Tue. Dec 7th, 2021


Determining whether or not artificial intelligence can be difficult (or difficult). So much so, that even experts sometimes do it wrong. That’s why Karen Hao, senior AI editor at MIT Technology Review, created a flowchart to explain everything. In this bonus content our host and his team have redesigned Hao’s Original reporting, Gamify it on a radio play.

Credit:

This episode was reported by Karen Hao. It was adapted for audio and produced by Jennifer Strong and Emma Silekens. The voices you hear are Emma Cillekens, as well as Eric Mongeon and Kyle Thomas Hemingway from our art team. We are edited by Michael Riley and Neil Firth.

Full transcript:

[:15 pre-roll]

[TR ID]

Jennifer: Hi there. I’m Jennifer Strong’s host We believe in machines.

It can be a little difficult to define what artificial intelligence is or isn’t. So much so, that even experts sometimes make mistakes. That’s why Karen Hao, senior AI editor at Tech Review, created a flowchart to illustrate this point… and together, we’re in this next episode… it’s stupid. It’s a joke. And we hope that helps.

I want to tell you about something really special that we have been working on for over a year now. It’s called The economy of extortion. This is a short podcast series about the ransomware epidemic created in collaboration with ProPublica. And it is now available where you want to listen.

[Show ID]

Emma Silicon: Ladies and gentlemen … Welcome to ‘This Is AI’ …

Players will ask questions like what is it … or not … AI … and … I’ve come up with an “assistant” to help with the answers …

Voice assistant: Hello.

Emma Silicon: Hello, Alexa.

Emma Silicon: And that’s exactly why we’re all on the same page … Artificial intelligence … in the broadest sense, refers to machines that can learn, reason, and work for themselves. They can make their own decisions when faced with new situations, much like humans and animals.

Emma Silicon: Now this bell … [SOT: ding] … means correctly identified AI … and this buzzer … [SOT: buzzer, crowd sigh] Well … nothing like that.

Emma Silicon: All right. So, let’s test your knowledge .. Ready … Set … Player One, go! ..

Eric Mangon: See ‘it’ …

Voice assistant: Yes.

Eric Mangon: It can detect what it looks like …

Voice assistant: No …[SOT: buzzer]

Emma Silicon: Okay, so it’s just a camera …

Eric Mangon: Okay, okay, but what will happen then? To be able to What does it look like?

[SOT: ding, ding, ding]

Emma Silicon: Yes – it’s computer vision and image processing. Player two!

Kyle Thomas Hemingway: Can you hear …

Voice assistant: Yes

Kyle Thomas Hemingway: Does it respond in a useful, intelligent way to listening?

Voice assistant: Yes

[SOT: DING DING DING]

Emma Silicon: So, this is NLP-natural language processing.

The goal of this type of AI is to help computers understand human language in ways that are useful.

But then what will happen No. Respond in a useful, intelligent way to what it hears. Could it also be AI?

Kyle Thomas Hemingway: If it replicates what you are saying …

[SOT: bell ding, ding, ding]

Emma Silicon: Yes! This is also AI — it is speech recognition, which is similar but works from spoken words instead of text. New round of questions! Player 1.

Eric Mangon: Can you read it?

Voice assistant: Yes

Eric Mangon: Is it reading what you type?

Voice assistant: No.

Eric Mangon: Is it reading text passages?

Voice assistant: Yes

Eric Mangon: Is it text analysis for patterns?

Voice assistant: Yes

[SOT: ding, ding, ding]

Emma Silicon: Yes, this is again NLP-natural language processing. Well done!

Kyle Thomas Hemingway: I’ll take that same question again – can it read?

Voice assistant: Yes

Kyle Thomas Hemingway: Is it reading what you type?

Voice assistant:: Yes

Kyle Thomas Hemingway: Does it respond in an intelligent, useful way?

Voice assistant: Yes

[SOT: ding, ding, ding]

Emma Silicon: This is also NLP-natural language processing. New Questions Please Player 1.

Eric Mangon: Could it be the cause?

Voice assistant: Yes

Eric Mangon: Is it looking for patterns in a huge amount of data?

Voice assistant: Yes

Eric Mangon: Does it use these patterns to make decisions?

Emma Silicon: Well, if not, that sounds like math 7.

Eric Mangon: But what if patterns are used to decide?

Voice assistant: Yes

[SOT: ding, ding, ding]

Emma Silicon: Then there is machine learning — which is what one learns through machine experience. All right. Final round!

Kyle Thomas Hemingway: Can it be removed?

Voice assistant: Yes.

[SOT: ding, ding, ding]

Kyle Thomas Hemingway: By itself, without help?

Voice assistant: Yes.

[SOT: ding, ding, ding]

Kyle Thomas Hemingway: Does it run based on what it sees and hears?

Voice assistant: Yes.

[SOT: ding, ding, ding]

Kyle Thomas Hemingway: Are you sure it’s not just a pre-programmed path?

Voice assistant: [Alexa] Hmmm. I’m not sure.

Emma Silicon: Very funny… but if so, that’s just a bot.

[SOT: buzzer, crowd sigh]

Kyle Thomas Hemingway: Okay, let’s try again. Is it moving along a pre-programmed path?

Voice assistant: No.

[SOT: ding, ding, ding]

Emma Silicon: Okay, so this is a smart robot, which means one that uses AI to make its own decisions.

Great …

And that’s the game.

Thanks for playing!

[Music up full]

Jennifer: We’ll be back – right after this.

[MIDROLL]

[MUSIC]

Jennifer: Many thanks to the talented voices of this episode, including our producer Emma Selekens, Eric Mongon and Kyle Thomas Hemingway. Editors Michael Reilly and Neil Firth.

Thanks for listening … I’m Jennifer Strong.

[Post Roll: TR ID]



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