2-weeks ago we covered the announcements around Google’s much vaunted AI search results. Since then, things haven’t been going so well, with numerous examples popping up of absurd answers such as suggesting putting glue on pizza (based on an old Reddit post), claiming that cats have been found on the moon, and stating that most doctors recommend eating rocks.
Yesterday Google published a detailed response and action plan. They claim the issues are due to misinterpreting the original queries, struggling with ‘satirical’ material, content voids, and over-reliance on user-generated output. Unlike us humans, perhaps the AI is yet to appreciate the highly ‘variable’ nature of the things we find online. In response, Google is implementing a series of improvements, including better detection of nonsensical queries, limiting the use of user-generated content, enhancing protections for sensitive topics like news and health, and making enhancements based on the latest identified patterns. But the concern many highlight is that its likely the more subtle errors, not picked up by these changes, that will have a bigger impact.
Google believes ‘AI Overviews’ are needed and beneficial as they provide quicker summaries that directly answer user queries. They are designed to “take the work out of searching by providing an AI-generated snapshot with key information” and helping users get collated answers when the information they are looking for comes from multiple sources. Google claim that their research shows people prefer these results. Of course, the other big driver here is that Google feel they need to keep pace with competitors such as Perplexity who are re-building the search experience around AI.
Perplexity’s new Pages feature announced this week takes a different approach – rather than brief summaries, it generates an entire customisable webpage based on prompts. The goal is more comprehensive research reports, building on the AI powered search engines approach of combining referenced sources with generated analysis. Pages makes this research more sharable, but the nature of AI generation still requires the user to read, and sense check the output, and often to review a few of the cited sources to ensure the model has got the right gist. This is a huge time saver for research, but not a panacea.
Takeaways: Google is likely to take things more slowly, but the processing of extensive and diverse web search results is still a killer feature for AI. Perplexity look to be leading on how this is going to work in practice. Whilst Google will need to rebuild trust in its AI search features, for us users, the reality is that we should never trust anything digital on first contact. As the old (1993) joke goes… on the Internet nobody knows you’re a dog… or perhaps a spacefaring cat.