Lesson 3B: Capabilities & limitations | AI Fluency: Framework & Foundations Course — Transcript

Full transcript

1. 0:12Let's now examine what generative AI can
2. 0:15and cannot do. Focusing on LLM such as
3. 0:18Claude, think of this as getting to know
4. 0:20a new colleague. Understanding their
5. 0:22strengths and limitations help you
6. 0:23collaborate more effectively. To start,
7. 0:26we'll focus on what these systems do
8. 0:28remarkably well. You might be amazed at
9. 0:31how versatile modern language models can
10. 0:33be. They're skilled with language in
11. 0:36ways that seemed impossible just a few
12. 0:38years ago. Crafting emails that capture
13. 0:41your voice, condensing lengthy reports
14. 0:43into clear summaries, translating
15. 0:46between languages, and explaining
16. 0:49complex topics across countless fields
17. 0:51from microbiology to marketing strategy.
18. 0:54What's particularly notable is how these
19. 0:56models can shift between different tasks
20. 0:59without needing additional training. The
21. 1:02very same system that helps you write
22. 1:04poetry or brainstorm ideas for your
23. 1:06birthday party can turn around and help
24. 1:09you understand quantum computing
25. 1:11concepts or analyze quarterly business
26. 1:15trends all through simple conversation.
27. 1:19These models can also maintain the
28. 1:21thread of a
29. 1:22conversation, remembering what you
30. 1:24discussed earlier and building upon it.
31. 1:28If you mention your project deadline in
32. 1:30passing, for example, and refer back to
33. 1:32it later within the conversation, the AI
34. 1:34typically understands what you're
35. 1:36talking about, much like a human
36. 1:38conversation partner would. Many modern
37. 1:41LLMs can now also reach beyond their own
38. 1:43knowledge by connecting to external
39. 1:45tools and information sources, allowing
40. 1:48them to search the web, process files,
41. 1:51or even use other applications to
42. 1:52enhance their
43. 1:54capabilities. This dramatically expands
44. 1:56what they can help with. However, just
45. 1:59like any technology, LLMs as exist today
46. 2:02also have certain
47. 2:03limitations. First, AI models are
48. 2:06bounded by their training data. LLMs
49. 2:09have a knowledge cutoff date based on
50. 2:11when they were trained, the point after
51. 2:14which they have no innate knowledge of
52. 2:16the world. For example, a model with a
53. 2:19cut off date of November 2024 means that
54. 2:22it wasn't trained on any data after
55. 2:24November
56. 2:252024. Imagine someone who went into a
57. 2:28retreat without internet access at a
58. 2:30specific date. They wouldn't know about
59. 2:32events that happened after they left.
60. 2:34Models need tools like web search to
61. 2:37learn more about recent
62. 2:39developments. Additionally, the training
63. 2:41process doesn't verify every fact in the
64. 2:43training data. This means models can
65. 2:46sometimes learn and reproduce
66. 2:48inaccuracies that were present in their
67. 2:50training data. They can also make
68. 2:52mistakes when trying to piece together
69. 2:54information they've learned. This leads
70. 2:56to what is often called a hallucination.
71. 3:00AI confidently stating something that
72. 3:02sounds plausible but is actually
73. 3:05incorrect. Unlike search engines that
74. 3:07simply retrieve existing
75. 3:09documents, LLMs generate responses based
76. 3:12on statistical patterns, sometimes
77. 3:15producing
78. 3:16hallucinations. Imagine a friend who
79. 3:18tells a story with absolute confidence
80. 3:20only to have the details completely
81. 3:22wrong. AI can sometimes be like that.
