Once again, Google has failed to deliver on its grand promises in the AI world. Their latest offering, the Gemma 2 2B model, is just another example of Google’s tendency to overpromise and underdeliver in the field of artificial intelligence.
While Google touted impressive benchmark results for Gemma 2 2B, real-world performance tells a different story. In my testing, this supposedly advanced model struggled with even basic tasks. It could barely play a game of 20 questions, a task that should be trivial for any competent language model.
The issues don’t stop there. Users attempting to work with Gemma 2 2B have encountered significant compatibility problems with Sparse Autoencoders (SAEs) in the Gemma Scope repository. The model outputs activations with 2048 dimensions, while the SAEs expect 2304 dimensions. This 256-dimension gap is causing headaches for developers and researchers alike.
Some have suggested padding the activations with zeros to bridge this gap, but this feels like a band-aid solution at best. It’s concerning that Google would release a model with such glaring issues, especially given their resources and supposed expertise in AI.
This situation is emblematic of a larger problem with Google’s AI efforts. They consistently produce models that look good on paper but fall short in practical applications. It’s a pattern we’ve seen time and time again, and it’s getting old.
While Google continues to disappoint, other companies and open-source projects are making real strides in AI development. Perhaps it’s time for the tech giant to step back and reassess its approach to AI, rather than continuing to release subpar models that fail to live up to the hype.
Have you had similar experiences with Google’s AI models? Share your thoughts and frustrations in the comments below. Let’s discuss how we can push for better, more reliable AI solutions in the face of these disappointments.
