A non-technical guide to Artificial Intelligence

Artificial Intelligence or AI is the hottest buzzword in most industries, and education is no exception. In schools, MOE is working on an “AI-enabled adaptive learning system to support teaching and learning”, while at home, many parents use websites like KidStartNow’s Pet Battle or Koobits to revise intelligently.

But have you ever wondered what exactly is AI? In this post, we give parents a non-technical rundown of AI using an example everyone can appreciate – getting our kids to read more.

WHAT IS ARTIFICIAL INTELLIGENCE?

AI has notoriously many definitions, but I like IBM’s explanation that AI is using computers to mimic the problem-solving and decision-making capabilities of the human mind.

To the average person, AI means robots like Skynet in Terminator or J.A.R.V.I.S in Ironman – intelligent machines that are indistinguishable from the human mind. That is called Strong AI, and what most don’t realise is we are still far from that. Rather, most AI applications today are Weak AI, which is focused on teaching machines to do a specific task like sweeping your floor or assessing your child’s Chinese pronunciation. 

Pro-tip: Note that Weak AI does not mean that the AI does the task poorly, just that its intelligence is confined to a narrow scope. For instance, chess-playing AI is stronger than the best human players but it is considered a Weak AI as it’s only good at playing Chess. 

Strong AI
Weak AI

In today’s post, we will be exploring two broad kinds of AI – Expert System and Machine Learning, with the goal of building an intelligent system that can choose a good book for a 6 year old girl to read.

EXPERT SYSTEM

Expert System is an old-school AI system that emulates the decision-making ability of a human expert, typically through if-else rules.

So let’s say I’m the robot, and my wife is training me to go to the bookstore to select a book that is both educational and also appealing to a 6 year old girl. You can think of me as a proxy for a robot.

My wife, being an expert on both shopping and what my daughter likes, could write down a list of rules that help me select the right book. For instance, I could

  1. Consider only books that are cheaper than $10, have pictures and do not have pinyin
  2. Reject books if they have more than two sentences per page or contain overly complex vocabulary (based on MOE syllabus)
  3. For each book, give 1 bonus point if it is about a topic my daughter likes (e.g. animals, princesses, fantasy). So a book with animals and princesses is worth 2 points.
  4. Select the book with the highest score. In the event of a tie, choose the cheapest book with the highest score.

Congratulations – we have just created a basic Expert System!

At this point, you might go – “Dan, that doesn’t sound very intelligent”. But while rule-based systems are rudimentary, they work well for certain domains like education and healthcare.

For instance, the KidStartNow vocab revision app combines rule-based systems with forgetting curve models to track the words your child knows and the optimal set of questions to review.

MACHINE LEARNING

Machine Learning is another kind of AI and is the cool kid on the block, and is basically teaching a computer to identify patterns from examples in data and make predictions (see youtube video below for a great explanation on what is Machine Learning).

Alright, let’s go back to the book selection example. What if my wife doesn’t actually know what sort of books our daughter likes – how should she train me to go to the bookstore to buy books?

One way would be to first show my daughter a list of books that we have at home, and for each book, ask if she likes it or not. After showing her enough books and recording her preferences, I will naturally gain an intuition of what she likes, which I can use to select a book with reasonable accuracy.

But wait, machines aren’t as smart as humans – we can’t simply tell a machine that my daughter likes Three Little Pigs, and have it automatically understand why. 

One thing we could do is associate each book with certain identifying features – for instance, a Three Little Pigs story would be a book about animals that has pictures, while the Frozen novel would be a book about princesses without pictures. This way, when we tell the machine that my daughter likes Three Little Pigs, it is able to start to reason “maybe she likes animal books with pictures”. And all we need to do is repeat the process with a large amount of books (aka data).

TitleCategoryPicturebookDaughter likes it?
Three Little PigsAnimalsYesYes
Three Little Pigs NovelAnimalsNoNo
CinderellaPrincessYesYes
Cinderella NovelPrincessNoYes
FrozenPrincessYesYes
Frozen NovelPrincessNoYes

Congratulations – we have just created a basic Machine Learning System that can predict what books my daughter will like!

WAIT, THAT WORKS?

You might be wondering: the approach we just described sounds relatively simple, and how could that possibly work? The answer is data.

In his AI course, famous AI scientist Andrew Ng talks about how the rising amount of data, together with cheap computation power and improvement in algorithms, is powering rapid improvements in Machine Learning performance, especially in the field of Deep Learning.

Given sufficient amounts of good data, we can train machines to do very specific tasks like personalising a Spotify music playlist or predict bank fraud. In the next section, we will talk about specifically how machine learning is used in the education space.

Machine Learning In Education

Speaking Mandarin is a big problem for many Singaporeans given that the majority of families now speak predominantly English at home. For many preschool parents, a concern is that their kids are speaking Chinese with an English or “ang-moh” accent. At KidStartNow, we are working on a machine learning audio pronunciation feature, where students can record and upload an audio clip, and our system can determine both accuracy of pronunciation as well as fluency and dictation.

Another use of machine learning in the education space is in universities, where AI can identify struggling students that are at risk of dropping out, so that officers can provide academic support. The way it works is that universities train a machine learning system with data from previous years, and it learns to predict at-risk dropouts from information like attendance records, grades and socio-demographic information (controversial).

SUMMARY

While Artificial intelligence has been extremely hyped over the last few years, we believe it has transformative potential in the education space, and hope this non-technical explanation has been helpful

At KidStartNow, we believe that the secret to improving in Chinese is through effective revision – that’s why every time your child uses our vocabulary revision app, we track his or her progress, and then use AI to personalise an optimal learning plan. If you are interested in finding out more about our app or regular Chinese enrichment classes at Bedok, please leave your details below and we will contact you within 2 working days.

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