策和执行基本任务，如解决问题，计划和学习。AI可以自动执行重复和平凡的管理任务，包括整个招聘过程中的筛选和申请人更新。这也是聊天机器人的兴起和视频放映的使用背后的原因。
聊天机器人：聊天机器人的简称，聊天机器人在人才获取方面越来越多。与苹果的Siri或亚马逊的Alexa一样，招聘中的聊天机器人使用人工智能（例如，机器学习 - 见下文）来理解问题并作出回应。Chatbots可以在不同的平台上使用，包括电子邮件，消息应用程序和通过您的申请人跟踪软件。Chatbots旨在模拟与您就业网站的访问者的对话，并正在迅速成为高容量招聘的基本技术工具。聊天机器人有效地使用，为您的招聘过程添加更吸引人的互动元素。今年早些时候进行的一项调查发现，在申请过程中，超过一半的候选人愿意与聊天机器人进行互动。
游戏化：游戏化将游戏的常见元素应用于其他在线活动领域，包括市场营销。在招聘过程中，毕业生雇主经常使用劳埃德银行集团，德勤和普华永道倍受青睐的Multipoly，以吸引年轻人才，创造更具吸引力的候选人经验。通过人力资源技术将游戏化融入您的招聘流程中。
机器学习：类似于人工智能，机器学习为AI提供了更智能的算法。在招聘中，机器学习可以减少您的聘用时间，并用于自动化候选人筛选，通常利用招聘分析中与最成功的人员相关的数据。人力资源软件中复杂的机器学习算法可用于通过语言选择甚至面部表情来评估候选人的潜在文化适应性。
人员分析：人员分析将数据和分析结合起来，深入了解与员工相关的一系列问题，包括领导力，绩效管理和招聘。要了解更多信息，请参阅我们以前的文章，其中提供了有关人员分析的更详细的介绍。
情绪分析：这可能不是你熟悉的词，但情绪分析解释了语言对人的影响，无论是消极的，积极的还是中立的。在招聘时可以用来分析你的工作岗位的措辞的影响。例如，去年我们报道说，在社交媒体平台Buffer的“工作岗位”中用'开发者'取代'黑客'这个词，看到女性候选人申请空缺的人数有所增加。在招聘软件中的人力资源分析提供了更多的洞察力，如何使用特定的话可以阻止人才适用于您的工作。使用情感分析的软件也可以提出更合适的词汇来吸引更多元化的人才库。
图灵测试：图灵测试是由科学家阿兰·图灵（Alan Turing）在1950年设想的，前提是“机器可以想象？今天，它指的是人工智能的潜力，以说服人们，而不是一个机器与人互动。其中最成功的例子是第三次获得2017年Loebner奖（基于图灵测试）的Mitsuku聊天机器人，但尚未说服评委是人类。 随着工作场所的自动化程度的提高，这是一个与AI有关的术语。
以上由AI 自动翻译。
Algorithms : An algorithm is a set of rules to be followed in a problem solving situation or calculation. Algorithms take large amounts of data generated during the hiring process and transform it into information that HR can use in candidate selection. In a previous study, candidates recruited via algorithms remained in their job 15% longer than those hired by HR. Algorithms help to improve candidate selection and reduce the potential for a bad hire.
Artificial Intelligence (AI) : Often referred to as the ‘fourth industrial revolution’, artificial intelligence (AI) is a machine that is capable of imitating intelligent human behaviour, which includes making decisions and performing basic tasks such as problem solving, planning and learning. AI automates repetitive and mundane admin tasks, including screening and applicant updates throughout the hiring process. It is also behind the rise in chatbots and the use of video screening.
Chatbots : Short for chat robot, chatbots are becoming more prolific in talent acquisition. Like Apple’s Siri or Amazon’s Alexa, chatbots in recruitment use artificial intelligence (eg, machine learning - see below) to comprehend questions and respond. Chatbots can be used across different platforms, including e-mail, messaging apps and through your applicant tracking software. Chatbots are designed to simulate conversations with visitors to your careers site and are rapidly becoming an essential tech tool for high volume recruitment. Used effectively, chatbots add a more engaging and interactive element to your hiring process. A survey carried out earlier this year found that over half of candidates are comfortable interacting with chatbots during the application process.[1]
Gamification : Gamification applies the common elements of game playing to other areas of online activity, including marketing. In recruitment it is frequently used by graduate employers, including Lloyds Banking Group, Deloitte and in PwC’s popular Multipoly to attract young talent and create a more engaging candidate experience. Incorporate gamification into your recruitment process through your HR technology.
Machine learning : Similar to artificial intelligence, machine learning provides AI with the algorithms that make it more intelligent. In hiring, machine learning can reduce your time to hire and is used to automate candidate screening, often utilising the data available in your recruitment analytics relating to your most successful hires. Sophisticated machine learning algorithms in HR software can be used to evaluate the potential cultural fit of a candidate through language choice and even facial expressions.
People analytics : People analytics combines data and analysis to gain insight into a range of issues related to your employees, including leadership, performance management and recruitment. For more insight, please see our previous article which provides a more detailed introduction to people analytics.
Sentiment analysis : It may not be a term you are familiar with but sentiment analysis interprets the effect that language has on people, whether negative, positive or neutral. In recruitment it can be used to analyse the impact of the wording of your job posts. For example, last we year we reported that by replacing the word ‘hacker’ with ‘developer’ in their job posts, social media platform Buffer saw an increase in the number of female candidates applying to their vacancies. HR analytics in your recruitment software provide more insight into how the use of specific words can deter talent from apply to your jobs. Software which uses sentiment analysis can also suggest more suitable words to attract a more diverse talent pool.
The Turing Test : The Turing Test was conceived by scientist Alan Turing in 1950 based on the premise 'can machines think?' Today it refers to the potential for artificial intelligence to convince people they are interacting with a person rather than a machine. One of the most successful examples is the Mitsuku chatbot which has been awarded the 2017 Loebner Prize[2] (based on the Turing Test) for the third time but has yet to convince the judges it is human. It's a term you may hear more of in relation to AI as automation in the workplace rises.
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