wind_dude t1_j43qjja wrote on January 12, 2023 at 11:25 PM

I used to be of the mind set that everything called AI is just ML, and we aren't even close to achieving AI. Well still technically true, now AI is AGI, and LLMs are AI. I've given up that fight. But yes basically synonyms in our current lexicon, just call ML AI to sound cooler to scifi fans and tech journalist.

To me...

AI is an abstract concept that a computer can achieve cognitive abilities, emotion, and problem solving to the same level of a human.

ML is statistical and mathematical models. Basically the limit of what can be achieved on logic based hardware.

Sirisian t1_j44fz5x wrote on January 13, 2023 at 2:24 AM

> now AI is AGI

It's not. Keep correcting people. AI is task or multi-task specific. It's perfectly fine for someone to say ChatGPT is a dialog AI, for example. It completes tasks (multiple in this case) using an artificial intelligence that happens to be created using machine learning techniques.

What you're describing in your second part is AGI. Non task-specific problem solving at the level of a human. The boundary between advanced AI, especially multi-task learning models, and AGI will get smaller and fuzzy in the coming decades, but there is a difference. There will be very advanced AIs that check a lot of boxes researchers create. When an AGI is created there are effectively no boxes left to check. The system can collect data, reason, and create a logical solution for any test way outside of its training data.

wind_dude t1_j4767yf wrote on January 13, 2023 at 5:15 PM

>ChatGPT is a dialog AI,

OpenAI doesn't even call chatGPT AI or dialog AI.

>Non task-specific problem solving at the level of a human. The boundary between advanced AI, especially multi-task learning models, and AGI will get smaller and fuzzy in the coming decades, but there is a difference.

That is closer to the original older definition of AI, I don't think I've seen one credible definition that doesn't include problem solving. AGI just started getting used because AI had been used to much to refer to stuff that isn't AI.

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

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.