Tuesday, January 16, 2018

AI just outperformed humans at reading, potentially putting millions of customer service jobs at risk of automation. Could it do the same in learning?

Something momentous just happened. An AI programme, from Alibaba, can now, for the first time, read a text and understand it better than humans. The purple line has just crossed the red line and the implications are huge.
Think through the consequences here, as this software, using NLP and machine learning, gets better ad better. The aim is to provide answers to questions. This is exactly what millions of people do in jobs around the world. Customer service in call centres, Doctors with patients, anywhere people reply to queries... and any interactions where language and its interpretation matter.
Health warning
First we must be careful with these results, as it depends on two things 1) the nature of the text 2) what we mean by ‘reading’. Such approaches often work well with factual texts but not with more complex and subtle texts, such as fiction, where the language is difficult to parse and understand, and where there is a huge amount of ‘reading between the lines”. Think about how difficult it is to understand even that last sentence. Nevertheless, this is a breakthrough.
The Test
It is the first time a machine has out-done a real person in such a contest. They used the Stanford Question Answering Dataset, to assess reading comprehension. The test is to provide exact answers to more than 100,000 questions. As an open test environment, you can do it yourself, which makes the evidence and results transparent. Alibaba’s neural network model, based on a Hierarchical Attention Network, which reads down through paragraphs to sentences to words, identifies potential answers and their probabilities. Alibaba has already used this technology in their customer service chatbot, Dian Xiaomi, to an average of 3.5 million customers a day on the Taobao and Tmall platforms. (10 uses for chatbots in learning).
Learning
Indeed, the one area that is likely to benefit hugely from these advances is education and training. The Stanford dataset does have questions that are logically complex and, in terms of domain, quite obscure, but one should see this development as great at knowledge but not yet effective with questions beyond this. That’s fine as there is much that can be achieved in learning.We have been using this AI approach to create online learning content, in minutes not months, through WildFire. Using a  similar approach, we identify the main learning points in any document, PPT or video, and build online learning courses quickly, with an approach based on recent cognitive psychology that focuses on retention. In addition, we add curated content.
Pedagogy
The online learning is very different from the graphics plus multiple-choice paradigm. Rather than rely on the weak ‘select from a list’ MCQs (see critique here), we get learners to enter their answers in context. It focuses on open-input and retention techniques outlined by Roedinger and McDaniel in Make It Stick.
Speed
To give you some idea of the sheer speed of this process we recently completed 158 modules for a global company, literally in days, without a single face-to-face meeting with the project manager. The content was then loaded up to their LMS and is ready to roll. This was good content and they are very happy with the results.
Pain relief
An interesting outcome of this approach to creating content was the lack of heat generated during the production process. There was no SME/designer friction, as that was automated. That’s one of the reasons we didn’t need a single face-to-face meeting. It allowed us to focus on getting it done and quality control.
Sectors
Organisations have been using this AI-created content as pre-training for face-to-face training for auditors in Finance, product knowledge and GMP in Manufacturing, health and safety, everything from nurse training to clinical guidelines in the NHS, apprenticeships in a global Hospitality company. All sorts of education and training in all sorts of contexts.
Conclusion

The breakthrough saw Microsoft and Baidu perform similarly, showing that the new AI-war is between China and the US. That’s a shame but we still have some edge here in Europe and the UK, if we could only overcome our tendency to see AI as a dystopian entity and start to use this stuff for social good, rather than being obsessed with ill-informed critiques. If we don’t, they will. These AI techniques have already hit the learning market. It is already automating the production of learning in that huge motherload of education and training: 101 courses and topics such as compliance, process, procedures, product knowledge and so on. Beyond this, AI-driven curation, which we use to supplement the core courses is also possible. If you want see how AI and WildFire can help you create content quickly, at much lower cost and increase retention, drop us a line and we’ll arrange a demo.

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