Generative AI crashed onto the scene very recently, and it has already shifted the power dynamic of many industries. Previously, predictive modeling required fluency in coding and statistics–which meant that it was only accessible to very large enterprises and consultants. Now, AI has democratized predictive analytics in many ways. But that comes at a cost.
Over the past year, the market has become flooded with black-box AI applications promising silver-bullet solutions. While some of these applications use robust proprietary technology, others are little more than re-skins of tools like ChatGPT. Buyers will need to become more discerning. Chirag Shah, a professor at the University of Washington and Co-Director at Responsibility in AI Systems & Experiences (RAISE), said it best: “when blind trust in these systems is combined with bias and a lack of transparency, you realize what a dangerous mix it can be.”
To businesses seeking to optimize business decisions with the help of AI, transparency matters.
One of the most hotly-debated topics right now is the issue of AI hallucination. It’s well known that generative AI has the potential to spread misinformation. Especially in a business setting, this could spell trouble. Consumer insights platform Native AI is getting ahead of this by introducing an early-to-market Synthetic Output Slider. This feature will allow users to toggle between high fidelity on one end and high creativity on the other. In other words, moving the slider to the right will increase the frequency in which AI invents contextual detail to sound more human, while moving the slider to the left will reduce predictions and stick only to known data within the data set.
Native AI’s Generative AI feature is called Digital Twins. These profiles are trained using a brand’s own product reviews, feedback surveys, social media discussion, or any other first or third party data, so they represent real customer opinions. When Native AI clients pose questions to their Digital Twins, it’s like they’re interacting with their customers (or competitors’ customers) in real time. The Synthetic Output Slider puts guardrails around the Digital Twins’ responses.
While it may seem like businesses should only seek known data, there are numerous cases when they would actually want predictive and assumptive data. For example, human responses to open-ended questions are full of tangential details that act as contextual clues. They provide a way to enter the mindset of the individual consumer. When AI shares a summary of collective responses, the “random” details that make it feel human are lost.
Sam Altman, CEO of OpenAI, recognizes the value of both. At a tech conference last June, he remarked, “there is a balance between creativity and perfect accuracy, and the model will need to learn when you want one or the other.”
Native AI believes that the model shouldn’t decide on behalf of the customer, but the customer should be able to decide for themself depending on the use case. “We have a lot of customers who use our solution for creative ideation and coming up with new hypotheses to validate, but we also have customers who use our platform to improve their products. Each use case requires a different level of prediction and creativity, so it makes sense to put the customer in control,” says Frank Pica, Native AI co-founder and CEO.
Even though Native AI is a self-serve tool, they have found that it’s important to take a consultative approach in the beginning to help clients who are new to AI or Data Science get the most out of the platform. “Some clients bring a lot of their own first party data, which can then be joined with third party sources to create robust consumer profiles,” says Native AI COO Sarah Sanders. “Other times, businesses just want to understand the motivations behind online product reviews. Either way, Native AI can be as robust or as simple as the client requires.”
Native AI believes that as the industry matures, businesses will demand greater control. The world is facing a steep learning curve with AI literacy. It’s imperative that businesses understand what happens to their data once it enters the “black box” of an AI algorithm, even if they don’t understand every line of code. To learn more about how your data can be used to deliver descriptive and predictive insights about consumer sentiment and feedback, visit gonative.ai.
