Lesson 3A: What is generative AI? (Deep Dive) | AI Fluency: Framework & Foundations Course — Transcript

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1. 0:02[music]
2. 0:12Hi, my name is Drew Bent and I'm a
3. 0:14teacher, programmer, and member of
4. 0:16technical staff at Enthropic. Welcome to
5. 0:18our exploration of generative AI. In
6. 0:21this video, we'll dive into what
7. 0:22generative AI actually is, how it works
8. 0:25under the hood, and the technological
9. 0:27breakthroughs that made these systems
10. 0:29possible. You might interact with
11. 0:31generative AI daily without fully
12. 0:33understanding what's happening behind
13. 0:35the scenes. Let's change that.
14. 0:37Generative AI refers to artificial
15. 0:40intelligence systems that can create new
16. 0:42content rather than just analyzing
17. 0:44existing data. For example, while
18. 0:47traditional AI might classify emails as
19. 0:49spam or not spam based on patterns,
20. 0:52generative AI can write a completely new
21. 0:54email for you. The first approach
22. 0:56analyzes and categorizes. The second
23. 0:59creates something new that didn't exist
24. 1:01before. This represents a fundamental
25. 1:04shift in AI capabilities.
26. 1:06Large language models or LLM like
27. 1:09anthropics cloud models are a prominent
28. 1:11type of generative AI. They're called
29. 1:14language models because they're trained
30. 1:16to predict and generate human language
31. 1:19and large because they contain billions
32. 1:21of parameters, mathematical values that
33. 1:24determine how the model processes
34. 1:26information, somewhat like synaptic
35. 1:28connections in your brain. The path to
36. 1:31today's generative AI wasn't sudden. It
37. 1:34involved three crucial developments
38. 1:36coming together at the right time.
39. 1:38First, there were algorithmic and
40. 1:40architectural breakthroughs that
41. 1:42fundamentally changed how AI systems
42. 1:44learn. While neural networks have been
43. 1:47around conceptually for decades, the
44. 1:50development of the transformer
45. 1:51architecture in 2017 was a gamecher.
46. 1:54This architecture excels at processing
47. 1:56sequences of text while maintaining
48. 1:58relationships between words across long
49. 2:01passages, which is critical for
50. 2:03understanding language in context.
51. 2:05Second, the explosion of digital data
52. 2:08provided the essential raw material for
53. 2:10training. Modern LLMs like Claude learn
54. 2:12from diverse sources such as websites,
55. 2:15code repositories, and other text that
56. 2:17represent human knowledge and
57. 2:18communication.
58. 2:20This vast tapestry of information helps
59. 2:22models develop a broad and nuanced
60. 2:24understanding of both language and
61. 2:27concepts. And third, massive increases
62. 2:31in computational power made it possible
63. 2:33to train these complex models on all
64. 2:35that data. Specialized hardware like
65. 2:37GPUs or graphics processing units and
66. 2:40TPUs or tensor processing units along
67. 2:43with distributed computing networks
68. 2:45often called clusters enable processing
69. 2:48that would have been impossible just a
70. 2:49few years earlier. The combination of
71. 2:52these three factors led to an important
72. 2:54discovery known as the scaling laws.
73. 2:57These empirical findings showed that as
74. 2:59models grew larger and trained on more
75. 3:01data with more computing power, their
76. 3:04performance improved in predictable
77. 3:05ways. More surprisingly, researchers
78. 3:08found that entirely new capabilities
79. 3:11began to emerge as these models grew
80. 3:13larger. Abilities no one explicitly
81. 3:15program, like reasoning through problems
82. 3:17stepby step or adapting to new tasks
83. 3:20with minimal instruction. Let's peek
84. 3:22under the hood at how these systems
85. 3:24actually work. During initial training,
86. 3:27also called pre-training, LLMs like
87. 3:29Claude analyze patterns across billions
88. 3:32of text examples. Imagine reading every
89. 3:34website and piece of text you could
90. 3:36find, not just to absorb information,
91. 3:38but to understand the statistical
92. 3:40relationships between words, phrases,
93. 3:42and concepts. At this stage, the model
94. 3:44essentially builds something like a
95. 3:45complex map of language and knowledge.
96. 3:48This pre-training process involves
97. 3:50showing the model text and asking it to
98. 3:52predict what comes next. Through many
99. 3:54iterations, the model gradually
100. 3:56refineses its predictions, learning the
101. 3:59patterns that make language coherent and
102. 4:01meaningful. After pre-training, models
103. 4:04undergo additional training called
104. 4:06fine-tuning, where they learn to follow
105. 4:08instructions, provide helpful responses,
106. 4:10and importantly, avoid generating
107. 4:12harmful content. This often involves
108. 4:15human feedback to improve the model's
109. 4:17performance, as well as reinforcement
110. 4:19learning, which uses rewards and
111. 4:21penalties to shape the model's behavior
112. 4:24toward being more helpful, honest, and
113. 4:27harmless. In the case of enthropics
114. 4:28models, once models are trained, they
115. 4:30are then deployed for you to interact
116. 4:32with. When you interact with Claude or
117. 4:34another LLM, you're providing a prompt,
118. 4:37which is text that the model reads and
119. 4:39then continues from based on patterns it
120. 4:41learned during training. The model isn't
121. 4:43retrieving pre-written answers from a
122. 4:45database. Instead, it's generating new
123. 4:47text that statistically follows from
124. 4:49what you've written. There's also a
125. 4:51practical limit to how much information
126. 4:53an LLM can consider at once, known as
127. 4:56the context window. Think of this as the
128. 4:58AI's working memory. The context window
129. 5:01includes your prompts, the AI responses,
130. 5:04and any other information you've shared
131. 5:06in your conversation. While AI companies
132. 5:08continue to grow the context window to
133. 5:11allow for longer context documents and
134. 5:13conversations, these limits remind us
135. 5:15that these systems don't have unlimited
136. 5:17access to information and cannot use
137. 5:20content beyond its current context
138. 5:22window without specialized tools like
139. 5:25web search. Bringing this together, the
140. 5:27three characteristics that make modern
141. 5:29generative AI so powerful include,
142. 5:32first, its ability to process vast
143. 5:34amounts of information during training,
144. 5:37allowing it to learn complex and nuanced
145. 5:39patterns in language and knowledge.
146. 5:41Second, its incontext learning ability.
147. 5:44LLMs can adapt to new tasks based on
148. 5:47instructions or examples in your prompt
149. 5:49without requiring additional training.
150. 5:51And third, emerging capabilities that
151. 5:53arise from scale. As these models grow
152. 5:56larger, they develop abilities that
153. 5:57weren't explicitly designed into them,
154. 6:00sometimes surprising even their
155. 6:01creators. In the next video, we'll
156. 6:03explore what these systems can and can't
157. 6:05do well, along with their most common or
158. 6:08valuable applications.
159. 6:11[music]