Generative AI tools have revolutionized the way we perceive artificial intelligence, as they can produce content closely resembling human creations, from text to images and even videos. However, the applications of generative AI extend beyond mere imitation. Modern AI relies on recognizing patterns in data to answer questions and make predictions. For instance, OpenAI’s ChatGPT leverages generative AI to generate more data, adhering to the patterns found in its training data.
While real-world data is invaluable, it presents challenges like collection costs and privacy concerns. Think about creating a dataset for training facial recognition algorithms; thousands of individuals need to be photographed, and their consent obtained, followed by rigorous checks to mitigate potential biases.
Enter synthetic data
Synthetic data offers a solution to these challenges. It’s artificially generated data that closely mimics real-world data and can serve many of the same purposes. Snowflake, a prominent “data-as-a-service” company, has taken this concept a step further by incorporating synthetic, AI-generated datasets into its offerings.
Synthetic data is information created artificially to possess the characteristics of real-world data, without containing actual real-world data. Generative AI is well-suited for this task, analyzing datasets and generating synthetic data that closely matches them. This approach enables businesses to train AI algorithms, conduct tests, and simulations without exposing sensitive or private information found in real-world data.
Applications abound
Synthetic data finds applications across various industries. In finance, it trains fraud detection algorithms to identify deliberately falsified transactions. In healthcare, it preserves patient data confidentiality. In retail and marketing, it creates synthetic customers for analyzing purchasing behavior. Business leaders, facing challenges with real-world data accessibility, complexity, and availability, increasingly turn to synthetic data. Partially synthetic datasets, augmenting real-world data with synthetic data, have become more common than fully synthetic datasets.
Snowflake’s role in generative synthetic data
Snowflake, a major player in B2B data brokerage, has expanded its offerings to include access to synthetic datasets generated by AI algorithms. For example, it offers a synthetic human face dataset created by Synthesis AI, featuring 5,000 diverse human faces. This addresses concerns over biases in facial recognition algorithms, allowing datasets to be customized for inclusiveness and representation.
Generative algorithms have accelerated the scaling of datasets, making them customizable for various global customer needs. Snowflake also provides synthetic financial data from Clearbox AI, including simulated mortgage applications augmented by generative AI-generated data.
Snowflake acknowledges the pivotal role of AI-generated synthetic data in its future. As generative models advance, they will increasingly mirror the real world, offering more cost-effective and efficient insights to businesses.
Beyond synthetic data and other generative AI applications at snowflake
Snowflake has gone beyond synthetic data, offering tools based on generative AI to its customers. The acquisition of Neeva, a search startup founded by former Google employees, has led to the implementation of natural language querying for datasets. Users can now engage with their data through conversations, extracting insights by asking questions rather than relying on traditional data science analysis.
Additionally, Snowflake has partnered with Nvidia to create a platform enabling users to build generative AI applications such as chatbots and search engines, accessing Snowflake’s vast data resources. Another initiative involves developing a Document AI tool for querying and extracting meaning from documents, using technology acquired from the purchase of the Swedish natural language platform Applica in 2022.
Snowflake is leveraging generative AI not only for synthetic data creation but also for building tools that facilitate data analysis and extraction. The company’s commitment to harnessing generative AI reflects its vision for innovative data solutions and enhanced data utilization in various industries.
