chief167 t1_j45eo3z wrote on January 13, 2023 at 7:39 AM

Reply to comment by wind_dude in [D] Has ML become synonymous with AI? by Valachio

AI is basically decision making. Giving information, how does a machine learn from its environment, take decisions, without human oversight. How does a machine adapt itself with more experience.

ML is just a way to create models.

For example the SLAM algorithm is an important algorithm in AI, because it allows robots to map their environment. However, this is not ML at all.

Another example of AI is knowledge graphs, like the earliest chess engines. A perfect chess AI can be made without any machine learning at all.

It's important to keep making the distinction.

MustachedLobster t1_j46hfdz wrote on January 13, 2023 at 2:36 PM

Slam exactly fits the definition of ml.

The more data you give it, the better the map gets, and the better we expect localisation to be.

It has no generalisation at all, but it is learning something very specific about a particular environment.

chief167 t1_j46k3aa wrote on January 13, 2023 at 2:55 PM

Slam is pure Mechatronics which I don't consider ml.

MustachedLobster t1_j47oa1s wrote on January 13, 2023 at 7:04 PM

It exactly matches Mitchell's definition of ml though.

> A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

https://towardsdatascience.com/what-is-machine-learning-and-types-of-machine-learning-andrews-machine-learning-part-1-9cd9755bc647#:~:text=Tom%20Mitchell%20provides%20a%20more,simple%20example%20to%20understand%20better%20.

Localisation error decreases the more data you have.