The media would have us believe that AI can do absolutely anything; it creates recipes, writes books, identifies diseases, drives cars. Conversely, pop culture paints a pretty dark picture of AI – mainly enslaving and killing humans. Both suggest that AI is a computer that has a mind of its own. That’s not the case. The technology that we call ‘AI’ in 2019 is nowhere near that advanced, but it is still a big leap forward in what computers are capable of.
To explain, we need to look at what machines can already do. For example, they’re good at following instructions. When you press the button on a coffee machine it receives the instruction ‘make coffee’ and it carries it out. There’s nothing particularly intelligent about that.Computers are just machines capable of carrying out multiple instructions. Essentially, that’s what code is – a long list of instructions. Take the password to your computer. The computer knows that your password is ‘learningpool123’ and it’s told that, if what you type in matches the password, it should let you in, and if it doesn’t then it shouldn’t.
By combining many of these simple instructions, we can make complex applications. It’s like a factory assembly line, each person has a simple task: attach Piece A to Piece B, wait for the conveyor belt to move along, repeat. It may be simple, but when you combine hundreds of these tasks, you end up with a car.
A simple calculator is able to do a tricky calculation instantly because, when it comes to memory and accuracy, it’s more intelligent than a human. Only a human, however, will then turn the calculator upside down and laugh at the fact that it says ‘boobies’. This is because it actually takes a lot of thinking. Firstly, we recognise that these digits, when inverted, resemble letters. We then see that these letters spell the word ‘boobies’, and we laugh because of our understanding of the cultural taboos around nudity. It might be simple and childish, but we’re drawing on a huge amount of information and making links between those unrelated pieces of information. When it comes to pattern spotting, humans have always been far more intelligent than computers.If you combine a computer’s accuracy and memory with the pattern spotting of a human, you essentially get Sherlock Holmes. Sherlock quickly acquires a lot of information by observing a situation.
Frayed threads on their sleeve, the mud on their shoes looks like it’s from the Shetland Islands, their breath smells faintly like whiskey.
He cross-references this with the vast amount of information in his memory.
Drunks leaning on a bar tend to get frayed sleeves, there’s a very nice brewery on Shetland.
And then he makes his deductions based on the links between them.
The person had a recent trip to the Shetlands, which is where the murder was committed!
Now, we’re getting computers to do the same thing, and that’s what AI is. In fact, IBM’s AI service is named “IBM Watson” after Sherlock’s companion.Facebook knows your likes, dislikes, age, address, who you’re friends with, where you work, things you’ve mentioned in messages, and so much more. And there are 2.27 billion people on Facebook. Imagine cross-referencing all that data and the conclusions you could draw from it. Why imagine it? It’s actually happening. Facebook use this incredible technology to decide what adverts to send people. Which feels like using a lightsaber to slice your bread. We’re using this technology to benefit learners by improving recall, creating a personalised learning experience, and making information immediately accessible.
Read our next blog post to find out how we’re achieving this.
Matt started his L&D career writing courses but soon discovered that his interest in tinkering with new technology could turn into a full-time role.
He’s spent the last year immersed in the world of chatbots and conversational UX, drawing on the dialogue skills he developed during his Drama and Creative Writing degree to create an e-learning bot with personality and purpose.
He also works with the Stream team to ensure that our LXP is built on sound learning theory.
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