Generative AI Hype Explained

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This question came up in many of the sessions on Day 1 of Google Cloud Next. Here’s a collation of all the answers that were shared during 2 talks by Amit Zavery, Helene Ambiana, Thomas Kurian, Asheem Chandna, Ravi Mahtre, Sarah Wang, James Kalani Lee

1. Accessibility

Simple apps like DALL-E and ChatGPT have made interacting with generative AI accessible to the public in an unprecedented way. User adoption happened at a record pace.

2. Demand drivers

Business needs for automation, creativity augmentation, and cost savings have made GenAI apps “hot”.

3. Algorithm innovations

Architectures like transformers, attention mechanisms, and reinforcement learning have allowed breakthroughs in generative modelling.

4. Compute power

The exponential increase in compute resources, GPUs, and cloud infrastructure has enabled the training of complex generative models that were infeasible before. (PS. The compute power is costly, not everyone can afford to pre-train LLMs)

5. Data availability

Vast amounts of data from sources like social media, internet usage, and digitization of information have provided the raw material to train powerful generative models.

In summary, the stars have aligned in terms of data, compute power, algorithms, business demand and accessibility — creating the perfect conditions for the generative AI revolution happening today.

The hype is justified given the immense possibilities these technologies are starting to unlock.


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