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UNDERSTANDING AI MICROCOURSE

GENERAL AI

General AI is AI that tries to do any task that a human can do - in the way a human might do it. Is this possible? Learn about what AI might be capable of.

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In this microcourse, you’ll learn about one of the fundamental ideas in AI: general AI.

Could a computer ever be better than a human at a specific task? Of course - and you will see some examples in the AI BASICS microcourse. This kind of AI is often called weak AI.
 
An AI that could work just like the human mind does and could intelligently do many of the tasks that a human can do would be strong AI, or as it is often known, General AI. 
 
But is this possible? Could we build AI that is as intelligent as (or maybe more intelligent than) a human?
 
So far, no one has managed to do this, but some AI systems are very intelligent indeed.
 
In this microcourse, we will learn about general AI by first looking at the difference between rules-based AI and pattern-matching (or machine learning) AI. You’ll see and try out some examples of both of these in this microcourse.
 
We will learn that rules-based AI can solve simple problems, but if we want to do things that are as complex as those that humans do, we need something more powerful that can learn how to solve problems.
 
Let’s get started and learn about the difference between rules-based and pattern matching AI before we think about whether general AI is even possible.
RULES BASED AND PATTERN MATCHING AI
 
Watch this short video from Haileybury teacher Toan Huynh that looks at the differences between these two types of AI.
RULES BASED AI
 
Let’s learn more about rules-based AI by looking how an AI plays a simple game. 
 
Can you beat this AI-powered naughts and crosses program? (Click on the link, and wait a little while after the ready screen).
AI-powered Naughts and Crosses program
Could you beat it? Does the AI always win? And, do you think it matters who goes first?
 
Now watch the following video which talked about how computers play games - naughts and crosses but also more sophisticated games like Go. 
How do you think I programmed the naughts and crosses game to never lose? Do you think believe the naughts and crosses program is ‘intelligent’.
 
Now watch the following video from Haileybury teacher Jeff Plumb.
 
You should be able to see that the program doesn’t choose the simplest way to win. Does this change your previous answer about 'intelligence'?
 

CREATE YOUR OWN AI USING MACHINE LEARNING

We’re going to use Teachable Machine, a simple machine learning AI system, to build an image classifier.
 
Follow along with Haileybury teacher Toan Huynh as he leads you through the process and how machine learning techniques teach the AI how to recognise a cat or a dog.
 
Pause the video as you do the same steps on the Teachable Machine website. You'll also need to use Google Image Search.
Now it’s your chance to build your own Teachable Machine image classifier.
 
You’ll need to
 
  • Create at least 2 classes of images you’d like to recognise and tell apart. For example Spaghetti vs Lasagna, Car vs Truck, Shirt vs Blouse or AFL ball vs Soccer ball. If you’re ambitious, you can try even more than 2 classes - for example: elephant vs rhino vs hippo

  • Search for and download at least 20 images for each class

  • Train the classifier

  • Test at least 5 images for each class (for a total of 10 images) which were not included in the training images

Now record the results for each test like this:

What was really in the image: (e.g. spaghetti)
What Teachable Machine classified it as: (e.g. lasagna)
Was it correct or incorrect:  (e.g. incorrect)

Once you are done, please save your Teachable Machine as a file that you can share with us later (see the screenshot below) and you can You can show off your Teachable Machine and share it in the AI EXPLORATORY microcourse. 
Teachable machine screenshot
CAN WE CREATE GENERAL AI?
 
You should see that the example of Teachable Machines seems to be more intelligent than simple rules-based AI. It certainly does something that humans can do - figure out what is a cat.
 
As you will see in the AI BASICS microcourse, some of the more sophisticated AI techniques - including machine learning, deep learning and neural networks - can do things that humans can do and often faster and more accurately. They can recognise faces, diagnose diseases and create images that look real but are, in fact, not real at all (what are called ‘deepfakes’).
 
But does AI do it in the same way that humans do it? Does it matter? Could machine learning make AI as smart - or smarter than - humans? Is AI ‘intelligent’?
 
Can we create a powerful general AI?
 
That's not an easy question to answer.
THE TURING TEST
 
One scientist thought he had the answer.
 
The mathematician Alan Turing proposed something called the imitation game (or the Turing Test). It’s a thought experiment - something to make you think about a problem.
 
The idea behind it was to figure out if a computer could fool a human into thinking that it was a human. If so, the computer could be considered to be intelligent.
 
Imagine three participants in the game: someone who has to ask questions, a computer, and another human.
 
The only way they can communicate is through text. The questioner aims to figure out by asking questions, which is the computer and which is the human.
 
If the computer doesn’t get found out, it has passed the test and can be considered intelligent.
 
Could you figure it out? What questions would you ask?
 
Recently, Amazon launched The Alexa Prize, a competition for university students. Teams are challenged to design conversational AI that Alexa customers can interact with for 20 minutes or more.
 
So maybe it's not about fooling anyone - but making it easier and more interesting to interact with AI.
 
Watch this video to learn more about the Alexa Prize. 
You can learn more about the Turing Test in this video, and why not ask Hailey in AI ETHICS microcourse about what she thinks about the Turing Test.
 
For the moment, here's a video that summaries the Turing Test.
THE CHINESE ROOM
 
But here's another point of view in the form of a thought experiment from philosopher John Searle. (Scientists like making up thought experiments).
 
Imagine a person, who does not speak Chinese, is sitting in a closed room. There is a book with all of the rules, phrases, and instructions of the Chinese language in the room.
 
Another person, who is fluent in Chinese, passes notes written in Chinese into the room.
 
With the help of the book, the person inside the room can understand the notes written in Chinese, select the appropriate response and pass it back to the Chinese speaker.
 
Does the person in the room really speak Chinese?
 
It seems like it in one way because they give the correct responses. But it really was just a process of matching questions with appropriate responses. It could be done with the kind of rule-based AI we saw earlier.
 
John Searle, who thought up the Chinese Room, says that even if the responses are all perfect, this is still not the same as being intelligent in the same way that a human is.
 
A human is more than just pattern-matching - and even more than machine learning - and so general AI is not possible, no matter how smart AI becomes.
 
 
This video gives you a bit more detail on the Chinese Room.
As you’ll learn about in the AI ETHICS microcourse, there are lots of problems that come up when we build smarter and smarter machines.

And most of them are becoming more important to think about as machines get smarter and smarter, doing more of the things that humans do.
QUESTIONS TO CONSIDER
 
Do you think machine learning AI is ‘smarter’ than rules-based AI?
 
Can a computer ever be considered to be intelligent?
 
Does it really matter if AI works in exactly the same way as a human mind does?
 
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