These are the 8 main drivers that make Bigdata different from ChatGPT:

  • Access to the latest information
  • Enhanced accuracy in specialized domains
  • No need for re-training
  • Full transparency
  • Data privacy and security
  • Customization and flexibility
  • Energy efficiency
  • Cost effectiveness
  • Scalability
  • Collaborative workflow

Bigdata.com offers several advantages over models like GPT-4, especially in contexts where up-to-date, detailed, and highly specific information is crucial. Here are some of the key benefits:

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Data Privacy & Security

Unlike ChatGPT, Bigdata will never use your data for training.

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Access to the latest information

Bigdata with real-time Retrieval Augmented Generation can access and incorporate the most current data and information available across both public and private sources. This is a significant advantage over static models like ChatGPT-4, which are limited to the knowledge they were trained on up until a certain date. For instance, ChatGPT-4’s training only includes data up until April 2023 and has limited web search and slow plugin capabilities.

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Enhanced accuracy in specialized domains

Since Bigdata can pull in information from external sources, it can provide more accurate and detailed responses in specialized areas. This is particularly beneficial for topics that require expertise or current data, such as financial services, medical advice, legal information, technological advancements, and recent news events or publications.

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No need for re-training

Maintaining and updating the knowledge of Bigdata is more straightforward and less costly than GPT. Instead of retraining the entire model, the system can be updated by modifying the external data sources or improving the retrieval algorithms.

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Full transparency

Unlike services like ChatGPT, known for their ‘black box’ operations, Bigdata embodies transparency by allowing users to be in control of the content and sources for each of their queries. The ability to cross-verify information through provided citations enhances user trust as they are not required to take the information at face value. This control not only empowers users but enhances accuracy and trust, making Bigdata a superior choice for informed decision-making. On the other hand, ChatGPT’s training on internet text exposes it to potential biases from those unknown sources, which may result in unintentionally biased or inaccurate outputs.

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Customization & flexibility

Bigdata can be tailored to specific needs or domains by accessing specialized databases or content sources. This allows for a more customized response based on the latest data usuals available behind paywalls or internal databases.

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Energy efficiency

Training an LLM like GPT4 is an extremely resource-intensive process, requiring substantial computational power and energy. In contrast, Bigdata leverages a combination of a smaller language model and external data sources. This approach reduces the computational resources needed for training, as the model can retrieve up-to-date information from external databases or the internet, rather than needing to learn and store all that information internally.

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Cost effectiveness

Bigdata is more scalable in terms of cost compared to GPT4. Since it relies on external data sources for the latest information, the underlying language model doesn’t need to be as large or comprehensive as a standalone LLM. This can make scaling up to accommodate more users or queries more cost-effective.

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Scalability

Bigdata is more scalable in terms of cost compared to GPT4. Since it relies on external data sources for the latest information, the underlying language model doesn’t need to be as large or comprehensive as a standalone LLM. This can make scaling up to accommodate more users or queries more cost-effective.

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Collaborative workflow

ChatGPT is a single-user-oriented tool. While users can share information they’ve obtained from ChatGPT with their team, the system does not inherently support real-time collaboration between users across an organization. There’s no built-in mechanism to have multiple users asking questions, sharing insights, or contributing to a research project simultaneously as they do with Bigdata.