In the whirlwind of technological advancements, artificial intelligence (AI) often becomes the scapegoat for broader societal issues. It’s an easy target, a non-human entity that we can blame for job displacement, privacy concerns, and even ethical dilemmas. However, this perspective is not only simplistic but also misdirected.

The crux of the matter isn’t AI itself, but the economic system under which it operates - capitalism. It’s capitalism that dictates the motives behind AI development and deployment. Under this system, AI is primarily used to maximize profits, often at the expense of the workforce and ethical considerations. This profit-driven motive can lead to job losses as companies seek to cut costs, and it can prioritize corporate interests over privacy and fairness.

So, why should we shift our anger from AI to capitalism? Because AI, as a tool, has immense potential to improve lives, solve complex problems, and create new opportunities. It’s the framework of capitalism, with its inherent drive for profit over people, that often warps these potentials into societal challenges.

By focusing our frustrations on capitalism, we advocate for a change in the system that governs AI’s application. We open up a dialogue about how we can harness AI ethically and equitably, ensuring that its benefits are widely distributed rather than concentrated in the hands of a few. We can push for regulations that protect workers, maintain privacy, and ensure AI is used for the public good.

In conclusion, AI is not the enemy; unchecked capitalism is. It’s time we recognize that our anger should not be at the technology that could pave the way for a better future, but at the economic system that shapes how this technology is used.

  • kometes@lemmy.world
    20·
    7 months ago

    Maybe work on proving “AI” is actually a technological advancement instead of an overhyped plagiarism machine first.

    • Lmaydev@programming.dev
      13·
      7 months ago

      LLMs real power isn’t generating fresh content it’s their ability to understand language.

      Using one to summarise articles gives incredibly good results.

      I use Bing enterprise everyday at work as a programmer. It makes information gathering and learning so much easier.

      It’s decent at writing code but that’s not the main selling point in my opinion.

      Plus they are general models to show the capabilities. Once the tech is more advanced you can train models for specific purposes.

      It seems obvious an AI that can do creative writing and coding wouldn’t be as good at either.

      These are generation 0. There’ll be a lot of advances coming.

      Also LLMs are a very specific type of machine learning and any advances will help the rest of the field. AI is already widely used in many fields.

    • kromem@lemmy.world
      7·
      7 months ago

      Furthermore, simple probability calculations indicate that GPT-4’s reasonable performance on k=5 is suggestive of going beyond “stochastic parrot” behavior (Bender et al., 2021), i.e., it combines skills in ways that it had not seen during training.

      Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello. Although the network has no a priori knowledge of the game or its rules, we uncover evidence of an emergent nonlinear internal representation of the board state.

      So you already have research showing that GPT LLMs are capable of modeling aspects of training data at much deeper levels of abstraction than simply surface statistics of words and research showing that the most advanced models are already generating novel and new outputs distinct from anything that would be in the training data by virtue of the complexity of the number of different abstract concepts it combines from what was learned in the training data.

      Like - have you actually read any of the ongoing actual research on the field at all? Or just articles written by embittered people who are generally misunderstanding the technology (for example, if you ever see someone refer to them as Markov chains, that person has no idea what they are talking about given the key factor of the transformer model is the self-attention mechanism which negates the Markov property characterizing Markov chains in the first place).

    • A_Very_Big_Fan@lemmy.world
      4·
      7 months ago

      instead of an overhyped plagiarism machine first.

      If I paint an Eiffel Tower from memory, am I plagiarizing?

      If it’s not plagiarism when humans do it, it’s not plagiarism when a machine does it.

      • assassin_aragorn@lemmy.world
        2·
        7 months ago

        Of course it is. A machine is not a human.

        If you want to make this argument, then AI companies should be required to treat their AI models like employees. Paid for 40 hours a week of work, extra for overtime.

        If it’s human to have “memory” that isn’t subject to plagiarism, then it’s human enough to be paid hourly.

        • A_Very_Big_Fan@lemmy.world
          5·
          7 months ago

          I don’t need to be paid to make a painting, I’ll just do it for fun or because a good friend wanted it.

          Why does a machine doing something that I do for fun constitute plagiarism?