a16z | Big Ideas 2024: AI Interpretability: From Black Box to Clear Box with Anjney Midha @a16z | Uploaded December 2023 | Updated October 2024, 7 hours ago.
Anjney Midha, General Partner at a16z, believes that mechanistic interpretability (a fancy term for "reverse engineering" AI models) will take center stage in 2024.
In this discussion, we move beyond the black box and explore pivotal questions: Why do AI models make specific statements? What influences the success of certain prompts? Most crucially, how can we control these models in real-world scenarios?
Topics Covered:
00:00 - Big Ideas in Tech 2024
01:39: AI Interpretability: From Black Box to Clear Box
02:21: What do we and don’t understand about LLM black boxes and interpretability
04:23 - Research in interpretability
06:43 - Features represented in the outputs from LLMs
08:16 - Unlocks in interpretability
11:49 - The engineering challenges
14:10 - Scaling mechanistic interpretability research
17:27 - A new focus on explainability
Resources:
View all 40+ big ideas: a16z.com/bigideas2024
Find Anish on Twitter: twitter.com/anjneymidha
Stay Updated:
Find a16z on Twitter: twitter.com/a16z
Find a16z on LinkedIn: linkedin.com/company/a16z
Subscribe on your favorite podcast app: a16z.simplecast.com
Follow our host: twitter.com/stephsmithio
Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
Anjney Midha, General Partner at a16z, believes that mechanistic interpretability (a fancy term for "reverse engineering" AI models) will take center stage in 2024.
In this discussion, we move beyond the black box and explore pivotal questions: Why do AI models make specific statements? What influences the success of certain prompts? Most crucially, how can we control these models in real-world scenarios?
Topics Covered:
00:00 - Big Ideas in Tech 2024
01:39: AI Interpretability: From Black Box to Clear Box
02:21: What do we and don’t understand about LLM black boxes and interpretability
04:23 - Research in interpretability
06:43 - Features represented in the outputs from LLMs
08:16 - Unlocks in interpretability
11:49 - The engineering challenges
14:10 - Scaling mechanistic interpretability research
17:27 - A new focus on explainability
Resources:
View all 40+ big ideas: a16z.com/bigideas2024
Find Anish on Twitter: twitter.com/anjneymidha
Stay Updated:
Find a16z on Twitter: twitter.com/a16z
Find a16z on LinkedIn: linkedin.com/company/a16z
Subscribe on your favorite podcast app: a16z.simplecast.com
Follow our host: twitter.com/stephsmithio
Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
