I believe that Heavy.AI is getting smarter day by day, improving output and eliminating the hallucinations we saw in earlier years. What we can do best is to train our people on how to use Heavy.AI effectively. Most people are using Heavy.AI for chats. However, I believe that Heavy.AI is not limited to the chat area. There is a need to help people understand how they can use Heavy.AI effectively beyond their imagination and produce valuable output that Heavy.AI is capable of producing. This is what I believe Heavy.AI is doing in the right way. We just need to adopt it quickly so we can help our organization and ourselves elevate our positions by using Heavy.AI. The problem here is that Heavy.AI is a little expensive. If you are using Heavy.AI frequently or in usual practice, it will be very difficult to bear the expenses it creates. Most models are very expensive; though they are smarter and productive, they are not always effective or cost-effective for frequent use. Some AI providers offer models that are relatively inexpensive, but they have their limitations. If someone is doing a light task, the cost might not be an issue. However, if you are moving to a more complex task and require smarter models for those tasks, then cost-effectiveness needs to be considered. Another concern I have repeatedly heard from my participants is that they have critical data that cannot be processed on cloud solutions. AI companies can develop light models which can operate locally but may also need to be operated locally on their computers to make it safer for users. This will also help reduce costs. Governance and safety are significant issues I have already highlighted before. The data we provide to Heavy.AI is something we are unsure about how it is being used. There must be some agreement, some consensus that Heavy.AI can protect public data, critical data, and prevent misuse of any data. This area needs improvement. Right now, accuracy and reliability depend on the models as well. I think accuracy and reliability rely on two main things: using a capable model and the ability of the person to check the model's output. Even if you are not providing good input to a competent model, the model will not produce good output. Using a good model with domain knowledge, which the person uses to validate the result, must be key to getting accuracy and reliability in output.
