4 ms·
No dataset, no training, no layers = not AI
by column 7y ago
No dataset, no training, no layers = not AI
- gwd 7y agoClassically speaking, any time you gave computers a goal to optimize for, it was called "AI"; a large amount of AI research in the 80's and 90's was about how to optimize search. Thus SQLite's website, for instance, (correctly) says: "The query planner is an AI that tries to pick the fastest and most efficient algorithm for each SQL statement." Optimizing database queries was a common topic for artificial intelligence research in the 80's and 90's. It's just that now we're so used to the idea of a computer searching for the best flight, the best map, the best way to optimize your code, the best way to execute your SQL query, that it doesn't seem like "magic" any more. When you say "no dataset, no training" you're talking about machine learning. And when you say "layers", you're talking about deep learning. [1] https://sqlite.org/queryplanner.html https://sqlite.org/queryplanner.html
- VBprogrammer 7y agoGenetic algorithms might have fallen out of favour recently but I don't think anyone would exclude them from AI as a category. The field in my experience has always contained a lot of planning and inference which you seem to be excluding.
- TeMPOraL 7y agoLR absolutely has datasets and training. You train a regression model in much the same way as you train any other ML model, DNNs included - you use some data to derive its parameters, use the rest to validate. LR typically has number of layers = 1, though I bet someone imaginative enough could create "deep linear regression", if that was useful for anything.
- sacado2 7y agoThe guys who coined the term "AI" strongly disagree.