Is AI About to “Eat Everything”? | AI Reality Check
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What is the main topic discussed in this episode?
Last week, the AI Safety and Evaluation Organization, METR, that's M-E-T-R, released a new update on their famous AI time horizon chart. Look, I'm going to load it on the screen here for people who are watching.
What does the METR chart measure?
And when you zoom in, you can see these points on this chart starting around 2025 begin to go up. And then when we get to 2026, they go way up. And then the last update go way up again. Now, this graph looks scary. Even if you don't know what it means, it does create a strong sense of digital ick. And as you can imagine, the Internet jumped into action to try to amplify that uneasy feeling.
What do these measurements actually capture?
Now, in a recent essay posted to his newsletter, Gary Marcus did a good job of rounding up some of the more, shall we say, concerned responses to this latest update to Meter's latest graph. Let me show you a couple here. Here's one. A tweet that said, AI power is doubling every 103 days now.
How are the models getting better?
It's going to eat everything. Nothing will be spared. We are on the threshold of truly ergodic alien intelligences in which human input will be nothing but a liability. All right. Here's another example that Gary pointed out. The tweet simply says, TikTok. It has a expertly drawn graph that shows highest intelligence on Earth by time. And you see there's a point where it goes up, up, crosses a tripwire, and then shoots straight up, where human brains become smart enough to create ASI, which is artificial superintelligence. Then below it is a version of that time horizon graphs.
Does this mean AI is about to 'eat everything'?
And they're like, look, doesn't that look similar? The line goes up, the line goes up. So I guess we're about to have artificial superintelligence conquer the world now many more tweets out there in response this time horizon update they all give you the same sense that this meter chart is capturing an intelligence explosion that a we're not ready for b that will change everything and c that vindicates every bold or crazy thing anyone has ever claimed about ais and his capabilities but is this right Well, it's Thursday, which means it's time for an AI reality check episode of this show, which seems like a perfect time to look closer at what exactly the meter time horizon chart is showing and what exactly that means.
What is the significance of the recent AI-related tweets?
As always, I'm Cal Newport, and this is Deep Questions, the show for people seeking depth in a distracted world.
All right, so the first question we want to ask here is, what is it exactly that the meter time horizon chart is actually showing? All right, so I spent time reading about it. The good news is meter actually is very transparent. They publish very detailed collections of notes describing their methodology and what goes into their chart. So it was actually quite a pleasure to get answers to these questions. So what are they actually showing on this chart? Well, here's what they did. They came up with a collection of what they call software tasks. These are well-defined challenges that you can solve by writing and or analyzing computer code. All right? Then for each of these tasks, they went out and asked a collection of human programmers to go do the task.
Hey, go do this. I believe the instruction was as quickly as you can. And then they asked them, how long did it take you to complete this task? They would then... Take the geometric mean, so they would average those answers, and whatever the mean was, whatever the average was, is the human time duration that they would label that task with. So if it, on average, took people two hours to complete a various task, they would say this is a two-hour task. They then said, let's evaluate different large language model-based tools on these tasks. Now, of course, a given large language model can't do anything except spit out tokens. So they would take each large language model and combine it with a – they call it a scaffold, but what we would call today also a coding harness.
So a program – that can call the LLM to try to solve programming challenges. This is like Cloud Code or Cursor or Codex. These are all coding harnesses. So the coding harness, when you give it the problem, for example, will query an LLM and say, give me a plan for tackling this problem.
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Chapters
7 chapters
1
What is the main topic discussed in this episode?
0:00–0:16
2
What does the METR chart measure?
0:16–0:47
3
What do these measurements actually capture?
0:47–1:06
4
How are the models getting better?
1:06–1:44
5
Does this mean AI is about to 'eat everything'?
1:44–2:30
6
What is the significance of the recent AI-related tweets?
2:30–10:31
7
What are the implications of the METR chart for AI development?
10:31–31:44
Speakers
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