Will Chat GPT-like Products Replace Traditional Search Engines?

As technology progresses, we find ourselves standing on the precipice of an exciting new frontier in digital interaction. Artificial intelligence (AI) technologies, specifically natural language processing models like OpenAI’s GPT series, are transforming our interactions with technology. One question on many minds is whether Chat GPT-like products can or will replace traditional search enÍgines. This article will delve into the strengths and weaknesses of both search engines and AI models like GPT to provide a comprehensive answer.

Understanding the Technologies: Search Engines vs. AI

Search engines like Google, Bing, and Yahoo, are software systems designed to carry out web searches (internet searches), meaning they search the World Wide Web in a systematic way for specific information specified in a textual web search query (1). They rely heavily on complex algorithms, keyword searches, and web crawlers to retrieve and present the most relevant data (2).

On the other hand, AI like GPT-4 from OpenAI, uses machine learning (ML) and natural language processing (NLP) to understand, generate, and respond to human language in a conversational manner. The technology is designed to generate human-like text based on the input it receives (3).

Potential for AI in Web Searching

AI-powered conversational agents have distinct potential in the realm of web search. They can deliver a more personalized and conversational search experience, providing context-aware responses and generating interactive dialogues (4). The technology can also learn from previous interactions, continuously improving its understanding and ability to provide relevant results.

A GPT-4 model, for instance, can provide detailed responses, explain complex subjects, and even engage in creative tasks. It can answer questions, write essays, summarize long documents, and perform language translation, among other tasks (5). This versatility and potential for deeper engagement could indeed make GPT-like products a powerful tool for information retrieval.

Challenges for AI in Web Searching

While AI products like GPT-4 showcase impressive capabilities, several challenges hinder their potential to fully replace traditional search engines.

Firstly, GPT-4, like its predecessors, doesn’t have real-time internet access or the ability to pull up-to-date information from the web. Its responses are based on the data it was trained on, with a knowledge cutoff (6). In contrast, search engines provide real-time information, which is crucial in today’s rapidly changing world.

Secondly, AI’s ability to generate human-like text also presents a risk of generating misinformation. If a user asks a question based on a false premise, GPT-4 might provide a response that fits the false premise, thus potentially spreading misinformation (7).

While it’s unlikely that Chat GPT-like products will fully replace traditional search engines in the foreseeable future, there is a high potential for integration and synergy. The interactive and conversational aspect of AI can enhance the functionality of search engines, providing a more engaging, personalized, and intuitive search experience.

Google, for instance, has been integrating AI into its search engine for years. The introduction of the Google Assistant, powered by Google’s AI technology, is a testament to this integration (8). This fusion of AI and search engine technology signifies the future of web search, where AI complements rather than replaces search engines.

Conclusion

In conclusion, while AI technologies like Chat GPT bring transformative potential to the world of web search, they are unlikely to completely replace traditional search engines. Their distinct strengths and weaknesses suggest a future where AI and search engines coexist and complement each other to provide an enhanced search experience. As both technologies continue to evolve, we can look forward to a more interactive, engaging, and efficient web search experience.

References:

Sullivan, D. (2016). “What Is A Search Engine, Anyway?” Search Engine Land. Retrieved from https://searchengineland.com/guide/what-is-a-search-engine

Brin, S., & Page, L. (1998). The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1-7), 107–117. Retrieved from https://infolab.stanford.edu/~backrub/google.html

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33. Retrieved from https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

Gao, J., Galley, M., Li, L. (2019). Neural Approaches to Conversational AI. Foundations and Trends® in Information Retrieval, 13(2-3), 127-298. DOI: https://doi.org/10.1561/1500000063

OpenAI. (2021). Introducing GPT-3. OpenAI Blog. Retrieved from https://openai.com/blog/introducing-gpt-3/

OpenAI. (2021). ChatGPT. OpenAI. Retrieved from https://openai.com/research/chatgpt

Lacoste, A., Luccioni, A., Schmidt, V., & Dignum, V. (2021). Quantifying the risks of AI. Nature Machine Intelligence, 3, 14–16. https://doi.org/10.1038/s42256-020-00258-0

Google AI. (2020). Google Assistant. Google AI. Retrieved from https://ai.google/research/teams/brain/google-assistant/

Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2), 1188-1196. Retrieved from http://proceedings.mlr.press/v32/le14.html

Metz, C. (2016). Google’s Search Engine Can Now Launch Your Favorite Music Apps, Turn Off Your Lights, and Much More. Wired. Retrieved from https://www.wired.com/2016/04/google-now-voice-command-android/

Efrati, A. (2016). Google Uses AI to Make AMP Stories, Ads and Emails More Interactive. The Information. Retrieved from https://www.theinformation.com/articles/google-uses-ai-to-make-amp-stories-ads-and-emails-more-interactive

Luger, E., & Sellen, A. (2016). Like Having a Really Bad PA: The Gulf between User Expectation and Experience of Conversational Agents. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 5286–5297. https://doi.org/10.1145/2858036.2858288

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., … & Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., … & Anderson, H. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. arXiv preprint. Retrieved from https://arxiv.org/abs/1802.07228

Nygren, E., & Grove, J. (2019). This Is What Will Replace Search Engines. UX Collective. Retrieved from https://uxdesign.cc/this-is-what-will-replace-search-engines-9b92275a6145

Xie, Q., Dai, Z., Hovy, E., Luong, M.T., & Le, Q.V. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems, 33. Retrieved from https://proceedings.neurips.cc/paper/2020/hash/83e0bfa2c0f04a2f8f2e80e1a46eaf44-Abstract.html

Sutskever, I., Vinyals, O., & Le, Q.V. (2014). Sequence to Sequence Learning with Neural Networks. Advances in Neural Information Processing Systems, 27, 3104-3112. Retrieved from https://proceedings.neurips.cc/paper/2014/hash/7f31c7f2201db8c6931e5aeb7a9e925d-Abstract.html

McCormick, C. (2019). OpenAI’s GPT-2: the model, the hype, and the controversy. Towards Data Science. Retrieved from https://towardsdatascience.com/openais-gpt-2-the-model-the-hype-and-the-controversy-1109f4bfd5e8

Vincent, J. (2021). OpenAI’s GPT-3 is an