वीडियो पर वापस

Claude

What happens when you talk to AI?

प्लैटफ़ॉर्म
YouTube
अवधि
4:47
बोली जाने वाली भाषा
अमेरिकी अंग्रेज़ी
व्यूज़
40.7 लाख
औसत कट
9.34s
स्क्रीन पर चेहरा
69%
गहरे फ़्रेम
0%
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What happens when you talk to AI? की ट्रांसक्रिप्ट

फ़िल्म के अपने ऑडियो से एक स्पीच मॉडल द्वारा लिखा गया, ब्रांड द्वारा नहीं। उस पल पर जाने के लिए किसी भी पंक्ति पर क्लिक करें।

  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 पंक्तियाँ · 901 शब्द

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