It’s about incentives. Worker oppression in Monarchy requires a bad King, in Feudalism bad lords, in Capitalism bad shareholders, and in Socialism self-hating workers. If you shared your workplace, would you push to remove your rights? Or to screw over your customers? And then argue for that against everyone else you share power with? The incentives are plainly better in a worker owned economy.
tara
joined 2 years ago
Hi academic here,
I research AI - better referred to as Machine Learning (ML) since it does away with the hype and more accurately describes what’s happening - and I can provide an overview of the three main types:
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Supervised Learning: Predicting the correct output for an input. Trained from known examples. E.g: “Here are 500 correctly labelled pictures of cats and dogs, now tell me if this picture is a cat or a dog?”. Other examples include facial recognition and numeric prediction tasks, like predicting today’s expected profit or stock price based on historic data.
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Unsupervised Learning: Identifying patterns and structures in data. Trained on unlabelled data. E.g: “Here are a bunch of customer profiles, group them by similarity however makes most sense to you”. This can be used for targeted advertising. Another example is generative AI such as ChatGPT or DALLE: “Here’s a bunch of prompt-responses/captioned-images, identify the underlying way of creating the response/image from the prompt/image.
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Reinforcement Learning: Decision making to maximise a reward signal. Trained through trial and error. E.g: “Control this robot to stand where I want, the reward is negative every second you’re not there, and very negative whenever you fall over. A positive reward is given whilst you are in the target location.” Other examples including playing board games or video games, or selecting content for people to watch/read/look-at to maximise their time spent using an app.
So typically there are 4 main competing interpretations of what AI is:
These are from Norvig’s “AI: A Modern Approach”.
Alan Turing’s “Turing Test” tests whether a given agent is artificially intelligent (according to definition #1). The test involves a human conversing with the agent via text messages, and deciding whether the agent is human or not. Large language models, a form of machine learning, can produce chatbot agents which pass this test. Instances of GPT4 prompted sufficiently to text an assessor for example. The assessor occasionally interacts with humans so they are kept sufficiently uncertain.
By this point, I think that machine learning in the form of an LLM can achieve artificial intelligence according to definition #1, but that isn’t what most non-tech non-academic people mean by AI.
The mainstream definition of AI is what we would call Artificial General Intelligence (AGI). This is an agent that meets a given one of Norvig’s criteria for AI across multiple scenarios and situations that they have never encountered before.
Many would argue that LLMs like GPT4 do not meet the criteria for AGI because they are not general enough, unable to learn to play an Atari game for example, or to learn an entirely unseen language to fluency.
This is the difference between an LLM and a fictional AGI like Glados or Skynet.
Additionally forms of machine learning exist like k-means clustering, which identify related groups within a dataset as their only function. I would assert these are not AI, although a weak argument could be made that they are thinking “rationally” enough to meet definition #4.
Then there are forms of AI which are not machine learning, such as heuristic agents - agents that are hard coding with reasoning by humans - such as the chess playing Stockfish, or the AI found in most video games.
Ultimately AI can describe machine learning if “AI” is understood as something which meets one or more of Norvig’s definitions. But since most people say AI when they mean AGI, I think “machine learning” is a better term. Less undeserved hype, less marketing disinformation, and generally better at communicating what is being talked about.