Kovendo

  A classification model's precision and recall can change when its decision threshold changes. A default threshold is not necessarily the optimal choice for every machine learning application. When evaluating a machine learning classification model, accuracy alone does not always tell the complete story. Imagine an email spam detector that correctly classifies 98% of emails. That sounds impressive. But what if it incorrectly sends important customer emails to the spam folder? Or imagine a disease detection model that finds almost every patient who may have a disease, but also incorrectly flags many healthy patients. This is where precision and recall become important. Precision and recall are two of the most useful metrics for understanding classification models. They are closely connected to the true positives, false positives, and false negatives found in a confusion matrix. If you are new to confusion matrices, start with Mastering the Confusion ...

  Crystallized and Fluid Intelligence: AI vs Human Intelligence AI inference has become dramatically cheaper. Stanford's 2025 AI Index reported that the cost of querying a model with GPT-3.5-level performance on the MMLU(Massive Multitask Language Understanding) benchmark fell by more than 280 times between November 2022 and October 2024. That number represents more than cheaper software. It shows how quickly artificial intelligence is moving from an experimental technology into an everyday intelligence tool. But an important question remains: Is artificial intelligence becoming intelligent in the same way humans are intelligent? To answer that question, we need to look beyond AI benchmark scores and understand two important concepts from psychology: crystallized intelligence and fluid intelligence. Humans use both. AI systems are also beginning to demonstrate capabilities that look somewhat similar to both. However, the similarities can be misleading. AI can know eno...

  The biggest mistake advanced marketers make with AI is starting with the question, “Which AI tool should I use?” Start instead with: Which marketing decision is costing us the most money, time or growth? Then determine whether AI can improve that decision. A practical AI marketing implementation should follow this framework: Business problem → Data → AI model/tool → Workflow → Experiment → KPI → Financial impact → Scale For example, if an e-commerce company has a 2.2% conversion rate, the objective is not to “use AI.” The objective could be: Increase conversion rate from 2.2% to 2.8% without increasing CAC. AI then becomes the mechanism used to achieve the business objective.  If you already have understanding on AI powered Digital Marketing then you are in the right place to implement the knowledge and gain growth for your online business. Implementation Example 1: Build an AI-Powere...

AI is already being used by 66% of marketers globally , yet the biggest competitive advantage is no longer simply knowing how to use ChatGPT or generate social media posts. The real advantage comes from connecting AI with customer data, marketing automation, predictive analytics, paid advertising, SEO, personalization, experimentation, and revenue attribution to build a marketing system that continuously learns and improves. For advanced marketers, AI-powered digital marketing is therefore not about replacing a marketer with an AI tool. It is about redesigning the entire marketing engine so that research, segmentation, content production, campaign optimization, customer journeys, conversion analysis, and budget allocation become increasingly intelligent and data-driven. McKinsey reports that generative AI is already most widely used in marketing and sales among business functions, with 42% of surveyed organizations reporting regular gen-AI use in marketing and sales. ...