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Ask HN: What's the difference between ML and AI?

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Re: Ask HN: What's the difference between ML and AI?

#3
There is an outdated separate meaning (roughly AI being the broader field of emulating intelligence with computers, ML the subset of AI focusing on statistical learning based on lots of examples), but at this point they're both marketing terms, and on their own mean nothing. I'd discard them and look at what the job description is actually saying

Re: Ask HN: What's the difference between ML and AI?

#7
I think of ML as more of a data and infra role, while AI engineer is more algorithms and research to make use of the the output and setup made by the ML engineers.

Could be wrong tho since I’m neither AI nor ML engineer lol. I just layman who puts down the pipes for the AI and ML people to set up their stuff.

Re: Ask HN: What's the difference between ML and AI?

#8
Cybernetics is the use of computers and other tools to amplify human intellect.

Machine learning is a branch of cybernetics.

AI is a branch of machine learning that hopes one day to use circular causality to replace the need for humans in the loop.

AI safety is the branch of AI that worries that AI won't align with humans in terms of incentive and motivations. Their main concern is circular causality in AI.

Re: Ask HN: What's the difference between ML and AI?

#9
https://www.coursera.org/articles/machine-learning-vs-ai

AI is an umbrella term covering a variety of interrelated, but distinct, subfields. Some of the most common fields you will encounter within the broader field of artificial intelligence include:

    Machine learning (ML): a subset of AI in which algorithms are trained on data sets to become machine learning models capable of performing specific tasks. 

    Deep learning: A subset of ML, in which artificial neural networks (AANs) that mimic the human brain are used to perform more complex reasoning tasks without human intervention.  

    Natural Language Processing (NLP): A subset of computer science, AI, linguistics, and ML focused on creating software capable of interpreting human communication. 

    Robotics: A subset of AI, computer science, and electrical engineering focused on creating robots capable of learning and performing complex tasks in real world environments.
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