I think the biggest mistake around improving the system is that most people are spending too much time on the actual synthesis without actually understanding whether or not the data is being retrieved correctly. To avoid this:
Create synthetic questions for each text chunk in your database
Amplify Partners was running a survey among 800+ AI engineers to bring transparency to the AI Engineering space. The report is concise, yet it provides a wealth of insights into the technologies and methods employed by companies for the implementation of AI products.
Highlights
👉 Top AI use cases are code intelligence, data extraction and workflow... See more
core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.
Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more
These two components might be some of the most important ideas to improve all of AI.
The study leveraged large language models (GPT-3.5) to identify and analyze critical aspects of emerging corporate risks using earnings call transcripts — with respect to Political, Climate-related, and AI risks.
More than 69,000 earnings call transcripts were used in the study, and the GPT model was instructed to make human-readable risk summaries... See more
How much do you think we’re going to get tons of specialization versus no, once the brain gets big enough and we do these fine tunings, that’s going to be it. It will be like AWS, GCP, and Azure.
Ali: I think the answer is closer to the latter. It’s going to have lots of specialization. Having said that, it’s not a dichotomy in the sense that maybe... See more