The AI Search Race has begun. Throughout history, we’ve seen countless races between the East and West. The space race, arms race, who will be first to walk on the moon race, and so many more. The AI Search Engine Race has begun.
Now, as artificial intelligence is reaching impressive levels of maturity and working products are being adopted and recognised, search engines are doing everything they can to use the technology to improve user experience.
The massively successful ChatGPT service awakened the world to the impressive powers AI can wield.
The AI Search Engine Race
Google have long been experimenting with AI, from their Google Lens tools, to MusicLM. Microsoft, the owners of the Bing search engine have recently announced a third round of investment into OpenAI, with rumours they will incorporate ChatGPT into Bing search.
Baidu, China’s leading search engine however are moving up a gear.
ChatGPT is banned in China, so Baidu are developing their own competitor, according to Bloomberg Technology Reporter, Zheping Huang.
Baidu have already spent Billions developing their AI capabilities, however they’ve been struggling to find a workable use case for it. Incorporating it into their search engine services could be the answer.
Google vs Bing vs Baidu
In terms of Traffic, here are the monthly stats for the 3 global search engine leaders, for now. All stats are according to SimilarWeb.com.
- Google – 86.4 Billion
- Baidu – 4.9 Billion
- Bing – 1.2 Billiom
How AI Can Improve Search Engines
Artificial Intelligence is poised to revolutionize the future of search engines. With its advanced capabilities in machine learning and natural language processing, AI will enable search engines to provide even more accurate and relevant results to users. In the future, search engines will continue to incorporate AI technologies, leading to a more personalized and intuitive search experience. From predictive search suggestions to improved relevance ranking, AI will play a crucial role in shaping the future of search engines.
- Natural language processing (NLP) for better understanding of queries
- Personalization based on user behaviour and search history
- Improved relevance ranking through machine learning algorithms
- Image and voice search capabilities
- Predictive search suggestions
- Integration with other sources of data for comprehensive results
- Semantic search to understand the context and meaning behind queries
