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% of them interact with a chatbot, a program that simulates a human conversation, on a daily basis while another survey indicates that many people prefer to use chatbots 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, 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 connection. For this reason, you will often find Big Data and AI are employed together and support each other.
The term “Big Data” refers to data sets that are so voluminous and complex that traditional data processing application software is 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 drive artificial intelligence.
3 Dimensions of Big Data
There are three dimensions to Big Data known as Velocity, Variety, and Volume.
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!
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.
Here 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 data coming from your 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 is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
In other words, machine learning focuses on the development of computer programs that can access large amounts of data and change their behavior/programming based on that information, without human intervention. Uses for machine learning in talent development include:
- Diagnose 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
A Few Examples of AI in Use in Talent Development Today
Here are just a few examples that are already in use. Many early adopters are in the higher education space, but the ideas work equally well in corporate training or K-12 education.
Jill Watson, the teaching assistant at the University of Georgia, communicates with students via email. https://www.recode.net/2016/6/1/11830980/ibm-watson-ai-teaching-assistant-jill-georgia-tech
Virtual tutors can help each learner move at a pace that is right for him/her.
Penn State is using chatbots 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 81% accuracy rate when compared to human graders:
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 chatbot 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 and/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.
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. More ideas here: https://blogs.sap.com/2017/07/26/machine-learning-7-use-cases-for-education-learning/
Build a Bot
There are dozens of platforms that let you create free chatbots 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.
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 secretly harboring a desire to test the waters with a chatbot or other educational AI application. You won’t find them unless you put yourself out there and join the conversation. One place to start is by attending TLDC 18. This conference is focused on the convergence of neuroscience and AI this year, so you’ll find plenty of conversations, workshops and panel discussion to educate, engage and inspire you.
Will You Be Replaced by a Chatbot?
While there is a wide 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.