In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand significant computational resources and memory. This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed "blending", a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just three models of moderate size (6B/13B paramaeters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ paramaters). This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of thirty days. The findings underscore the potential of the "blending" strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands.

In this special guest feature, Glen Rabie, CEO of Yellowfin, discusses how alerts are commonly used as a basic business intelligence tool, but there's a better alternative: AI-driven automated analytics. AI has the power to parse the data behind dashboards and send a signal when significant activity happens. Here are five reasons why AI-driven automated analytics are better than alerts in today's evolving business landscape. Yellowfin is an Analytics and Business Intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics.

A/B tests are a crucial tool for evaluating experiments in marketing domain. It is performed by subjecting different population samples to variations of an offer. The results are observed at the end of experiment to select the best performing variant. Marketing teams need to spend a lot of time in crafting these experiments. It also takes time and money to wait for results to iterate and improve.