82. 3:25Another important constraint is the
83. 3:27context window we mentioned earlier. As
84. 3:29a reminder, that's the amount of
85. 3:31information an AI can process at one
86. 3:33time. Every LLM has a maximum limit to
87. 3:36how much information it can consider
88. 3:38during a single interaction. If this
89. 3:40limit is exceeded, the AI won't be able
90. 3:42to remember information that falls
91. 3:43outside the window, usually on a first
92. 3:46in first out basis. Depending on the
93. 3:48size of the model, this can limit its
94. 3:50ability to process large documents or
95. 3:52remember the entire conversation.
96. 3:54Furthermore, unlike traditional software
97. 3:57that produces identical outputs given
98. 3:59the same inputs, LLM are somewhat
99. 4:02unpredictable by default, also known as
100. 4:06non-deterministic. Ask the same question
101. 4:08twice and you might get slightly
102. 4:09different responses each time. This
103. 4:12variability stems from the nature of how
104. 4:14these models generate text. They're
105. 4:16making probabilistic decisions about
106. 4:19what text should come next based on
107. 4:21patterns in their training data and
108. 4:23certain settings that developers can
109. 4:25tweak. This creative variability can be
110. 4:28great for brainstorming and generating
111. 4:30diverse ideas, but requires awareness
112. 4:33when consistency or accuracy are
113. 4:36critical. Some LLM interfaces also offer
114. 4:39settings to control this randomness when
115. 4:41needed. This setting is often referred
116. 4:44to as temperature. Additionally, while
117. 4:46these models are improving rapidly,
118. 4:49they've historically shown limitations
119. 4:51with complex reasoning tasks,
120. 4:53particularly with mathematical or
121. 4:55logical problems requiring multiple
122. 4:57steps. The good news is that newer
123. 5:00reasoning or extended thinking models
124. 5:02specifically designed to think stepby
125. 5:05step are showing strong progress in
126. 5:07these areas. And finally, while models
127. 5:10like Claude can now access external
128. 5:12tools, they may still lack access to
129. 5:15specific data sources or specialized
130. 5:17tools that would be needed for certain
131. 5:19tasks. It's like having a brilliant
132. 5:21colleague who can't access your
133. 5:23company's internal database. Their
134. 5:26ability to help will be limited no
135. 5:28matter how smart they are. If a model
136. 5:30doesn't have access to a piece of data
137. 5:32or tool that is needed to answer a
138. 5:34question, then it should not come as a
139. 5:36surprise that it won't be able to help
140. 5:38answer the question. The field of
141. 5:41generative AI is rapidly evolving.
142. 5:44Researchers are working to address
143. 5:45current limitations through techniques
144. 5:48like retrieval augmented generation
145. 5:51which connects models to external
146. 5:54knowledge and data sources as well as
147. 5:56expanding their ability to use tools and
148. 5:59improving their reasoning capabilities.
149. 6:01That said, some limitations will likely
150. 6:04remain for the foreseeable future, even
151. 6:06if we don't know exactly what those
152. 6:08limitations will be. Understanding what
153. 6:10AI can or cannot do is essential for AI
154. 6:13fluency and helps you determine when and
155. 6:16how to best incorporate these systems
156. 6:18effectively into your work and daily
157. 6:20life. The most effective applications
158. 6:22will leverage the complimentary
159. 6:23strengths of humans and AI. We bring
160. 6:26critical thinking, judgment, creativity,
161. 6:29and ethical oversight that AI may
162. 6:31struggle to replicate. While AI offers
163. 6:33speed, scale, pattern recognition, and
164. 6:36the ability to process vast amounts of
165. 6:38information. These complimentary
166. 6:40strengths will evolve as the technology
167. 6:42evolves. That's why continued learning
168. 6:45and experimentation are so valuable.
169. 6:48They help you stay a breast of these
170. 6:49changes and discover new possibilities.
171. 6:52In these exercises across this course,
172. 6:54you'll have a chance to explore these
173. 6:56concepts firsthand through conversations
174. 6:58with Claude. This direct experience will
175. 7:01help you develop an intuitive feel for
176. 7:04what generative AI can do, can't do, and
177. 7:07how best to work with it.