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