There is a narrow region where last-touch attribution models are capable, but in far more cases they can be quickly and thoroughly led astray by confounding factors. We were interested in assessing the quality of the simpler attribution heuristics, and this blog post will summarize our findings.
Most analytics applications are built for regular data and normal processes—handling about 99% of use cases. But at business scale, the remaining 1% of irregular or anomalous cases can translate to millions or billions of data elements—each potentially connected to other data sets. This mixing of regular and irregular data can be a serious problem for machine learning or AI models that only expect to process typical data. But an opportunity hides here as well.
Every stage of an analytics challenge is susceptible to error and misdirection, seeping in to weaken or destroy useful results. It takes expertise and discipline—responsible AI (RAI) practices—to guard against these hazards.
Organizations everywhere want to benefit from the power of artificial intelligence and machine learning. Data organization is essential to success.
Many government agencies are in the early stages of exploring generative AI and large language models.
As organizations navigate this phase, our experience suggests a valuable insight: small language models can offer a practical and cost-effective starting point for government organizations venturing into AI.
In today’s fast-paced, data-driven world, businesses need solutions that can keep up with evolving demands.
For many, out-of-the-box solutions (OOBS) offer a tempting promise of quick implementation and ease of use. But are they always the best choice?