RAG: retrieval augmented generation

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In financial services, there is a notable shift away from cloud solutions as companies are opting to enhance their own data center capabilities. However, across different industries, many organizations continue to pursue cloud migration, recognizing the inefficiency of maintaining extensive data centers. The understanding of AI within enterprises remains immature, with widespread apprehension about its reliability. A prevalent concern is the phenomenon of 'hallucination' in AI models, leading to the belief that the technology is fundamentally flawed. Despite advancements, these models can still produce inaccuracies, drawing parallels to human fallibility in memory and perception. The occurrence of these errors, referred to as hallucinations, is being monitored through established benchmarks, which indicate a significant decrease in hallucination rates in tandem with improvements in model performance. The introduction of Retrieval Augmented Generation (RAG) represents a paradigm shift in this domain, promising enhanced accuracy in AI outputs.

Aidan Gomez is the Co-founder & CEO at Cohere, the leading AI platform for enterprise, having raised over $1BN from some of the best with their last round pricing the company at a whopping $5.5BN. Prior to Cohere, Aidan co-authored the paper “Attention is All You Need,” which introduced the groundbreaking Transformer architecture. He also collaborated with a number of AI luminaries, including Geoffrey Hinton and Jeff Dean, during his time at Google Brain, where the team focused their efforts on large-scale machine learning.

In Today's Episode with Aidan Gomez We Discuss:

1. Compute vs Data: What is the Bottleneck:

Does Aidan believe that more compute will result in an equal increase in performance?
How much longer do we have before it becomes a case of diminishing returns?
What does Aidan mean when he says "he has changed his mind massively on the role of data"? What did he believe? How has it changed?

2. The Value of the Model:

Given the demand for chips, the consumer need for applications, how does Aidan think about the inherent value of models today? Will any value accrue at the model layer?
How does Aidan analyze the price dumping that OpenAI are doing? Is it a race to the bottom on price?
Why does Aidan believe that "there is no value in last year's model"?
Given all of this, is it possible to be an independent model provider without being owned by an incumbent who has a cloud business that acts as a cash cow for the model business?

3. Enterprise AI: It is Changing So Fast:

What are the biggest concerns for the world's largest enterprises on adopting AI?
Are we still in the experimental budget phase for enterprises? What is causing them to move from experimental budget to core budget today?
Are we going to see a mass transition back from Cloud to On Prem with the largest enterprises not willing to let independent companies train with their data in the cloud?
What does AI not do today that will be a gamechanger for the enterprise in 3-5 years?

4. The Wider World: Remote Work, Downfall of Europe and Relationships:

Given humans spending more and more time talking to models, how does Aidan reflect on the idea of his children spending more time with models than people? Does he want that world?
Why does Aidan believe that Europe is challenged immensely? How does the UK differ to Europe?
Why does Aidan believe that remote work is just not nearly as productive as in person?