Tue. 01/09 – Do We Need ChatGPT In Our Cars?
Tech Brew Ride Home
Amalgamated Internets, LLC
4.7 • 1K Ratings
🗓️ 9 January 2024
⏱️ 18 minutes
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| 0:00.0 | Welcome to the Tech Meam ride home for Tuesday, January 9th, 2024. I'm Brian McCullough today. |
| 0:08.8 | Open AI responds to the lawsuit from the New York Times. Do you need chat geepity in your car? |
| 0:13.6 | VW thinks you do. Sony teases a spatial VR headset. Apple only wants you to call Vision |
| 0:19.6 | Pro Spatial Computing and how you two can sign up to get a demo of the Vision Pro in a couple of weeks. |
| 0:25.0 | Here's what you missed today in the world of tech. |
| 0:27.0 | Open AI has responded to the New York Times's lawsuit over AI training data. |
| 0:37.8 | Open AI asserts training is fair use and there is an opt outout that so-called regurgitation is a rare bug and they make |
| 0:47.1 | the claim that the New York Times, quote, manipulated its models to get them to quote their |
| 0:51.8 | past articles verbatim quote while we disagree with the |
| 0:56.1 | claims in the New York Times lawsuit we view it as an opportunity to clarify our |
| 0:59.7 | business our intent and how we build our technology our position can be summed up in these four points which we flush out below |
| 1:05.0 | one we collaborate with news organizations and are creating new opportunities |
| 1:09.0 | two training is fair use but we provide an opt-out because it's the right thing to do. |
| 1:13.0 | Three, regurgitation is a rare bug that we are working to drive to zero and |
| 1:18.1 | four the New York Times is not telling the full story. Training AI models using publicly available internet |
| 1:23.8 | material is fair use as supported by longstanding and widely accepted |
| 1:27.4 | precedence. We view this principle as fair to creators necessary for |
| 1:30.8 | innovators and critical for U.S. competitiveness. |
| 1:33.8 | Memorization is a rare failure of the learning process that we are continually making progress |
| 1:38.0 | on, but it's more common when particular content appears more than once in training data like if pieces of it appear on |
| 1:44.5 | lots of different public websites. So we have measures in place to limit |
| 1:48.0 | inadvertent memorization and prevent regurgitation in model outputs. We also |
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