Key Points From This Episode:
- She shares her professional journey that eventually led to the founding of Gradient Ventures.
- How Anna would contrast AI Winter to the standard hype cycles that exist.
- Her thoughts on how the web and mobile sectors were under-hyped.
- Those who decide if something falls out of favor; according to Anna.
- How Anna navigates hype cycles.
- Her process for evaluating early-stage AI companies.
- How to assess whether someone is a tourist or truly committed to something.
- Approaching problems and discerning whether AI is the right answer.
- Her thoughts on the best application for AI or MLR technology.
- Anna shares why she is excited about large language models (LLMs).
- Thoughts on LLMs and whether we should or can we approach AGIs.
- A discussion: do we limit machines when we teach them to speak the way we speak?
- Quality AI and navigating fairness: the concept of the Human in the Loop.
- Boring but essential data tasks: whose job is that?
- How she feels about sensationalism.
- What gets her fired up when it is time to support new companies.
- Advice to those forging careers in the AI and ML space.
Tweetables:
“When that hype cycle happens, where it is overhyped and falls out of favor, then generally that is – what is called a winter.” — @AnnapPatterson [0:03:28]
“No matter how hyped you think AI is now, I think we are underestimating its change.” — @AnnapPatterson [0:04:06]
“When there is a lot of hype and then not as many breakthroughs or not as many applications that people think are transformational, then it starts to go through a winter.” — @AnnapPatterson [0:04:47]
@AnnapPatterson [0:25:17]
Links Mentioned in Today’s Episode:
Anna Patterson on LinkedIn
‘Eight critical approaches to LLMs’
‘The next programming language is English’
‘The Advice Taker’
Gradient
How AI Happens
Sama
