A machine can learn patterns from many examples, but it can be mistaken, so people decide.
Rules for every case would never end. A pointy ear does not always mean cat, so the rules break.
Machine learning is a different idea: the machine learns patterns from many examples, like a hundred photos each labelled cat or dog.
It does not understand pets. It only finds patterns in what it is shown, and it can be sure and mistaken.
People check a machine's work, and for anything that matters, people decide.
These are some of the questions on the sets. The printable sheets have more and an answer key.
False. A machine finds patterns. It does not understand pets.
Many different photos. Varied examples teach a machine more than copies do.
Nearly every cat photo had a sofa, so it learned sofa means cat. It found a pattern nobody meant.
People. People check a machine's work, and people decide.
4. 9 - 5 = 4.
Play pet sorter with a grown-up. They show you six labelled pictures or objects, like cats and dogs, or fruit and vegetables. Then they show a new one. Guess which group it belongs to. Were you ever sure and mistaken? What would help you learn more?
Play pet sorter together: show six labelled pictures or objects, like cats and dogs or fruit and vegetables, then a new one to guess. Ask whether your child was ever sure and mistaken, and what would help them learn more.
