But AI is not immune to mistakes: sometimes it makes factual errors, ignores logic, or even produces an empty set of words. As examples show, the neural network understands words like “flashlight” or “stone” literally. Therefore, robotic descriptions cannot be left — the editor needs to refine the texts, give them liveliness and “zest”. Examples of growth in conversions, orders and profits How to drive 551 applications for 100k budget in B2B. Case on selling batteries How to bypass the "Few impressions" status and receive applications from low-frequency queries in Yandex.
Direct. Case study on car repair Responding to reviews using neural networks Monitoring reviews on the marketplace is a monotonous but necessary task. Before [Login to see the link] the introduction of neural networks, the process looked like this: you had to log into your account several times a day, track new reviews, and write a response to them. You can even exclude a person from this process altogether: there are special Telegram bots. They analyze the review, find out whether it is positive or negative, and write a response to it.
But this is a new tool, and it has not yet learned to work with ambiguous and ironic reviews. You can follow the algorithm of such a bot using the example of Review Helpbot. Training AI-Targetologist Course: Automate Advertising Setup with Neural Networks Course on selling websites. Create landing pages without experience in programming, copywriting and design. Course on Yandex Direct: Set up advertising more effectively than 97% of marketers Creating a visual using neural networks Neural networks have learned to generate images quite successfully.