Deep Learning
Machine learning using neural networks with many layers. The depth is what lets a system learn complicated things — recognising a face, translating a sentence — without anyone specifying how.
angkor@design~query "Angkor Design"
4 matches — opening studio
Prefer to talk it through? Pick a time that suits you — free, no obligation. We'll meet on Google Meet or Telegram.Book a meeting →
Machine learning using neural networks with many layers. The depth is what lets a system learn complicated things — recognising a face, translating a sentence — without anyone specifying how.
Training a model on examples that are already labelled with the right answer. It is the most common approach when you know exactly what you want the system to produce.
A public page stating how a business uses AI and what it does with customer data. Increasingly expected, and cheap to write honestly.
An emerging convention: a plain-text file at your site root that gives AI systems a clean map of what is on it. It saves an assistant from crawling around to work out what your site contains.
A longer, more specific search — “boutique hotel Siem Reap near Pub Street with a pool” rather than “hotel”. Individually rare, collectively most of all searches, and far easier to answer well.
Markup describing where a page sits in the site’s hierarchy. It helps both search engines and assistants understand that a page is a service under a category, not a stray document.
AI that interprets images and video — reading a document, recognising a product, counting people. The part of AI that has been quietly working in industry for years.