Vice President of AI & Quantum Computing, Paul Smith-Goodson dives in as explores how Google responded to ChatGPT by using foundation models and generative AI to create innovative products and improve its existing offerings.
Google has been using standard AI to enhance its Workspace products since 2015. At the same time Google was revamping its AI strategy in response to ChatGPT, Microsoft was also busy overhauling its products for the same reason.In light of OpenAI's recent progress in generative AI, Microsoft was quick to capitalize on its existing relationship with the company by an early integration of the technology into its products.
Creating complicated and advanced products using generative AI isn't typically something a company can do in a matter of a few weeks. Google was successful because it had been conducting AI research since the early 2000’s.Bard is Google’s answer to ChatGPT. It is a conversational AI search tool that uses a lightweight version of Google's Language Model for Dialogue Applications .
Few companies have the resources or skills required to develop and train foundation models, or to perform the tuning required to create and maintain useful generative AI models. After the debut of ChatGPT, Google must have recognized this was an area where enterprise limitations existed, so it created plans to make generative AI more accessible and easier to create.
To support the capabilities needed to implement generative AI at the enterprise level, Google also created new platforms with needed features and capabilities, including search and conversational AI, foundational models and complete enterprise control. In a short amount of time, Google rapidly retooled its products and recreated an aggressive roadmap, improving many products through the application of generative AI. To recap:
Google created Bard, a large language model that is Google's version of ChatGPT. Bard is more powerful and more practical than ChatGPT because it has been trained on a larger dataset of code and text, its information is more current, it can access and process information from the real world through Google Search and it is constantly being improved and updated.
Although not an element of Safe AI, it is equally important for the protection of users that they retain end-to-end sovereignty and maintain control over their data in Vertex. Google has safeguards in place to ensure customer data is not mixed with the training data that is used for foundation models. With that assurance, Vertex AI provides users with the necessary compliance requirements needed by many industries.
“I think that a better approach would be to have a more open and transparent discussion about AI research, and to develop guidelines for responsible AI development. We should also invest in research on the potential risks of AI and develop strategies to mitigate those risks.
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