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Claude

What happens when you talk to AI?

Plateforme
YouTube
Durée
4:47
Langue parlée
anglais américain
Vues
4,1 M
Coupe médiane
9.34s
Visage à l'écran
69%
Images sombres
0%
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Transcription de What happens when you talk to AI?

Transcrit par un modèle vocal à partir du son du film, et non rédigé par la marque. Cliquez sur une ligne pour y amener la lecture.

  1. 0:00When you send a message to an AI,
  2. 0:02there's a moment where it appears to be thinking,
  3. 0:04but what's actually happening?
  4. 0:06Is it reading the entire internet?
  5. 0:08Is it copying answers from a database?
  6. 0:10Is it just a fancier search engine?
  7. 0:13I'm Jane, and I work on user experience here at Anthropic,
  8. 0:16the company that makes Claude.
  9. 0:18Here's what's actually going on.
  10. 0:20AI models like Claude work by prediction.
  11. 0:23When you send a message, the model reads that message
  12. 0:26and draws on everything it learned during training
  13. 0:28to write back a response a little bit at a time.
  14. 0:31You'll see it appear word by word,
  15. 0:33and each one is chosen based on everything that came before it.
  16. 0:36Here's what surprises most people.
  17. 0:39The model writes one word at a time,
  18. 0:41but it doesn't think one word at a time.
  19. 0:44We'll come back to that in a moment.
  20. 0:46You've likely seen something like this before.
  21. 0:48When your phone's keyboard suggests Ben
  22. 0:50after you type, how have you, that's prediction too.
  23. 0:54The keyboard has learned which words tend to follow which.
  24. 0:56but there's a key distinction here.
  25. 0:58Your simple predictive keyboard
  26. 1:00is only looking at the last two or three words.
  27. 1:03It has no idea what you're trying to say
  28. 1:05or where the sentence is going.
  29. 1:07An AI model goes much further than this.
  30. 1:09It's been trained on an enormous amount of text
  31. 1:12and other kinds of information.
  32. 1:13This is known as training data.
  33. 1:15And when an AI model predicts,
  34. 1:17it isn't just looking at the last two or three words
  35. 1:20the way your keyboard does.
  36. 1:21What it's actually doing turns out to be much deeper
  37. 1:23and we'll see exactly how in a moment.
  38. 1:25Long before you ever talked to it, the model went through billions of rounds of the same exercise.
  39. 1:33See some text, guess the next word, see how close it got, adjust slightly, go again.
  40. 1:41Later, in a second stage called fine-tuning, the model's full answers are rated,
  41. 1:46sometimes by people, sometimes against a written set of guidelines,
  42. 1:50and the model is nudged toward answers that are more useful and less likely to mislead
  43. 1:55or cause harm.
  44. 1:57Both processes contribute to a model's
  45. 1:59generative capabilities.
  46. 2:01All of that takes place up to a certain date
  47. 2:04called the training cutoff,
  48. 2:06after which the model doesn't reliably know
  49. 2:08about facts or information without leaning on other tools
  50. 2:11such as internet search.
  51. 2:13While the model can search the internet
  52. 2:16to access information beyond the cutoff
  53. 2:18if the tool has search available,
  54. 2:20you can't assume the model has done a web search
  55. 2:22to craft its response.
  56. 2:24To be sure, you can ask it to, and when it does,
  57. 2:27it will typically share sources that you can review
  58. 2:30to confirm the output's accuracy.
  59. 2:32Now back to how the model thinks.
  60. 2:34Predict the next word sounds almost mechanical,
  61. 2:37like the model is just reacting one word,
  62. 2:39then the next, then the next.
  63. 2:40But to predict the next word well,
  64. 2:42you can't just look at the last few words.
  65. 2:44You have to work out where the sentence is going,
  66. 2:46what the paragraph is arguing,
  67. 2:48what a good answer would actually be.
  68. 2:49The model takes into account everything that came before,
  69. 2:52your uploaded documents, memory system prompt, your prompts, your conversation history,
  70. 2:56before outputting the next word or even syllable.
  71. 2:59And then that whole process repeats for the next one and so on until the response is done.
  72. 3:04So why should you care about any of this?
  73. 3:07Because when you understand that an AI model is a prediction system,
  74. 3:11you are able to work with it and interpret its outputs much better.
  75. 3:14The fact that it can write you something that's never existed before makes sense
  76. 3:18once you remember it's generating,
  77. 3:20not just pulling answers from somewhere.
  78. 3:22The fact that it sometimes states something false
  79. 3:25with total confidence, same reason.
  80. 3:27It's producing what a good answer would look like,
  81. 3:30and usually that lines up with reality.
  82. 3:33Occasionally it doesn't,
  83. 3:34which is why your judgment still matters.
  84. 3:37Knowing all that, a few habits
  85. 3:38that can get you better results.
  86. 3:40One, give it context to work with.
  87. 3:42Tell it who you are, what you're working on,
  88. 3:44and what a good result looks like.
  89. 3:46All of that becomes part of the pattern.
  90. 3:48Two, remember the cutoff.
  91. 3:50The model's knowledge stops at a certain date,
  92. 3:52so for anything recent like prices or the news,
  93. 3:55it could be out of date unless the tool tells you it searched the web.
  94. 3:58Three, ask for options.
  95. 4:00Because it's generating, not retrieving information,
  96. 4:03there's no single stored answer, so ask for a few versions of the draft
  97. 4:06or have it try again in a different tone.
  98. 4:08We encourage you to explore.
  99. 4:10Four, and above all, double-check the AI's outputs.
  100. 4:14A confident tone or a polished-looking result
  101. 4:16is just how the output comes out.
  102. 4:18It isn't proof that the output is accurate or done well.
  103. 4:21Consider the stakes of your question
  104. 4:23and double-check facts if wrong information would cause an issue.
  105. 4:26So that's the basic shape of how AI works.
  106. 4:29Every time you send a message,
  107. 4:30you're handing it the start of a pattern,
  108. 4:32and it completes that pattern
  109. 4:33based on everything it learned in training.
  110. 4:36Different models will likely complete that pattern in different ways,
  111. 4:39so it's worth experimenting to see what works for you.
  112. 4:42We'll keep sharing our research on this topic on Anthropik's blog.
112 lignes · 901 mots

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