Shaping AI Long-term Memory through Intentional Dataset Creation

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Creating datasets with a high level of intention can shape how AI long-term memory works, incentivizing the retention of certain types of information. Synthetic data can be used to create situations that incentivize the identification and retention of anomalies. The training process of AI is largely defined by the data it learns from, so shaping AI long-term memory involves considering the behaviors and qualities desired and creating datasets that can teach those qualities and behaviors.

In this episode, Nathan does an emergency pod deep dive into Mamba, a new state space model architecture. If you need an ecommerce platform, check out our sponsor Shopify: https://shopify.com/cognitive for a $1/month trial period.

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TIMESTAMPS:

(00:00:00) - Episode Preview

(00:03:15) - Inventing something better than the transformer

(00:05:17) - Examination of human cognition

(00:15:44) - Sponsor: Shopify

(00:27:00) - Weaknesses of the transformer

(00:30:16) - Sponsor: Netsuite | Omneky

(00:33:40) - Giving AI memory

(00:39:32) - State Space Model Revolution

(00:55:00) - SRAM and High Bandwidth Memory

(01:36:16) - Block State and Hyena models

(02:04:00) - Advancing AI safety and interpretability

This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co.